Tag: taxi

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.

Cabs to airports

Early yesterday morning I had a minor scare when Mega Cabs stood me up. I had a flight to catch at 7 am to Mumbai, and had booked a Mega Cab for 5am. This was after consulting a few friends who are frequent travellers on Monday mornings, who advised that finding an Uber or Ola at 5am is not particularly straightforward. I must mention that I haven’t done business trips for a while, which means I haven’t had to catch 7am flights, so the last time I took one such flight was before Ola/Uber became big in Bangalore (October 2014). And I’ve always preferred Mega to Meru since their cabs are relatively better maintained and more prompt.
And then Mega stood me up. The assigned driver Nagesh N never called me, and when I called him, didn’t pick up. I didn’t panic, since I knew I could get a cab on Uber or Ola, except that neither had any cabs available. I called Mega customer care, who promised an alternate cab at 5:15 (still leaving enough time to get to the airport and catch my flight). But then I received an SMS saying that I’ll get a cab at 6:15. Rather than arguing with Mega, I tried Uber once again, and this time I was in luck, finding a cab that would take me to the airport at a surge of 1.8X (80% more than the “normal” fare).
So on the way to the airport I got talking to Kumar, my Uber driver, about the economics of cab rides in Bangalore, and airport trips. As I had mentioned in my earlier post on Uber’s new pricing model, the reduction in per kilometer fare and increase in per rupee fare has meant that an airport run is normally not remunerative for an Uber driver. Add to this the fact that Uber’s bonus payments to drivers are on a “per trip” basis rather than a percentage or distance basis, that a driver reaching the airport at around 6am has to wait for at least a couple of hours to get a passenger to ride back to the city, and that Uber’s new bonus structures that began today not paying much incentives for trips before 7 am (this was told to me by Kumar), drivers have responded by simply not switching on their Uber systems at 5 in the morning, when the likelihood that any trip is an airport run approaches 1.
This is clearly inefficient, and  consequence of bad pricing on behalf of Uber. On the one hand, drivers are denied opportunities to carry customers over long distances, which is an airport run. On the other, customers are inconvenienced thanks to the lack of cabs, and have to rely on the otherwise rather unreliable and mostly unused Meru or Mega cabs, whose cars are of poor quality and drivers unresponsive. A lose-lose situation. All thanks to bad regulation (read my post in Pragati on how Uber is like a parallel regulator).
The solution is rather simple – an airport surcharge. Any trips to or from the airport on Uber can be slapped a further surcharge (of Rs. 200, perhaps). Such a surcharge will make the ride remunerative for drivers, while at the same time still keeping Uber much cheaper than the likes of Meru or Mega. In fact, this morning’s trip, after the 1.8X surge, cost me Rs. 780, which is cheaper than what it would have cost me if Nagesh N of Mega Cabs had not ditched me, and I could pay in a “cashless” manner, directly from my Paytm account. It’s a surprise that Uber hasn’t yet figured this out, given all their “data science” prowess!
Update: 
A friend who I met on the flight told me that in his town (Whitefield) it’s not hard to find an Uber/Ola cab at 5am on Mondays, except that the drivers cancel rides once they figure out it’s for an airport drop. Again pointing to the fact that incentives are not aligned for maximum throughput

Baumol Disease Index

In his excellent take on why Rohit Sharma’s 264 is bad for cricket, Niranjan Rajadhyaksha writes about the Baumol’s Cost Disease. This phenomenon, which was first described by William Baumol and William Bowen in the 1960s, describes the increase in cost of labour in industries that have seen little productivity. This has to do with an increase in productivity in other sectors which pushes up the clearing price of labour, which increases the costs of industries that have seen no improvements in productivity.

Based on this, we can construct an index on how industrialised an economy is, which I’m going to christen “Baumol Disease Index”. The basic idea is to pick a sector that is likely to be unaffected by productivity changes over the long term, and look at the median salary of workers in that sector in different countries and across different points in time. This can help us compare the relative levels of industrialisation and productivity in different countries, and in the same country over time.

