Index Insurance Research Articles

The economic potential for area‐yield crop insurance: An application to maize in Ghana

AbstractRainfall index insurance can enable farm households to manage production risk, but demand in developing countries remains low at market prices, in part because the insurance trigger may not correlate well with individual farm losses. Area‐yield crop insurance, which links payouts to average yield in a geographic zone, attempts to increase demand by more accurately targeting insurance payouts to production shortfalls. However, shifting from an exogenous weather‐based to an endogenous yield‐based index introduces concerns of asymmetric information, which can lead to market failures that constrain supply from providers. These features are inversely related: larger insurance zones inhibit index manipulation, but average yield is less informative about any individual plot. We quantify this tradeoff for maize in Ghana using a spatial yield model calibrated to match observed production. Insurers must demarcate zones of no more than 5000 farmers for area‐yield insurance to outperform weather insurance. The framework presented in this paper allows assessment of the relationship between index performance and asymmetric information in new crop insurance products.

Climate variability through the lens of applied weather index insurance in Senegal-a novel perspective on the implications of decadal variation

IntroductionWeather-based index insurance is a financial instrument which allows smallholder farmers to protect themselves against climate shocks such as droughts and floods. In many cases, insurance indices are based on one or more earth observation datasets (e.g., rainfall, soil moisture, vegetative health) which are partly covering periods of more than 40 years. While remote sensing products and their associated data have improved over this time, understanding the historical climate variability and trends remains an essential piece in ensuring the development of indexes that best represent farmers’ risks. From a practical perspective, shortening time series to limit the risk of understudied climate variability, such as the Atlantic Multidecadal Variability, sometimes seems to be a quick solution. However, shorter time series jeopardize the overall robustness of the index. Therefore, understanding the links between climate variability, index design, and implications for farmers is key. Weather-based index insurance products in Sahelian West Africa usually face a challenge in robustly quantify underlying climatic decadal variation in seasonal rainfall.MethodsThis study analyzes the influence of decadal shifts in rainfall patterns in Sahelian West Africa, in particular Senegal, on index insurance calibration and design, concluding with practical recommendations for the next generation of drought risk finance instruments in the region.ResultsOur findings indicate that decadal variability has not led to a clear decrease in payouts in recent years compared to earlier years, despite an overall increase in seasonal rainfall. Rather, we find that interannual variability has increased which may be a more critical factor for assessing farmers’ agricultural risk than the increase in total rainfall.DiscussionFocusing on key moments of the cropping calendar in the design of an index shows that an increase in the total average rainfall per season does not result in fewer payouts.

Envisaging Indian farmers' desires from agricultural index insurance integrating rank sum weighting method and MoSCoW technique: an approach to requirements prioritization

Envisaging Indian farmers' desires from agricultural index insurance integrating rank sum weighting method and MoSCoW technique: an approach to requirements prioritization

Blended insurance scheme: A synergistic conventional-index insurance mixture

Conventional indemnity-based insurance (“conventional insurance”) and index-based insurance (“index insurance”) represent two primary insurance types, each harboring distinct advantages depending on specific circumstances. This paper proposes a novel blended insurance whose payout is a mixture of the two, to achieve enhanced risk mitigation and cost efficiency. We present the product design framework that employs a multi-output neural network (NN) model to determine both the triggering type and the index-based payout level. The proposed framework is then applied to an empirical case involving soybean production coverage in Iowa. Our results demonstrate this blended insurance could generally outperform both conventional and index insurance in enhancing policyholders' utility.

Data-driven determination of plant growth stages for improved weather index insurance design

PurposeThis paper aims to improve the delimitation of plant growth stages in the context of weather index insurance design. We propose a data-driven phase division that minimizes estimation errors in the weather-yield relationship and investigate whether it can substitute an expert-based determination of plant growth phases. We combine this procedure with various statistical and machine learning estimation methods and compare their performance.Design/methodology/approachUsing the example of winter barley, we divide the complete growth cycle into four sub-phases based on phenology reports and expert instructions and evaluate all combinations of start and end points of the various growth stages by their estimation errors of the respective yield models. Some of the most commonly used statistical and machine learning methods are employed to model the weather-yield relationship with each selected method we applied.FindingsOur results confirm that the fit of crop-yield models can be improved by disaggregation of the vegetation period. Moreover, we find that the data-driven approach leads to similar division points as the expert-based approach. Regarding the statistical model, in terms of yield model prediction accuracy, Support Vector Machine ranks first and Polynomial Regression last; however, the performance across different methods exhibits only minor differences.Originality/valueThis research addresses the challenge of separating plant growth stages when phenology information is unavailable. Moreover, it evaluates the performance of statistical and machine learning methods in the context of crop yield prediction. The suggested phase-division in conjunction with advanced statistical methods offers promising avenues for improving weather index insurance design.

