Multi-peril Crop Insurance Research Articles

Implications of crop yield distributions for multiperil crop insurance rating in Ghana: a lasso model application

PurposeThe objective of this paper is to examine crop yield predictions and their implications on MPCI in Ghana. Farmers in developing countries struggle with their ability to deal with agricultural risks. Providing aid for farmers and their households remains instrumental in combatting poverty in Africa. Several studies have shown that correctly understanding and implementing risk management strategies will help in the poverty alleviation agenda.Design/methodology/approachThis study examines the importance of crop yield distributions in Ghana and its implication on multiperil crop insurance (MPCI) rating using the Lasso regression model. A Bonferroni test was employed to test the independence of crop yields across the regions while the Kruskal-Wallis H test was conducted to examine statistical differences in mean yields of crops across the ten regions. The Bayesian information criteria and k-fold cross-validation methods are used to select an appropriate Lasso regression model for the prediction of crop yields. The study focuses on the variability of the threshold yields across regions based on the chosen model.FindingsIt is revealed that threshold yields differ significantly across the regions in the country. This implies that the payment of claims will not be evenly distributed across the regions, and hence regional disparities need to be considered when pricing MPCI products. In other words, policymakers may choose to assign respective weights across regions based on their threshold yields.Research limitations/implicationsThe primary limitation is the unavailability of regional climate data which could have helped in a better explanation of the variation across the regions.Originality/valueThis is the first study to examine the implications of regional crop yield variations on multiperil crop insurance rating in Ghana.

Cultivating Support: An Ex-Ante Typological Analysis of Farmers’ Responses to Multi-Peril Crop Insurance Subsidies

AbstractIn Europe, uptake of multi-peril crop insurance remains limited, leading to discussions on premium subsidies. A study of 228 Saxony farmers investigated their attitudes and willingness to pay for a multi-peril crop insurance in 2022. Using a cluster analysis based on farmers’ satisfaction with current risk management as well as market supply of tools, perceived benefits of the insurance, and views on governmental intervention, four farmer segments were identified. Each showed varied attitudes toward insurance subsidies, willingness to pay, and farm characteristics. As an initial ex-ante study on this topic, the insights are invaluable for insurance providers and policy makers.

A Bibliography of Key Final Agency Determinations of the United States Department of Agriculture Risk Management Agency

This Article is the first law review article to comprehensively examine Final Agency Determinations (FADs) of the United States Department of Agriculture. A key part of the administrative process within the Risk Management Agency of USDA, FADs contribute to the interpretation and understanding of the Common Crop Insurance Policy, which is the federally-reinsured multi-peril insurance contract. This Article surveys ten of the most significant recent FADs and emphasizes the importance of FADs to litigated disputes between insurance providers and insureds with regard to the federal crop insurance program. Overall, understanding of FADs is critical for stakeholders with the multi-peril crop insurance program.

Is Agricultural Insurance Fulfilling Its Promise for the Developing World? A Review of Recent Evidence

Innovations in agricultural index insurance have raised expectations that the private sector can overcome shortcomings associated with more traditional indemnity-based products like multiperil crop insurance and strengthen agricultural risk management at scale across developing countries. This article updates previous reviews on agricultural insurance but differs in that it goes beyond the prognosis that recent innovations can help make insurance more commercially viable. As such, it addresses two important challenges that have received limited attention. First, it distinguishes different types of farm households and recognizes that many are excluded from the insurance market, describing additional innovations that can help make insurance more accessible to these excluded groups. Second, it acknowledges that insurance for catastrophic risks is unaffordable for most farmers and summarizes new developments in disaster assistance and safety net programs that can provide broader protection against these risks. The review concludes that cost-benefit analyses of subsidized insurance programs will be crucial for guiding public spending decisions.

