农业生产风险评估及农业保险费率厘定的不确定性：研究进展和破解之道

鉴于准确厘定农业保险费率在保障农业保险持续健康发展中的重要性,20世纪80年代起学界围绕农业生产风险的科学评估和农业保险精算开展了大量研究,但尚不完善,评估结果仍存在较大不确定性。为提高农业保险定价的可信度,推动我国农业保险精算技术的发展,本文作者对国内外学界关于农业生产风险评估和农业保险费率厘定的最新研究进行了综述,在文献梳理的基础上分析了农业保险费率厘定不确定产生的根源及应对之策。研究发现,数据的稀缺性、技术问题的模糊性以及风险评估和保险定价的空间不匹配性是造成农业生产风险评估和农业保险费率厘定结果存在不确定性的3个主要原因,提高农业保险定价结果的可信度成为国外学者最新研究的努力方向。在大数据时代,降低农业保险费率厘定不确定性、提高农险定价信度最终需要依靠大数据技术,融合多种数据资源,但农业保险的精算定价并不能完全解决逆选择问题,还需要提供更加多样更具有弹性的农业保险产品进行配合。

The Uncertainty of Agricultural Yield Risk Assessment and Agricultural Insurance Pricing: Literature Review and Wayforward

As the importance of making an accurate rate to the sustainable development of agricultural insurance programs, lots of literature on agricultural risk assessment and agricultural insurance pricing had been conducted since the 1980s. Yet, uncertainty still existed regarding the risk assessment results and/or the agricultural insurance premium. With the purpose of improving the credibility of Chinese agricultural insurance pricing, we firstly conduct a literature review on the recent development in the field of agricultural risk assessment and insurance pricing, and then put forward the uncertainty sources for agricultural insurance pricing, followed by a solution. It is found that the data scarcity, the fuzziness in dealing with technical issues, and the unmatched spatial scale of risk assessment and pricing are the three reasons for the uncertainty of agricultural risk assessment and insurance pricing, and improving the agricultural insurance pricing credibility has been emerging as a hot topic in recent literature. Reducing the uncertainty of agricultural insurance pricing can be achieved in the big data era with the help of data mixing technology and data-intensive research. While making a sound agricultural insurance rate cannot overcome the essential adverse selection problem which could hamper the agricultural insurance sustainable development, however, it can be partly addressed by providing more flexible agricultural insurance products with alternative coverage levels.