Investment banks, scientific research and cows

I’ve commented earlier on this blog about how investment banks indirectly fund scientific research – by offering careers to people with PhDs in pure sciences such as maths and physics.

The problem with a large number of disciplines is that the only career opportunity available to someone with a PhD is a career in academia. Given that faculty positions are hard to come by, this can result in a drop in number of people who want to do a PhD in that subject, which has the further effect of diminishing research in that subject.

Investment banks, by hiring people with pure science PhDs, have offered a safety net for people who haven’t been able to get a job in academia, as a consequence of which more people are willing to do PhDs in these subjects. This increases competition and overall improves the quality of research in these topics.

Beef is like investment banks to the dairy industry. I recall an article (can’t recall the source and link to it, though) which talked about V Kurien of Amul going to a meeting called by the Union government on banning cow slaughter. Kurien talked about his mandate from his cooperative being that everything was okay as long as cow slaughter wasn’t banned – for that would kill the dairy industry.

Prima facie (use of latin phrase on this block – check)  this might sound like a far-fetched analogy (research to cows). However, cow slaughter has an important (positive) role to play in encouraging the dairy industry.

When you buy a cow, you aren’t sure how good it is in providing milk, until you’ve put it through a few cycles of childbirth and milking. If after purchase it turns out that the cow is incapable of producing as much milk as you were promised, it turns out to be a dud investment – like getting a PhD in a field with few non-academic opportunities and not being able to get a faculty position.

When cow slaughter is permitted, however, you can at least sell the cow for its meat (when it is still healthy and fat) and hope to recover at least a part of the (rather hefty) investment on it. This provides some kind of a “safety net” for dairy farmers and encourages them to invest in more cows, and that results in increasing milk production and a healthier dairy industry.

This is not all. Legal slaughter means that there is a positive “terminal value” that can be extracted from cows at the end of their milking lives. Money can also be made off the male calves (cruel humans have made the dairy industry one-to-many. Semen from stud bulls is used to impregnate lots of cows, and most bulls never get to fuck) which would otherwise have negative value.

A ban on killing cows implies a removal of these safety nets. Investing in cows becomes a much more risky business. And lesser farmers will invest in that. To the detriment of the dairy industry.

There are already reports that following the ban on cow slaughter in Maharashtra last year, demand for cows is going down as farmers are turning to the more politically pliable buffaloes.

Similarly, with the investment banking industry seeing a downturn and the demand for “quants” going down, it is likely that the quality of input to graduate programs in pure science might go down – though it may be reasonable to expect Silicon Valley to offer a bailout in this case. Cows have no such luck, though.

Simulating segregation

Back in the 1970s, economist Thomas Schelling proposed a model to explain why cities are segregated. Individual people choosing to live with others like themselves would have the macroscopic impact of segregating the city, he had explained.

Think of the city as being organised in terms of a grid. Each person has 8 neighbours (including the diagonals as well). If a person has fewer than 3 people who are like himself (whether that is race, religion, caste or football fandom doesn’t matter), he decides to relocate, and moves to an arbitrary empty spot where at least 3 new neighbours are like himself. Repeat this a sufficient number of times and the city will be segregated, he said.

Rediscovering this concept while reading this wonderful book on Networks, Crowds and Markets yesterday, I decided to code it up on a whim. It’s nothing that’s not been done before – all you need to do is to search around and you’ll find plenty of code with the simulations. I just decided to code it myself from first principles as a challenge.

You can find the (rather badly written) code here. Here is some sample output:

Sample output

As you can see, people belong to two types – red and blue. Initially they start out randomly distributed (white spaces show empty areas). Then people start moving based on Schelling’s rule – if there are less than 3 neighbours of the same kind, you move to a new empty place (if one is available) which is more friendly to you. Over time, you see that you get a segregated city, with large-ish patterns of reds and blues.

