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!

Why restaurant food delivery is more sustainable than grocery delivery

I’ve ranted a fair bit about both grocery and restaurant delivery on this blog. I’ve criticised the former on grounds that it incurs both inventory and retail transportation costs, and the latter because availability of inventory information is a challenge.

In terms of performance, grocery delivery companies seem to be doing just fine while the restaurant delivery business is getting decimated. Delyver was acquired by BigBasket (a grocery delivery company). JustEat.in was eaten by Foodpanda. Foodpanda, as this Mint story shows, is in deep trouble. TinyOwl had to shut some offices leading to scary scenes. Swiggy is in a way last man standing.

Yet, from a fundamentals perspective, I’m more bullish on the restaurant delivery business than the grocery delivery business, and that has to do with cost structure.

There are two fundamental constraints that drive restaurant capacity – the capacity of the kitchen and the capacity of the seating space. The amount of sales a restaurant can do is the lower of these two capacities. If kitchen capacity is the constraints, there is not much the restaurant can do, apart from perhaps expanding the kitchen or getting rid of some seating space. If seating capacity is the constraint, however, there is easy recourse – delivery.

By delivering food to a customer’s location, the restaurant is swapping cost of providing real estate for the customer to consume the food to the cost of delivery. Apart from the high cost of real estate, seating capacity also results in massive overheads for restaurants, in terms of furniture maintenance, wait staff, cleaning, reservations, etc. Cutting seating space (or even eliminating it altogether, like in places like Veena Stores) can thus save significant overheads for the restaurant.

Thus, a restaurant whose seating capacity determines its overall capacity (and hence sales) will not mind offering a discount on takeaways and deliveries – such sales only affect the company kitchen capacity (currently not a constraint) resulting in lower costs compared to in-house sales. Some of these savings in costs can be used for delivery, while still possibly offering the customer a discount. And restaurant delivery companies such as Swiggy can be used by restaurants to avoid fixed costs on delivery.

Grocery retailers again have a similar pair of constraints – inventory capacity of their shops and counter/checkout capacity for serving customers. If the checkout capacity exceeds inventory capacity, there is not much the shop can do. If the inventory capacity exceeds checkout capacity, attempts should be made to sell without involving the checkout counter.

The problem with services such as Grofers or PepperTap, however, is that their “executives” who pick up the order from the stores need to go through the same checkout process as “normal” customers. In other words, in the current process, the capacity of the retailer is not getting enhanced by means of offering third-party delivery. In other words, there is no direct cost saving for the retailer that can be used to cover for delivery costs. Grocery retail being a lower margin business than restaurants doesn’t help.

One way to get around this is by processing delivery orders in lean times when checkout counters are free, but that prevents “on demand” delivery. Another way is for tighter integration between grocer and shipper (which sidesteps use of scarce checkout counters), but that leads to limited partnerships and shrinks the market.

 

It is interesting that the restaurant delivery market is imploding before the grocery delivery one. Based on economic logic, it should be the other way round!

Getting BRT to work

Dedicated bus lanes are neither a necessary nor a sufficient condition for BRT

After significant success in Ahmedabad and spectacular failure in Delhi, Pune is the latest city in India to embark on a “Bus Rapid Transport” (BRT) project. As the name suggests, the point of a BRT is to provide fast and convenient transport to people on buses that ply on existing roads, with some sections of some roads being reserved for buses.

However, in popular imagination, BRT has become synonymous with bus lanes (a lane of road reserved for buses), and the whole controversy in Delhi (which caused the project to be shelved) was about a lane of an arterial road being reserved for buses. In fact, however, a dedicated bus lane is neither a necessary nor a sufficient condition for implementation of BRT.

The attraction of BRT is that it comes with low infrastructure cost – unlike a train or monorail (or even a tram) line, there is not much investment required in terms of physical infrastructure. The challenge with BRT, however, is that its buses are liable to get stuck in traffic (just like every other vehicle) which might prevent it from living up to its middle name.

For this reason, certain changes are made to traffic patterns so that BRT indeed remains rapid. For example, traffic signals on arterial bus routes might be designed to give priority to the directions where buses travel. You might have bus stops in the middle of the road for people to get on to buses. And you might reserve lanes on roads for buses. Once again note that the last named is not a necessary condition for BRT.

What BRT should deliver is a dense and reliable network of buses. On arterial and other key roads, frequency of buses should be extremely high. Our current model of point-to-point and hub-and-spoke based bus routes need to be given up in favour of a more dense network, where it might be quicker for people to change multiple buses to get to their destination. This also warrants a change in the ticketing system, using a zone-based ticket than the current point-to-point ticket, and moving ticketing offline.

