Index Insurance Products Research Articles

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.

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.

On the Risk Efficiency of a Weather Index Insurance Product for the Brazilian Semiarid Region

Abstract Weather index insurance (WII) has long been advertised as a viable alternative to crop yield insurance. WII products were first developed to assist climate-vulnerable farmers from developing countries where establishing a well-structured crop insurance market is expressively difficult due to the poor transport infrastructure and the prevalence of sparsely distributed small-scale farms. In Brazil, the semiarid region stands out as the one that concentrates the ideal conditions for the implementation of a WII product since it houses thousands of climate-vulnerable farmers. With this in mind, we designed and priced a WII product for farmers from the semiarid region of Brazil and posteriorly investigated its risk efficiency. To do so, we first investigated crop yield responses to aridity, enabling the selection of locations for which the WII product was posteriorly assessed. Second, we grouped selected locations into specific contracts according to geographical proximity and evaluated each of these contracts to attest the risk efficiency of the proposed WII product using the method of stochastic efficiency with respect to a function (SERF), which identifies utility efficient alternatives for a range of risk attitudes. Our results show that the WII product may be effective in protecting farmers from adverse variations in production revenue, possibly being attractive for utility-maximizer farmers that are sufficiently risk averse.

Understanding German farmers’ intention to adopt drought insurance

Climate risks, particularly droughts and heat waves, negatively affect agricultural incomes worldwide. Drought insurance is promising to mitigate resulting income losses at the farm level. As the proportion of German farmers insured against drought is low, policymakers and insurers aim to increase the appeal of drought insurance to farmers. However, to accelerate their efforts in this regard, more information regarding farmers' intention to adopt drought insurance beyond current adoption is needed. To obtain initial insights, we surveyed 127 German farmers in a risk management context and applied a modified transtheoretical model of behavioral change. This revealed detailed information on the gradual adoption of drought insurance. Given a heterogenous distribution among the gradual stages of adoption, a binomial logit model was estimated instead of an ordered logit to investigate farmers' current intention to adopt drought insurance. Furthermore, the machine learning technique of least absolute shrinkage and selection operator (LASSO) was applied to select the most relevant features to be used as explanatory variables in the estimation. The results show that farmers' gender and risk attitude, land tenure, how severely they were affected previously by weather risks, and the level of trust in index insurance products have a statistically significant effect on farmers' intention to adopt drought insurance. Additionally, this study provides insights into farmers' reasons against drought insurance. As such, the results are important to policymakers considering policy intervention, insurers interested in farmers' intention to insure and to researchers focusing on farmers’ adaptation to climate change.

Plant growth stages and weather index insurance design

AbstractGiven the assumption that weather risks affect crop yields, we designed a weather index insurance product for soybean producers in the US state of Illinois. By separating the entire vegetation cycle into four growth stages, we investigate whether the phase-division procedure contributes to weather–yield loss relation estimation and, hence, to basis risk mitigation. Concretely, supposing stage-variant interaction patterns between temperature-based weather index growing degree days and rainfall-based weather index cumulative rainfall, a nonparametric weather–yield loss relation is estimated by a generalized additive model. The model includes penalized B-spline (P-spline) approach based on nonlinear optimal indemnity solutions under the expected utility framework. The P-spline analysis of variance (PS-ANOVA) method is used for efficient estimation through mixed model re-parameterization. The results indicate that the phase-division models significantly outperform the benchmark whole-cycle ones either under quadratic utility or exponential utility, given different levels of risk aversions. Finally, regarding hedging effectiveness, the expected utility ratio between the phase-division contract and the whole-cycle contract, and the percentage changes of mean root square loss and variance of revenues support the proposed phase-division contract.