The interesting thing to note is that there is no “complete segregation” – there is no one large red patch and one large blue patch. Secondly, segregation seems rather slow at first, but soon picks up pace. You might also notice that the white spaces expand over time.

This is for one specific input, where there are 2500 cells (50 by 50  grid), and we start off with 900 red and 900 blue people (meaning 700 cells are empty). If you change these numbers, the pattern of segregation changes. When there are too few empty cells, for example, the city remains mixed – people unhappy with their neighbourhood have no where to go. When there are too many empty cells, you’ll see that the city contracts. And so forth.

Play around with the code (I admit I haven’t written sufficient documentation), and you can figure out some more interesting patterns by yourself!

Capitalism and Freedom and JNU

This piece by David Henderson has a very powerful quote by Milton Friedman. Quoting in full:

In the circumstances envisaged in the socialist society, the man who wants to print the paper to promote capitalism has to persuade a government mill to sell him the paper, a government printing press to print it, a government post office to distribute it among the people, a government agency to rent him a hall in which to talk and so on. Maybe there is some way in which one could make arrangements under a socialist society to preserve freedom and to make this possible. I certainly cannot say that it is utterly impossible. What is clear is that there are very real difficulties in preserving dissent and that, so far as I know, none of the people who have been in favor of socialism and also in favor of freedom have really faced up to this issue or made even a respectable start at developing the institutional arrangements that would permit freedom under socialism. By contrast, it is clear how a free market capitalist society fosters freedom.

Think about the ongoing protests at Jawaharlal Nehru University, a far-left-of-centre university, regarding the rally they took out last week and the government crackdown thereafter. While the current protests there have little to do with economics, and mostly about government control, given that a large section of the university has a mostly leftist anti-capitalist agenda, it’s a good example to take.

So where did the students and faculty of JNU obtain the resources to organise their protest marches? Some posters and banners might have been handmade, but many would’ve been bought (or made to order) from capitalist banner manufacturers.

The protests were largely covered by capitalist media houses which gave them further ballast, and acted as a force multiplier. Discussions on capitalist TV channels and newspapers (some of them publicly listed) added legitimacy to the protests.

Protestors would have needed a way to coordinate regarding the time and location and manner of protests. While old-fashioned methods such as notice boards and offline meetings could have been used, it is far more likely (and far easier) that the protestors used a capitalist social network (such as WhatsApp or Telegram (though admittedly the latter is not-for-profit, but it’s just that its owners are not optimising for profits) ) to coordinate their protests, using smartphones and computers made by capitalist manufacturers and sold by capitalist shopkeepers.

In other words, capitalism is a necessary condition for any kind of freedom, especially freedoms directed against the state. In a wholly state-owned economy, last week’s protests would have been far harder, if not impossible.

The state-owned media could have been one-sided in the coverage. The state-owned banner manufacturers could have refused to sell to the protestors. State-owned social media would have snooped on and subverted attempts to organise (if not block them altogether). I’m only picking a few examples here.

The next time you think you can have social freedom without capitalism, think again. It is capitalists driven by profit motives who provide anti-state activists the necessary tools to express their freedom.

Bias in price signals from ask only markets

Yesterday I listened to this superb podcast where Russ Roberts of the Hoover Institution interviews Josh Luber who runs Campless, a secondary market for sneakers (listen to the podcast, it isn’t as bizarre as it sounds). The podcast is full of insights on markets and “thickness” and liquidity and signalling and secondary markets and so on.

To me, one of the most interesting takeaways of the podcast was the concept that the price information in “ask only markets” is positively biased. Let me explain.

A financial market is symmetric in that it has both bids (offers to buy stock) and asks (offers to sell). When there is a seller who is willing to sell the stock at a bid amount, he gets matched to the corresponding bid and the two trade. Similarly, if a buyer is willing to buy at ask, the ask gets “taken out”.

The “order book” at any time thus contains of both bids and asks – which have been unmatched thus far, and looking at the order book gives you an idea of what the “fair price” for the stock is.

