The market for gay relationships

The market for homosexual relationships is an interesting one from the analysis perspective. Like the market for heterosexual relationships, it is a matching market (we are in a relationship if and only if I like you AND you like me). Unlike heterosexual relationships, it is not a “bipartite” market, since both the nominal “buyer” and the “seller” in a transaction will come out (no pun intended) of the same pool (gay people of a particular sex).

The other factor that makes this market interesting (purely from an analysis perspective – it’s bad for the participants) is that there is disapproval at various levels for homosexual relationships. Until today, for example, it was downright criminal to indulge in gay sex in India. Even where it is legal, there is massive social and religious opposition to such relationships (think of the shootout at the gay bar in Florida, for example).

Social disapproval has meant that gays sometimes try to keep their sexuality under wraps. Historically, it has been a common practice for gays to enter into heterosexual marriages, and pursue relationships outside. In fact, there is nothing historical about this – read this excellent piece by Srinath Perur on gays in contemporary hinterland Karnataka, for whom Mohanaswamy, a collection of short stories with a gay protagonist, was a kind of life changer.

Organising a market for an item that is illegal, or otherwise frowned upon, is difficult, since people don’t want to be found participating in it. If I were a gay man looking for a partner, for example, I couldn’t go around openly looking for one if I didn’t want my family to know that I’m gay. So the first task would have been discovery – “safe spaces” where I would be happy to expose my sexuality, and where I could also meet potential partners.

When demand and supply exist, buyers and sellers will find a way to meet each other, though often at high cost. One such “way” for homosexual people has been the gay bar. Though not explicitly advertised, such bars act as focal points (I have a chapter on focal points in my book) for gay people.

They also act as an “anti focal point” (a topic I HAVEN’T covered in the book, for a change!) for heterosexual people who want to stay away because they don’t want to be hit on by gay people (thus reducing market congestion – another topic I cover in my book). Similarly other cultural activities have acted as focal points for gay people to get together and meet each other.

Like in heterosexual relationship markets (this is the link to a sample chapter from the book), the advent of dating apps has revolutionised gay dating, as apps such as Tinder and Grindr have provided safe spaces where gays can look for relationships “from the comfort of their homes”. There are studies that show that Grindr has changed the nature of relationships among gay men, and how these apps have “saved lives” in places such as India where homosexuality was criminal until today.

Today’s Indian Supreme Court ruling will have a massive positive impact on gay relationships in India. For starters, there are still millions of people in the closet – while apps such as Tinder and Grindr allowed more people to participate in these markets (since this could be done without really “coming out”), that gay sex was a criminal act would have led to some people to err on the side of caution (and deprive themselves of the chance of a relationship). Gay people who were worried about criminality, but not that much about social sanctions, will now be more willing to come out, leading to an increase in the market size.

Barring congestion (when “bad counterparties” prevent you from finding “good counterparties”),  the likelihood of finding a match in a market is generally proportional to the number of possible counterparties. Since gay relationship markets are not bipartite, we can say that the likelihood of finding a good match varies by the square of the number of market participants (and this brings in the Indian Prime Minister’s infamous 2ab term). In other words, it not only allows the people now coming into the market to find relationships, but it also allows existing players to find better relationships.

Then, there is the second order effect. Decriminalisation will mean that more people will come out of the closet, which will mean more people will find homosexuality to  be “normal” leading to better social mores (to take a personal example, I used to use the word “gay” as a pejorative (to mean “uncool”) until I encountered my first openly gay acquaintance – someone with whom I share on online mailing list). And as social attitudes towards homosexuality change, it will lead to more people coming out of the closet, setting off a virtuous cycle of acceptance of homosexuality.

In other words, today’s decision by the Indian Supreme Court is likely to set off a massive virtuous cycle in the liquidity of the market for homosexual relationships in India!

PS: It is a year since my first book was published, so we are running a promotional offer where you can buy the Kindle version for one dollar (or Rs. 70).

 

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!

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.

Platform as a platform

This afternoon, as I was getting off the tube, I looked at the railway platform, and wondered how it compared to “platforms” as we now know in the context of “platform economics“. For those of you under a rock, platform economics talks about the economics of “platforms” that bring together two sides of a market to interact.

In that sense, Uber is a platform connecting drivers to passengers. Ebay is a platform connecting buyers and sellers of used goods. Paypal is a platform connecting people who want to pay and those who want to receive payment. And so forth (these are all textbook examples nowadays).

So is the railway platform a platform? And if not, is it correct that we refer to entities that run two-sided markets as platforms (arguably, the most intuitive meaning of the word “platform” in the last hundred or so years has been in the railway context)? These were some of the questions I grappled with as I walked along the length of the platform at Ealing Broadway.

