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PurposeThe purpose of this paper is to examine factors affecting the use of forage index insurance. Forage is a difficult crop to insure, and index insurance may be well suited for forage insurance and has been implemented in several countries, including Canada, the USA and France. Despite being a promising risk management tool, forage index insurance participation rates in Canada, and other countries are low relative to crop insurance participation rates for grain and oilseed producers.Design/methodology/approachA survey was conducted with 87 beef and cattle producers from Alberta and Saskatchewan, Canada. A probit regression model was used, and a number of variables were included to examine the use of forage index insurance.FindingsIn total, 6 of 11 variables in the model are found to be statistically significant in explaining forage producers’ use of forage index insurance. Results suggest that producers who maintain lower feed reserves are more likely to purchase forage index insurance. Also, producers with higher levels of knowledge of crop insurance and a more positive attitude toward forage insurance are more likely to use forage index insurance. Furthermore, producers are more likely to use forage index insurance if they perceive drought and weather risk as being of greater importance, and if they are younger. The importance of the variable forage index insurance premium price was statistically insignificant. This could be due to the effect of subsidization, reducing the importance of price for the decision to purchase. Similarly, the use of other subsidized risk management policies, including a whole-farm margin policy (e.g. the government program and AgriStability), did not reduce forage index insurance use. A possible explanation for this is that the subsidization of the policies may make it profitable to purchase both, despite the overlapping coverage.Practical implicationsThese results may be useful for policy makers interested in increasing forage index insurance participation rates, as forage index insurance participation rates have historically been low relative to grain and oilseed producers.Originality/valueThis study is believed to be one of the first studies regarding the use of forage index insurance by forage producers. Producers can be exposed to catastrophic risks such as drought or other extreme weather events, and forage index insurance may be an effective means to manage these risks. Index insurance determines payments using an index that is correlated to producers’ actual yields. A downside of this method is basis risk, which is the mismatch between the insured index and the producer’s actual yield. Research has focused on basis risk and developing improved methods to reduce basis risk. However, less research has investigated the other important factors that may contribute to forage index insurance use. Producers may have a different risk management environment regarding forage production compared to other farm activities, and these differences have largely not been examined.

Despite increasing adoption of earth observations data to inform disaster response and recovery, deciding which measurements to use—and how—remains an open question. An increasing number of flood insurance programs have been using observable proxies—or indices—to activate payouts. However, convincing evaluation of important design features, including choice of index data, are lacking. This study investigates five potential flood data sets at national and regional scales in a simulated index‐based insurance program in Bangladesh: gridded precipitation, river‐height and modeled inundation from the national flood agency, and two satellite data sets of surface‐water‐extent (one state‐of‐practice, the other state‐of‐the‐art). We demonstrate that data choice determines the accuracy and timeliness of indexed payouts, as well as the uncertainty associated with their likelihood, which influences program costs. For example, while river‐height and satellite water‐extent indices activated payouts during the two worst floods in the 20‐year study period (2004 and 2007), the precipitation‐index activated for just one of them. Furthermore, our state‐of‐the‐art satellite index activated on average 1 week earlier and with 21% lower uncertainty than the satellite‐index used in practice. We propose that practitioners leverage the divergence‐of‐evidence among multiple data sets to identify regions where there is lower confidence in making accurate and timely payouts, which can help inform additional programming such as back‐up payout mechanisms. Beyond insights for practitioners leveraging insurance to protect Bangladeshi communities threatened by extreme monsoon floods, this work offers techniques to assess the sensitivity of indexed programs to different data and scales in other flood‐prone regions. Plain Language Summary Data from ground instruments and satellites are used by governments, insurers, and non‐governmental organizations to respond to climate disasters. Increasingly, insurance programs have been using observed flood conditions to generate indices that determine who receives payouts and when. However, choosing the most appropriate measure of flooding is not straightforward and existing approaches typically lack rigor or transparency around this choice. Investigating five data sets in Bangladesh, we find that the choice of data for the index controls the years in which payouts are made and how timely the payouts are. For example, indices using river‐height from ground instruments and surface‐water from satellites paid out during the two worst floods in the study period. On the other hand, a rainfall index failed to pay out in the major 2004 floods. We also find that using our novel machine learning water detection method results in more timely payouts than the satellite method that has been used in practice. We encourage practitioners to use the disagreement between data sets to identify areas with lower confidence that payouts will be made when needed and to plan additional actions. Beyond Bangladesh, our study presents ways practitioners can assess the appropriateness of different data sets in other flood‐prone regions. Key Points Data choice directly controls the accuracy, timeliness, and uncertainty of index‐based flood insurance payouts Our state‐of‐the‐art index activated payouts earlier and with lower uncertainty than the satellite‐index previously used in Bangladesh Divergence of evidence among multiple data sets can be used to inform additional actions in regions with lower confidence in payouts