Can satellite-retrieved data increase farmers' willingness to insure against drought? – Insights from Germany

CONTEXTAs the demand for index insurance in Europe remains low, the use of satellite-retrieved data has received considerable research attention because it can reduce the basis risk, increase hedging effectiveness and thus potentially increase the attractiveness of index insurance to farmers. However, there is limited knowledge on farmers' demand and specific preferences for satellite-based index insurance. OBJECTIVEThis study aims to obtain initial insights into farmers' preferences for satellite-based index insurance. In particular, farmers' overall preferences for satellite-based compared to precipitation-based index insurance are investigated. The objective is further deepened by investigating the effect of partially subsidizing index insurance. Finally, we focus on differences in preferences caused by the farmer's individual specific factors as this allows to identify the potential target groups for satellite-based index insurance. METHODSAs satellite-based index insurance is still hard to find in Europe, a discrete choice experiment has been conducted with 127 German farmers. In a hypothetical scenario, farmers had to choose whether or not to insure themselves against drought by purchasing hypothetical index insurance products. Specifically, based on a labeled design, they had to choose between a satellite-based and a precipitation-based index insurance, or could choose not to use either alternative (opt-out). We include other insurance attributes such as premium, strike level and payout as they were rated as important by the farmers. RESULTS AND CONCLUSIONMixed logit model results show a preference for both index insurance products against no insurance. Particularly, the average farmer has a statistically significant higher preference for the satellite-based index insurance. In addition, subsidizing satellite-based index insurance has a higher effect on farmers' preferences. Moreover, the latent class model identifies heterogeneous preferences among farmers. Insurers can be advised to incorporate satellite data into the design of index insurance. By integrating them into existing index insurance products or by developing purely satellite-based index insurance, insurers could tailor index insurance better to farmers' preferences. Policymakers can be advised that farmers are differentially sensitive to subsidies. Especially farmers with a stronger preference for the satellite-based index insurance derive a huge increase in utility from subsidies. SIGNIFICANCEThe study provides initial insights into farmers' preferences for satellite-based index insurance that can guide insurers regarding the design of index insurance products and give policymakers insights into how governmental support would change farmers' demand for index insurance.

Urban Flood Loss Assessment and Index Insurance Compensation Estimation by Integrating Remote Sensing and Rainfall Multi-Source Data: A Case Study of the 2021 Henan Rainstorm

To address the problems of traditional insurance compensation methods for flood losses, such as difficulty in determining losses, poor timeliness, a complicated compensation process and moral hazard, an urban flood index insurance tiered compensation model integrating remote sensing and rainfall multi-source data was proposed. This paper first extracted the area of water bodies using the Normalized Difference Water Index and estimates the urban flood area loss based on the flood loss model of remote sensing pixels. Second, the tiered compensation mechanism triggered by rainfall was determined, and the urban flood index insurance tiered compensation model was constructed using remote sensing and rainfall multi-source data. Finally, the economic losses and flood insurance compensation in urban flood were estimated. The results show that: (1) the geo-spatial distribution of flood-affected areas by remote sensing inversion is consistent with the actual rainfall characteristics of Henan Province, China; (2) based on the flood losses model of remote sensing pixels, the estimated flood losses for Henan Province are CNY 110.20 billion, which is consistent with the official data (accuracy ≥ 90%); and (3) the proposed model has good accuracy (R2 = 0.98, F = 1379.42, p < 0.05). The flood index insurance compensation in Henan Province is classified as a three-tier payout, with a total compensation of CNY 24,137 million. This paper can provide a new approach to estimate large-scale urban flood losses and the scientific design of flood index insurance products. It can also provide theoretical and technical support to many countries around the world, particularly those with underdeveloped flood insurance systems.

Revisiting preferences for agricultural insurance policies: Insights from cashew crop insurance development in Ghana

