WHY YOU CAN'T FIND A TAXI IN THE RAIN AND OTHER LABOR SUPPLY LESSONS FROM CAB DRIVERS

I replicate and extend the seminal work of Camerer et al. ("Labor Supply of New York City Cabdrivers: One Day at a Time,'' Quarterly Journal of Economics, 112 [1997], 407-441), who find that the wage elasticity of daily hours of work for New York City taxi drivers is negative and conclude that their labor supply behavior is consistent with reference dependence. In contrast, my analysis of the complete record of all trips taken in NYC taxi cabs from 2009 to 2013 shows that 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. I find substantial heterogeneity across drivers in their elasticities, but the estimated elasticities are generally positive and rarely substantially negative. I find that new drivers with smaller elasticities are more likely to exit the industry, whereas drivers who remain quickly learn to be better optimizers (have positive labor supply elasticities that grow with experience). These results are consistent with the neoclassical optimizing model of labor supply and suggest that consideration of gain-loss utility and income reference dependence is not an important factor in the daily labor supply decisions of taxi drivers.