The number of index insurance pilots in developing countries has grown tremendously in recent years, but there has been little progress in our understanding of the quality of those products. Basis risk, or remaining uninsured risk, is a widely recognized but rarely measured feature of index insurance product quality. This article uses eight semi-annual seasons of longitudinal household data to examine the distribution of basis risk associated with an index-based livestock insurance (IBLI) product in northern Kenya. We find that IBLI coverage reduces exposure to covariate risk due to large shocks and mitigates downside risk substantially for many households, even at commercial premium rates. But index insurance is no magic bullet; insured households continue to face considerable basis risk. Examining the components of basis risk, we find that IBLI reduces exposure to covariate risk due to high loss events by an average of 63%. The benefits of reduced covariate risk exposure are relatively small, however, due to high exposure to seemingly mostly random idiosyncratic risk, even in this population often thought to suffer largely from covariate shocks. The result is that IBLI policyholders are left with an average of 69% of their original risk due to high loss events. This research underscores the need for caution when promoting index insurance as a risk mitigation tool, as well as the importance of product quality evaluation.

Extreme weather events cause considerable damage to the livelihoods of smallholder farmers globally. Whilst index insurance can help farmers cope with the financial consequences of extreme weather, a major challenge for index insurance is basis risk, where insurance payouts correlate poorly with actual crop losses. We analyse to what extent the use of crop simulation models and crop phenology monitoring can reduce basis risk in index insurance. Using a biophysical process-based crop model (Agricultural Production System sIMulator (APSIM)) applied for rice producers in Odisha, India, we simulate a synthetic yield dataset to train non-parametric statistical models to predict rice yields as a function of meteorological and phenological conditions. We find that the performance of statistical yield models depends on whether meteorological or phenological conditions are used as predictors and whether one aggregates these predictors by season or crop growth stage. Validating the preferred statistical model with observed yield data, we find that the model explains around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level, outperforming vegetation index-based models that were trained directly on the observed yield data. Our methods and findings can guide efforts to design smart phenology-based index insurance and target yield monitoring resources in smallholder farming environments.

With uninsured risk representing a major hurdle to investment, productivity growth, and poverty reduction in developing country smallholder agriculture, index-based agricultural insurance has offered the promise of overcoming the hurdles of traditional indemnity-based insurance for this context. In spite of extensive experimentation, take-up has been disappointingly low without large and sustained subsidies. We show that existing constraints on take-up can partially be overcome using revised contract designs, advanced technology for better measurement, improved marketing, and better policy support. However, because index insurance is likely to remain expensive in that context, we suggest that improved index insurance be combined with stress tolerant seed varieties and new risk-oriented savings and credit products that build on the complementarities between what can be offered by index insurance and these other instruments to cope with shocks and manage risk.

This article investigates the impact that indemnity payments from index insurance have on the asset recovery of households following a catastrophic weather disaster. Our focus is on the Index-Based Livestock Insurance (IBLI) in Mongolia. We analyze the effect of IBLI indemnity payments after a once-every-50-year winter disaster struck Mongolia over the winter of 2009/2010. The analysis is based on three waves of a household panel survey implemented in western Mongolia two to five years after the shock. We employ the bias-corrected matching estimator to account for selection into purchasing IBLI. Results indicate that pastoralist households purchasing IBLI before the shock recover faster from shock-induced asset losses than comparable uninsured households. We find a significant, positive, and economically large effect of IBLI indemnity payments on herd size one to three years after the shock. Four years after the shock, the effect vanishes. Results are robust to defining post-shock livestock recovery in various ways, as well as the choice of covariates and the use of alternative propensity score estimators. An analysis of shock-coping strategies suggests that IBLI appears to have relieved households from credit constraints. In addition, indemnity payments helped herders avoid selling and slaughtering animals, thus smoothing their productive asset base. Our article is among the first to provide evidence on the beneficial effects of index insurance after a weather shock in a developing economy.

