Can high-value agriculture narrow the urban-rural income gap? A policy-based evidence from China
Zijing Guo et al.
Abstract
Purpose This study examines whether, how and under what conditions high-value agriculture (HVA), as embodied in the characteristic agricultural product advantageous areas (CAPAs) policy, helps reduce the urban–rural income gap (URIG). Design/methodology/approach Exploiting panel data covering 1,723 counties in China from 2014 to 2022, this study employs a staggered difference-in-differences (DID) approach based on the quasi-natural experiment of the CAPAs policy to evaluate whether HVA narrows the URIG. It further identifies the underlying mechanisms, heterogeneous effects across five product types and the synergies between CAPAs and other rural development policies. Findings The CAPAs policy significantly reduces the URIG. This effect is primarily achieved through improving factor allocation, strengthening agricultural branding and fostering specialized agricultural entities. The impact varies across product types–CAPAs focused on grains, economic crops and livestock products significantly contribute to narrowing the gap. In contrast, those centered on forestry products tend to exacerbate inequality, and CAPAs for aquatic and horticultural products are statistically insignificant. Furthermore, policies such as socialized agricultural services, rural e-commerce, agricultural credit and agricultural insurance substantially enhance the effectiveness of CAPAs in reducing the URIG. Originality/value This study aims to fill the gap in causally identifying the impact and mechanisms of HVA on the URIG, while also examining agricultural product heterogeneity and policy complementarities, thereby providing insights for the governance of urban–rural transformation in developing economies.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.