HuLi / Nanjing University of Finance and Economics
Faster on-demand services have enabled gig workers, such as food delivery and ride-hailing drivers, to fulfill more orders, thereby increasing their income. However, high delivery speeds have led to a significant number of independent workers being involved in traffic incidents, incurring substantial costs. In this study, we employ a game-theoretic model to analyze the impact of mandatory insurance policies, as widely implemented measures for labor welfare security, on the participating decisions and driving behaviors of drivers with heterogeneous opportunity costs, as well as the profits of on-demand platforms. Specifically, this study investigates an on-demand platform supply chain consisting of time-sensitive customers, independent drivers, a single platform, and a commercial insurance firm under three scenarios in which: (i) all drivers are not covered by insurance (Case B); (ii) the platform pays the insurance premium for all participating drivers (Case P); and (iii) all drivers are required to pay the insurance premium for themselves (Case D). Our analyses yield three relevant insights regarding the impact of mandatory insurance policies. First, platforms paying the premium can lead to the highest platform profits compared to the other two cases when both drivers' risk exposure to traffic incidents and consumers' time sensitivity is significant. Second, it is commonly believed that drivers bearing the insurance premium can mitigate moral hazard problems; however, our results show that this effect does not hold when drivers' risk exposure due to incidents is relatively high. Thirdly, mandatory insurance policies always decrease driver welfare because drivers' income decreases, and the size of the driver supply is restrained mainly due to high commercial insurance premiums.