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.