The entry of for-hire vehicles (FHV) through on-demand taxi service companies such as Uber and Lyft have disrupted the traditional taxi service industry. The unique business model of these companies allows cab drivers to ply on the road without the need for the coveted million-dollar taxi medallions. Thus, the heavily regulated taxi industry saw a rise in the number of taxis on the road. This increase in the supply of taxis resulted in demand for rides shifting from the incumbent taxis, such as the Yellow taxis of New York, to the new entrants in the market, Uber, Lyft, and the like. Since the Yellow taxis depend on their daily trips for their earnings, a fall in the number of trips will consequently result in a fall in their earnings. Thus, leading to the general belief that the entry of the new entrants resulted in a decline in earnings of the incumbent taxis. In this study, we show that the presence of these new on-demand taxi service companies does disrupt the traditional taxi market by taking away the demand from incumbent taxi drivers. However, the incumbent taxi drivers can learn on the job and hence can increase their overall total earnings despite the fall in demand.
This study uses trip level data of Yellow taxis and FHVs (comprising of Uber, Lyft, and the like) in New York City for 2015-2019. We use this trip level data to compute Yellow taxi driver’s earnings, trip count, and the distance traveled. We calculate the trip count of FHVs, and we use this to measure the intensity of FHV’s presence in a given taxi zone and time. Using OLS, we compute the effect of FHV’s presence on Yellow taxi driver’s earnings, distance traveled, and trip count.
The number of Yellow taxis plying on the roads of New York City at a time is fixed by the number of medallions, which is, in turn, regulated by the Taxi and Limousine Commission. Most of the Yellow taxi drivers lease a taxi daily. This encourages drivers to set a daily wage target, and upon reaching that target, they go off the streets. This form of labor supply discourages taxi drivers from maximizing their earnings and utility by working more on high paying days and less on low paying days. The entry of additional taxis in the form of Uber, Lyft, and the like increased the number of cabs, thereby changing the dynamics of the market by increasing the supply of taxis. Hence, we expect the demand would now be spread across the increasing supply base. This translates to reduced demand for an individual Yellow taxi, and one would expect that they will be unable to meet their daily earnings targets or be forced to reduce their daily earnings targets. Eventually, resulting in a fall in their earnings per trip.
Our results support the initial hypothesis of reduced earnings. An increase in FHV trips by 24 rides result in a fall of Yellow taxi’s average hourly fare per trip by 8 dollars, and a reduction in Yellow taxi’s average distance traveled per trip by 20%. These results are for the entire city of New York. However, these results are not the same when we consider parts of the city at a time. That is, the effect of FHV’s presence differs by taxi zones. 1 In taxi zones, where traditionally (before Uber’s entry), you have high Yellow taxi activity (demand), we notice a fall in demand for Yellow taxi with an increase in FHV’s activity (FHV trip count). That is, with an increase in FHV trips by 24 rides, the Yellow taxi’s average hourly fare per trip falls by 10 dollars. While the average distance traveled per trip falls by 13%. These results hint towards a reduction in the supply of Yellow taxis with an increase in the supply of FHVs. That is a substitution between the supply of Yellow taxis and FHVs.
When we consider taxi zones where traditionally you have had low Yellow taxi activity, the supply of Yellow taxi rises with the same increase in FHV’s activity. An increase in FHV trips by 24 rides will result in a reduction of Yellow taxi’s average hourly fare per trip by 49 dollars. However, in these taxi zones, the average distance traveled per trip increases by over 220%. Here, the results point towards an increase in the supply of Yellow taxis with an increase in the supply of FHVs. That is, the supply of Yellow taxis and FHV are complementary to each other.
Manhattan and Brooklyn are two boroughs where we, traditionally, notice high Yellow taxi activity. This can be seen through the heatmap below. Thus, we re-run our results by comparing Manhattan and Brooklyn against the other three boroughs: Bronx, Queens, and Staten Island. These results confirm our previous findings; a substitution between the supply of Yellow taxis and FHVs in Manhattan and Brooklyn and a complementary effect between the supply of Yellow taxis and FHVs in the other three boroughs.
So far, we have looked at the average hourly earnings per trip and distance traveled per trip for an individual taxi driver. However, another crucial element of a taxi driver’s total earnings is the number of trips it makes in a shift. When we account for the number of trips made, we are able to calculate the overall earnings and total distance traveled by all taxi drivers during a given shift. These aggregate results indicate that the total hourly earnings and total distance traveled increases by 1348 dollars and 116 miles, respectively, with a concurrent rise in FHV trips by 24 rides. We are thereby indicating that overall, the presence of Uber, Lyft, and the like increases the demand for Yellow taxis.
Figure 1 Heatmap depicting Yellow taxi trips by taxi zone before Uber’s entry. A darker shade indicates a higher number of trip
A heightened FHV presence leads to a fall in earnings and distance traveled for an individual taxi driver. However, this individual-level effect of FHV is heterogeneous as Yellow taxi drivers are learning on the job and moving towards areas where they face less competition by the new entrants and increased demand. Further, upon accounting for the number of trips made by Yellow taxi drivers, we find that the total earnings and distance traveled of all taxi drivers increases with an increase in FHV trips.
We suggest that this phenomenon can be explained by a behavioral shift that the presence of Uber, Lyft, and the like have brought about. We postulate that the people are now habituated to taking taxi rides for commutes and hence, increasing the total demand for taxi rides. The increase in demand must be significant enough for it to spill over onto Yellow taxis despite the rise in taxi supply. Testing of this theory is beyond the scope of this study, and we leave it open for future studies to explore.
Previous studies have documented a reduction in the total earnings of the incumbent taxi driver; therefore, this study is contrary to these previous studies. Though promising, we have only used New York city taxi trip data to arrive at the results. Hence, they are not indicative of a similar phenomenon in other cities/countries. Due to data limitations, we could not calculate the distance or the fare for FHVs, nor could we estimate our results at an individual driver level. With an increase in the availability of high-resolution data covering more geographic regions, we will be able to answer these questions for more cities and with better precision.
About the Author:
Rohin Nandakumar is a Research Associate at SRITNE.
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