COPULA-BASED STATISTICAL MODELLING OF SYNOPTIC-SCALE CLIMATE INDICES FOR QUANTIFYING AND MANAGING AGRICULTURAL RISKS IN AUSTRALIA

COPULA-BASED STATISTICAL MODELLING OF SYNOPTIC-SCALE CLIMATE INDICES FOR QUANTIFYING AND MANAGING AGRICULTURAL RISKS IN AUSTRALIA

A Bootstrap Simulation for comparison of Group Risk Plan and Multi-Peril Crop Insurance Policy

Agricultural insurance is one of the solutions for farmers to avoid risks such as climate change, pest, disease and price fall which increase the risk of crop failure. Indonesian Ministry of Agriculture (MoA) and Jasindo have been using subsidized Multi-Perils Crop Insurance (MPCI) and faced some disadvantages that mainly due to high risk moral hazard, adverse selection, and high administrative costs. Our research provides an alternative policy area yield index insurance known as Group Risk Plan (GRP) to handle this problem. To find homogeneous basis risk in GRP, the data was classified into two categories: up to and above two hectares. Bootstrapping approach was used to find MPCI and GRP indemnity amount for each term of harvesting period. In addition, both parametric estimation and goodness of fit test indicated appropriate distribution used by MPCI and GRP in each term of harvesting period as well. To compare the aggregate loss distribution, the conditional value at risk CV aR(X) measures the mean of excess loss exceeding V aR(X). It reflects how risky and measures the amount of money insurance company should retain. Simulations indicated that MPCI has greater CV aR(X) than GRP, which implies the higher likelihood of loss. The insurance company should have enough reserve for MPCI to cover that loss. On the other hand, the GRP policy that minimizes moral hazard and adverse selection thereby reduces administrative costs and offers potential to market at lower costs, may encourage farmers to do good farming practices that will result in the lower likelihood of loss. Therefore, GRP could be considered as an alternative crop insurance policy in Indonesia.

The impacts of multiperil crop insurance on Indonesian rice farmers and production

AbstractThis article analyzes the effect of multiperil crop insurance policy for risk‐averse Indonesian rice farmers located in Tuban and Gresik Regencies of the East Java Province. Based on the model, comparative static analysis of a change in policy variables (coverage levels and premium subsidies) on input use through the coupling, wealth, and insurance effects are presented. The comparative static results are largely ambiguous and left as empirical questions. Consequently, the model is numerically simulated to quantify the effects of different coverage levels and subsidy rates on input use, expected net insurance payments, and certainty equivalents. The empirical analysis shows that MPCI crop insurance results in a decline in expected yield for coverage levels above about 82.5% for both regencies. Furthermore, higher subsidy rates amplify the reduction in input use and yield. Therefore, incomplete coverage with relatively low premium subsidies is the best policy to minimize the impact on input use and yield. However, from the farmers’ perspective, the optimal policy combination results from the highest coverage level and subsidy, which offer the largest expected net insurance payments and certainty equivalent.

Index insurance benefits agricultural producers exposed to excessive rainfall risk

Managing the risks of climate variability on crop production is central to ensuring financially viable farming systems and sustainable food production. Insurance provides a mechanism to manage and transfer climate risks. However, traditional multi-peril crop insurance (MCPI) is often too expensive and so other methods, such as index insurance, are being explored as a cheaper way to insure farmers against climate induced crop losses. Here we investigate the potential financial benefits of index insurance (protecting against excessive rainfall) for agricultural producers, namely sugar cane farmers in Tully, northern Australia. We used 80 years of historical climate and yield data to develop an excessive rainfall index. The index was developed and tested using generalized additive regression models (allowing for non-linear effects) and quantile regression, which allows relationships with lower quantiles (i.e. low yield events) to be assessed. From the regression models we derived relationships between the excessive rainfall index and sugar cane yield losses that were converted to insurance fair premiums (i.e. premiums that cover expected losses). Finally, we used efficiency analysis, based on Conditional Tail Expectation (CTE), Certainty Equivalence of Revenue (CER) and Mean Root Square Loss (MRSL), to quantify financial benefits to farmers if they purchased excessive rainfall index insurance. The regression model predicted sugar cane yields well (cross-validated R2 of 0.65). The efficiency analysis indicated there could be financial benefit to sugar cane farmers if they were to use excessive rainfall index insurance. Index insurance (based on the assumption of a fair premium) could make farmers better off by $269.85 AUD/ha on average in years with excessive rainfall (i.e. years with rainfall over the 95th percentile). Index insurance could offer a viable method for managing the financial risks posed by excessive rainfall for sugar cane producers in northern Australia. We are not aware of any other study demonstrating the potential benefits of excessive rainfall index insurance in the literature, but our results suggest this type of insurance may be viable for sugar cane producers, and other crops, in parts of the world where extreme rainfall poses a risk to the financial sustainability of production.

