The effects of overconfident yield forecasting on cross-coverage level crop insurance demand

PurposeThis paper examines the effect of overconfident yield forecasting (optimism bias) on crop insurance coverage level choices across both yield and revenue insurance.Design/methodology/approachThis study simulates a representative producer’s preferred coverage level for both yield and revenue insurance under three potential models of decision-making and four potential manifestations of overconfident yield forecasting. The study then uses this framework to examine how coverage level choices change as overconfidence increases (decreases).FindingsAs overconfidence increases, producers prefer lower levels of crop insurance coverage than they would otherwise prefer, with extreme overconfidence inducing farmers to buy no insurance at all. While overconfidence affects cross-coverage demand for revenue and yield insurance similarly, this effect is more pronounced for yield insurance. Cross-coverage level demand for revenue insurance is relatively stable across changes in the correlation between prices and yields.Practical implicationsThis research has important implications for crop insurance subsidy design and crop insurance demand modeling.Originality/valueThere is a growing body of literature suggesting that producers are overconfident with regard to their future yield risk and that this bias reduces their willingness to pay for risk management tools such as crop insurance. This is the first study to look at how such overconfidence affects cross-coverage level demand for crop insurance.

Optimal strategies for a capital constrained contract-farming supply chain with yield insurance

We consider a capital-constrained contract-farming supply chain with a risk-averse farmer and a risk-neutral agro-dealer, where the farmer faces some yield uncertainty that can be covered by insurance. Using the Stackelberg model, we derive the optimal strategies on the insured level, production and wholesale price. The result shows that farmers with low risk aversion tend not to be insured, while those with high risk aversion tend to insure. Further analysis indicates that, as the degree of the farmer's risk aversion increases, the farm size decreases, but the yield per unit area and the wholesale price of the agricultural product increases. In addition, yield insurance and premium subsidies can lead to a decrease of the yield per unit area. However, the expansion of the farm size can compensate for the inhibitory effect of the decrease of yield per unit area on the total yield, and thus the total yield increases. We also find that when the premium subsidy rate is low, the yield insurance's value to farmers is negative. Moreover, the yield insurance's value to farmers increases with respect to the bank's interest rate.

Determinants of Adoption of Crop Insurance: Evidence from Bolangir District in Odisha

Pradhan Mantri Fasal Bima Yojana (PMFBY) is an area-based crop yield insurance scheme introduced in Odisha in Eastern India since kharif, 2016. The present study aims to identify the factors that determine the adoption of PMFBY among farmers and examine the operational efficiency of the scheme. The study is based on a field survey conducted in the drought-prone Bolangir district in Western Odisha. A total of 200 households were interviewed with questionnaires, which included 80 loanee and 80 non-loanee PMFBY users and 40 non-users. Probit regression is fitted to identify the factors that determine the adoption of PMFBY. Higher caste farmers with greater farm size, larger household incomes and indebtedness and risk-averse farmers are more likely to adopt PMFBY. Farmers having more dependence on non-farm income are less inclined to buy crop insurance. Farmers cite various reasons for dissatisfaction with the scheme such as: delay in compensation payment, large loss assessment unit and non-coverage of individual and independent risks.

Moral Hazard in Agricultural Yield Insurance

In agricultural yield insurance practices, there are two main categories of insurance products which differed from the targeted insured yield, namely area-yield based insurance and individual-based insurance. A common knowledge is that individual-yield based insurance has more flexibility that could meet the real demand of insureds, while having much more severity of moral hazard and higher administration costs. Relatively, area-yield based insurance has lower risk of moral hazard, but obtaining bias, or so-called basis risk at the same time. In this paper, we use an improved modified Miranda Decomposition Model to establish a theoretical framework of farmers behaviors when assuming their goals are to maximize the expected rate of return in agricultural production process under both individual-yield and area-yield insurance. The results show that these two distinct arrangements may cause different motivation to farmers, seducing them act or not act in moral hazard manner.

On the Robustness of PERT Fittings in Agricultural Yield Insurance

In agricultural insurance practice, risk and indemnity payment are often incurred from individual farmer’s yield. However, high administration cost and data scarcity are simultaneously quite often seen, which form huge burdens for insurers to adequately rate insurance products. Under this circumstance, some methods that could be used to estimate farmers’ yields, in particular, their distributions, are urgently needed. Among these methods, a so called PERT fitting technique often prevails due to its simplicity which only requires very little knowledge about the yield history, that is frequently implemented by both academics and practitioners. However, the very limited information used would sometimes cause severe bias, in other words, the reliability of this method is yet to be examined. In this paper, I used Monte Carlo experiments to test the robustness of PERT fittings under Var and CTE risk measures in different scenarios. The result proves that PERT method is indeed robust and trustworthy.