Tag: uber

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.

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

Uber’s new pricing structure

So Uber has changed its pricing structure in Bangalore. Earlier they nominally charged Rs. 50 fixed, Rs. 15 per kilometer and Rs. 1 per minute, and then slapped a 35% discount on the whole amount. From today onwards the new fare structure is Rs. 30 fixed, Rs. 8 per kilometer and Rs. 1 per minute, without any further discounts. They’re marketing it using the Rs. 8 per kilometre number.

I took a ride this afternoon under the new fare structure, and the bill was Rs. 152, about the same as it would have been under the old fare structure. In that sense, I guess this was an “average ride”, in terms of the distance by time covered. This was the kind of ride where their assumption on distance travelled per unit time (in coming up with their new formula) was exactly obeyed!

So how do we compare the old and new formulae? We can start by applying the discount on the nominal numbers of the old formula. That gives us a fixed cost of Rs. 32.5, a per kilometer cost of Rs. 9.75 and a per minute cost per 65 paise. We can neglect the difference in fixed cost. Comparing this to the new cost structure, we find that the passenger now gets charged a lesser amount per kilometre, but a higher amount per minute.

In face, taking the “slope” between the old and new rates, the per kilometre cost has come down by Rs. 1.75 while the per minute cost has risen by 35 paise. Taking slope, this implies that Uber has assumed a pace of a kilometre per five minutes, or twelve kilometre per hour.

So if your journey is going to go slower than twelve kilometres per hour, on average, you will end up paying more than you used to earlier. If your journey is faster than twelve kilometres per hour, then you pay less than you did under the previous regime.

A few implications of the new fare structure are:

1. Peak hour journeys are going to cost more, for they are definitely going to go slower than twelve kilometres an hour
2. Your trips back from the pub should now be cheaper, for late nights when the roads are empty you’ll travel significantly faster than twelve kilometres an hour
3. What does this imply for the surge pricing in the above two cases? I think the odds of a surge during peak office hours will come down (since the “base price” of such a trip goes up, which will push down demand), and  the odds of a surge late on a Friday or Saturday night might go up (since base fare has been pushed down for that).
4. The Rs. 30 fixed cost implies that if a driver travels at 12 km/hr when looking for a new ride, the gap between rides for a driver is 11.5 minutes (if the driver spends X minutes, he will travel X * 12/ 60 kilometres in that time. The compensation for this combination is X + X*12/60 * 8, which we can equate to 30. This gives us X = 11.54).
5. Trips to/from the airport will now be cheaper, for you can travel much faster than 12 km/hr on that route. So Uber will become even more competitive for airport runs. Again this might increase probability of a surge at peak flight times.

I continue to maintain that Uber has the most rational price structure among all on-demand taxi companies, since the fare structure fairly accurately mirrors drivers’ opportunity costs. Ola doesn’t charge for the easily measurable time, and instead charges for “waiting time”, which is not well defined. Ola also has a very high minimum fare (Rs. 150). I wonder how they’ll play it if their planned acquisition of TaxiForSure goes through, since TaxiForSure was playing on the short trip model (with minimum fares going as low as Rs. 49). Given the driver approval before a ride, though, I doubt if anyone actually manages to get a Rs. 49 ride from TaxiForSure.

Times continue to be interesting in the on-demand taxi market. We need to see how Ola responds to this pricing challenge by Uber!

What sets Uber apart from other marketplaces

While at the gym this evening I was thinking of marketplaces.  To give some context, the reason I went there was that there were too many thoughts running around my head, so I needed to focus on something mindless or something that required so much concentration that I could only hold one other thought in my mind at that point in time. In fact, when you go “under the bar” (do a back squat),  even that one thought will vanish – you need all your physical and mental energy to complete the squat.

Anyway so I was thinking of marketplaces, and marvelling at the kind of impact companies like Uber and Ola have had. They have been an absolute gamechanger in their business in that it has completely changed the way that people and cabs get matched to each other. This was a matching that had been extremely inefficient in the past, but with these apps, they have become better by an order of magnitude. And it is this order of magnitude that sets apart Uber/Ola from other marketplace businesses.

And as I was moving between weights, I had another thought – the trick with Uber/Ola as a marketplace is that it is near impossible to do “side deals”. The ultimate nightmare for a platform/marketplace provider is to let the two sides “discover each other” and conduct further deals “offline”. This can be the bane of services such as dating services, where once you go on your first date (as recommended by OkCupid or Tinder), the dating service need not know anything about your relationship after that! They’ve “lost” you. In fact, talking to someone from the industry recently, I learnt that they do dating rather than marriage since in the former there is the hope of “repeat happy customers”.