 

The fashion so far in India (with Ahmedabad being a possible exception) is to announce arterial roads as “BRT corridors” and start off the BRT services by reserving lanes on these roads for buses, without bothering about linkages and networks at either end. The problem with this is that the losers of the road space “pay” immediately, but the benefits of BRT are not immediately forthcoming.

A better method of implementation would be to make reservation of bus lanes the last step in BRT. The first should be to increase the density of buses and creation of networks. The problem with this is that it requires investment and the expanded (and densified) network might run far below capacity for a while. Yet, as the network expands (even without dedicated lanes), people will begin to see the benefits and convenience offered, and demand for BRT will increase.

Two things will happen – firstly, the expanded and densified network of buses will start crowding out (literally) private vehicles on the road. Secondly, people will see the relative benefits of taking these buses and these buses will start filling up. As these two effects take place, there will come a point when lanes can be reserved for buses without slowing down any of the rest of the traffic.

What we need, in other words, is “system thinking“, and to look at BRT as a solution to move people to their destination in a more efficient manner. Once policymakers recognise that bus lanes are only a means to this end, we can expect BRT to implemented in a proper fashion.

On Sub-nationalism

Pramit Bhattacharya has a nice piece in MintOnSunday about the positives of sub-nationalism, which fosters provision of public and common goods. He cites academic research to contrast Kerala and Uttar Pradesh, which were similar in the mid-19th century, but now have significantly differing levels of public goods.

The research Bhattacharya cites argues that the linguistic sub-nationalism that was formed in Kerala in the mid 19th century was responsible for the state’s high levels of public goods and development. The absence of such sub-nationalism has resulted in weak institutions and weak development in UP, he says.

He ends the piece saying that sub-nationalism is not always a good thing and can lead to secessionist tendencies. He cites the example of Assam, where sub-nationalism has actually hampered development rather than fostering it.

This discussion reminds me about last year’s “unofficial” referendum in Catalunya about whether to secede from Spain. The vote was unofficial since the Spanish Parliament didn’t authorise it, but there were strong signs of Catalan nationalism when I visited Barcelona last October. The Yellow, Red and Blue flag of Catalan nationalism hung from several windows. There was a clock in one of the main squares counting down to the referendum (which finally didn’t matter).

And while there were several emotional reasons for the demand for secessions, including repression at the hands of the “Castilians”, one of the main reasons was economic – the share of national spending on Catalunya was far less than the proportion of Catalunya’s contribution to the Spanish National Budget. The feeling of “why should we subsidise the rest of the country?” was rampant.

This little story illustrates both the positive and negative aspects of sub-nationalism. The negative is easy to see from the above – strong sub-nationalism leads to a strong “us and them” sentiment towards the rest of the country, and the region begins to resent the rest of the country, especially if the latter gets a larger share of the national pie. And this can lead to secessionist tendencies as is evident in Catalunya.

The positive thing about sub-nationalism, on the other hand, is that it subsumes groupism at smaller levels. A strong sub-nationalist feeling means that people think of themselves as members of that sub-nationalist group, and solidarity to any “lower level” groups weakens.

The problem with high solidarity among small groups is that it may lead to provision of private goods at the expense of public goods. When a place is strongly divided by caste, for example, each caste group wants to maximise the interest of the particular caste, and thus invests in a way that the caste gets a bigger share of the seemingly fixed pie.

When the solidarity is at the level of a state or region, on the other hand, the best way to develop the region or state is to provide for public goods or welfare schemes that span the entire state or region, and this leads to an expansion of the pie and the overall development of the region. In other words, when the “us” is a largish geographical area, it is more likely that investments happen in terms of public goods for the area rather than private goods.

Coming back to the example of Kerala, the strong Malayali subnationalism of the mid 19th century had the effect of pushing down casteism. Consequently, the groupism happened at a level (“Malayalis”) that was larger and more diverse than the caste-level groupism that happened elsewhere (like in UP) where there was no strong sub-nationlist movement. The lack of sub-nationalism in a place like UP has meant that casteist divisions in the region have remained strong, and solidarity at that level doesn’t lead to public goods or development.

Think of the nation as a hierarchy, of sub-nations and sub-sub-nations and so forth. And each person’s loyalty is divided in different extents up and down the person’s “chain”. And among these different layers, it is a zero sum game. Thus, strong loyalties at a particular level are resented both by levels higher and lower, and justifiably so. But the higher the level at which the loyalty remains, the better it is for the provision of public goods and development. Chew on it.