I've written before about some obvious signs of widespread inefficiency in the traditional taxi market, as illustrated in the photo above that show the dozens of taxis that line up every day at The Mayflower Hotel in DC (and at every other major hotel in DC) and sit idly for very long periods of time waiting for passengers. In this post from last August "Why are a dozen taxis lined up every day at The Mayflower Hotel?" I wrote:
In a dynamic Uber (UBER) world, the available drivers respond to demand, and are directed by Uber to areas where there are a lot of passengers. And when demand is really high, surge pricing goes into effect to attract even more drivers to areas of high demand. But when there are always a dozen taxis sitting around idly at The Mayflower (and most other DC hotels), that just seems like an outdated form of transportation inefficiency, an inefficient excess supply, a failure to balance supply and demand, and something that would never happen in a ride-sharing world of much greater transportation efficiency.
There's now evidence of just how inefficient legacy taxis are compared to UberX when measured by two capacity utilization rates: a) the fraction of time taxi and UberX drivers have a fare-paying passenger in their cars and b) the percentage of total miles driven by taxi and UberX drivers with a passenger in their cars. The anecdotal evidence of taxi inefficiency I observe almost every day in DC is now confirmed more formally in a new NBER research paper by Judd Cramer and Alan B. Krueger titled "Disruptive Change in the Taxi Business: The Case of Uber," here's the abstract:
In most cities, the taxi industry is highly regulated and utilizes technology developed in the 1940s. Ride sharing services such as Uber and Lyft, which use modern internet-based mobile technology to connect passengers and drivers, have begun to compete with traditional taxis. This paper examines the efficiency of ride sharing services vis-à-vis taxis by comparing the capacity utilization rate of UberX drivers with that of traditional taxi drivers in five cities. The capacity utilization rate is measured by the fraction of time a driver has a fare-paying passenger in the car while he or she is working, and by the share of total miles that drivers log in which a passenger is in their car. The main conclusion is that, in most cities with data available, UberX drivers spend a significantly higher fraction of their time, and drive a substantially higher share of miles, with a passenger in their car than do taxi drivers. Four factors likely contribute to the higher capacity utilization rate of UberX drivers: 1) Uber's more efficient driver-passenger matching technology; 2)the larger scale of Uber than taxi companies; 3) inefficient taxi regulations; and 4) Uber's flexible labor supply model and surge pricing more closely match supply with demand throughout the day.
From the Findings section of the paper:
Figure 1 (reproduced above to the right of the photo) summarizes estimates of the mileage-based capacity utilization measure for Los Angeles and Seattle, the only two cities for which we have been able to obtain information on taxi drivers' miles. In Los Angeles, taxi drivers have a passenger in the car for 40.7% of the miles they drive, while UberX drivers have a passenger in the car for 64.2% of their miles, resulting in a 58% higher capacity utilization rate for UberX drivers. In Seattle, UberX drivers achieve a 41% higher capacity utilization rate than taxis in terms of share of miles driven with a passenger in the car [39.1% for taxis vs. 55.2% for UberX].
And when capacity utilization is measured by time with a passenger:
Across the five cities (LA, NYC, Seattle, San Francisco and Boston), UberX drivers have a passenger in their car around half the time that they are working, whereas taxi drivers have a passenger in their car anywhere from 32% of the time in Boston to nearly half the time in New York City.
From the paper's Conclusion:
There are several possible reasons why UberX drivers may achieve significantly higher capacity utilization rates than taxi drivers. First, Uber utilizes a more efficient driver-passenger matching technology based on mobile internet technology and smart phones than do taxis, which typically rely on a two-way radio dispatch system developed in the 1940s or sight-based street hailing. Second, in most cities Uber currently has more driver partners on the road than the largest taxi cab company. Apart from the technology, there are network efficiencies from scale, as pure chance would likely result in an Uber driver being closer to a potential customer than a taxi driver from any particular company given the larger scale of Uber. Third, inefficient taxi licensing regulations can prevent taxi drivers who drop off a customer in a jurisdiction outside of the one that granted their license from picking up another customer in that location. Fourth, Uber's flexible labor supply model and surge pricing probably more closely matches supply with demand during peak demand hours and other hours of the day.
The concluding section of the paper also raises some important implications of Uber's significantly higher capacity utilization rates compared to traditional taxis: a) Uber's greater efficiency translates to lower fares (e.g. UberX drivers in LA could charge 37% less than taxis and generate the same revenue per hour) and b) greater operational efficiency translates to less traffic congestion and less wasteful fuel consumption in cities like LA, where for every mile driven by taxis with a passenger in the car, they travel 1.46 miles without a passenger; the comparable figure for UberX drivers is 0.56 miles.
Finally, the authors' findings also have implications for occupational licensing laws, which are frequently impossible to repeal directly due to the entrenched power of politically connected, government-protected industry cartels like Big Taxi. However, new innovative technologies like Uber and Lyft (LYFT) as one example, are bringing about disruptive changes that are weakening (and could eventually eliminate) inefficient, unnecessary and counterproductive occupational licensing laws that for many, many generations were impervious to change.
Related: Beyond their much greater operational efficiency which lowers prices and reduces congestion, ride-sharing services are also providing other unintended side-benefits. For example, Uber and Lyft are being given credit for encouraging redevelopment of neighborhoods around downtown Nashville and helping to revitalize the city's urban core, see story here, here's a quote: "What it's doing for the neighborhoods near downtown Nashville is amazing. This is changing the game," said developer Khira Turner.