Platform Training and Learning by Doing and Gig Workers’ Incomes

Empirical Evidence From China’s Food Delivery Riders

Bibliographic Data

ID16911684
AuthorsQi Zheng (0000-0003-3838-046X, Capital University of Economics and Business, corresponding author), Jing Zhan (0000-0002-7339-6267, Capital University of Economics and Business), Xinying Xu (0000-0001-5968-5989, University of International Business and Economics)
Year2024
Volume14
Issue3
Publication date2024-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSAGE Open (JOURNAL)
Journal identifiersISSN: 2158-2440 • E-ISSN: 2158-2440
PublisherSAGE Publications (PUBLISHER • US)
DOI10.1177/21582440241284555
OpenAlexW4402897514
LanguageEN
References cited24

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

Business · China · Empirical evidence · Food delivery · Geography · Digital Economy and Work Transformation · Employment and Welfare Studies · Psychology · Retirement, Disability, and Employment · Marketing

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