Publication Type

Journal Article

Version

acceptedVersion

Publication Date

7-2019

Abstract

With the popularization of ride-sharing services, drivers working as freelancers on ride-sharing platforms can design their schedules flexibly. They make daily decisions regard- ing whether to participate in work, and if so, how many hours to work. Factors such as hourly income rate affect both the participation decision and working-hour decision, and evaluation of the impacts of hourly income rate on labor supply becomes important. In this paper, we propose an econometric framework with closed-form measures to estimate both the participation elasticity (i.e., extensive margin elasticity) and working-hour elasticity (i.e., intensive margin elasticity) of labor supply. We model the sample self-selection bias of labor force participation and endogeneity of income rate and show that failure to control for sample self-selection and endogeneity leads to biased estimates. Taking advantage of a natural experiment with exogenous shocks on a ride-sharing platform, we identify the driver incentive called “income multiplier” as exogenous shock and an instrumental variable. We empirically analyze the impacts of hourly income rates on labor supply along both extensive and intensive margins. We find that both the participation elasticity and working-hour elasticity of labor supply are positive and significant in the dataset of this ride-sharing platform. Interestingly, in the presence of driver heterogeneity, we also find that in general participation elasticity decreases along both the extensive and intensive margins, and working-hour elasticity decreases along the intensive margin.

Keywords

Ride-sharing platforms, Labor supply, Income elasticity, Sample selection, Endogeneity

Discipline

Databases and Information Systems | Operations Research, Systems Engineering and Industrial Engineering | Transportation

Research Areas

Intelligent Systems and Optimization

Publication

Transportation Research Part B: Methodological

Volume

125

First Page

76

Last Page

93

ISSN

0191-2615

Identifier

10.1016/j.trb.2019.04.004

Publisher

Elsevier

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1016/j.trb.2019.04.004

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