Model and analysis of labor supply for ride-sharing platforms in the presence of sample self-selection and endogeneity

• Main Authors: SUN, Hao, WANG, Hai, WAN, Zhixi
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2019
• Subjects: Ride-sharing platforms; Labor supply; Income elasticity; Sample selection; Endogeneity; Databases and Information Systems; Operations Research, Systems Engineering and Industrial Engineering; Transportation
• Online Access: https://ink.library.smu.edu.sg/sis_research/4515; https://ink.library.smu.edu.sg/context/sis_research/article/5518/viewcontent/1_s20_S0191261518306106_main.pdf
• Institution: Singapore Management University

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.