Institute of Finance Engineering in School of Management, Jinan University, Guangzhou, 510632, People's Republic of China.
School of Management, Jinan University, Guangzhou, 510632, People's Republic of China.
PLoS One. 2024 May 20;19(5):e0301977. doi: 10.1371/journal.pone.0301977. eCollection 2024.
Based on panel data from 2009 to 2021, covering 30 provinces in China, we have been constructed the Rural Financial Risk Index using the objective entropy weighting method to study rural financial risk in China systematically from the perspective of spatial distribution. Specifically, we discuss the spatial distribution, regional differences and dynamic evolution of rural financial risk across Chinese four different regions divided into the Northeast, East, Central and West. It's found that Local government debt and Land transfer income are the two primary determinants influencing the level of rural financial risk in China. Furthermore, we conclude the ranking value of rural financial risk across four regions that the central exhibits the highest level, followed by the West, the East, and finally the Northeast, where the reasons for such ranking results as follows. Firstly, although the highest level of risk among provinces in the West is equivalent to that in the Central, there exists a smaller minimum rural financial risk in the former compared to the latter. Then, it should be noted that there's a low-low agglomeration of rural financial risk in the Northeast, while it demonstrates a high-high agglomeration in the Central according to the Moran Index test analysis. Again, there's a declining trend in rural financial risk disparity within the region and an upward trend is observed when comparing different regions (except the East vs West), especially increase largely between the Northeast and Central in past two years after analyzing the decomposition of Dagum Gini coefficient. Moreover, we study the absolute differences and dynamic evolution in different four regions through three-dimensional diagram of kernel density estimation, and it's found that the change of rural financial risk in four regions moved to the right as a whole, while the tail distribution remains inconspicuous. The absolute difference is diminishing in the Northeast, and the two-level differentiation characteristics tend to weaken as a whole in the Central, with a disordered wave peak height observed in both the East and West. Finally, the article presents pertinent policy implications but limitations according to the research findings.
基于 2009 年至 2021 年的面板数据，涵盖中国 30 个省份，我们使用客观熵权法构建了农村金融风险指数，从空间分布的角度系统研究了中国农村金融风险。具体来说，我们讨论了中国四大不同地区（东北、东部、中部和西部）农村金融风险的空间分布、区域差异和动态演变。结果发现，地方政府债务和土地出让收入是影响中国农村金融风险水平的两个主要决定因素。此外，我们得出了四个地区农村金融风险排名值，中部最高，其次是西部、东部，最后是东北，其排名结果的原因如下。首先，尽管西部省份的风险水平最高与中部相当，但前者的农村金融风险最小。然后，应该注意的是，根据 Moran 指数检验分析，东北存在农村金融风险的低-低集聚，而中部则存在高-高集聚。再次，区域内农村金融风险差距呈下降趋势，而不同区域之间（东部与西部除外）则呈上升趋势，特别是在过去两年，东北与中部之间的差距大幅增加，达格姆基尼系数分解分析。此外，我们通过核密度估计三维图研究了不同四个区域的绝对差异和动态演变，发现四个区域的农村金融风险变化整体向右移动，而尾部分布仍然不明显。东北的绝对差异在缩小，中部的两级分化特征整体趋于减弱，东部和西部的波峰高度无序。最后，根据研究结果提出了相关政策建议和局限性。