For those of you who’re not in the know, I’ve written a book on market design. The Takshashila Institution is publishing it, and the book should be out fairly soon (manuscript is complete, but there’s still plenty to do). In that book, I have a chapter on taxi marketplaces such as Uber/Lyft/Ola, and how they’ve transformed the efficiency of the taxi market. Before I introduce these characters, though, I draw the history of the taxi market.

In that, I talk about taxi stands. Taxi stands work in the way of Thomas Schelling’s focal points. Passengers go there because they know empty taxis will go there. Taxi drivers looking for passengers go there because they know passengers looking for taxis will go there. This way, rather than waiting at a random place looking for either a passenger or a ride, going to the taxi stand is rational. And in that sense, taxi stands are a platforms.

In a way, railway platforms are platform in the same sense. Think of a train that wants to pick up passengers, and passengers who want to travel on a train. If there were no designated pick up points, trains would stop at random places, which passengers would have to guess. While engine drivers could see passengers waiting by the side, stopping at random places might have meant that the train would have had to go empty.

From this perspective, railway platforms act as platforms – they are focal points where trains and passengers come together. Passengers wait there because they know trains stop there, and vice versa. And helpfully, there is an actual physical platform that elevates passengers to the height of the train door so they can get on and off easily!

Isn’t this a wonderful way to have complicated a rather simple concept?

Market depth, pricing and subsidies

A few days back I had written about how startups should determine how much to subsidise their customers during the growth phase – subsidise to the extent of the long-term price. If you subsidise too much initially, elasticity might hit you when you eventually have to raise prices, and that can set you back.

The problem is in determining what this long-term sustainable price will be. In “one-sided markets” where the company manufactures or assembles stuff and sells it on, it is relatively easy, since the costs are well known. The problem lies in two-sided markets, where the long-term sustainable price is a function of the long-term sustainable volume.

A “bug” of any market is transaction costs, and this is especially the case in a two-sided market. If you are a taxi driver on Ola or Uber platform, the time you need to wait for the next ride or distance you travel to pick up your next customer are transaction costs. And the more “liquid” the market (more customers and more drivers), the lesser these transaction costs, and the more the money you make.

In other words, the denser a market, the lower the price required to match demand and supply, with the savings coming out of savings in transaction costs.

So if you are a two-sided market, the long-term sustainable price on your platform is a function of how big your market will be, and so in order to determine how much to subsidise (which is a function of long-term sustainable price), you need to be able to forecast how big the market will be. And subsidise accordingly.

It is well possible that overly optimistic founders might be too bullish about the eventual size of their platform, and this can lead to subsidising to an extent greater than the extent dictated by the long term market size. And some data points from the Indian “marketplace industry” show that this has possibly happened in India.

Having remained credit card only for a long time now, Uber has started accepting cash payments – in order to attract customers who are not comfortable transacting money online. This belated opening shows that Uber perhaps didn’t hit the numbers they had hoped to, using their traditional credit card / wallet model.

Uber has problems on the driver side, too, with an increasing number of its drivers turning out to be rather rude (this is anecdata from several sources, I must confess), refusing rides, fighting with passengers, etc. Competitor Ola has started buying cars and loaning them to drivers, perhaps indicating that the driver side of the market hasn’t grown to their expectations. They are all indicative of overestimation of market size, and an attempt to somehow hit that size rather than operating at the lower equilibrium.

So an additional risk in running marketplaces is that if you overestimate market size, you might end up overdoing the subsidies that you provide to build up the market. And at some point in time you have to roll back those subsidies, which might lead to shrinkage of the market and a possible death spiral.

Now apply this model to your favourite marketplace, and tell me what you think of them.

Barriers to entry in cab aggregation

The news that Reliance might be getting into the cab aggregation game got me thinking about the barriers to entry in this business. Considering that it is fundamentally an unregulated industry, or rather an industry where players actively flout regulations, the regulatory barrier is not there.

Consequently, anyone who is able and willing to make the investment and set up the infrastructure will be able to enter the industry. The more important barrier to entry, however, is scale.

Recently I was talking to an Uber driver who had recently switched from TaxiForSure. The latter, he said had lost “liquidity” over the last couple of months (after the Ola takeover), with customers and drivers deserting the service successively in a vicious cycle. Given that cab aggregation is a two-sided market, with prominent cross-sided network effects (number of customers depends on number of cabs and vice versa), it is not possible to do business if you are small, and it takes scale.

For this reason, for a new player to enter the cab aggregation business, it takes significant investments. The cost of acquisition for drivers and passengers is still quite high, and this has to be borne by the new player. Given that a significant number of drivers have to be initially attracted, it takes deep pockets to be able to come in.

