Please use this identifier to cite or link to this item: http://arks.princeton.edu/ark:/88435/dsp014b29b826g
 Title: Why You Can't Find a Taxi in the Rain and Other Labor Supply Lessons from Cab Drivers Authors: Farber, Henry Issue Date: Feb-2015 Series/Report no.: Working Papers (Princeton University. Industrial Relations Section) ; 583a 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. 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. Additionally, using a discrete choice stopping model, the probability of a shift ending is strongly positively related to hours worked but at best weakly related to income earned. These results are consistent with the neoclassical optimizing model of labor supply and suggest that consideration of gain-loss utility and reference dependence is not an important factor in these labor supply decisions.I explore heterogeneity across drivers in their labor supply elasticities and consider whether new drivers diﬀer from more experienced drivers in their be-havior. 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). URI: http://arks.princeton.edu/ark:/88435/dsp014b29b826g Appears in Collections: IRS Working Papers

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