Who do you subsidise?

One basic rule of pricing is that it is impossible for all buyers to have the same consumer surplus (the difference between what a buyer values the item at and what he paid). This is because each buyer values the item differently, and is thus willing to pay a different price for it. People who value the item more end up having a higher consumer surplus than those who value it less (and are still able to afford it).

Dynamic pricing systems (such as what we commonly see for air travel and hotels) try to price such that such a surplus is the same for all consumers, and equal to zero, but they never reach this ideal. While the variation in consumer surplus under such systems is lower, it is impossible for it to come to zero for all, or even a reasonable share of, customers.

So what effectively happens is that customers with a lower consumer surplus end up subsidising those with a higher consumer surplus. If the former customers didn’t exist, for example, the clearing price would’ve been higher, resulting in a lower consumer surplus for those who currently have a higher consumer surplus.

Sometimes the high surplus customer and the low surplus customer need not be different people – it could be the same person at different times. When I’m pressed for time, for example, my willingness to pay for a taxi is really high, and I’m highly likely to gain a significant consumer surplus by taking a standard taxi or ride-hailing marketplace ride then. At a more leisurely time, travelling on a route with plenty of bus service, I’d be willing to pay less, resulting in a lower consumer surplus. It is important to note, however, that my low surplus journey resulted in a further subsidy to my higher surplus journey.

When it comes to markets with network effects (whether direct, such as telecommunications, or indirect, like any two-sided marketplace), this surplus transfer effect is further exacerbated – not only do low-surplus customers subsidise high-surplus customers by keeping clearing price low, but network effects mean that by becoming customers they also add direct value to the high surplus customers.

So when you are pleasantly surprised to find that Uber is priced low, the low price is partly because of other customers who are paying close to their willingness to pay for the service. When you pay an amount close to the value you place on the service, you are in turn subsidising another customer whose willingness to pay is much higher.

This transfer of consumer surplus can be seen as an instance of bundling, but from the seller’s side. Since a seller cannot discriminate effectively among customers (even with dynamic pricing algorithms such as Uber’s surge pricing), the high-surplus customers come bundled with the low-surplus customers. And from the seller’s perspective, this bundling is optimal (see this post by Chris Dixon on why bundling works, and invert it).

So the reason I thought up this post is that there has been some uncertainty about ride-hailing marketplaces in Bangalore recently. First, drivers went on strike alleging that they weren’t being paid fairly by the marketplaces. Then, a regulator decided to take the rulebook too literally and banned pooled rides. As i write this, a bunch of young women I know are having a party, and it’s likely that they’ll need these ride-hailing services for getting home.

Given late night transport options in Bangalore, and the fact that the city sleeps early, their willingness to pay for a safe ride home will be high. If markets work normally, they’re guaranteed a high consumer surplus. And this will be made possible by someone, somewhere else, who stretched their budget to be able to afford an Uber ride.

Think about it!

Cross-posted at RQ

Truly Madly: Review

So the wife and I both decided to sign up on the dating app TrulyMadly, she to conduct research for her matchmaking service, and me as part of my research for the book that I’m currently revising. Based on our collective usage of our respective apps for about an hour, here are some pertinent observations.

  • Sexism: The wife can see salaries of men she is getting matched with, while I don’t get to see salaries of women being recommended to me. Moreover, women are allowed to “lurk” (and not have a public profile) on the platform, but no such thing for men. I’m surprised no one has called out TrulyMadly on their sexism
  • Job board: To list on the app you need to indicate your profession and job, and how much you are making. So if you are a woman on this site, apart from getting to check out men, you get to check out what jobs pay how much, and it’s not inconceivable that you use the app to find yourself a job.
  • Judgments: This should possibly go down under sexism again. Anyway, the wife has mentioned her qualifications as “MBA”, and she is only being shown men who are graduates of top B-schools in India. No such thing for me – women shown to me had all kinds of qualifications. It’s like TrulyMadly has decided that women should only date men who are at least as well qualified as them. Moreover, the app also decides that men can only date women who are shorter than them, though there’s a setting somewhere to change this.
  • Age bar: Based on my age (which I entered as 34), the app decided that I should only be allowed to check out women between the ages of 26 and 34. These can be moved around, in case I have fetishes outside this age range, but I’m shocked that they are not aware of the N/2+7 rule – based on which the lower limit should’ve been set at 24 (34/2+7) and not 26.
  • Gender imbalance: The app gave up on me after I rejected some half a dozen women, after which I deactivated my account and deleted the app. The wife’s app, however, continues to go strong, as she might have rejected some two or three dozen men by now (apart from having done research on what jobs pay how much). Just goes to show the gender imbalance on the app. I can imagine this leading to a lot of frustrated people, of both genders.

Ok that’s it for now. Any more insights you can read in my book (I hope to get it out in the next month or two)!

Moral of the story: Product management pays better than category leader.