It is similar with a service such as Airbnb, where once you’ve located a B&B you like, you can cut airbnb out of the deal from the next time on. Of course availability and stuff matter, but given how much in advance you book, a quick call to check availability is a small cost vis-a-vis the benefit of cutting out the middle man.

The beauty of Uber/Ola, however, is that it is impossible to do deals offline. Yes, after a ride, the driver and the passenger have each other’s numbers. But the next time the passenger wants a ride, the probability that the same driver is in the vicinity and free to give a ride is infinitesimal. So the passenger has to go to the app again. Moreover, it is the app that takes care of the pricing (using GPS, etc.) – something that is impossible to estimate if you try to cut out the app.

So when people say that they are building the “Uber for <some other service>”, in most cases it is not exactly the case – not all marketplace transactions are like Uber. For to be like Uber, you need to be an instant matching mechanism that changes the way the industry fundamentally operates; and you need a mechanism that keeps deals “online” by force.

Chew on it!

Rating systems need to be designed carefully

Different people use the same rating scale in different ways. Hence, nuance is required while aggregating ratings taking decisions based on them

During the recent Times Lit Fest in Bangalore, I was talking to some acquaintances regarding the recent Uber rape case (where a car driver hired though the Uber app in Delhi allegedly raped a woman). We were talking about what Uber can potentially do to prevent bad behaviour from drivers (which results in loss of reputation, and consequently business, for Uber), when one of them mentioned that the driver accused of rape had an earlier complaint against him within the Uber system, but because the complainant in that case had given him “three stars”, Uber had not pulled him up.

Now, Uber has a system of rating both drivers and passengers after each ride – you are prompted to give the rating as soon as the ride is done, and you are unable to proceed to your next booking unless you’ve rated the previous ride. What this ensures is that there is no selection bias in rating – typically you leave a rating only when the product/service has been exceptionally good or bad, leading to skewed ratings. Uber’s prompts imply that there is no opportunity for such bias and ratings are usually fair.

Except for one problem – different people have different norms for rating. For example, i believe that there is nothing “exceptional” that an Uber driver can do for me, and hence my default rating for all “satisfactory” rides is a 5, with lower scores being used progressively for different levels of infractions. For another user, for example, the default might be 1, with 2 to 5 being used for various levels of good service. Yet another user might use only half the provided scale, with 3 being “pathetic”, for example. I once worked for a firm where annual employee ratings came out on a similar five-point scale. Over the years so much “rating inflation” had happened that back when I worked there anything marginally lower than 4 on 5 was enough to get you sacked.

What this means is that arithmetically averaging ratings across raters, and devising policies based on particular levels of ratings is clearly wrong. For example, when in the earlier case (as mentioned by my acquaintance) a user rated the offending driver a 3, Uber should not have looked at the rating in isolation, but in relation to other ratings given by that particular user (assuming she had used the service before).

It is a similar case with any other rating system – a rating looked at in isolation tells you nothing. What you need to do is to look at it in relation to other ratings by the user. It is also not enough to look at a rating in relation to just the “average” rating given by a user – variance also matters. Consider, for example, two users. Ramu uses 3 for average service, 4 for exceptional and 2 for pathetic. Shamu also uses 3 for average, but he instead uses the “full scale”, using 5 for exceptional service and 1 for pathetic. Now, if a particular product/service is rated 1 by both Ramu and Shamu, it means different things – in Shamu’s case it is “simply pathetic”, for that is both the lowest score he has given in the past and the lowest he can give. In Ramu’s case, on the other hand, a rating of 1 can only be described as “exceptionally pathetic”, for his variance is low and hence he almost never rates someone below 2!

Thus, while a rating system is a necessity in ensuring good service in a two-sided market, it needs to be designed and implemented in a careful manner. Lack of nuance in designing a rating system can result in undermining the system and rendering it effectively useless!

How much surge is too much surge?

I had gone for a wedding in far-off Yelahanka and hailed an Uber on the way back. The driver was bragging about how it’s easy to find an Uber at any time anywhere in Bangalore, when I pointed out to him that earlier in the evening when I was on my way to the wedding I’d failed to find one, and had taken an Ola instead.

He was surprised that an Uber wasn’t available in Jayanagar when I told him that there were cars available but at a 1.7X surge, and given the distance I was to travel I found it more economical to take an Ola which was offering a ride at a flat Rs. 50 premium. To this, the driver said that he had also noticed that demand sharply dropped off once the level of surge went beyond 1.5X, and at such surges supply would easily outstrip demand.

Now I’m no fan of Ola’s pricing – I think the flat Rs. 50 premium during peak hours is unscientific, but I wonder if the level of Uber’s surges makes sense. From a pure microeconomic standpoint, it is easy to see where Uber is coming from – raise price until quantity demanded matches quantity supplied and let the market clear. The question, however, is if this kind of a surge makes sense from a behavioural standpoint.