The development and uptake of agricultural insurance products by farmers in developing countries has been universally and disappointingly low. This paper investigates farmers’ preferences and willingness to pay for a variety of agricultural insurance products, including indemnity insurance, index insurance, benchmark insurance, and hybrid (indemnity-index) insurance in the Bono and Bono East Regions of Ghana. We employed hybrid latent class and multiple indicators, multiple causes (MIMIC) models using discrete choice experimental data from 383 cashew growers. The results show that cashew farmers are heterogeneous in their preferences, with a majority advocating for agricultural insurance against key perils such as wildfires, high wind speed and excess rainfall. Hybrid (indemnity-index) insurance product is highly preferred and valued by cashew farmers advocating for agricultural insurance, followed by index insurance product. Farmers are quite sensitive to premiums, expected payout, type of perils covered by the insurance and loss assessment criteria. Social and behavioural constructs relating to trust in insurance companies, subjective knowledge about agricultural insurance, and perceived agricultural insurance benefits are significant determinants of farmers’ preferences for agricultural insurance products. The findings imply that it has become very necessary for agricultural insurance product developers, underwriters, and insurers in developing countries to gain more insight on farmers’ social and behavioural constructs related to agricultural risk, insurance knowledge and trust. We suggest that agricultural insurance product developers and policy-makers involved in agricultural insurance development should improve farmers’ understanding of basis risk and the concept of agricultural insurance, as well as the potential benefits of farm insurance. In this way, we can improve the uptake of agricultural insurance products by farmers in developing countries.

German farmers' perceived usefulness of satellite-based index insurance: insights from a transtheoretical model

PurposeIndex insurance is promising to mitigate drought-related income losses in agriculture. To reduce the basis risk of index insurance, the integration of satellite data is of growing interest in research. The objective of this study is to obtain preliminary evidence regarding farmers' perceived usefulness (PU) of satellite-based index insurance.Design/methodology/approachBy modifying the transtheoretical model of change to a transtheoretical model of PU, German farmers' gradual PU of satellite-based index insurance was investigated.FindingsThe results show that the average farmer perceives satellite-based index insurance as useful. It can be particularly seen that a higher level of education in an agricultural context as well as higher trust in index insurance products increases farmers' gradual PU. Moreover, higher relative weather-related income losses increase farmers' gradual PU.Research limitations/implicationsIt is recommended to apply latent variables when conducting future investigations regarding farmers' PU.Originality/valueTo the best of the authors' knowledge, this is the first study to explore farmers' PU of upcoming satellite-based index insurance by modifying and applying the transtheoretical model in a new way.

Mission of International Agribusiness Insurance: Modern Challenges and Opportunities for Ukraine

An urgent problem today is the reproduction of a sustainable ability to meet the demand for food products in different countries of the world. The solution to this global problem lies in the search for optimal financial and economic tools for timely counteraction to agricultural risks. Many years of international experience prove that such a tool is insurance. The purpose of the study is to conceptualise the mission of international agribusiness insurance, specify modern challenges, and identify opportunities for Ukraine. The methodology of this study was based on the use of various methods, in particular: abstract and logical, epistemological, comparative, structural and functional, modelling, formalisation, induction and deduction, regression and system analysis. The uniqueness of insurance lies in its ability to accumulate the necessary financial resources and distribute them to the needs of compensation for losses in the case of insurance events distributed in space and time. Consequently, the conducted research is aimed at substantiating the mission of international agribusiness insurance, identifying current challenges and existing opportunities in Ukraine. The main advantages and disadvantages of various models of interaction of stakeholders in the international agricultural insurance market are established. The paper highlights the world experience in positioning classic and index insurance products on the market. It was found out that the current level of agribusiness insurance in Ukraine does not correspond to its potential opportunities. Based on the parameters of the constructed regression model, it is established that the basis for further expansion of insurance coverage of agricultural risks is an increase in the number of concluded and renegotiated agricultural insurance contracts, an increase in the liability limit (insurance amounts) of companies, ensuring actuarial balance, and an increase in the amount of state subsidies. The practical implementation of these measures should be considered in the context of food security at the regional, national, and global levels

An empirical study of the key factors affecting herders' purchasing decision on weather index insurance—A case study from inner Mongolia autonomous region, China

The weather index insurance for mutton sheep (WIMS) is the first weather index insurance product that addresses the increased feeding cost-related risk of grassland animal husbandry herders in China. In 2019, the WIMS was implemented as a pilot project in 11 county regions of Xilin Gol League, Inner Mongolia, China. Since the implementation of this product, the purchasing rate has varied from region to region, as the highest purchasing rate was 84.3%, while the lowest was only 1.8%. This research uses the double hurdle model to identify the factors affecting herders' purchasing behavior. This paper reports a survey study of 535 participants herders in the Xilin Gol League. The result indicates that herders' age, disaster risk evaluation, insurance awareness, pasture area, and neighborhood purchasing decision significantly impact herders' purchasing propensity. Disaster evaluation, insurance awareness, pasture area and location significantly impact the purchasing degree of herders. The study suggests that young herders and herders with large pasture areas should be motivated to purchase insurance. In addition, new methods should be applied for the publicity of this insurance product; Investments should be focused on the automatic weather monitoring station projects in pastoral areas to reduce the basis risk of the insurance.