Why PayTM is winning the payments “battle” in India

For the last one year or so, ever since I started using IMPS at scale, and read up the UPI protocol, I’ve been bullish about Indian banks winning the so-called “payments battle”. If and when the adoption of electronic payments in India takes off, I’ve been expecting banks to cash in ahead of the “prepaid payments instruments” operators.

The events of the last one week, however, have made me revise this prediction. While the disruption of the cash economy by withdrawal of 85% of all notes in circulation has no doubt given a major boost to the electronic payments industry, only some are in a position to do anything about this.

The major problem for banks in the last one week has been that they’ve been tasked with the unenviable task of exchanging the now invalid currency, taking deposits and issuing new currency. With stringent know-your-customer (KYC) norms, the process hasn’t been an easy one, and banks have been working overtime (along with customers working overtime standing in line) to make sure hard currency is in the market again.

While by all accounts banks have been undertaking this task rather well, the problem has been that they’ve had little bandwidth to do anything else. This was a wonderful opportunity for banks, for example, to acquire small merchants to accept payments using UPI. It was an opportune time to push the adoption of credit card payment terminals to merchants who so far didn’t possess them. Banks could’ve also used the opportunity to open savings accounts for the hitherto unbanked, so they had a place to park their cash.

As it stands, the demands of cash management have been so overwhelming that the above are literally last priorities for the bank. Leave alone expand their networks, banks are even unable to service the existing point of sale machines on their network, as one distraught shopkeeper mentioned to me on Saturday.

This is where the opportunity for the likes of PayTM lies. Freed of the responsibilities of branch banking and currency exchange, they’ve been far better placed to acquire customers and merchants and improve their volume of sales. Of course, their big problem is that they’re not interoperable – I can’t pay using Mobikwik wallet to a merchant who can accept using PayTM. Nevertheless, they’ve had the sales and operational bandwidth to press on with their network expansion, and by the time the banks can get back to focussing on this, it might be too late.

And among the Prepaid Payment Instrument (PPI) operators again, PayTM is better poised to exploit the opportunity than its peers, mainly thanks to recall. Thanks to the Uber deal, they have a foothold in the premium market unlike the likes of Freecharge which are only in the low-end mobile recharge market. And PayTM has also had cash to burn to create recall – with deals such as sponsorship of Indian cricket matches.

It’s no surprise that soon after the announcement of withdrawal of large currency was made, PayTM took out full page ads in all major newspapers. They correctly guessed that this was an opportunity they could not afford to miss.

PS: PayTM has a payments bank license, so once they start those operations, they’ll become interoperable with the banking system, with IMPS and UPI and all that.

Moving towards a cashless economy

In any transaction, the process of payment is a pain. It is a necessary step, of course, in that payment is what completes the transaction, but the process of payment is not something that adds any value to the transaction. If money could be magically be transferred from buyer to seller at the end of a transaction, both transacting parties would be happy.

In this context, any chosen method of payment, be it cash or credit card or cheque or bank transfer, involves some degree of pain for the transacting parties.

In case of cash, there’s the problem of counting out the money, cross checking it, finding exact change, being able to handle currency without the fear of being robbed, and making sure the currency is not counterfeit. Cheques have a credit risk, since they can bounce, not to speak of the time it takes to write one, and the time it takes for the money to get transferred.

Bank transfer requires parties to have bank accounts, and the ability of transacting parties to tell each other their account details. Credit cards have the most explicit pain of transaction – the transaction fees the merchants need to pay the acquiring bank – apart from the time and pain of swiping, entering the PIN, etc.

The reason India has so far been a primarily cash economy is that the pain of transacting through cash has been far lower than the pain through other means. Apart from the pains mentioned above, cash also has the advantage of anonymity, speed of transaction and ability to hide from the tax authorities.

So if we have to turn India closer to a cashless economy, as the current union government plans to do, we need to either increase the pain of transacting in cash, or reduce the pain of transacting through another means. The Unified Payments Interface (UPI), which was launched with much fanfare earlier this year but has spectacularly failed to take off, seeks to reduce pain of cashless transactions. The government’s efforts to get people open bank accounts through the Pradhan Mantri Jan Dhan Yojana (PMJDY) also seeks to reduce pain in non-cash transactions.

The government’s recent effort to withdraw legal tender of Rs. 500 and Rs. 1000 notes, on the other hand, seeks to increase the cost of transacting in cash – 85% of the current stock of cash in India needs to get banked in the next 50 days. This, however, is not a repeatable exercise – it can simply remove confidence in the rupee and drive people to alternate (formal or informal) currencies.

So what can be done to move India to a more cashless economy? The problem with small change has already played its part, with most auto rickshaw and taxi drivers in Mumbai supposedly willing to accept payment in digital wallets such as PayTM. If the stock for the new Rs. 2000 and Rs. 500 notes released is low, and most people have to transact using Rs. 100 notes, that will again increase the pain of transacting in cash, since the cost of handling cash might go up.