Short- and long-term weather prediction based on a hybrid of CEEMDAN, LMD, and ANN.

Agriculture is one of the major economic sectors in Africa, and it predominantly depends on the climate. However, extreme climate changes do have a negative impact on agricultural production. The damage resulting from extreme climate change can be mitigated if farmers have access to accurate weather forecasts, which can enable them to make the necessary adjustments to their farming practices. To improve weather prediction amidst extreme climate change, we propose a novel prediction model based on a hybrid of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), local mean decomposition (LMD), and artificial neural networks (NN). A detailed comparison of the performance metrics for the short- and long-term prediction results with other prediction models reveals that the three-phase hybrid CEEMDAN-LMD-NN model is optimal in terms of the evaluation metrics used. The study's findings demonstrate the efficiency of the three-phase hybrid CEEMDAN-LMD-NN prediction model in decision-system design, particularly for large-scale commercial farmers, small-holder farmers, and the agricultural index insurance industry that require reliable forecasts generated at multi-step horizons.

Climate risk adaptation through livestock insurance: evidence from a pilot programme in Nigeria

ABSTRACT This study investigates the option of choosing index-based livestock insurance (IBLI) to mitigate the adverse effects associated with climate change in Kwara State, Nigeria. Previous studies indicate that the failure to include farmers during the early stage of pilot programme design is a major factor in Africa’s poor participation rate in index insurance. We conducted a survey with 392 farming households across eight of the state's 16 Local Government Areas (LGAs). This survey employed a contingent valuation method (CVM) to assess the value respondents are willing to pay for such insurance and add to the growing literature on IBLI uptake. We discuss important issues ranging from the cost of the IBLI premium to the impact of social capital and access to alternative sources of income on adoption. Our findings show that farmers are willing to pay a 1.3% premium for IBLI, which is lower than the current premium charged for traditional agricultural insurance in Nigeria which typically ranges from 2% to 5%. The results also highlight the need to consider insurance uptake and access to credit as complementary measures, rather than substitute strategies for managing the risks posed by severe climate shocks and extremes.

LOSS MODEL OF CLIMATE INSURANCE BASED ON EFFECT OF GROWING DEGREE DAYS INDEX

Climate change is a threat to agriculture, especially food crops such as rice. Climate index insurance is an alternative to cover the risk of agricultural losses due to crop failure due to climate change factors. The observed climate index is the effect of growing degree days which measures the impact of temperature on plant growth and development. The data used in this study is daily temperature data at Climatology Station Class 1 Darmaga, Bogor and Meteorological Station Class 3 Citeko, West Java, during the gadu (rice that is planted in the Gadu/Dry season) planting period. In determining the amount of loss, the average daily temperature on growing degree days is calculated using a combination of a time series model and a deterministic model. The deterministic model describes the trend and seasonality of the time series at each station. The parameters contained in the model will be estimated using least-square. To see the dependence of temperature at different stations using a normal bivariate distribution. The result show that the amount of loss based on the index of growing degree days per unit rupiah per degree Celsius (℃) for Meteorological Station Class 3 Citeko only occurs for certain percentages, namely 80%, 90%, and 95%, while for Climatology Station Class 1 Darmaga Bogor it can occur for each percentage. This indicates that the amount of losses obtained will depend on determining the strike level by using the mean and standard deviation of the growing degree days index distribution. Furthermore, these findings suggest that Climatology Station Class 1 Darmaga Bogor have higher risk of crop failure due to climate change than Meteorological Station Class 3 Citeko.

The Regulation of Index-Based Agricultural Insurance Products as a Risk Management Tool for Small Holder Farmers in Cameroon

The regulation of index based agricultural insurance for small holder farmers in Cameroon are as a result of the risk they are exposed to like weather variability and related risks associated with their activities especially droughts, floods, pests and input costs and more importantly the limits of indemnity based agricultural insurance. Like any other poor and low income farmers with small land holdings their risk exposures can only be managed through good laws especially with the use of index insurance as a risk management tool. This article examines the CIMA Code as a regulatory instrument for agricultural insurance as far as small holder farmers are concerned. Book VII of the CIMA Code introduced microisurance in 2012 as a panacea to indemnity insurance which enabled the codification and development of index insurance by member states. The implementation of Index-based agricultural insurance faces considerable challenges that must be overcome to effectively service farmers in remote areas in a country like Cameroon. These challenges includes amongst others: regulatory insufficiency, limited historical rainfall and crop yield data, information on livestock mortality and limited infrastructures just to mention a few. It is recommended inter-alia that these aforementioned could be addressed through codification, infrastructural developments, inclusion of non-farming activities like banking and micro finance institutions amongst others.

