Highlights Drivers on ride-sharing platforms make daily decisions regarding whether to participate in work, and if so, how many hours to work. 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 on ride-sharing platforms. We develop an approach to address challenges of sample self-selection bias and endogeneity of hourly income rate. We find that both the participation elasticity and working-hour elasticity of labor supply are positive and significant in the dataset of a ride-sharing platform. Interestingly, 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.

    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 regarding 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.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

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


    Beteiligte:
    Sun, Hao (Autor:in) / Wang, Hai (Autor:in) / Wan, Zhixi (Autor:in)


    Erscheinungsdatum :

    2019-04-08


    Format / Umfang :

    18 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Locality Filtering for Efficient Ride Sharing Platforms

    Tosoni, Francesco / Ferragina, Paolo / Marino, Andrea et al. | IEEE | 2022


    Capacity sharing for ride-sourcing platforms under competition

    Li, Xiaonan / Li, Xiangyong / Shi, Junxin | Elsevier | 2023


    RIDE-SHARING ASSISTANCE SYSTEM, RIDE-SHARING ASSISTANCE APPARATUS AND RIDE-SHARING ASSISTANCE METHOD

    SEKI YUSHI / HASHIMOTO RYOKO / TERAHATA KATSUYA et al. | Europäisches Patentamt | 2019

    Freier Zugriff

    Ride-sharing system and ride-sharing method

    MATSUO YOSHIAKI | Europäisches Patentamt | 2023

    Freier Zugriff

    SELF CLEANING OF RIDE SHARING VEHICLE

    BAKER ALEX W / BAUMBARGER MICHAEL A / OLCHOVY JACOB J et al. | Europäisches Patentamt | 2021

    Freier Zugriff