Bridging the urban–rural income divide through entrepreneurship: evidence from a double machine learning approach in China - Hao, Shuai; Liu, Liping; Wang, Guogang; Wang, Xizhao (Frontiers in Sustainable Food Systems, 2025)
Narrowing the urban–rural income gap in a sustainable and inclusive manner remains a longstanding concern in development economics. This study investigates how entrepreneurial activity can contribute to narrowing the urban–rural income gap in China, with a focus on technological spillovers and structural transformation. Drawing on a county-level panel dataset from 2000 to 2022, we apply a Double Machine Learning (DML) framework for causal inference. The empirical results show that entrepreneurship significantly reduces the urban–rural income gap, and the findings are robust to a series of validity checks. Mechanism analysis reveals two key pathways through which entrepreneurship helps narrow the income gap. First, it enhances resource allocation efficiency via knowledge and technology spillovers. Second, it promotes industrial upgrading in rural areas. Heterogeneity analysis shows that the effects are particularly pronounced in central and western regions. Across industries, labor-intensive entrepreneurship exerts the strongest equalizing effect, while technology-intensive sectors rely more on spillover channels. The impact of resource-intensive entrepreneurship is comparatively weaker and may be accompanied by negative externalities. This study provides novel empirical evidence on how entrepreneurship can support coordinated urban–rural development and informs the design of regionally and sectorally differentiated innovation policies.