Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We study learning by doing by New York City taxi drivers, who have substantial discretion over their driving strategies and receive compensation closely tied to their success in finding customers. In addition to documenting learning overall by these entrepreneurial agents, we exploit our data's breadth to investigate the factors that contribute to driver improvement across a variety of situations. New drivers lag further behind experienced drivers when in difficult situations. Drivers benefit from accumulating neighborhood-specific experience, which affects how they search for their next customers.