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

doi:10.3864/j.issn.0578-1752.2021.22.006

摘要/Abstract

摘要：

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。

关键词: 农业保险, 农业生产风险评估, 农业保险定价, 费率厘定, 不确定性

Abstract:

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.

Key words: agricultural insurance, agricultural yield risk assessment, agricultural insurance pricing, insurance ratemaking, uncertainty

张峭, 王克. 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[J]. 中国农业科学, 2021, 54(22): 4778-4786.

ZHANG Qiao, WANG Ke. The Uncertainty of Agricultural Yield Risk Assessment and Agricultural Insurance Pricing: Literature Review and Wayforward[J]. Scientia Agricultura Sinica, 2021, 54(22): 4778-4786.

0
/ 收藏文章 0 / 推荐

导出引用管理器 EndNote|Reference Manager|ProCite|BibTeX|RefWorks

链接本文: https://www.chinaagrisci.com/CN/10.3864/j.issn.0578-1752.2021.22.006

https://www.chinaagrisci.com/CN/Y2021/V54/I22/4778

图/表 1

参考文献 62

[1]	MAHUL O, STUTLEY C J. Government Support to Agricultural Insurance: Challenges and Options for Developing Countries. The World Bank: Washington D.C. 2010
[2]	KNIGHT T O, COBLE K H. Survey of US multiple peril crop insurance literature since 1980. Review of Agricultural Economics, 1997, 19(1): 128. doi: 10.2307/1349683. doi: 10.2307/1349683
[3]	GLAUBER J W. Crop insurance reconsidered. American Journal of Agricultural Economics, 2004, 86(5): 1179-1195. doi: 10.1111/ajae.v86.5
[4]	张峭. 中国农作物生产风险评估及区划理论与实践. 北京: 中国农业科学技术出版社, 2013.
	ZHANG Q. The Theory and Practice of Chinese Crop Yield Risk Assessment and Regionalization. Beijing: China Agricultural Science and Technology Press, 2003. (in Chinese)
[5]	张峭, 徐磊, 王克. 加强农业生产风险评估与区划保障农保持续健康发展. 人民网, 2011.
	ZHANG Q, XU L, WANG K. Promoting the sustainable development of Chinese agricultural insurance program by enhancing the crop yield risk assessment and regionalization. People Newspaer, 2011. (in Chinese)
[6]	COBLE K H, KNIGHT T O, GOODWIN B, MILLER M F, REJESUS R M. A Comprehensive Review of the RMA APH and COMBO Rating Methodology, 2010, RMA USDA.
[7]	RACINE J, KER A. Rating crop insurance policies with efficient nonparametric estimators that admit mixed data types. Journal of Agricultural and Resource Economics, 2006, 31(1): 27-39.
[8]	王克, 张峭. 农业生产风险评估方法的评述及展望. 农业展望, 2013(2): 38-43.
	WANG K, ZHANG Q. Agricultural yield risk assessment: review and outlook. Agricultural Outlook, 2013, 9(2): 38-43. (in Chinese)
[9]	肖宇谷, 王克, 王晔. Bootstrap方法在农业产量保险费率厘定中的应用. 保险研究, 2014(9): 21-28.
	XIAO Y G, WANG K, WANG Y. The application of bootstrap method in ratemaking procedure of crop yield insurance. Insurance Studies, 2014(9): 21-28. (in Chinese)
[10]	叶涛, 谭畅, 刘杨宾. 基于县域单产数据的种植业保险定价模型关键假设检验. 保险研究, 2014(6): 3-10.
	YE T, TAN C, LIU Y B. Testing for the key assumptions of crop insurance pricing models using county-level yield data. Insurance Studies, 2014(6): 3-10. (in Chinese)
[11]	王克, 张峭. 农作物单产风险分布对保险费率厘定的影响: 以新疆3县(市)棉花单产保险为例. 中国农业大学学报, 2010, 15(2): 114-120.
	WANG K, ZHANG Q. Influence of flexible crop yield distributions on crop insurance premium rate: A case study on cotton insurance in three counties of Xinjiang Province. Journal of Agricultural University, 2010, 15(2): 114-120. (in Chinese)
[12]	王克, 张峭. 基于数据融合的农作物生产风险评估新方法. 中国农业科学, 2013, 46(5): 1054-1060.
	WANG K, ZHANG Q. A new approach to assess crop yield risk based on mixed source of data. Scientia Agricultura Sinica, 2013, 46(5): 1054-1060. (in Chinese)
[13]	VILLENEUVE R. Issues in Agricultural Insurance, in 2015 SOA Annual Meeting. 2015.
[14]	叶涛, 聂建亮, 武宾霞, 李曼, 史培军. 基于产量统计模型的农作物保险定价研究进展. 中国农业科学, 2012, 45(12): 2544-2551.