Does subsidized MPCI crowds out traditional market-based hail insurance in the Netherlands?

PurposeThe purpose of this paper is to determine which factors influence the choice to adopt subsidized multi-peril crop insurance (MPCI) in the Netherlands and whether prior hail insurance uptake is one of the determinants of MPCI adoption. In addition, it is analyzed whether subsidized MPCI has reduced disaster relief spending.Design/methodology/approachCross-sectional survey with 512 respondents using a stratified design comprising MPCI adopters and non-adopters sampled from the Dutch national census data base. The national census, including information on subsidized MPCI adoption from 2010 up to and including 2015, was supplemented with information on (prior) traditional market-based hail insurance uptake, and other underlying determining factors were elicited. Logistic regression analysis was used to determine which factors influence the choice to adopt MPCI.FindingsAnalysis of MPCI adoption reveals that subsidized MPCI mainly substituted for market-based hail insurance uptake up to now. Growers who did not insure against hail in the past were hardly reached. Approximately, three-quarter of MPCI adopters insured hail prior to market introduction of MPCI. In the arable sector, MPCI adoption was 2.89 (p<0.01) more likely for prior hail insurance adopters compared to non-adopters, while it was 9.67 (p<0.01) more likely in the fruit sector.Research limitations/implicationsIn the arable sector, it is expected that MPCI uptake in the coming years will reach more prior non-adopters of hail insurance as demand is expected to increase. Prior hail insurance adopters in the arable sector can be seen as the early MPCI adopters. In the fruit sector, adoption rates are already at a relative high level and a further significant increase by targeting non-adopters of hail insurance is not likely.Originality/valueGovernmental support has crowded out to some extend traditional market-based hail insurance in the Netherlands. Since the Common Agricultural Policy of the European Union is creating more momentum to subsidize crop insurance more member states with a long history of a mature hail insurance market may be confronted with similar crowding-out effects.

Are the Federal Crop Insurance Subsidies Equitably Distributed? Evidence from a Monte Carlo Simulation Analysis

This study hypothetically analyzes the distribution of the premiums paid and thus the subsidies received by farmers participating in the Risk Management Agency (RMA) multi-peril crop insurance program. The results show a wide spread in the effective subsidy levels, to where some producers might not be receiving any subsidies at all (i.e., they actually pay close to their full actuarially fair premium), while others only pay a small fraction of their actuarially fair premium. More importantly, the results show that “shrinkage” estimators such as the one used by the RMA have the unintended negative consequence of disproportionally subsidizing farmers who are less effective in managing risk. Producers whose farms exhibit higher downside yield variability receive much more generous subsidies than those with lower levels of yield variability.

Predicting Corn Grain Yield Using Silage Starch Content and Crop Adjuster Methods

Multi‐peril crop insurance (MPCI) requires that if corn (Zea mays L.) is insured for grain and the producer wants to use it for another purpose, that is, silage, the producer must have the corn grain yield appraised before corn silage harvest. This appraisal requires an adjuster to determine the weight of grain corn, and based on stage of maturity, apply a factor to convert the determined weight to the weight of mature corn at 155 g kg−1 moisture. This study was designed to test the hypothesis that corn grain yield can be estimated accurately before grain is combine harvested using the USDA–Risk Management Agency (RMA) methods and routine silage quality measurements for starch. Corn was established at two locations from 2006 to 2008. Management factors involving planting date, hybrid, and harvest timing were applied to create a range in yield and forage quality. Both RMA methods and the starch‐based method underestimated combine grain yield by 4 to 36% at silage harvest stages between 75 and 25% kernel milk (KM). At silage harvest, the starch method estimated final combine grain yield more accurately than the RMA maturity line weight (MLW) method, but when applied after silage harvest or just before grain harvest, the RMA Weight method was the better predictor of final combine grain yield. With careful assessment, starch‐analysis of silage theoretically could produce an equitable estimate of grain yield for insurance adjustments.