The point is that the “base fare” (“1X”) is “anchored” in the customer’s mind, and thus any decision he takes in terms of willingness to pay is made keeping this “anchor” in mind. And when the quoted price moves too far from the anchor (beyond 1.5X, say), the customer deems that it is “too expensive”, and decides that waiting for a few minutes for fares to drop (or using a competing app) is superior to paying the massive premium.

I suppose that Uber would have noticed this. That there is a “cliff” surge price beyond which there is a massive drop off in volume of matchings. The problem is that if they restrict their surges to this “cliff value” they might be leaving money on the table by not being able to match the market. On the other side, though, if the surge is so high that the volume of transactions drops sharply, it results in much lower commissions for Uber! I’m assuming that a solution to this problem is on the way!

And I’ve found that it’s always harder to find a taxi on a Sunday. The problem is that because demand is lower, supply is also lower (this is a unique characteristic of “two-sided markets”) because of which the chances of finding a match are harder, and transaction costs are higher. I wonder if it makes sense for taxi aggregators to levy a “Sunday premium” (perhaps with Uber holding a day-long minimum of 1.2X surge or something) to compensate for this lack of liquidity!

Finally some sensible Uber regulation

Ever since Uber launched, regulators worldwide haven’t had a clue as to how to regulate it – it has been such a big disruptor in the taxicab market. Some countries and cities have taken to banning it outright (the list is too long to post links here). Others (such as some states in India) have tried to get Uber to register itself as a “taxicab company”.

The problem with all these regulations is that the Uber model (being replicated by firms such as Ola and TaxiForSure in India) is a fundamental gamechanger. As I have written in this earlier post, the on-demand model propagated by Uber implies that a number of the inefficiencies in the taxicab market don’t exist any more. In this context, trying to regulate it by moving it back to the earlier (extremely inefficient) model is extremely regressive. The right way to regulate is to create a level playing field for taxicab aggregators (which includes Uber) and move the market to a regime where the new technology-enabled efficiencies are made good use of.

And that is precisely what Los Angeles has done. In a rather progressive move (which ought to be copied by other states and cities and countries), the city has decreed that all city-based cab operators need to offer app-based booking services. The interesting bit in the regulation (see link above) is that drivers who fail to install the e-hail app are actually going to be fined.

What this will lead to is that the local taxi market is itself going to become more efficient which should definitely increase both profitability for the local cab industry and also availability of local cabs to the people of LA. What this will also do is to give people of LA a choice between using Uber and the traditional taxi app, which will lead to an improvement in Uber’s service levels. As things stand now I don’t see any downside from this LA regulation.

I hope the model succeeds in LA and other cities see the brilliance of the model and accept the efficiencies brought into the market thanks to this model and adopt similar regulation. I see this kind of regulation coming into the Bangalore market though the backdoor though. Ola already helps match auto rickshaws to customers and now TaxiForSure is also getting into that market. Will this mean that autos won’t have to line up for hours together in front of Lalbagh gate for passengers arriving in the city by bus?

Oh, and LA is not the first city to implement regulations requiring taxis to be “hailable” via an app. When I visited Singapore in November 2013, I found that cabs in the city worked the same way. Locals had an app which they would use to call taxis. The problem there though was that the app was only available to locals (your android/iOS had to be registered in Singapore for you to be able to even install the app), which made it a nightmare for us tourists to move around.

Oh, and while on the topic, a good revenue source for companies such as Ola or TaxiForSure would be to provide the technology backbone to cities that are seeking to use app-based hailing services for their cabs.

 

Inefficiencies in the auto rickshaw market and Uber

Taxi marketplaces such as Uber and Ola address inefficiencies and failures in the auto rickshaw / taxi market

Weary after a long cold night journey you get off the overnight bus from Chennai at Lalbagh’s Double Road gate, and look around for auto rickshaws. There are some ten of them around. The drivers are equally weary, having woken up early and left their homes to stand in the cold, hoping to find passengers alighting from buses. They want to get compensated for this, and quote you a fare that includes such compensation. All of them quote similar fares. You grudgingly bargain and agree, and conclude that Bangalore’s auto drivers are bastards.

Alternate scenario: as the bus reached Madivala, ten minutes away from Lalbagh Double Road gate at that time of the morning, you pull out your app and ask for a taxi to pick you up from Double Road gate in ten minutes’ time. The driver has been up, but resting at home. He leaves home now, just in time to be there at Double Road gate by the time you get off there. Off you get into the car and go.

You have to get to work and try catching an auto rickshaw. The guy asks for extra money for he has to take you through traffic-laden roads, which are a tax on his time, which the regulated fare doesn’t compensate him for. You bargain, get in, and conclude that auto drivers are bastards.

