Willingness to Pay for Livestock Husbandry Insurance: An Empirical Analysis of Grassland Farms in Inner Mongolia, China

Haibin Dong, Saheed Olaide Jimoh, Yulu Hou and Xiangyang Hou
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Haibin Dong: Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China
Saheed Olaide Jimoh: Sustainable Environment Food and Agriculture Initiative, Lagos 104101, Nigeria
Yulu Hou: Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Xiangyang Hou: Institute of Grassland Research, Chinese Academy of Agricultural Sciences, Hohhot 010010, China

Sustainability, 2020, vol. 12, issue 18, 1-13

Abstract: Livestock husbandry insurance (LHI) is increasingly gaining acceptance in developing countries, relative to its efficacy in mitigating the covariate risks faced by households in vulnerable agrarian communities. However, this risk-mitigating tool has received little research attention in the context of Chinese herders. The current study focused on the status, and determinants of herders’ willingness to purchase LHI. We used a contingent valuation approach to collect data from 450 households across three grassland types in Inner Mongolia. Descriptive statistics and binary logistic regression models were used to analyze the collected data. We show that herders’ level of awareness and acceptance of LHI are below expectations. Our results further indicated that herders with higher education level, livestock number, risk perception level, awareness, and contracted grassland area are more likely to purchase LHI. Policymakers and insurers should design programs that will educate herders on LHI while taking cognizance of other critical factors that influence households to purchase insurance. This will go a long way in scaling-up the attractiveness of LHI to herders and the agrarian community at large.

Keywords: risk; insurance; livestock; grassland; policy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
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