Assessing pricing assumptions for weather index insurance in a changing climate

Weather index insurance is being offered to low-income farmers in developing countries as an alternative to traditional multi-peril crop insurance. There is widespread support for index insurance as a means of climate change adaptation but whether or not these products are themselves resilient to climate change has not been well studied. Given climate variability and climate change, an over-reliance on historical climate observations to guide the design of such products can result in premiums which mislead policyholders and insurers alike, about the magnitude of underlying risks. Here, a method to incorporate different sources of climate data into the product design phase is presented. Bayesian Networks are constructed to demonstrate how insurers can assess the product viability from a climate perspective, using past observations and simulations of future climate. Sensitivity analyses illustrate the dependence of pricing decisions on both the choice of information, and the method for incorporating such data. The methods and their sensitivities are illustrated using a case study analysing the provision of index-based crop insurance in Kolhapur, India. We expose the benefits and limitations of the Bayesian Network approach, weather index insurance as an adaptation measure and climate simulations as a source of quantitative predictive information. Current climate model output is shown to be of limited value and difficult to use by index insurance practitioners. The method presented, however, is shown to be an effective tool for testing pricing assumptions and could feasibly be employed in the future to incorporate multiple sources of climate data.

The Effectiveness of Area-Based Yield Crop Risk Insurance in China

ABSTRACT Area-based yield insurance (AYI) is a kind of index insurance that can eliminate asymmetric information problems (adverse selection and moral hazard) and reduce transaction cost. It is important in managing crop risk for developing countries such as China, which has many small farms. However, the biggest challenge in index insurance is basis risk. In this regard, can AYI be used in China? Is AYI competitive with traditional multi-peril crop insurance (MPCI)? What are the principal factors affecting the effectiveness of AYI? Following and extending the approaches suggested by Miranda in 1991, this article compares the effectiveness of AYI and MPCI in terms of risk reduction per premium. An empirical model was established, indicating the relationship between the efficiency of AYI and the explanatory variables, using data from a survey of 108 wheat farmers from two counties in Hebei Province, China. The results provide salutary suggestions to area-based index insurance design and development in China.

Analysis of multi peril crop insurance loss ratiosAnalysis of multi peril crop insurance loss ratios

In the first part of this paper, the distribution of loss ratios for five crops was analyzed. The results show that the truncated shifted Pareto distribution can be used for crop insurance loss ratios approximation. In the second part, the relationship between weather conditions and crop insurance loss ratios was tested. Results suggest that this dependency is weak. It shows that crop insurance loss ratios are affected not only by weather conditions – monthly growing degree days and monthly precipitation – but also by other factors.

Is geographic diversification sufficient to limit contract grower risk?

Lending and financial institutions have looked for a variety of ways to expand their portfolios into agriculture, but because of the risks associated with lending to farmers who lack traditional forms of collateral, they face price and yield risks, causing these inroads to be limited. Market-based instruments are readily available for price risk. Organised exchanges offering the most basic of these instruments, futures and options, have operated for a long time, providing transparency to the market and low-cost risk transfer tools for those able to access them. While the use of price risk management instruments is an incomplete solution, it has sufficient merits on its own and will make the overall burden of risk more bearable. The use of these instruments and multi-peril crop insurance products is expensive and does not provide full protection for financial lending institutions to limit their credit risk exposure. This article determines whether geographic diversification would be sufficient as a risk management tool for lending institutions to limit their credit risk.

Weather Derivatives in the Wine Industry

The wine industry is very sensitive to climate risk, mainly due to the grape crop. Industry players usually cover this risk through single-peril and multi-peril crop insurance policies. Insurance policies pose many problems of efficiency, as pointed out in the literature, and do not usually cover the temperature risk. The aim of this paper is to understand whether weather derivatives, mainly developed for the energy industry, can also be useful to cover temperature risk for the wine industry. I have discovered that a weather derivative useful for this task is similar to a cooling degree day (CDD) contract in the energy industry, and that a possible hedging strategy is a straddle consisting of combination of a call and a put on a suitable temperature index. In the case history presented, relating to the DOC Oltrep-Pavese (OP) Bonarda red grape, the straddle covers the temperature risk effectively, although it is unable to reduce volatility related to the crop economic value.