Abstract Drought-induced risk endangers farmers in arid and semi-arid regions. Insurance is recognized as an appropriate policy alternative to support farmers facing with financial losses associated with production reduction. In this context, present study developed an ex ante index-based insurance program to deal with drought-induced risk of production losses. We applied this model to wheat growers in Iran. After the calibration of the contract parameters, an insurance scheme was optimized and tested. We showed that optimal insurance contracts generate low gain of certain equivalent income, high compensation, and a high basis risk. The best contract was not proportional to the complexity of the proposed index. The insurance program studied is recommended as a proper alternative for currently applying yield insurance. Resumo O risco induzido pela seca coloca em perigo os agricultores em regiões áridas e semiáridas. O seguro é reconhecido como uma alternativa política apropriada para dar suporte aos agricultores que enfrentam perdas financeiras associadas à redução da produção. Nesse contexto, o presente estudo desenvolveu um programa de seguro baseado em índice ex ante para lidar com o risco de perdas de produção induzido pela seca. Aplicamos este modelo aos produtores de trigo no Irã. Após a calibração dos parâmetros do contrato, um esquema de seguro foi otimizado e testado. Mostramos que os contratos de seguro ideais geram baixo ganho de certa renda equivalente, alta compensação e alto risco de base. O melhor contrato não foi proporcional à complexidade do índice proposto. O programa de seguro estudado é recomendado como uma alternativa adequada para a aplicação atual do seguro de rendimento.

Named peril index insurance has great potential to address unmet risk management needs for agricultural insurance in developing economies, potentially contributing to increased agricultural sustainability and improved food security. However, the development and appraisal of index insurance business lines is not without challenges. Insurers must rigorously evaluate the quality of the products they offer and take care to ensure that distributors and policyholders understand the benefits and limits of the purchased coverage. Without these important steps to ensure responsible insurance practices, insurers can damage the implementation and potential of index insurance in the market. Risk Modeling for Appraising Named Peril Index Insurance Products: A Guide for Practitioners helps stakeholders in the named peril index insurance industry appraise new and existing products. Part 1 of the guide provides a summary of the insights and decisions required for the insurer to make an informed decision to launch and expand an index insurance business line. Insurance managers are the primary audience for part 1. Part 2 provides a step-by-step guide to calculating the decision metrics used by the insurance manager in part 1. These metrics are calculated using probabilistic modeling that provides insights into risks related to the index insurance product. Actuarial analysts are the primary audience for part 2. In an increasingly competitive insurance market, creative product development and imaginative business strategies are becoming the norm. This guide will help emerging market insurers who seek to stay on the cutting edge to successfully and sustainably penetrate new market segments.

PurposeThe purpose of this study is to explore and prioritize the barriers that affect weather index-insurance (WII) adoption among customers by utilizing interpretive structural modelling (ISM) and fuzzy-MICMAC.Design/methodology/approachThis paper utilized the combined approach in two phases. In first phase comprehensive literature study and expert mining method have been performed to identify and validate WII adoption barriers. In second phase, ISM has been utilized to examine the direct relationships among WII adoption barriers in order to develop a structural model. Further, fuzzy-MICMAC method has been utilized to analyse indirect relationships among barriers to explore dependence and driver power.FindingsThis study has identified 15 key barriers of WII adoption among customers and developed a structural model based on binary direct relationship using ISM. Later, the outcomes of ISM model have been utilized for analysing the dependence and driver power of each WII adoption barriers in cluster form using fuzzy-MICMAC. The customer awareness related WII adoption barrier are mainly at the top level, WII demand related barriers are in the centre and WII supply related barriers at the bottom level in ISM model.Practical implicationsThe findings offered important insights for WII insurers to understand mutual relationships amongst WII adoption barriers and assists in developing strategy to eliminate dominant key barriers in order to enhance their customer base.Originality/valueBased on best of author's knowledge this paper firstly integrates the ISM fuzzy-MICMAC method into identification and prioritization of barriers that affects WII adoption among customers.

The suitability of insurance products often depends greatly on individual circumstances. This paper examines the challenges of heterogeneity in a relatively new product, weather-indexed insurance. This index insurance product has been launched in over a dozen countries, with the goal of enabling households engaged in agricultural activity a means to manage risk. Using data from a large-scale field experiment, we build and calibrate a model which accounts for household investment decisions, including the scope for self-insurance via labor markets to (risky) wage work. Our results show that insurance is most valuable to households with reduced access to wage labor, or to those who face wages that are sensitive to rainfall risk. These findings have important implications for areas where index insurance is most effective.