	YE T, NIE J L, WU B X, LI M, SHI P J. Crop insurance premium rating based on yield simulation models. Scientia Agricultura Sinica, 2012, 45(12): 2544-2551. (in Chinese)
[15]	肖宇谷. 农业保险中的精算模型研究. 北京: 清华大学出版社, 2018.
	XIAO Y G. The Actuarial Model in Agricultural Insurance. Beijing: Tsinghua University Press, 2018. (in Chinese)
[16]	WOODARD J D, SHERRICK B J, SCHNITKEY G D. Actuarial impacts of loss cost ratio ratemaking in US crop insurance programs. Journal of Agricultural and Resource Economics, 2011, 36(1): 211.
[17]	张峭, 王克, 宋建国. 中国农业保险保障分析与评价. 北京: 中国金融出版社, 2020.
	ZHANG Q, WANG K, SONG J G. The Analysis and Evaluation of Chinese Agricultural Insurance. Beijing: China Financial Publication House, 2020. (in Chinese)
[18]	庹国柱. 农业保险协议理赔该清理了. 中国银行保险报, 2020.
	TUO G.Z. The claim deal of Chinese agricultural insurance should be cleaned up. China Banking and Insurance News, 2020. (in Chinese)
[19]	VILLENEUVE R. Restating Losses in Crop Insurance, in International Agricultural Risk. Finance and Insurane Conference, Vancouver Canada. 2013.
[20]	ELAINE H A, GOODWIN B. Big assumptions for small samples in crop insurance. Agricultural Finance Review, 2014, 74(4): 477-491. doi: 10.1108/AFR-09-2014-0025
[21]	YE T, NIE J L, WANG J, SHI P J, WANG Z. Performance of detrending models of crop yield risk assessment: evaluation on real and hypothetical yield data. Stochastic Environmental Research and Risk Assessment, 2015, 29(1): 109-117. doi: 10.1007/s00477-014-0871-x
[22]	陈军, 赵思健, 聂谦. 区域农业产量风险低估的评价研究. 保险研究, 2020(2): 19-29.
	CHEN J, ZHAO S J, NIE Q. A study on the underestimated evaluation of regional agricultural yield risk. Insurance Studies, 2020(2): 19-29. (in Chinese)
[23]	叶涛, 史培军, 王静爱. 种植业自然灾害风险模型研究进展. 保险研究, 2014(10): 12-23.
	YE T, SHI P J, WANG J A. A review on crop natural disaster risk models. Insurance Studies, 2014(10): 12-23. (in Chinese)
[24]	GOODWIN B K, HUNGERFORD A. Copula-based models of systemic risk in u.s. agriculture: implications for crop insurance and reinsurance contracts. American Journal of Agricultural Economics, 2015, 97(3): 879-896. doi: 10.1093/ajae/aau086
[25]	PORTH L, ZHU W K, TAN K S. A credibility-based Erlang mixture model for pricing crop reinsurance. Agricultural Finance Review, 2014, 74(2): 162-187. doi: 10.1108/AFR-04-2014-0006
[26]	REJESUS R M, COBLE K H, MILLER M F, GOODWIN B K, AND KNIGHT T O, Accounting for weather probabilities in crop insurance rating. Journal of Agricultural and Resource Economics, 2015, 40(2): 306-324.
[27]	SHEN Z W, ODENING M, OKHRIN O. Can expert knowledge compensate for data scarcity in crop insurance pricing? European Review of Agricultural Economics, 2015, 43(2): 237-269. doi: 10.1093/erae/jbv015
[28]	KER A P, TOLHURST T N, LIU Y. Bayesian estimation of possibly similar yield densities: Implications for rating crop insurance contracts. American Journal of Agricultural Economics, 2015, 98(2): 360-382. doi: 10.1093/ajae/aav065
[29]	WOODARD J D, VERTERAMO-CHIU L J. Efficiency impacts of utilizing soil data in the pricing of the federal crop insurance program. American Journal of Agricultural Economics, 2017, 99(3): 757-772. doi: 10.1093/ajae/aaw099
[30]	张峭, 王克. 我国农业自然灾害风险评估与区划. 中国农业资源与区划, 2011, 32(3): 32-36.
	ZHANG Q, WANG K. Assessment and regional planning of Chinese agricultural natural disaster risks. Chinese Journal of Agricultural Resources and Regional Planning, 2011, 32(3): 32-36. (in Chinese)
[31]	张峭, 王克, 张希. 农作物灾损风险的评估方法研究. 上海农业学报, 2010, 26(增刊): 22-26.
	ZHANG Q, WANG K, ZHANG X. Research on the assessment approach of agricultural disaster and loss risk. Acta Agriculturae Shanghai, 2010, 26(Suppl.): 22-26. (in Chinese)
[32]	王月琴, 赵思健, 聂谦. 山西沁县谷子综合天气指数保险研究. 保险研究, 2019(4): 15-26.
