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. 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).
This paper is based on my Albert Rees Lecture at the annual meeting of the Society of Labor Economists, May 2, 2014, Arlington, VA. The author thanks participants in workshops at the Federal Reserve Bank of Atlanta, Princeton University, MIT, and Harvard University for their helpful comments. The views expressed herein are those of the author and do not necessarily reflect the views of the National Bureau of Economic Research.
Henry S. Farber, 2015. "Why you Can’t Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers," The Quarterly Journal of Economics, vol 130(4), pages 1975-2026.