Maybe I’ll Just Take a Cab? : Ashoka University’s Shuttle Debacle
By Ayushman Roy (UG24), Illustrated by Atharv Apte (ASP 24)
On March 12, 2023, Ashoka University’s transport department announced the expanded role that their partnership with Uber was to take — Uber was tasked with the formalization and modernization of the user experience. The transport department claimed that the partnership was supposed to “result in an overall enhanced user experience led by convenient booking and tracking of shuttles” (Transport Department 2023). Presumably, the rationale behind the change was the issue of the weekend rush, where shuttle queues were frequently cut and wait-times sometimes stretched over three hours. However, the formalization of the shuttle services has resulted in severe unintended consequences while simultaneously failing to solve the problem it was initially tasked with. The shuttle queues may have dissipated but the actual availability of shuttles has eroded; a lamented aggravation of the problem has taken place. In this article, I argue that prematurely rushing into formalization may cause decision makers to exhibit sub-optimal market behavior, resulting in outcomes that aren’t Pareto optimal.
Probability of Seat ∝ Max Waiting Time=Willingness to Pay
Before the introduction of Uber, the shuttle services worked on a first-come first-serve basis. It was not a perfect system by any means — people frequently cut queues and the boarding areas were always overcrowded — but it worked. If you wanted a confirmed shuttle seat, then you just went earlier and waited. Hence, the wait was effectively a non-monetary cost associated with using the service. The equation given above describes the relationship — if you had a high willingness to pay, then the maximum you would wait for the shuttle would be much lower than a person with a low willingness to pay. Hence, the market was pareto-optimal, a situation where no one can be made better off without making someone else worse off, under the supply constraints it was subjected to. However with the introduction of Uber, the market underwent several fundamental changes. By elimination of the wait-times and instead associating the cost of the service with a booking mechanism that closely mirrors the course registration system of the University, the formalization led to the erosion of social preferences of the users which resulted in new problems of frequent no-shows and some instances of seat-hoardings. Uber liberalised the concept of a seat — you no longer needed to wait, and hence, your willingness to pay no longer determined whether you would get a seat or not. Hence, seat availability was now transformed into a public good from a more commodified older version where the usage of shuttles involved some personal costs through wait times. This transformation was not accompanied by a similar transformation among the users, who still considered it as a commodity that only just became cheaper. Hence, users could now book multiple seats for multiple shuttles, practically insuring their seats in case they were late for their shuttle.
This user behaviour, when practiced as a herd, caused an artificial supply shortage that forced people to either abandon their travel plans or take a paid Uber depending on their willingness to pay. I call this a “herd behaviour” because a small group of people overbooking the shuttles cause some shuttles to get booked completely, and hence, people who did not find a shuttle this time make sure they get enough “insurance” next time by overbooking the shuttles; a vicious cycle gets established. Empirical evidence released by the transport department proves my hypothesis: around 40% of seat bookings do not realise to actual ridership, and furthermore, these discrepancies have increased month-on-month (Transport Department 2023). These non-pareto optimal outcomes are a result of the premature modernisation of the shuttle service where technological advancements in the user experience have not been accompanied by a similar increment in shuttle frequency nor capacity leading to a supply-side bottleneck. The change to non-pareto optimal outcomes cannot be dismissed as mere transitory, and will persist unless some changes are made to improve the supply of shuttles.
Furthermore, the increased abstraction and invisibilization of the cost through the elimination of wait-times led to the erosion of the perceived publicness of the service, although the service was the exact same. People acted like payoff-optimizing economic agents with limited social preferences because they perceived the shuttle seat as something more private and commodified. Hence, people became liberal with their shuttle-booking behaviour which led to the overbooking of shuttles without a substantial increase in occupancy. The situation may have further aggravated through the use of penalties, like blacklisting and seat-rationing, placed for no-shows and other non-social behaviour. Samuel Bowles’ book “The Moral Economy” presents experimental evidence that proves the erosion of social preferences by crowding-out through incentives and fines (Bowles 2016). Hence, the transport department may have only contributed to the overbooking problem by categorically crowding-out the effect of social preferences through the implementation of inadequate and loophole-prone penalties.
The Ashokan shuttle debacle materialises itself through some interesting sociological consequences across campus. Firstly, a fairly rudimentary non-monetary secondary market has been established for shuttle seats through inter- and intra-batch group-chats. The evolution of these markets will provide excellent primary material for market development and other related analysis — it remains ambiguous whether these markets will eventually accommodate some form of monetary instruments or not, given current reliance on altruistic participation and stagnant shuttle supply. Secondly, the alternative shift towards paid Uber cabs have consequences across environmental, sociological and economic domains. The visual manifestations of the prevalent economic and social inequalities across campus has been aggravated by the shuttle crisis — the divide materialises through choices between cabs and rideshares. Students with lower disposable money, usually the ones on significant financial aid, disproportionately face the effects of seat scarcity. The Uber cabs that mirror the shuttle routes cost anywhere between ₹500–1000 and become unaffordable for most students. They are left with no alternative option, and hence, resort towards the use of risky rideshares cheaply available across the highway. When we look through the problem of intersectionality that the above line of facts present, we truly understand the potential crucial consequences such a nonchalant formalisation can bring about — how would economically-disadvantaged students commute when faced with immediate travel needs?
References:
Ashoka University Transport Department. Update on Shuttle Services, March 12, 2023.
Ashoka University Transport Department. “Shuttle Seat Utilization.” Sonipat, November 2023.
Bowles, Samuel. The Moral Economy: Why Good Incentives Are No Substitute for Good Citizens, 2016.
