Market-making in on-demand markets

I’ve written a post on LinkedIn about the need for market-making in on-demand markets. I argue that for a market to be on-demand for one side, you require the other side to be able to provide liquidity. This liquidity comes at a cost and the side needs to get compensated for it. Driver incentive schemes at Ola/Uber and two-part electricity tariffs are examples of such incentives.

An excerpt:

In a platform business (or “two sided market”, or a market where the owner of the marketplace is not a participant), however, the owner of the market cannot provide liquidity himself since he is not a participant. Thus, in order to maintain it “on demand”, he should be able to incentivise a set of participants who are willing to provide liquidity in the market. And in return for such liquidity provided, these providers need to be paid a fee in exchange for the liquidity thus provided.

Read the whole thing! :)

Pricing and waiting in line

Every time I have to stand in what seems like an exceedingly long line for something, I wonder if they’ve got the pricing wrong. If they had their economics straight, I reason, they would raise prices to an extent where the market just “clears”, and there is no need for a line.

In this context, this piece by Tyler Cowen comes in handy, where he talks about the various advantages of lines and waiting in line. Apart from some superfluous stuff such as lines making us more patient and waiting in line being less painful now thanks to smartphones, Cowen makes some very interesting points. For example,

Higher prices also skew the customer mix toward wealthier and thus older people, who exert less influence over the purchasing decisions of their peers. They are less likely to text about a concert, put it on their Facebook pages or talk up its reputation to dozens of friends at parties. The younger buyers are usually the ones who make places trendy, thus many sellers use lower prices, with lines if need be, to lure in those individuals and cultivate their loyalties.

The above passage illustrates why it is sometimes necessary to keep prices lower than the “clearing price” and let lines form (as long as you have an orderly way of dealing with the lines). Essentially, by raising price until a point where the market just clears, you are optimising the revenues for that particular day or point in time.

However, if you are a “going concern”, as all businesses are normally assumed to be, you don’t optimise for revenues or profits on a particular day. What you optimise for is long-term sustainable profit, and you do what it takes in order to maximise that.

As Cowen says above, by keeping prices lower than clearing price, you draw crowds that are likely to talk about the experience of your offering, thus giving you free advertising. As the “flash sales” conducted by Xiaomi (where phones sold out in a few seconds after sales opened) show, lines can end up being reported in the press which creates free publicity for you – leading to greater future sales.

Then there is (Cowen touches upon this) the signalling effect of the line itself – that so many people are waiting in line for something signals that there’s something inherently worthy about the product, and results in increasing demand (and more people in the line!). The line is an act of discovery – you may not go to a food card if you didn’t see the line in front of it.

There is also the issue of price elasticity – beyond certain levels, prices can be extremely elastic, in that if you raise prices at a particular margin, demand drops significantly (this has to do with “price barriers” in people’s minds – possibly a behavioural issue). So it becomes impossible for you to set the price at the precise level where your establishment just fills up. So you have a choice between not filling up your capacity or getting people to stand in line. And the latter is more profitable.

The lesson from this is that you should think long term when you are analysing pricing decisions, and not optimise for maximising instantaneous profits! Read the full piece by Cowen. It’s well worth it.

Sociology and economics

A few years back I was interviewing a sociology graduate for a scholarship and loudly exclaimed that it was absurd that she had a masters in sociology while not knowing much economics – she had mentioned that her courses in sociology (bachelors and masters) had no “papers” (the word used by students of certain prominent Indian universities when they mean “courses”. The choice of words possibly indicates their priorities) in economics.

It is a result of my prior – everything I know and have learnt about sociology and social behaviour is from the realm of economics and game theory (iterated prisoners’ dilemma and derivatives). I’ve learnt it from reading blogs (Marginal Revolution, Econlog, etc.) and from pop economics books written by authors such as Steven Levitt and Malcolm Gladwell. So every time I think of a sociology problem I can’t think of any method apart from economic reasoning to attack it.

However, it turns out that the use of economic reasoning for sociological analysis is rather recent, and started only with the work of Chicago economist Gary Becker, who wrote a series on love and marriage. Becker’s wife had died, and he was a single father, when he wrote his series of papers on this topic. This is supposed to be one of the first steps in the “creep” of economics into (now) related disciplines such as sociology and political science. This has been uncharitably called by Becker’s critics as “economic imperialism“.