Perversely, if crime and robberies increase, that will again make people wary of handling cash. In fact, as this excellent piece in the New Yorker claims, the reason Sweden has moved largely cashless is that people got scared of handling cash after a series of cash robberies a few years ago. The cost of higher crime, however, means this is not a desirable way to go cashless.

It’s been barely three days since the new Rs. 500 and Rs. 2000 notes have been released, and there are already reports of counterfeiting in these notes. Given the framework I’ve proposed in this blogpost, it is not inconceivable that these rumours have been planted – when people become more wary of receiving large currency (thanks to the fear of counterfeiting), they want to reduce the use of such physical currency.

It’s perverse, I know, but nothing can be ruled out! As I’ve repeatedly pointed out, increased use of cash has a fiscal cost (in terms of printing and maintaining currency, apart from people not paying taxes), so the government has an incentive to stamp it out.

 

 

May a thousand market structures bloom

In my commentary on SEBI’s proposal to change the regulations of Indian securities markets in order to allow new kinds of market structures, I had mentioned that SEBI should simply enable exchanges to apply whatever market structures they wanted to apply, and let market participants sort out, through competition and pricing, what makes most sense for 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.

Now, Matt Levine, who has been commenting on market structures for a long time now, makes a similar case in his essay on the Chicago Stock Exchange’s newly introduced “speed bump”:

A thousand — or at least a dozen — market structures can bloom, each subtly optimized for a different type of trader. It’s an innovative and competitive market, in which each exchange can figure out what sorts of traders it wants to favor, and then optimize its speed bumps to cater to those traders.

Maybe I should now accuse Levine of “borrowing” my ideas without credit! 😛

 

Intermediation and the battle for data

The Financial Times reports ($) that thanks to the rise of AliPay and WeChat’s payment system, China’s banks are losing significantly in terms of access to customer data. This is on top of the $20Billion or so they’re losing directly in terms of fees because of these intermediaries.

But when a consumer uses Alipay or WeChat for payment, banks do not receive data on the merchant’s name and location. Instead, the bank record simply shows the recipient as Alipay or WeChat.

The loss of data poses a challenge to Chinese banks at a time when their traditional lending business is under pressure from interest-rate deregulation, rising defaults, and the need to curb loan growth following the credit binge. Big data are seen as vital to lenders’ ability to expand into new business lines.

I had written about this earlier on my blog about how intermediaries such as Swiggy or Grofers, by offering a layer between the restaurant/shop and consumer, now have access to the consumer’s data which earlier resided with the retailer.

What is interesting is that before businesses realised the value of customer data, they had plenty of access to such data and were doing little to leverage and capitalise on it. And now that people are realising the value of data, new intermediaries that are coming in are capturing the data instead.

From this perspective, the Universal Payment Interface (UPI) that launched last week is a key step for Indian banks to hold on to customer data which they could have otherwise lost to payment wallet companies.

Already, some online payments are listed on my credit card statement in the name of the payment gateway rather than in the name of the merchant, denying the credit card issuers data on the customer’s spending patterns. If the UPI can truly take off as a successor to credit cards (rather than wallets), banks can continue to harness customer data.

Aswath Damodaran, Uber’s Valuation and Ratchets

The last time I’d written about Aswath Damodaran’s comments on Uber’s valuation, it was regarding his “fight” with Uber investor Bill Gurley, and whether his valuation was actually newsworthy.

Now, his latest valuation of Uber, which he concludes is worth about USD 28 Billion, has once again caught the attention of mainstream media, with Mint writing an editorial about it (Disclosure: I write regularly for Mint).

I continue to maintain that Damodaran’s latest valuation is also an academic exercise, and the first rule of valuation is that “valuation is always wrong”, and that we should ignore it.

However, in the context of my recent piece on investor protection clauses in venture investments (mainly ratchets), it is useful to look at Damodaran’s valuation of Uber, and how it compares to Uber’s valuation if we were to account for investor protection clauses.

“True value” of Indian unicorns after accounting for investor protection. Source: Mint

When Uber raised $3.5 Billion from Saudi Arabia’s Public Investment Fund earlier this year, the headline valuation number was $62.5 Billion. Given the late stage of investment, it is unlikely that the investor would have done so without sufficient downside protection – at the very least, they would want a “full ratchet” (if the next investment happens at a lower valuation, then they get additional shares to compensate for their loss). This is a conservative assumption since late stage (“pre-IPO”) investments usually have clauses more friendly to the investor, usually incorporating a minimum “guaranteed return”.

Plugging these numbers into the model I’ve built (pre-money valuation of $59 Billion and post-money valuation of $62.5 billion), the valuation of the put option written by existing investors in favour of Uber comes to around $1.28 Billion. Accounting for this option, the total value of the company comes out to $39.6 Billion.

Damodaran’s valuation, based on his views, principles and numbers, is $28 Billion. Assuming that investors and management of Uber are aware of the downside protection clauses and its impact on the company’s valuation, Damodaran’s valuation is not that much of a discount on Uber’s true valuation!