	WANG Y Q, ZHAO S J, NIE Q. A study on synthetic weather index insurance for millet in Qinxian, Shanxi Province. Insurance Studies, 2019(4): 15-26. (in Chinese)
[33]	赵思健, 张峭, 聂谦, 郑茗曦. 农作物气象灾害风险识别与评估研究. 灾害学, 2018, 33(2): 51-57.
	ZHAO S J, ZHANG Q, NIE Q, ZHENG M X. Weather-related diaster risk recognizing and assessment of crop. Journal of Catastrophology, 2018, 33(2): 51-57. (in Chinese)
[34]	牛浩, 陈盛伟. 山东省玉米气象产量分离方法的多重比较分析. 山东农业科学, 2015, 47(8): 95-99.
	NIU H, CHEN S W. Multiple comparative analyses of separation methods for meteorological yield of corn in Shandong Province. Shandong Agricultural Sciences, 2015, 47(8): 95-99. (in Chinese)
[35]	吴海平, 李士森, 李晓涛, 任金政. 混合数据信度优化模型及其在农作物保险定价中的应用: 以河北省县级玉米保险费率厘定为例. 金融理论与实践, 2020(12): 110-115.
	WU H P, LI S S, LI X T, REN J Z. Mixed data optimal model and its application in crop insurance pricKing: taken the county-level maize insurance pricing of Hebei Province as an example. Financial Theory and Practice, 2020(12): 110-115. (in Chinese)
[36]	JUST R E, WENINGER Q. Are crop yields normally distributed? American Journal of Agricultural Economics, 1999, 81(2): 287-304. doi: 10.2307/1244582
[37]	HARRI A, ERDEM C, COBLE K H, KNIGHT T O. Crop yield distributions: A reconciliation of previous research and statistical tests for normality. Review of Agricultural Economics, 2009, 31(1): 163-182. doi: 10.1111/raec.2009.31.issue-1
[38]	WOODARD J D, SHERRICK B J, SCHNITKEY G D. Revenue risk reduction impacts of crop insurance in a multi crop framework. Applied Economic Perspectives and Policy, 2010, 32(3): 472-488. doi: 10.1093/aepp/ppq014
[39]	GOODWIN B K, KER A P. Nonparametric estimation of crop yield distributions: Implications for rating group-risk crop insurance contracts. American Journal of Agricultural Economics, 1998, 80(1): 139-153. doi: 10.2307/3180276
[40]	KER A P, GOODWIN B K. Nonparametric estimation of crop insurance rates revisited. American Journal of Agricultural Economics, 2000, 82(2): 463-478. doi: 10.1111/ajae.v82.2
[41]	ZHANG Q, WANG K. Evaluating production risks for wheat producers in Beijing. China Agricultural Economic Review, 2010, 2(2): 200-211. doi: 10.1108/17561371011044306
[42]	任金政, 李晓涛. 基于异方差调整的粮食单产风险分布对保险费率厘定的影响研究. 保险研究, 2019(9): 74-87.
	REN J Z, LI X T. The impact of crop unit yield risk distribution based on heteroscedasticity adjustment on premium rates. Insurance Studies, 2019(9): 74-87. (in Chinese)
[43]	BOKUSHEVA R. Measuring dependence in joint distributions of yield and weather variables. Agricultural Finance Review, 2011, 71(1): 120-141. doi: 10.1108/00021461111128192
[44]	TOLHURST T N, KER A P. Innovation Induced Volatility: 65 Years of Increasing and Asymmetric Crop Yield Volatility in the US Corn Belt. 2017.
[45]	ZHU W. Actuarial Ratemaking in Agricultural Insurance[D]. University of Waterloo, 2015.
[46]	ZHU W J, TAN K S, PORTH L. Agricultural insurance ratemaking: Development of a new premium principle. North American Actuarial Journal, 2019: 23(4): 512-534. doi: 10.1080/10920277.2019.1618340
[47]	PARK E J, BRORSEN B W, HARRI A. Using bayesian Kriging for spatial smoothing in crop insurance rating. American Journal of Agricultural Economics, 2019, 101(1): 330-351. doi: 10.1093/ajae/aay045
[48]	TOLHURST T N, KER A P. On technological change in crop yields. American Journal of Agricultural Economics, 2015, 97(1): 137-158. doi: 10.1093/ajae/aau082
[49]	BECHTEL A, YOUNG D L. The importance of using farm level risk estimates in crop enrollment decisions. Western Agricultural Economics Association, 1999.
