Based on the classic reservation wage theory, this paper proposes the concept of reservation wage on platform and utilizes the carhailing platforms survey data to empirically investigate the impact and mechanism of heterogeneous work experience on reservation wages, and to examine the impact of the gig economy on the overall welfare rather than income of gig workers. The results reveal that the drivers who transferred from the laidoff and unemployed status have the lowest reservation wages. Coming from industries with excess capacity and having negative job search experience significantly reduce the reservation wages of the gig workers, and the former has a greater effect. Further, the risk attitude towards the platform and the degree of family support are important mechanisms, and the type of gig workers (survival or development) has a differentiating effect. Finally, the paper describes the gig workers' current status of labor supply and labor surplus. Based on this description, this paper discusses policy implications about helping gig workers transfer and improve employment quality.

This paper examines the basic demographic and labor supply characteristics of digital platform workers in China using drivers' survey data on U carhailing platform.A preliminary explanation of their differences in labor supply at the individual level is presented.The result shows that overall the platform workers do not provide a high level labor supply and their labor supply shows a dispersed distribution.There is also inconsistence in the distribution of labor supply intensity and labor supply continuity.Drivers' differences in education and hukou status can well explain their differences in labor supply intensity but fail to account for their differences in labor supply continuity on the platform.The competitive income on platform can well motivate drivers with lower education levels and non-local hukou,but it cannot explain the labor supply behavior of drivers with better education background.Digital platforms workers are featured by large internal heterogeneity.The issues of social risk bearing and social assimilation for digital platform workers call for more attention.