Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods

Zheng, Qiujie; Wang, H. Holly; Shi, Qiuyue

doi:10.1080/00036846.2014.894630

Cited by 7 publications

(7 citation statements)

References 21 publications

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“…For example, yield insurance systems could be set up in order to compensate high yield losses for risky crops 24 25 26 . Such systems require accurate estimations of yield anomaly distributions in order to derive reliable risk premiums 17 18 19 20 . Our study indicates that, in Europe, risk premiums are likely to be higher for most of the legume species compared to major non-legume species.…”

Section: Discussionmentioning

confidence: 99%

“…A rigorous analysis of such risks is crucial for decision makers to define and promote efficient agro-environmental policies for grain legumes in Europe. An accurate estimation of yield loss distribution is also a key element for developing insurance systems against yield loss 17 18 19 20 . To date, only qualitative information from either country-level surveys or expertize is available on this topic 12 13 14 15 16 21 .…”

mentioning

confidence: 99%

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Estimating variability in grain legume yields across Europe and the Americas

Cernay

Ben-Ari

Pelzer

et al. 2015

Sci Rep

114

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Grain legume production in Europe has recently come under scrutiny. Although legume crops are often promoted to provide environmental services, European farmers tend to turn to non-legume crops. It is assumed that high variability in legume yields explains this aversion, but so far this hypothesis has not been tested. Here, we estimate the variability of major grain legume and non-legume yields in Europe and the Americas from yield time series over 1961–2013. Results show that grain legume yields are significantly more variable than non-legume yields in Europe. These differences are smaller in the Americas. Our results are robust at the level of the statistical methods. In all regions, crops with high yield variability are allocated to less than 1% of cultivated areas. Although the expansion of grain legumes in Europe may be hindered by high yield variability, some species display risk levels compatible with the development of specialized supply chains.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Estimating variability in grain legume yields across Europe and the Americas

Cernay

Ben-Ari

Pelzer

et al. 2015

Sci Rep

114

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“…Hou et al (2011) used the subjective perception of risk and loss as the proxy of risk aversion during testing adverse selection of crop insurance in northern China. Different contract design may also influent farmers' purchase behavior, Zheng et al (2014) shown that the hypothetical two-crop insurance contract can be more affordable to farmers than traditional individual crop insurance contracts.…”

Section: Risk Aversionmentioning

confidence: 99%

Risk aversion in crop insurance program purchase decisions

Lyu

Barré

2017

CAER

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Purpose The purpose of this paper is to contribute to the aforementioned literature on the linkage between economic activity and human preference by estimating the cross-sectional determinants of farmers’ participation in participation in crop insurance programs (CIPs) and identifying the impediments preventing the remaining farmers from participating. Design/methodology/approach Using the unique data sets of risk preference experiments and maize producer surveys pertaining to the maize production areas of China, this paper explores the determinants of farmers’ CIPs and scrutinizes the role of risk aversion in farmers’ CIP purchase decisions under the expected utility maximization framework. And a “non-zero threshold probit model” is used for the analysis. Findings The results show that risk aversion plays an important role in CIP purchase decision-making, not only in the form of its direct effect but also with regard to the interaction term and expected loss. Furthermore, if the insured amount is high enough, then risk aversion will no longer affect insurance purchase. Additionally, purchase experience, CIP environment (village purchase ratio), and contract items (insured amounts) are significant determinates in these decisions. There is no significant evidence to suggest that serious adverse selection exists in the sampling areas. Originality/value One theoretical model is established which considered not only general variables like farmers’ production and household information, but also conditions and terms in the insurance policies. The unique experimental method is used to measure farmers’ risk aversion. Both the role of risk aversion and its’ interaction terms with others in CIP participation are scrutinized to identify complicated influences under the context of real society.

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“…Many insurance companies with the support of governments deliver insurance programs. Several studies regarding Chinese agricultural insurance have been published (Zhong et al 2007;Turvey and Kong 2010;Boyd et al 2011;Okhrin et al 2013;Zheng et al 2014). However, none has addressed the information asymmetry issues mentioned above.…”

Section: Hog Production and Insurance In Chinamentioning

confidence: 99%

Cheating? The Case of Producers’ Under‐Reporting Behavior in Hog Insurance in China

Zhang

Cao

Wang³

2018

Canadian J Agri Economics

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Information asymmetry exists in virtually every insurance setting. The institutional arrangement of hog insurance in China offers a unique opportunity to investigate the farmer's behavior of under‐reporting the actual number of finished hogs on one hand, and the insurer's efficiency in determining the actual numbers on the other. Using data on 444 hog operators synchronized from farm production survey and insurance records, results showed that farmers report on average 11.5% fewer hogs to the insurance company. The level of under‐reporting is positively associated with the size of operation. Farmers with longer farming experience and more conservative risk attitude report more accurately. The under‐report behavior is also partially attributed to a farmer's limited capacity of accurate estimation. Due to information barrier, the insurance company is only able to recover 18.6% of the under‐report at the indemnity payment stage. Results are robust after controlling for potential sample selection problems. It is suggested that technical supports, public programs and premium incentive designs in repeated insurance should be considered to promote more accurate reports.

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Estimating bivariate yield distributions and crop insurance premiums using nonparametric methods

Cited by 7 publications

References 21 publications

Estimating variability in grain legume yields across Europe and the Americas

Estimating variability in grain legume yields across Europe and the Americas

Risk aversion in crop insurance program purchase decisions

Cheating? The Case of Producers’ Under‐Reporting Behavior in Hog Insurance in China

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