[50]	KNIGHT T O, COBLE K H, GOODWIN B K, REJESUS R M, SEO S. Developing variable unit-structure premium rate differentials in crop insurance. American Journal of Agricultural Economics, 2010, 92(1): 141-151. doi: 10.1093/ajae/aap002
[51]	WANG H H, ZHANG H. Model-based clustering for cross-sectional time series data. Journal of Agricultural, Biological, and Environmental Statistics, 2002, 7(1): 107. doi: 10.1198/108571102317475099
[52]	CLAASSEN R, JUST R E. Heterogeneity and distributional form of farm-level yields. American Journal of Agricultural Economics, 2011, 93(1): 144-160. doi: 10.1093/ajae/aaq111
[53]	COBLE K H, DISMUKES R, THOMAS S. Policy implications of crop yield and revenue variability at differing levels of disaggregation, in Selected Paper for presentation at the American Agricultural Economics Association Annual Meeting, Portland, Oregon, July 29-August 1, 2007. 2007: Portland, Oregon.
[54]	叶涛, 牟青洋, 史培军. 编制全国农业保险区划、支撑农业保险高质量发展//中国保险学会. 加快农业保险高质量发展政策解读和成果汇编. 北京: 中国金融出版社, 2020: 186-199.
	YE T, MOU Q X Y, SHI P J. Supporting the high-quality development of China agricultural insurance by drafting the national agricultural insurance regionalization//Chinese Insurance Society. The Collection of Papers on Promoting China Agricultural Insurance High-quality Development. Beijing: China Financial Publication House, 2020: 186-199. (in Chinese)
[55]	WANG H H, MAKUS L D, CHEN X M. The impact of US commodity programmes on hedging in the presence of crop insurance. European Review of Agricultural Economics, 2004, 31(3): 331-352. doi: 10.1093/erae/31.3.331
[56]	POPP M, RUDSTROM M, MANNING P. Spatial yield risk across region, crop and aggregation method. Canadian Journal of Agricultural Economics, 2005, 53(2.3): 103-115. doi: 10.1111/cjag.2005.53.issue-2-3
[57]	RUDSTROM M, POPP M, MANNING P, GBUR E. Data aggregation issues for crop yield risk analysis. Canadian Journal of Agricultural Economics, 2002, 50(2): 185-200. doi: 10.1111/cjag.2002.50.issue-2
[58]	ZHANG Q, LI Y, WANG K. The measure of data aggregation bias for crop production risk assessment// HUANG C. Emerging Economies, Risk and Development, and Intelligent Technology—Proceedings of the 5th International Conference on Risk Analysis and Crisis Response. USA: CRC Press, 2015: 35-42.
[59]	邓仲华, 李志芳. 科学研究范式的演化——大数据时代的科学研究第四范式. 情报资料工作, 2013(4): 19-23.
	DENG Z H, LI Z F. The evolution of scientific research paradigm: the fourth paradigm of scientific research in the era of big data. Information and Documentation Services, 2013(4): 19-23. (in Chinese)
[60]	黄欣卓, 彭康珺. 第四研究范式: 大数据时代的社会科学研究——“大数据与社会科学转型高端学术研讨会”综述. 清华社会科学, 2019, 1(1): 254-264.
	HUANG X Z, PENG K J. The fourth paradigm of scientific research: social sciences research in the era of big data—the workshop review of big data and social sciences research. Tsinghua Social Sciences, 2019, 1(1): 254-264. (in Chinese)
[61]	RAMIREZ O A, COLSON G. Premium estimation inaccuracy and the distribution of crop insurance subsidies across participating producers// Agricultural & Applied Economics Association's 2013 AAEA & CAES Joint Annual Meeting. Washington D.C, 2013.
[62]	MAISASHVILI A, BRYANT H L, JONES J P H. Implications of alternative crop insurance subsidies. Journal of Agricultural and Applied Economics, 2020, 52(2): 240-263. doi: 10.1017/aae.2019.46

Metrics

Viewed

Full text

951

HTML			PDF

Just accepted	Online first	Issue	Just accepted	Online first	Issue
0	0	6	0	0	945

From	Others	local

Times	673	278
Rate	71%	29%

Abstract

636

Just accepted	Online first	Issue

0	0	636

From	Others	local

Times	543	93
Rate	85%	15%

Cited

Web of Science	Crossref	ScienceDirect	Search for Citations in Google Scholar >>


This page requires you have already subscribed to WoS.

Shared