The accelerating diffusion of robotics technology is exerting an increasingly disruptive impact on the labor market, heightening the risk of technological unemployment. Data indicates that China’s overall unemployment rate rose from 2.9% to 4.2% between 2010 and 2024. Consequently, whether robot applications induce technological unemployment in the Chinese labor market remains a critical question to be addressed. Concurrently, it is worth examining whether grassroots trade unions, as a vital institutional arrangement for protecting the rights of workers, can play a role in stabilizing employment.

This paper investigates the impact of robot applications on urban unemployment and uncovers the role that trade unions play in this dynamic process. The results show that robot applications significantly exacerbate the risk of urban unemployment, with this adverse shock being more pronounced in regions characterized by stricter household registration thresholds and higher levels of marketization. Furthermore, vulnerable groups—including low-skilled, non-state-sector, elder, and female workers—bear more severe shocks from technological unemployment. Further analysis demonstrates that while higher trade union density and enhanced trade union representation help mitigate the risk of technological unemployment, this mitigating effect faces structural limitations. The findings suggest that the employment-stabilizing role of trade unions is increasingly prominent.

The marginal contributions of this paper are twofold: First, by extending the analysis of robotics’ labor market effects from the employment dimension to the unemployment dimension, it provides new empirical evidence for understanding labor market adjustments in China under automation, thereby supplementing the existing literature. Second, by incorporating trade unions into the analytical framework linking robot applications and urban unemployment, it introduces an institutional perspective to observe technology-driven labor market fluctuations, representing a significant extension of prior research.