<p>With the rapid advancement of automation technology, the labor market is undergoing significant transformations, particularly affecting migrant workers. The widespread adoption of industrial robots has intensified job displacement risks, forcing many migrant workers to turn to the gig economy as a livelihood alternative. This paper empirically examines the impact of robots on migrant workers' participation in the gig economy and analyzes the crucial role social networks play in this process. The results show that industrial robots significantly enhance migrant workers' probability of entering the gig economy. Heterogeneity analysis reveals that migrant workers with moderate skills benefit the most, while marital status and gender also influence the effectiveness of robots. Furthermore, the impact varies between new-generation and traditional migrant workers. Mechanism analysis indicates that automation-induced unemployment pressure is a key driver of gig work entry, with social networks playing a critical facilitating role. Overall, the findings provide important theoretical insights into employment transitions under technological disruption and offer practical implications for the development of the gig economy and related policy design.</p>

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Industrial robots, social networks, and the gig economy

  • Chunyang Su,
  • Lin Zhang

摘要

With the rapid advancement of automation technology, the labor market is undergoing significant transformations, particularly affecting migrant workers. The widespread adoption of industrial robots has intensified job displacement risks, forcing many migrant workers to turn to the gig economy as a livelihood alternative. This paper empirically examines the impact of robots on migrant workers' participation in the gig economy and analyzes the crucial role social networks play in this process. The results show that industrial robots significantly enhance migrant workers' probability of entering the gig economy. Heterogeneity analysis reveals that migrant workers with moderate skills benefit the most, while marital status and gender also influence the effectiveness of robots. Furthermore, the impact varies between new-generation and traditional migrant workers. Mechanism analysis indicates that automation-induced unemployment pressure is a key driver of gig work entry, with social networks playing a critical facilitating role. Overall, the findings provide important theoretical insights into employment transitions under technological disruption and offer practical implications for the development of the gig economy and related policy design.