Why You Can't Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers


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. Using the model of expectations-based reference points of
Koszegi and Rabin (2006), I distinguish between anticipated and unanticipated
daily wage variation and present evidence that only a small fraction of wage
variation (about 1/8) is unanticipated so that reference dependence (which is
relevant only in response to unanticipated variation) can, at best, play a limited
role in determining labor supply. 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 nega-
tive. 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).

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