In order to construct this index, we will take into account one sector which has a lot of “human input” and is unlikely to see much improvement in productivity thanks to mechanisation. My first choice for this was for employees of a company like McDonalds (taking off on The Economist’s Big Mac Index) but then that sector is not that insulated from greater productivity.

We could use the original example that Baumol and Bowen used, which is performing arts, but then performing arts is a winner takes all market – Iron Maiden will be able to command much higher ticket prices compared to the local orchestra thanks to their history and brand and perception of quality. So performing arts is not a great example, either.

Another good choice would be government bureaucrats, since their work is unlikely to be much affected by productivity. But then we’ve had some computerisation and that must have increased some productivity, and ability to be productive and willingness to be productive don’t always go hand in hand!

What about drivers? Despite the efforts towards development of driverless cars, these are unlikely to really take off in the next couple of decades or so, and so we can assume that productivity will remain broadly constant. The other advantage of drivers is that while salaries are tricky to measure (and we need to depend on surveys for those, with mostly unreliable results), taxi fares in different cities are public information, and it is not hard to separate such fares out into cost of fuel, cost of car and cost of driver’s time. This way, measurement of an average taxi driver’s income in different cities and countries, and at different points in time, should not be really difficult.

So, I hereby propose the Baumol Disease Index. It is the per month pre-tax expected income for a driver in a particular city after taking into account costs of fuel and car. This number is going to be imputed from taxi prices. And is going to be a measure of general levels of productivity and industrialisation in an economy. Sounds good?

And while we are on the topic of indices, you should read this excellent leader in last week’s The Economist on the profusion of indices. And since we have a profusion anyway, adding this one additional index shouldn’t hurt! And this one (Baumol Disease Index) measures something that is not measured by too many other indices, and is simple to calculate!

Howzzat?

The “Per Person” catch

Every time a travel agent sends you an itinerary for a tour package, look for the units of the cost. Usually it’s quoted in US Dollars per person. The funny thing is that this is how it is quoted even when it is just an accommodation package where two or three of you are going to share a room.

I wonder if this is a way to encourage more spending, since the customer perceives the total cost to be a much smaller number when he sees “per person” than when he sees an all-inclusive number.

Like for a forthcoming trip, the travel agent sends me an email saying “the hotel will send a taxi to pick you up at the airport at a cost of EUR 50 per person”!!

On a similar note, I realize travel agents love to bundle. When costs across several hotels and trains and taxis are bundled together and presented to you as an aggregate (“per person”, again), it is easy for them to pass on overheads to you without you figuring out where exactly that overhead went.

There have been times in the past when I’ve received packages from travel agents, then tried to purchase each component of that package online, and found that the total cost of buying the parts separately is approximately half the bundled cost that travel agents impose!

Two kinds of immigration

There are fundamentally two kinds of immigration – local job-creators and local job-competitors. The former are primarily middle and upper middle class people, who create jobs locally in terms of employing people (directly) to provide services for them – like maids, cooks, drivers, laundrymen, etc. The latter are primarily working class people who migrate in order to provide local services. They work as maids, cooks, drivers, etc.

Already existing local service providers welcome the immigration of job-creators. That means they now have the opportunity to push up their asking prices, since there is now more competition for their services. There is little economic opposition to the immigration of job-creators. The opposition to them is usually cultural – witness the rants of middle class “native” Bangaloreans like me against “koramangala people”.

Job-competitors, on the other hand are not so welcome. While they usually don’t contribute too much to the “culture” of the city, they compete directly economically against already existing local service providers. There is a clear economic rationale for local service providers to oppose the entry of more such providers, and since the local service providers are usually numerous and politically active, it is easier to oppose the entry of such job-competitors.

In the 1960s, for example, Shiv Sena started out by targeting South Indian middle class people. However, that campaign didn’t last long, since the “masses” (mostly local service providers) realized that it was economically counterintuitive for them to target middle class people. Hence, gradually over time, the rhetoric changed and the targets are now immigrant job-competitors. So you have Shiv Sena guys beating up Bihari taxi drivers, etc. And since this targeting of immigrant job-competitors is economically advantageous to the “masses”, it is likely to be more sustainable than the targeting of immigrant middle class people.