Index Insurance for Forage, Pasture, and Rangeland: A Review of Developed (USA and Canada) and Developing (Kenya and Ethiopia) Countries

Index insurance for forage, pasture, and rangeland has gained ground in policy and academic circles. Stakeholders promote it as an innovative risk management tool for enhancing resilience to drought-induced perils and providing a way for consumption smoothing to livestock producers in drought vulnerable ecosystems. Index insurance, which avoids market failures such as moral hazard, adverse selection, and transactional cost, has been piloted and implemented all over the world. To support future development and research on index-based insurance in livestock systems, operational index insurance for forage, pasture, and rangeland systems in developed (USA and Canada) and developing (Kenya and Ethiopia) countries are reviewed and compared. This paper finds some similar characteristics (huge subsidy payments—ranging from 50 to 100 percent, significant government role, low adoption, insufficient payouts, data challenges, etc.), of this product between the two regions. A major difference between the PRF and NDVI is the number of choices available to users of rainfall index insurance who face close to 3000 choice options, while NDVI users have less than 5 choice options available for them. Based on these insights, we highlight opportunities where the two regions can benchmark and improve upon their respective index insurance schemes—index-based livestock insurance (IBLI) in developing and rainfall index insurance for forage in developed regions.

A Research Paradigm of Weather Index Insurance for Grassland Animal Husbandry: A Pathway to Increased Income for Herders in China’s Inner Mongolia

This paper aims to theoretically and empirically test whether the Weather Index Insurance for Mutton Sheep (WIMS) which protects the herders from increased feeding costs resulting from drought and snow disasters promotes the income of herders in China’s Inner Mongolia or not. We have applied the OLS, PSM, and quantile regression models using field survey data from 261 herders in Xilin Gol, Inner Mongolia Autonomous Region for our objective. The findings demonstrate that the WIMS significantly increases the overall and high-income herders’ income, but has no significant effect on herders’ income at other income levels.. According to the results, we recommend that as the subsidized agricultural insurance product, the government should expand the pilot region of WIMS to furtherly test its income effect and consider reducing or exempting insurance premiums for low-income herders to reduce their financial pressure.

Evaluation of Weather Yield Index Insurance Exposed to Deluge Risk: The Case of Sugarcane in Thailand

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

Effects of weather index insurance adoption on household food consumption and investment in agricultural inputs in Ethiopia

Weather index insurance is an innovative pro-poor climate risk management strategy that can benefit the poor. But evidence on the welfare impact of this innovation is scant. This study examines the impact of adoption of weather index-based insurance (IBI) for crops on household food consumption and investment in agricultural inputs using the difference-in-difference estimator. Results indicate that adoption of insurance has causally increased household food consumption and investment in high-risk high-return agricultural inputs. Further, accounting for the intensity of adoption, results suggest that repeated uptake of weather index insurance has cumulative lasting effect on household food consumption and investment in agricultural inputs.

Can revenue index insurance outperform yield index insurance?

AbstractIndex insurance programs in developing countries have focused almost entirely on agricultural production risk (i.e., yield) while largely avoiding output marketing risk (i.e., price). This omission may miss an important constraint on smallholder investment and may partially explain underwhelming demand for yield‐based insurance policies. Here, we explore the viability of an area‐revenue index insurance policy and how its performance may compare to that of an area‐yield index insurance policy. Using data from Ghana, we estimate reduced‐form regression analysis and calibrate a simulation model, generating several important results. We show that there is a negative correlation between farm investment and covariate price risk. Moreover, our simulation predicts that in many market contexts, area‐revenue index insurance suffers from less basis risk, exhibits higher demand, and is more effective at crowding in advanced input adoption compared to area‐yield index insurance. Our results also demonstrate important contexts in which area‐yield index insurance outperforms area‐revenue index insurance. We therefore find that revenue insurance may be a valuable and impactful product in Ghana but would not outperform area‐yield index insurance in all contexts.

