Do NYC cab drivers quit too early when it rains?
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A BIG DATA ANALYSIS WITH NEW YORK CITY CAB DATA
Students of judgment and decision making (aka behavioral economics) are familiar with the idea that cab drivers work until they hit an income target and then quit, ignoring opportunities to make more money on especially profitable days, such as when it rains. They probably get the idea from hearing people talk about the paper Labor Supply of New York City Cabdrivers: One Day at a Time. We say “hearing people talk about the paper” because that paper does not say that cab drivers make more money when it rains and is otherwise quite cautious (e.g., it concludes “because evidence of negative labor supply responses to transitory wage changes is so much at odds with conventional economic wisdom, these results should be treated with caution.”)
A working paper and presentation by Princeton economist Henry Farber looked at recently released data on every cab ride taken in New York City from 2009-2013 (about 900 million trips, which he down-samples). Some of Farber’s conclusions are:
- Increase in demand and reduction in supply make it difficult to find a taxi in the rain.
- But wage is no higher when it rains.
- Lower supply is not the result of drivers stopping after having reached their target.
- Lower supply is result of less pleasant driving in the rain.
ABSTRACT
In a seminal paper, Camerer, Babcock, Loewenstein, and Thaler (1997) find that the wage elasticity of daily hours of work New York City (NYC) taxi drivers is negative and conclude that their labor supply behavior is consistent with target earning (having reference dependent preferences). I replicate and extend the CBLT analysis using data from all trips taken in all taxi cabs in NYC for the five years from 2009-2013. The overall pattern in my data is clear: drivers tend to respond positively to unanticipated as well as anticipated increases in earnings opportunities. This is consistent with the neoclassical optimizing model of labor supply and does not support the reference dependent preferences model.
I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers differ from more experienced drivers in their behavior. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and only rarely substantially negative. I also find that new drivers with smaller elasticities are more likely to exit the industry while drivers who remain learn quickly to be better optimizers (have positive labor supply elasticities that grow with experience).
REFERENCE
Farber, Henry S. (2014). Why You Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers. National Bureau of Economic Research Working Paper Series No. 20604 http://www.nber.org/papers/w20604
H/T Eric Jaffe http://www.citylab.com/weather/2014/10/why-new-yorkers-cant-find-a-taxi-when-it-rains/381652/
Photo credit: https://www.flickr.com/photos/chrisschoenbohm/6186211082/
For almost 7 years, I represented a large group of Toronto city cab drivers in a lawsuit.
Here is what I learned about why you cannot get a cab in the rain, in Toronto.
(Note that a cab owner can rent out his/her cab to a driver.)
1. There are a number of drivers who either not licensed as a taxi driver or don’t have the appropriate car insurance.
2. Many of these drivers aren’t very good on the road. They cannot afford an accident. So, they stay home in bad weather.
3. Most are renting cabs which are licensed to be at Airport.
4. When there is bad weather, three things happen.
First, there are too few cabs at the Airport because the unlicensed drivers stay at home.
Second, the independent cab drivers -who are not affiliated with a brokerage- tend to stay at home nobody is flagging a cab & most customers are using the brokerage and calling.
Finally, in response to the lack of Airport cabs, the authorities relax and ask for City cabs to go to the airports -making it harder to flag a cab downtown.
The dynamics in each city would be different depending upon the licensing structure.
November 19, 2014 @ 9:34 pm
When I thought of taxi drivers not driving in the rain, I thought it was because of lack of customers. It is true then that they could still make money, but why risk their cab? If it is raining really hard, there is an increased risk of crashing so I think it would be better to just not take the risk. Not driving in the rain because it is unpleasant seems like a valid excuse to me.
April 6, 2015 @ 9:49 am
The primary focus of Farber’s article is on the impact of precipitation on cab driver hours worked and wages earned. However, analyzing average daily cab driver wages while controlling for precipitation, driver experience, etc. is incorrect. The analysis should be done at a much more disaggregate and local level, i.e., hour by hour within a day, specific to location within the city. The reason for this should seem obvious: precipitation can occur throughout the day and can vary in intensity by GPS location. Precipitation is just as likely to occur when a driver is starting a shift as it is when he is making a decision to end a shift. Moreover, “precipitation” isn’t just precipitation. Snow day behavior should be different from rain day behavior. Not to mention that the intensity of the precipitation (e.g., light snow that doesn’t stick vs days long blizzards vs heavy rain with flooding) can have significant and widely differing impacts on driver decision-making. Driver strategy should play a role here as well insofar as some drivers prefer driving days vs nights, while some only work Manhattan while others prefer to sit in the queues at the airports. Finally, driving conditions can be significant in shaping decisions. E.g., traffic speed is a consequence of many more factors than just weather and the impact of traffic speed may be the single most important consideration with respect to shift termination, especially late in the shift.
April 12, 2015 @ 8:36 am