Managing weather- and market price-related financial risks in algal biofuel production

Large-scale algae production has garnered interest due to its potential as a biofuel feedstock. Previous research assessing the profitability of algae products has been mostly based on values averaged over time, but algae production and resulting financial returns exhibit significant variability due to weather and fluctuations in selling prices for algae-based products. In other sectors, producers often reduce weather- and market price-related financial risk with financial instruments such as insurance, but little research has been performed on the design of insurance products to protect algae producers. This study develops a novel index-based insurance instrument that pays-out during unfavorable weather and market conditions, then explores the instrument's effectiveness, combined with a cash reserve, in reducing revenue stream variability for an algae producer. Results indicate that a biophysically based index-insurance product tailored to the specific financial risks in algae production can reduce variability in net revenues and can do so at a lower cost than relying solely on cash reserves, the most common financial risk management tool. Assessing the performance of index-insurance in algae production is particularly timely given the passage of the 2018 Farm Bill, which newly opens opportunities for the USDA to provide crop insurance to algae producers.

Designing development interventions: The application of service design and discrete choice experiments in complex settings

The persistence of problems such as endemic poverty, rising inequalities, climate change and biodiversity loss demands us to find solutions which are embedded in a highly complex web of interacting social, technological, and ecological processes. Service design (SD), an approach to directly involve citizens in the development and improvement of services and systems, shows promise as a tool to support the design of interventions to address complex development challenges in the Global South. In this paper we describe how service design was used alongside discrete choice experiments (DCEs) to inform the design of a Weather Index Insurance product for small holder farmers in Uganda. As part of the service design process, we used archetypes to capture and articulate the multiple vulnerabilities of farmers and quickly test prototype insurance packages to identify important design features. DCEs tested promising design features in a manner that complemented as well as triangulated the service design phase. The results of both phases were used to inform the design of a WII product that has been taken up by major insurance providers in Uganda. The approach complements and builds on qualitative work typically done to inform DCEs by opening up space for research participants to question core assumptions, and by involving respondents directly in the process of designing a future service.

Linking farmers’ risk attitudes, livelihood diversification and adoption of climate smart agriculture technologies in the Nyando basin, South-Western Kenya

Climate smart agriculture (CSA) technologies are innovations meant to reduce the risks in agricultural production among smallholder farmers. Among the factors that influence farmer adoption of agricultural technologies are farmers' risk attitudes and household livelihood diversification. This study, focused on determining how farmers' risk attitudes and household livelihood diversification influenced the adoption of CSA technologies in the Nyando basin. The study utilized primary data from 122 households from two administrative regions of Kisumu and Kericho counties in Kenya. The study employed the multivariate probit (MVP) and ordered probit (OP) models and descriptive statistics in data analysis using Stata 14.0. Results from the study indicated that farmers’ risk attitudes had a significant negative influence in the adoption of terraces, ridges and bunds as well as the intensity of adoption of given CSA technologies. Household livelihood diversification had a significant negative influence in the adoption of stress tolerant livestock but did not have a significant effect on the intensity of adoption of given CSA technologies. The study recommends that relevant stakeholders should introduce an appropriate agricultural index insurance product to Nyando basin farmers to encourage the broader adoption of CSA technologies.

Risk, crop yields, and weather index insurance in village India

AbstractWe investigate the sources of variability in crop yields and their relative importance in the context of weather index insurance for smallholder farmers in India. Using parcel‐level panel data, multilevel modeling, and Bayesian methods we measure how large a role seasonal variation in weather plays in explaining yield variance. Seasonal variation in weather accounts for 14%–22% of total variance in crop yields, a value that has rarely been convincingly estimated in the economics literature. These calculations shed light on the relatively low rates of uptake of index insurance by resource‐constrained farmers. The results also provide direction for designing more suitable index insurance products.

