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

43 Pages Posted: 22 Oct 2014 Last revised: 20 Apr 2023

See all articles by Henry S. Farber

Henry S. Farber

Princeton University; National Bureau of Economic Research (NBER)

Multiple version iconThere are 2 versions of this paper

Date Written: October 2014

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).

Suggested Citation

Farber, Henry S., Why You Can't Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers (October 2014). NBER Working Paper No. w20604, Available at SSRN: https://ssrn.com/abstract=2513164

Henry S. Farber (Contact Author)

Princeton University ( email )

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