NDLI: Revisiting Labor Supply of New York City Taxi Drivers: Empirical Evidence from Large-Scale Taxi Data

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Revisiting Labor Supply of New York City Taxi Drivers: Empirical Evidence from Large-Scale Taxi Data

Content Provider	Semantic Scholar
Author	Morgul, Ender F. Ozbay, Kaan
Copyright Year	2015
Abstract	Taxicab activity patterns in urbanized regions have been subject to major changes over the past few years, especially after the introduction of online taxi-hailing applications. Demand-and-supply characteristics of taxicab services remain as a significant question to be analyzed when deciding on transportation policy improvements in big cities. On the other hand, labor supply theories that seek to explain possible income-targeting behavior in transitory wage changes have also been tested with taxicab driver data. However, empirical work on investigating the relation between income and work hours have resulted in conflicting findings mainly due to methodological differences and, to some extent, limited number of observations. One of the main limitations of taxi demand-supply questions have been lack of sufficient and reliable data. This paper presents an empirical assessment of taxicab drivers' labor supply using a novel large-scale data source from New York City. Electronically collected data provides detailed information about work hours of drivers and collected fares. The methodological framework employed in this paper was mainly borrowed from the previous literature and the findings were compared with results from earlier studies. Some of the results using the large-scale data were found to deviate from these earlier findings. Moreover, seasonal variations in labor supply response to transitory wage changes were empirically identified. The results of this study provide empirical support for the income-targeting hypothesis.
File Format	PDF HTM / HTML
Alternate Webpage(s)	http://engineering.nyu.edu/citysmart/trbpaper/15-3331.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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