Industry players were probably banking on the fact that with the industry already seeing consolidation (when Ola bought TaxiForSure), Venture Capitalists might stop funding newer businesses in this segment, and for that reason Uber and Ola might have a free rein. Ola had even stopped subsidising passengers in the meantime, reasoning (correctly for the time) that with their only competition being Uber they might charge market rates.

From this perspective it is significant that the new player who is entering is an industrial powerhouse with both deep pockets and with a reputation of getting their way around in terms of regulation. The first ensures that they can make the requisite investment (without resorting to VC money) and the second gives the hope that the industry might get around the regulatory troubles it’s been facing so far.

I once again go back to this excellent blog post by Deepak Shenoy on the cab aggregation industry. He had mentioned that what Uber and Ola are doing is to lay down the groundwork for a new sector and more efficient urban transport services. That they may not survive but the ecosystem they create will continue to thrive and add value to urban transport. Reliance’s entry into this sector is a step in making this sector more sustainable.

Will I switch once they launch? Depends upon the quality of service. Currently I’m loyal to Uber primarily because of that factor, but if their service drops and Reliance can offer better service I will have no hesitation in switching.

The ET article linked above talks about drivers cribbing about falling incentives by Uber and Ola. It will be interesting to see how the market plays out once the market stabilises and incentives hit long-run market rates (at which aggregators need to make a profit). A number of drivers have invested in cabs now looking at the short-term profits at hand, but these will surely drop with incentives as the industry stabilises.

Reliance’s entry into cab aggregation is also ominous to other “new” sectors that have shown a semblance of settling down after exuberant VC activity – in the hope that VCs will stop funding that sector and hence competition won’t grow. After the entry into cab aggregation, I won’t be surprised if Reliance Retail were to move into online retail and do a good job of it. The likes of Flipkart beware.

Pipes, Platforms, the Internet and Zero Rating

My friend Sangeet Paul Chaudary, who runs Platform Thinking Labs, likes to describe the world in terms of “pipes” and “platforms”. One of the themes of his work is that we are moving away from a situation of “dumb pipes”, which simply connect things without intelligence, to that of “smart platforms”. Read the entire Wired piece (liked above) to appreciate it fully.

So I was reading this excellent paper on Two-Sided Markets by Jean-Charles Rochet and Jean Tirole (both associated with the Toulouse School of Economics) earlier today, and I found their definition of two-sided markets (the same as platform business) striking. This is something I’d struggled with in the past (I admit to saying things like “every market is two-sided. There’s a buyer and a seller”), especially given the buzzword status accorded to the phrase, but it is unlikely I’ll struggle again. The paper says:

A necessary condition for a market to be two-sided is that the Coase theorem does not apply to the relation between the two sides of the markets: The gain from trade between the two parties generated by the interaction depends only on the total charge levied by the platform, and so in a Coase (1960) world the price structure is neutral.

This is an absolutely brilliant way to define two-sided markets. The paper elaborates:

Definition 1: Consider a platform charging per-interaction charges a^B and a^S to the buyer and seller sides. The market for interactions between the two sides is one-sided if the volume V of transactions realized on the platform depends only on the aggregate price level

a=a^B +a^S

i.e., is insensitive to reallocations of this total price a between the buyer and the seller. If by contrast V varies with a^B while a is kept constant, the market is said to be two-sided.

So for a market to be two-sided, i.e. for it to be intermediated by an “intelligent platform” rather than a “dumb pipe”, the volume of transactions should depend not only on the sum of prices paid by the buyer and seller, but on each price independently.

The “traditional” neutral internet, by this definition, is a platform. The amount of content I consume on Youtube, for example, is a function of my internet plan – the agreement between my internet service provider and me on how much I get charged as a function of what I consume. It doesn’t depend on the total cost of transmitting that content from Youtube to me. In other words, I don’t care what Youtube pays its internet service provider for the content it streams. Transaction costs (large number of small transactions) also mean that it is not practically possible for Youtube to subsidise my use of their service in this model.

Note that if buyers and sellers on a platform can make deals “on the side”, it ceases to be a platform, for now only the total price charged to the two matters (side deals can take care of any “adjustments”). The reason this can’t take place for a Youtube like scenario is that you have a large number of small transactions, accounting for which imposes massive transaction costs.

The example that Rochet and Tirole take while explaining this concept in their paper is very interesting (note that the paper was written in 2004):

…As the variable charge for outgoing traffic increases, websites would like to pass this cost increase through to the users who request content downloads…

..an increase in their cost of Internet traffic could induce websites that post content for the convenience of other users or that are cash-strapped, to not produce or else reduce the amount of content posted on the web, as they are unable to pass the cost increase onto the other side.

Note how nicely this argument mirrors what Indian telecom companies are saying on the Zero Rating issue. That a general increase in cost of internet access for consumers can result in small “poor” consumers to not consume on the internet at all, as they are unable to pass on the cost to the other side!

Fascinating stuff!