Price, information and product quality: Explaining index insurance demand in Burkina Faso

Index insurance is a promising avenue for addressing risk in developing countries, but pilot index insurance projects are rarely scaled-up because demand remains disappointingly low. This article studies an area-yield insurance product which generated high demand and sales in Burkina Faso, and was subsequently scaled up nationally. We exploit experimental and quasi-experimental variations to measure and compare the effect of price, information, and product quality on insurance demand. Our results suggest that price has an important effect on demand, while information also plays a role. However, the quasi-experimental variation in product quality across farmer groups does not affect insurance take-up. Demand is much lower during the scale-up, suggesting that implementation efforts during the pilot also matter. These findings show that high quality index insurance products can be attractive for farmers, but that price for value, along with other factors, remain major barriers to adoption. Our results also suggest that small-scale farmers may not fully understand the complex financial instruments offered to them commercially, with implications for the use of index insurance as a policy option to foster resilience.

An Application of Geographically Weighted Quantile Lasso to Weather Index Insurance Design

ABSTRACT Objective: this article studies the efficiency of a novel regression approach, the geographically weighted quantile lasso (GWQlasso), in the modeling of yield-index relationship for weather index insurance products. GWQlasso allows regression coefficients to vary spatially, while using the information from neighboring locations to derive robust estimates. The lasso component of the model facilitates the selection of relevant explanatory variables. Methodology: a weather index insurance (WII) product is developed based on one-month standardized precipitation index (SPI) derived from a daily precipitation dataset for 41 weather stations in the state of Paraná (Brazil) for the period from 1979 to 2015. Soybean yield data are also used for the 41 municipalities from 1980 to 2015. The effectiveness of the GWQlasso product is evaluated against a classic quantile regression approach and a traditional yield insurance product using the spectral risk measure (SRM) and the mean semi-deviation. Results: while GWQlasso proved as effective as quantile regression, it outperformed the yield insurance product. Conclusion: the GWQlasso is an alternative to the crop insurance market in Brazil and other locations with limited data.

The Role of Experience in Learning for Index Insurance Products: Evidence from Rural Kenya

The Role of Experience in Learning for Index Insurance Products: Evidence from Rural Kenya

Demand Behaviour for Weather Index Insurance Products in Regions Prone to Agricultural Droughts

Weather index insurance (WII) is a promising insurance scheme relevant for the agricultural sector, particularly in many low-income countries, including regions prone to agricultural droughts. WII policies might be more affordable to farmers, as the payouts in these insurance schemes are based on weather indices objectively determined for the specific agricultural regions, and therefore a costly individual loss assessment is not necessary. To successfully implement and scale up WII schemes, the development of transdisciplinary models properly addressing the complexity of relevant socio-natural processes and their interplay is necessary. We develop a stochastic model to simulate the demand for insurance policies in a drought-prone region under an assumption that weather and climate services, as tailored products for regions vulnerable to droughts, provide forecasts of the weather index on which the insurance scheme is based. Therefore, the simulated demand for the insurance policy might depend on the quality of the available weather index forecast. Presented modelling results suggest that both the income of individual producers and the dynamics of aggregate demand for insurance policies might be sensitive to the quality of the available weather index forecast. The developed modelling approaches can inform the design and implementation of WII schemes in drought-prone regions.

IDENTIFYING POTENTIAL REGIONS FOR A PRECIPITATION INDEX INSURANCE PRODUCT IN PARANÁ – BRAZIL: A HIERARCHICAL CLUSTERING APPROACH

In this article the availability and quality of public databases for soybean yields and daily rainfall in the state of Parana in Brazil is assessed in order to verify the feasibility of an index insurance product. The multiple imputation by chained equations (MICE) method is utilized to fill missing values in the rainfall dataset and study the existence of spatial and temporal patterns in the data by means of hierarchical clustering. The results indicate that Parana fulfills data requirements for a scalable weather index insurance with MICE and hierarchical clustering being effective tools in the pre-processing of precipitation data.