However, not all markets are symmetric this way. In fact, most markets are asymmetric in that they only contain asks – offers to sell. Think of your neighbourhood shop – the shopkeeper is set up to only sell goods, at a price he determines (his “ask”). When a buyer comes along who is willing to pay the ask price of a good, a transaction happens and the good disappears.

Most online auction markets (such as eBay or OLX) also function the same way – they are ask only. People post on these platforms only when they have something to sell, accompanied by the ask price. Once a buyer who is willing to pay that price is found, the item disappears and the transaction is concluded.

What makes things complicated with platforms such as OLX or eBay (or Josh Luber’s Campless) is that most sellers are “retail”, who don’t have a clear idea of what price to ask for their wares. And this introduces an interesting bias.

Low (and more reasonable) asks are much more likely to find a match than higher asks. Thus, the former remain in the market for much shorter amount of time than the latter.

So if you were to poll the market at periodic intervals looking at the “best price” for a particular product, you are likely to end up with an overestimate because the unreasonable asks (which don’t get taken out that easily) are much more likely to occur in your sample than more reasonable asks. This problem can get compounded by prospective sellers who decide their ask by polling the market at regular intervals for the “best price” and use that as a benchmark.

Absolutely fascinating stuff that you don’t normally think about. Go ahead and listen to the full podcast!

PS: Wondering how it would be if OLX/eBay were to be symmetric markets, where bids can also be placed. Like “I want a Samsun 26 inch flatscreen LCD TV for Rs. 10000”. There is a marketplace for B&Bs (not Airbnb) which functions this way. Would be interesting to study for sure!

The myth of affordable housing

Cities are unaffordable by definition because of the value that can be extracted by living in them. 

A few months back, my Takshashila colleague Varun KR (Shenoy) asked me if there is any city where housing is not prohibitively expensive. It wasn’t a rhetorical question. While answering “no”, I went off on a long rant as to why affordable housing is a myth, and why housing in urban areas is by definition expensive. I had been planning to blog it for a while but I get down to it only now.

Cities are expensive to live in due to a simple reason – lots of people want to live there. And why do lots of people want to live in cities? Because the density in cities means that there is a lot more economic activity happening per capita that results in greater productivity and happiness.

If you are in a rural area, for example, there are few services that you could afford to outsource, for the small scale means that it doesn’t make sense for people to provide that service. Even when such services exist, lack of competition might mean a large “bid-ask spread” and hence inefficiency. This means you are forced to do a lot more tasks which you suck at, leaving less time for you to do things you are good at and make money from.

Needs of a rural area also means that there is a natural limit on the kind of economic activities that can be remunerative there, so if your skills don’t lie in one of those, you are but forced to lead a suboptimal existence.

Larger agglomerations (such as cities), by putting people closer to each other, provide sufficient scale for more goods and services to become tradable. Transaction costs are reduced, and you can afford to outsource a lot more tasks than you could afford to in a rural area, thus boosting your productivity.

Economist and noted urban theorist Jane Jacobs, in her book “Cities and the Wealth of Nations”, argues that economic development occurs exclusively in cities and “city regions” and proceeds to demolish different theories by which people have tried to create economic value in remote areas (my review of the book here).

The larger a city is, the greater the benefits for someone who lives there, controlling for ability and skill. Thus, ceteris paribus, the demand for living in cities exceeds that of living in smaller agglomerations, which gets reflected in the price of housing.

It might be argued that what I have presented so far is only an analysis of demand, and supply is missing from my analysis. (I don’t understand who is on the left and who is on the right on this one but) One side argues that the reason housing is not affordable in cities is that strict regulations and zoning laws limit the amount of housing available leading to higher prices. The other side talks about the greed of builders who want to “maximise profits by building for the rich”, which leads to undersupply at the lower end of the market.

