Platform Training and Learning by Doing and Gig Workers' Incomes: Empirical Evidence From China's Food Delivery Riders.

This study focuses on the different impacts of platform training and learning by doing on gig workers' platform income. Based on survey data of China's delivery riders on the platform in 2020, via quantitative methods combined with the case study, it is found that the platform training is negatively correlated with riders' incomes, while learning by doing is positively correlated with their incomes. Workers with a high level of platform-income dependence earn more than those with an average level of dependence under the same platform training, or learning by doing. Overall, the incomes of the former are significantly lower than those of the latter, where the difference is mainly due to unobservable factors. Both platform training and learning by doing significantly reduce the income gap. In addition, the instrumental variable and the propensity score matching approaches are introduced to handle the endogeneity problem, and robust results are obtained. Plain language summary: Influence of learning by doing and platform training on gig workers' incomes This study looks at how two different ways of learning affect gig workers' earnings on platforms. We used data from a survey of delivery riders in China in 2020. We found that training provided by the platform tends to lower riders' earnings, while learning from actual work experience tends to increase earnings. Riders who rely more on their platform income earn more than those who don't, even after training or learning by doing. Both types of learning help reduce the income gap. We used special methods to make sure our results are accurate. [ABSTRACT FROM AUTHOR]