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Correlates and impact of crop insurance in India: evidence from a nationally representative survey
Agricultural Finance Review Pub Date : 2020-08-10 , DOI: 10.1108/afr-03-2020-0034
A.G. Adeeth Cariappa , Darshnaben P. Mahida , Priyanka Lal , B.S. Chandel

Purpose

The purpose of this paper is to identify the correlates of crop insurance adoption and estimate the impact on debt and farm income.

Design/methodology/approach

The authors used nationally representative data from National Sample Survey Office (NSSO), which consisted of 35,200 farming households. Logit and propensity score matching (PSM) (nearest neighbor, caliper and kernel matching) techniques were used.

Findings

With only around 5% of households insuring their crops and 87% of them not receiving claims, crop insurance in India has failed. Logit model estimates of correlates of adoption indicated that households with larger family size, lower social group, less education, lower standard of living and poor were more likely to be left out of the ambit of crop insurance. Further, propensity score estimates suggested that households with access to crop insurance had significantly lesser outstanding debt with positive effect on input costs and crop income. The authors’ results were in contrast to the risk balancing theory.

Practical implications

Results of our work encourage us to rethink and restructure the crop insurance policy design in India. With credit and insurance markets interlinked by design and as the risk balancing in the farm business found absent, policies to strengthen both the markets are the need of the hour. To encourage more farmers to take up crop insurance, revenue-based indemnity calculation could be tried in India.

Originality/value

Impact estimates from three different algorithms of matching were compared and tested for robustness. Consistent average treatment effect on treated (ATT) was considered for interpretation and policy implications. Since the data are from a nationally representative survey, results are believed to be of extreme value to policy makers and insurance providers as it can be generalized.



中文翻译:

印度农作物保险的相关性和影响:一项全国代表性调查的证据

目的

本文的目的是确定采用农作物保险的相关性,并估计其对债务和农场收入的影响。

设计/方法/方法

作者使用了国家样本调查办公室(NSSO)的全国代表性数据,该办公室由35200个农户组成。使用对数和倾向得分匹配(PSM)(最近邻,卡尺和内核匹配)技术。

发现

印度只有约5%的家庭为农作物提供保险,而其中87%的农户没有收到索赔,因此印度的农作物保险失败了。Logit模型对收养相关性的估计表明,家庭规模较大,社会群体较少,教育程度较低,生活水平较低和贫穷的家庭更有可能被排除在作物保险范围之外。此外,倾向得分估计值表明,拥有农作物保险的家庭的未偿债务明显减少,对投入成本和农作物收入产生了积极影响。作者的结果与风险平衡理论形成鲜明对比。

实际影响

我们的工作结果鼓励我们重新思考和调整印度的作物保险政策设计。由于信贷和保险市场是通过设计相互联系的,并且由于农场业务中缺乏风险平衡,因此需要同时加强这两个市场的政策。为了鼓励更多的农民购买农作物保险,可以在印度尝试基于收入的赔偿计算。

创意/价值

比较了来自三种不同匹配算法的影响估计,并测试了鲁棒性。考虑到对治疗(ATT)的一致平均治疗效果以进行解释和政策暗示。由于这些数据来自全国代表性的调查,因此,如果将结果概括起来,就可以认为对决策者和保险提供者而言,这些结果具有极高的价值。

更新日期:2020-08-10
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