So my exclamation that a masters program in sociology not including a course in economic reasoning being absurd would be valid only in very recent times, when syllabuses would have been updated to keep track of any such above “imperialism” and “creep”. Given the glacial pace at which Indian universities move, however, I think my remark might have actually come across as absurd!

PS: Read this excellent Lunch with FT interview of Gary Becker by Tim Harford.

How much surge is too much surge?

I had gone for a wedding in far-off Yelahanka and hailed an Uber on the way back. The driver was bragging about how it’s easy to find an Uber at any time anywhere in Bangalore, when I pointed out to him that earlier in the evening when I was on my way to the wedding I’d failed to find one, and had taken an Ola instead.

He was surprised that an Uber wasn’t available in Jayanagar when I told him that there were cars available but at a 1.7X surge, and given the distance I was to travel I found it more economical to take an Ola which was offering a ride at a flat Rs. 50 premium. To this, the driver said that he had also noticed that demand sharply dropped off once the level of surge went beyond 1.5X, and at such surges supply would easily outstrip demand.

Now I’m no fan of Ola’s pricing – I think the flat Rs. 50 premium during peak hours is unscientific, but I wonder if the level of Uber’s surges makes sense. From a pure microeconomic standpoint, it is easy to see where Uber is coming from – raise price until quantity demanded matches quantity supplied and let the market clear. The question, however, is if this kind of a surge makes sense from a behavioural standpoint.

The point is that the “base fare” (“1X”) is “anchored” in the customer’s mind, and thus any decision he takes in terms of willingness to pay is made keeping this “anchor” in mind. And when the quoted price moves too far from the anchor (beyond 1.5X, say), the customer deems that it is “too expensive”, and decides that waiting for a few minutes for fares to drop (or using a competing app) is superior to paying the massive premium.

I suppose that Uber would have noticed this. That there is a “cliff” surge price beyond which there is a massive drop off in volume of matchings. The problem is that if they restrict their surges to this “cliff value” they might be leaving money on the table by not being able to match the market. On the other side, though, if the surge is so high that the volume of transactions drops sharply, it results in much lower commissions for Uber! I’m assuming that a solution to this problem is on the way!

And I’ve found that it’s always harder to find a taxi on a Sunday. The problem is that because demand is lower, supply is also lower (this is a unique characteristic of “two-sided markets”) because of which the chances of finding a match are harder, and transaction costs are higher. I wonder if it makes sense for taxi aggregators to levy a “Sunday premium” (perhaps with Uber holding a day-long minimum of 1.2X surge or something) to compensate for this lack of liquidity!

Why the proposed Ola-TaxiForSure merger is bad news

While a merger intuitively makes economic sense, it’s not good for the customers. The industry is consolidating way too fast, and hopefully new challengers will arise soon

Today’s Economic Times reports that Ola Cabs is in the process of buying out competitor TaxiForSure. As a regular user of such services, I’m not happy, and I think this is a bad move. I must mention upfront, though, that I don’t use either of these two services much. I’ve never used TaxiForSure (mostly because I never find a cab using its service), and have used Ola sparingly (it’s my second choice after Uber, so use it only when Uber is not available).

Now, intuitively, consolidation in a platform industry is a good thing. This means that more customers and more drivers are on the same platform, and that implies that the possibility of finding a real-time match between a customer who wants a ride and a driver who wants to offer one is enhanced. The two-sided network effects that are inherent in markets like this imply super-linear returns to scale, and so such models work only at scale. This is perhaps the reason as to why this sector has drawn such massive investments.

While it is true that consolidation will mean lower matching cost for both customers and drivers, my view on this is that it’s happening too soon. The on-demand taxi market in India is still very young (it effectively took off less than a year back when Uber made its entry here. Prior to that, TaxiForSure was not “on demand” and Ola was too niche), and is still going through the process of experimentation that a young industry should.

For starters, the licensing norms for this industry are not clear (and it is unlikely they will be for a long time, considering how disruptive this industry is). Secondly, pricing models are still fluid and firms are experimenting significantly with them. As a corollary to that, driver incentive schemes (especially to prevent them from “multihoming”) are also  rather fluid. The process of finding a match (the process a customer and a driver have to go through in order to “match” with each other), is also being experimented with, though the deal indicates that the verdict on this is clear. Essentially there are too many things in the industry that are still fluid.