In an alternate scenario, you use an app-based taxi which calculates the fare as a linear combination of distance travelled and time taken, which means that the driver gets compensated for getting stuck in traffic without having to bargain for it. And without you having to think that the driver is a bastard.

In the evening you are trying to get an auto rickshaw from MG Road, and the guy asks for a premium. This premium is not reflective of costs, but the fact that demand for auto rickshaws in that area at that time is high, and that there will be customers willing to pay that premium. You conclude that the auto rickshaw driver is a bastard. Uber’s surge pricing (which can be steep at times) doesn’t evoke the same reaction from you. Uber has centralised knowledge of demand and supply so they can clear prices better, while the auto driver, lacking that knowledge, quotes a price that reflects his lack of market knowledge. And not having a good idea of what to charge, he might try to charge above market price.

What I’m trying to say here is that the local taxi/auto rickshaw market is inefficient, and ridden with failures. There is lack of information flow between demand and supply, which leads to inferior price negotiation, and the transaction cost of time and effort wasted on negotiation as opposed to using that time to travel! And when a market fails, the classic economic response is regulation, but in the case of taxi markets regulation is so poor (regulated prices do not reflect costs) that it enhances the market failure. The (badly) regulated prices anchor into people’s minds unrealistic expectations, and when auto drivers nudge them towards more realistic market prices, passengers assume that they (drivers) are bastards.

It is in this context that players like Uber and Ola (I’m not a fan of Ola’s pricing model, though) step in and try to resolve the market failure by improving flow of demand-supply information and setting “clearing prices” that compensate the driver in line with his costs. If you look closely, these companies are actually rescuing the local taxi market from its inherent inefficiencies and failures and bad regulation!

It is important, however, that no one market place ends up becoming a monopoly. As long as we have two or three different marketplaces, both customers and drivers have the choice of moving between one and the other, and this will ensure that these market places face market pressures from the two sides of the market, and if they “regulate” in an unfair manner, their participants will move to a competing marketplace, resulting in loss of business for the marketplace.

But then, considering the inherent network effects of the marketplace model, I don’t know how we can ensure that competition exists!

 

Uber, Meru and Service Taxes

The use of arbitrary barriers in regulation, like the Rs. 10 lakh limit on Service Taxes is counterproductive and can lead to a non-level playing field. More importantly such barriers encourage small-scale operations which can act against efficiency

A couple of months back, the Service Tax Department slapped a notice on Uber, demanding that the cab aggregation service pay service tax on its revenues. Cab services fall under the service tax net, and recently other cab service providers such as Meru and Mega have started adding a service tax component to their bills.

What queers the pitch in the case of Uber is who pays, and whether they pay at all. Uber claims to be an aggregation platform, bringing together cabbies and passengers, and says that it is the cabbies who are in charge of paying service tax on the revenues they make through the platform. From the Tax Department’s perspective though, going after thousands of cabbies demanding taxes is not very feasible, so they are trying to get Uber to pay the service tax.

More importantly, Service Tax becomes payable only if the annual revenues from the service cross Rs. 10 lakh and it is unlikely that too many of Uber’s cabbies will cross that threshold. So if we were to look at Uber strictly as an aggregator (which it actually is), it is unlikely that any service tax can be collected on its services!

What it also means that this gives platforms like Uber an unfair advantage over companies such as Meru which own their taxis – the latter’s revenue is much more than Rs. 10 lakh per annum and thus service tax has to be paid on the entire revenue! And this means that the playing field when it comes to taxi services is not level – for it is cheaper for an individual running a single taxi to offer service rather than a company offering a fleet.

This is similar to regulations in manufacturing that make it much more expensive (in terms of enhanced labour regulations and disclosures for companies beyond a certain size) for larger companies to operate vis-a-vis smaller ones. Even in the proposed relaxation of labour laws, a number of relaxations are to do with the minimum size of a company for doing the disclosures, and not with the easing of regulations themselves. All that it means is that just the threshold is raised – it becomes easier for companies to grow beyond their current levels of inefficiency, but they will soon hit a new level of inefficiency!

The problem for all this is the arbitrary fixing of slabs. An ostensible reason for fixing the minimum slab for service tax at Rs. 10 lakh is that enforcement for people earning less is going to be difficult. But as can be seen in the Uber case, this can lead to inefficient structures of industrial organisations, by keeping them small, and is hence not prudent. The government would do well to remove such arbitrary numbers from its regulation!

The other thing about service tax is that once your income crosses Rs. 10 lakh, you pay service tax on your entire income rather than the excess over 10 lakh, which is how income tax is structured. This is again inefficient, for someone who is making Rs. 9.8 lakh is now dissuaded from taking new business since it can literally subtract value! Another reason for arbitrary barriers to go.