Learning by Driving: Productivity Improvements by New York City Taxi Drivers
Author(s) -
Kareem Haggag,
Brian McManus,
Giovanni Paci
Publication year - 2016
Publication title -
american economic journal applied economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 12.996
H-Index - 82
eISSN - 1945-7782
pISSN - 1945-7790
DOI - 10.1257/app.20150059
Subject(s) - exploit , variety (cybernetics) , productivity , discretion , compensation (psychology) , marketing , business , computer science , economics , psychology , computer security , artificial intelligence , economic growth , political science , social psychology , law
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.
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