Bundling Genetic and Financial Technologies for More Resilient and Productive Small-Scale Farmers in Africa

Abstract Using a multi-year, spatially diversified randomised controlled trial spanning two African countries, this paper explores whether a complementary bundle of genetic and financial technologies can boost the resilience and productivity of small-scale farmers. The analysis shows that both moderate droughts and more severe yield losses undermine the resilience of control-group households, and that these shocks have long-lasting effects as farmers invest and produce less following shocks. Severe yield shocks also increase hunger and food insecurity. The genetic technology—drought-tolerant seeds—provides economically significant protection against mid-season drought and mitigates the long-term drop in farm productivity seen in the control group. The financial technology—satellite-based index insurance—offsets the long-term consequences of severe yield losses that are not mitigated by the drought-tolerant seeds. Finally, the analysis shows that treatment-group farmers who experienced shocks and saw the technologies in action subsequently increased their agricultural investment beyond pre-shock levels, an effect we call the resilience dividend. Unfortunately, this apparent experiential learning cuts both ways. Farmers who did not experience the efficacy of the risk management technologies backed away from using them. Our findings thus showcase how genetic and financial risk-mitigating technologies can offer farmers more complete protection, as well as the challenge of inducing sustained uptake of technologies that are midway between experience and credence goods and only infrequently reveal their benefits.

Crop index insurance as a tool for climate resilience: lessons from smallholder farmers in Nigeria

Crop farmers in arid and semi-arid regions face a covariate risk that is of paramount importance, namely, the threat of catastrophic crop loss triggered by natural disasters and climate hazards. Such an occurrence not only jeopardizes the livelihoods of these farmers but may also lead to persistent poverty. Promoting sustainable development requires control over climate-related shocks, which negatively affect the most vulnerable population in the developing world. This study investigates the option of choosing crop area-yield index insurance to mitigate the adverse effects associated with climate stress. In addition, we assessed the amount farmers are willing to pay for an insurance product that insures yields below 70% of the local average in the study area. Our results show that access to extension services and economic association membership offers farmers social capital and encourages their decision to purchase index insurance. We also find that the long-run welfare impact of index insurance policy on vulnerable households could be significant as an alternative insurance mechanism to traditional insurance. This study contributes to filling the gaps on the uptake of index insurance and provide guidance to policymakers in their approach to mitigating the effects of climate change on crop production in Nigeria.

Application of Index Insurance in Iran's Agriculture: case of wheat growers.

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.

Are Vietnamese coffee farmers willing to pay for weather index insurance?

Global coffee production experiences detrimental impacts of climate change. Weather index insurance (WII) offers an opportunity for coffee farmers to mitigate the climate risks in production and motivate them to adopt sustainable farming practices. This study explores Vietnamese farmers' willingness to participate and pay for WII schemes for coffee. A contingent valuation survey was employed on a sample of 151 farmers from the two largest coffee production areas: Lam Dong and Dak Lak provinces. The findings revealed that farmers are willing to pay, on average, US$92.30 per policy for a premium on insurance products. We also found that farmers in Lam Dong are willing to pay more than those in Dak Lak despite fewer Lam Dong farmers being willing to participate in the insurance schemes. The majority of farmers prefer drought to be insured within 3 months of coffee blossom, from February to April. Factors influencing farmers' decision to join the insurance schemes include education, farm size, climate change perception and experiences, and insurance knowledge. The study suggests that the current coffee industry co-contribution to insurance premiums could potentially be reduced by up to 90 %. However, caution must be taken when adjusting co-contribution, as farmers' willingness to pay is heterogeneous. In addition, raising awareness of the impact of climate change on crop production and insurance knowledge training is critical to ensure an increased number of participants in the schemes. The recommendations from this study will contribute to improving the design of coffee insurance products that are tailored to local needs and preferences and will assist in upscaling the products’ outreach in the Coffee Climate Protection Insurance Program.

Research on Photovoltaic Index Insurance Fraud Detection Method Based on LSTM-AR Model

Research on Photovoltaic Index Insurance Fraud Detection Method Based on LSTM-AR Model

Uma análise empírica da flutuação do preço da carne suína na China com o modelo autorregressivo de heterocedasticidade condicional

ABSTRACT: Pork price fluctuations are closely related to the national economy and people’s livelihoods in China. Based on the monthly pork price fluctuations in China from January 2011 to August 2022, this study uses ARCH family models to assess the characteristics and laws of these fluctuations in China. The pork price fluctuations show obvious clustering, with external shock information from the previous month affecting the pork price in the following period; the pork market price is characterized by risk compensation, with the high risk of pork supply driving the pork price up. In addition, the pork price fluctuations are characterized by asymmetry, with a greater impact of good than of bad news on the pork price. Due to the pork industry’ low entry threshold and the existence of sunk costs, positive information on the pork market has a stronger impact on price fluctuations than negative information. To guide pork supply, we recommend improving monitoring and early-warning mechanisms in the pork market to identify the pork price volatility threshold and measure the price volatility. In addition, price index insurance products should be constantly strengthened, with different types of insurance products being offered to meeting the insurance demand of various sectors in the pig meat supply chain.