Dynamic Labor Supply of Taxicab Drivers: a Semiparametric Optimal Stopping Model

Abstract

We estimate an optimal stopping model for taxicab drivers' labor supply decisions, using a large sample of shifts for drivers of New York City taxicabs. Our results show that both ``behavioral'' and ``neoclassical'' wage responses are present in the data, with the behavioral income-targeting story explaining shorter shifts, and the standard neoclassical model explaining longer shifts. Hence these findings partially reconcile the divergent reduced-form results in the existing literature. A methodological contribution of this paper is to develop a new closed-form estimator for dynamic discrete choice models in a semiparametric setting, in which the distribution of utility shocks is left unspecified.

Keywords: Dynamic discrete choice model, Closed form estimator, Optimal stopping, Taxicab industry, Labor supply, Negative wage elasticities, Semiparametric average derivative estimation

JEL Classification: C14, D91, C41, L91