The problem with consolidation at a time when paradigms and procedures are still fluid is that current paradigms (which may not be optimal) will get “frozen”, and customers (and drivers) will have to live with the inefficiencies and suboptimalities that are part of the current paradigms. It looks as if after this consolidation the industry will settle into a comfortable duopoly, and comfortable duopolies are never great for innovation and for finding more optimal solutions.

Apart from the network effects, the reasons for the merger are clear, though – in the mad funding cycle unleashed by investors into this industry, TaxiForSure was a clear loser and was finding itself unable to compete against the larger better-funded rivals. Thus, it was a rational decision for the company to get acquired at this point in time. From Ola’s point of view, too, it is rational to do the deal, for it would give them substantial inorganic growth and undisputed number one position in the industry. For customers and drivers, though, now faced with lower choice, it is not a great deal.

This consolidation doesn’t mean the end, though. The strength of a robust industry is one where weak firms go out of business and new firms spring up in their place in their attempt to make a profit. That three has become two doesn’t mean that it should remain at two. There is room in the short term for a number three and even possibly a number four, as the Indian taxi aggregation industry tries to find its most efficient level.

I would posit that the most likely candidates to emerge as new challengers are companies such as Meru or EasyCabs, which are already in the cab provider business but only need to tweak their model to include an on-demand component. A wholly new venture to take up the place that is being vacated by TaxiForSure, however, cannot be ruled out. The only problem is that most major venture capitalists are in on either Uber or Ola, so it’s going to be a challenge for the new challenger to raise finances.

\begin{shameless plug}
I’m game for such a venture, and come on board to provide services in pricing, revenue management, availability management and driver incentive optimisation. :)
\end{shameless plug}

 

Aggregate quality of life

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

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

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

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

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

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

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

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

More on the Swiss Franc move

The always excellent Matt Levine has reported in Bloomberg (with respect to the recent removal of the cap on the price of the Swiss Franc) that:

Goldman Sachs Chief Financial Officer Harvey Schwartz said on this morning’s earnings call that this was something like a 20-standard-deviation event

While mathematically this might be true, the question is if it makes sense at all. Since it is mathematically easy to model, traders look at volatility of an instrument in terms of its standard deviation. However, standard deviation is a good descriptor of a distribution only if the distribution looks something like a normal distribution. For all other distributions, it is essentially meaningless.

The more important point here is that the movement of the Swiss Franc (CHF) against the Euro had been artificially suppressed in the last three odd years. So from that perspective, whatever Standard Deviation would have been used in order to make the calculation was artificially low and essentially meaningless.

Instead, the way banks ought to have modelled it was in terms of modelling where EUR/CHF would end up in case the cap on the CHF was actually lifted (looking at capital and current flows between Switzerland and the Euro Area, this wouldn’t be hard to model), and then model the probability with which the Swiss National Bank would lift the cap on the Franc, and use the combination of the two to assess the risk in the CHF position. This way the embedded risk of the cap lifting, which was borne out on Thursday, could have been monitored and controlled, and possibly hedged.

There are a couple of other interesting stories connected to the lifting of the cap on the value of the CHF. The first has to do with Alpari, a UK-based FX trading house. The firm has had to declare insolvency following losses from Thursday. And as the company was going insolvent, they put out some interesting quotes. As the Guardian reports:

In the immediate aftermath of Thursday’s move by the Swiss central bank, analysts at Alpari had described the decision as “idiotic” and by Friday the firm had announced it was insolvent. “The recent move on the Swiss franc caused by the Swiss National Bank’s unexpected policy reversal of capping the Swiss franc against the euro has resulted in exceptional volatility and extreme lack of liquidity,” said Alpari.

The second story has to do with homeowners in Hungary and Poland who borrowed their home loans in Swiss Franc, and are now faced with significantly higher payments. I have little sympathy for these homeowners and less sympathy for the bankers who sold them the loans denominated in a foreign currency. I mean, who borrows in a foreign currency to buy a house? I don’t even …

There is a story related to that which is interesting, though. Though Hungary is more exposed to these loans than Poland, it is the Polish banks which are likely to suffer more from the appreciation in the CHF. The irony is that the Hungarian market was initially much more loosely regulated compared to the Polish market, where only wealthier people were allowed to borrow in CHF. But in Hungary, the regulator took more liberties in terms of forcing banks to take the hit on the exchange rate movement, and the loans were swapped back into the local currency a while back.

In related reading, check out this post by my Takshashila colleague Anantha Nageswaran on the crisis. I agree with most of it.