While zoning and building restrictions might artificially restrict supply and push up prices (San Francisco is a well-known example of a city with expensive housing for this reason), easing such restrictions can have only a limited impact. While it is true that increasing density might lead to an increase in supply and thus lower prices, a denser city will end up providing scale to far more goods and services than a less dense city can, thus increasing the value addition for people living there, which means more people want to live in these denser cities.

As for regulations that dictate that “affordable housing” be built, one needs to look no further than the “Slum Rehabilitation Apartments” that have been built in Mumbai on land recovered from slums (the usual deal is for a builder to commit to building a certain number of “affordable” houses for the erstwhile dwellers of the slums thus demolished apart from “conventional” housing). Erstwhile slumdwellers rarely occupy such apartments, for they are willing to accept a lower quality of life (in another slum, perhaps) in exchange for the money that can be generated by renting out these apartments.

This piece is far from over, but given how long it’s been, I’ll probably continue in a second part. Till then, I leave you with this thought – a city becoming an “affordable” place to live is a cause of worry for policymakers (and dwellers of the city itself) because it is an indicator that the city is not adding as much economic value as it used to.


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.

Inequality in income and consumption

My hypothesis is that while inequality in terms of income or wealth (measured in rupees/dollars) has been growing, consumption inequality is actually coming down. I hope to do a more detailed analysis using data, but I’ll stick to an anecdote for this this introductory blogpost.

The trigger for this thought came about a year back, at a meeting in one of the organisations I’m associated with. The meeting wasn’t terribly interesting, so I spent time checking out the guy sitting next to me, whose Net Worth I knew is at least a couple of orders of magnitude more than mine.

He was wearing a Louis Philippe shirt, and I have several shirts of that brand. He had a Parker pen, and I use a Parker too. He had a rather fancy watch whose brand I do not recall now, but my Seiko isn’t that bad in comparison. And he had an iPhone, which cost four times as much as the phone I used then (a Moto G), but not out of reach for me.

I can go on but the gist is that while our income and wealth levels were different by an order of magnitude, our consumption wasn’t all that far off. I must admit that I’m also a so-called “1-percenter” in terms of income (recall a study which said that 99th percentile of income in India is Rs. 12 lakh per annum), so I was also part of the power law tail, yet the marginal difference in consumption to income levels was strikingly low.

Since this is an introductory blog post on this topic, I posit that this is a more general trend and applies at many other levels. The thing with inequality is that income (and wealth) is usually distributed according to a Power Law (unless the state is extremely coercive and extractive), so as the economy grows, inequality as measured by measures such as the Gini coefficient is bound to increase (here’s a nice but hard-to-read paper by Nassim Nicholas Taleb on why the Gini coefficient is flawed for fat-tailed distributions such as the power law).

Yet, as the economy grows, more people are pushed beyond a “basic level” of income where they are able to afford “necessities” (and certain kinds of luxuries), so inequality as measured by consumption will actually be lower. The challenge is in measuring such inequality appropriately.

I’ll mention a couple of more anecdotes in support of this. One sector where inequality has fallen is in commute. Some rich old-time Bangaloreans look back in nostalgia at a time when there was no congestion on the streets of Bangalore, and how the city has since deteriorated. Yet, that congestion-free travel was then available only to the extremely wealthy (who could afford private vehicles) or lucky (my father waited for four years to get his first scooter because of limited supply). Public transport infrastructure was abysmal and buses infrequent.

Now, a larger proportion of the population can afford private vehicles and public transport has also improved (though not by much), making life better at the lower end of income/wealth levels. And the rich (who had exclusive access to roads in private cars earlier) are faced with higher congestion.

Another obvious example is the telephone. Very few people had them even twenty years back (we applied for ours in 1989, only to get “allotted” a phone in 1995), and now pretty much everyone has a basic mobile phone now (and with cheaper smart phones, even some relatively poor people own smart phones).

This is a theory worth pursuing. Need to analyse how to collect data and measure inequality, but I think there’s something to this hypothesis. Any thoughts will be welcome!