Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (10): 2585-2604.doi: 10.11821/dlxb202110017

• Poverty Reduction and Rural Development • Previous Articles     Next Articles

Spatial correlation of corn markets under the background of price support policy reform in China

DING Cunzhen()   

  1. College of Economics and Management, Shandong Agricultural University, Tai'an 271018, Shandong, China
  • Received:2020-08-18 Revised:2021-07-27 Online:2021-10-25 Published:2021-12-25
  • Supported by:
    National Natural Science Foundation of China(72073084)


Market spatial correlation is one of the key indicators that reflect the operation efficiency of corn markets. The implementation and reform of corn price support policy in China plays an important role in changing the spatial correlation of corn markets. Employing market price data at province level, this study first analyzed the change of corn market spatial correlations under the price support policy reform. Then, social network analysis was applied to describe the network characteristics of corn market spatial correlations, and quadratic assignment procedure (QAP) analysis was used to investigate key factors that affected the spatial correlation. The results indicated that, (1) overall, the spatial correlation of China's corn markets was relatively high, and there was an upward trend in recent years. The implementation of temporary corn storage policy exerted insignificant effect on this upward trend, but it led to a decrease in the spatial correlation between markets in policy-affected provinces and those in non-policy affected provinces. (2) The spatial correlation of corn markets presented a multi-threaded and complex network structure, which was closely related and stable as a whole. Provinces with large corn consumption in the central and eastern regions were at the center of the spatial correlation network and played as the central actor in the network. However, provinces in the western region and provinces implementing corn support policy had little effect on the spatial correlation network of corn markets, indicating their marginal and disadvantaged role in the network. (3) Geographical proximity, market distance, market power, information transmission effect and the implementation of temporary storage policy were the key factors that affected the spatial correlation of corn markets. Furthermore, the role of market power and information transmission effect in determining the spatial correlation has become increasingly important.

Key words: price support policy, spatial correlation, social network analysis method (SNA), quadratic assignment procedure (QAP), corn, China