Can expert knowledge compensate for data scarcity in crop insurance pricing?

Shen, Zhenjiang; Odening, Martin; Okhrin, Ostap

doi:10.1093/erae/jbv015

Cited by 20 publications

(13 citation statements)

References 43 publications

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“…In addition we observe a positive correlation between participation in insurance schemes with the variables "revenue variability" and "expected premium": the higher the revenue variability, the higher the likelihood of stipulating an insurance contract; the higher is the expected premium, which reflects a higher level of underlying risk, the higher is the participation in crop insurance program. This seemingly counterintuitive result is explained by the crop data scarcity which imposes higher premiums in Italy (Shen et al, 2016). In order to disentagle the effects of premiums we would need to rely on expert knowledge of the degree of riskiness: unfortunately, those data are not available.…”

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confidence: 99%

Imperfect information and participation in insurance markets: evidence from Italy

Santeramo

2018

AFR

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Participation in crop insurance programs is lowered by imperfect knowledge resulting in adverse selection and moral hazard problems. We aim at investigating how experience in insurance contracts may influence participation in the Italian crop insurance market. From Italian farm-level data we estimate a dynamic discrete choice model of participation to investigate the role of experience. The methodology, coupled with exploratory analysis of the data, allows one to compare the relevance of different sources of experience in the crop insurance decision making process. We found that experience tend to be a catalyst for insurance participation. Policy implications are discussed: in particular we discuss on the importance of bolstering uptake to exploit the advantages of the inertia and spillover effects that emerge from experience. To the best of our knowledge, the role of experience has been underinvestigated. Our analysis has the specific contribution of modeling the potential role of experience (exploited after buying an insurance contract) on uptake in crop insurance programs.Risk management in agriculture is receiving growing attention in developing and develoed countries, as attested by the increase in prominence of crop insurance programs, where participation has been enhanced through subsidies (Mahul and Stutley, 2010). While the world's largest risk management program -the United States of America's one -supports farmers through hedge funds, revenue insurance programs, mutual funds, and weather indexes, in the European Union, the tools are still not much diffused. The EU

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Section: See Tablementioning

confidence: 99%

Imperfect information and participation in insurance markets: evidence from Italy

Santeramo

2018

AFR

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“…10 We observe a positive correlation between participation in insurance schemes with the variables "Revenue variability" and "Expected premi": the higher the revenue variability, the higher the likelihood of buying insurance contracts; the higher is the expected premium, which reflects a higher level of underlying risk, the higher is the participation in crop insurance programs. This seemingly counterintuitive result is explained by crop data scarcity, which is likely to impose higher premia in some areas of Italy (Shen, Odening, and Okhrin 2016). In order to disentangle the effects of premia we would need to rely on expert knowledge of the degree of riskiness: unfortunately, those data are not available.…”

Section: Applied Economic Perspectives and Policymentioning

confidence: 99%

I Learn, You Learn, We Gain Experience in Crop Insurance Markets

Santeramo

2018

Applied Eco Perspectives Pol

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The relevance and the impact of experience in insurance markets are underinvestigated. From Italian farm-level data we estimate a dynamic discrete-choice model of participation to investigate the role of experience. The methodology, coupled with exploratory analysis, allows one to compare how different sources of experience influence the crop insurance decision making process. We found that direct experience is a catalyst for insurance participation for medium and large farms. The experience indirectly acquired is also relevant, especially for small farms. Policy implications include the importance of information campaigns and of bolstering uptake to exploit the advantages of the inertia and spillover effects that emerge from experience.

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“…Besides some empirical work in maintenance optimisation (Bunea & Bedford, 2002), the majority of experiences for 695 eliciting copulas, such as the first approach presented above, comes from banking and insurance (Shen et al, 2015;Arbenz & Canestraro, 2012;Regis et al, 2011;Böcker et al, 2010), an area in which the popularity of copulas has increased lately (Genest et al, 2009). Here, expert judge-700 ment is typically used to assess conditional and joint probabilities of (extreme) loss events.…”

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confidence: 99%

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

Werner

Bedford

Cooke

et al. 2017

European Journal of Operational Research

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Many applications in decision making under uncertainty and probabilistic risk assessment require the assessment of multiple, dependent uncertain quantities, so that in addition to marginal distributions, interdependence needs to be modelled in order to properly understand the overall risk. Nevertheless, relevant historical data on dependence information are often not available or simply too costly to obtain. In this case, the only sensible option is to elicit this uncertainty through the use of expert judgements. In expert judgement studies, a structured approach to eliciting variables of interest is desirable so that their assessment is methodologically robust. One of the key decisions during the elicitation process is the form in which the uncertainties are elicited. This choice is subject to various, potentially conflicting, desiderata related to e.g. modelling convenience, coherence between elicitation parameters and the model, combining judgements, and the assessment burden for the experts. While extensive and systematic guidance to address these considerations exists for single variable uncertainty elicitation, for higher dimensions very little such guidance is available. Therefore this paper offers a systematic review of the current literature on eliciting dependence. The literature on the elicitation of dependence parameters such as correlations is presented alongside commonly used dependence models and experience from case studies. From this, guidance about the strategy for dependence assessment is given and gaps in the existing research are identified to determine future directions for structured methods to elicit dependence. (Anca M. Hanea), O.MoralesNapoles@tudelft.nl (Oswaldo Morales-Nápoles) cause variables in the model are correlated, or indirectly when an uncertainty analysis of model parameters is carried out to explore model robustness. Both cases exhibit complex interrelations and dependencies which need to be considered if assumptions such as independence are not 10 justifiable.However, it is often not straightforward to either model or quantify dependence. In particular whenever no relevant historical data are available, the only sensible way to achieve uncertainty quantification is through eliciting ex-15

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Can expert knowledge compensate for data scarcity in crop insurance pricing?

Cited by 20 publications

References 43 publications

Imperfect information and participation in insurance markets: evidence from Italy

Imperfect information and participation in insurance markets: evidence from Italy

I Learn, You Learn, We Gain Experience in Crop Insurance Markets

Expert judgement for dependence in probabilistic modelling: A systematic literature review and future research directions

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