Rainfall Index Insurance Research Articles

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.

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.

Sequenced Crop Evapotranspiration and Water Requirement in Developing a Multitrigger Rainfall Index Insurance and Risk-Contingent Credit

Abstract Weather index insurance (WII) has been a promising innovation that protects smallholder farmers against drought risks and provides resilience against adverse rainfall conditions. However, the uptake of WII has been hampered by high spatial and intraseasonal basis risk. To minimize intraseasonal basis risk, the standard approaches to designing WII based on seasonal cumulative rainfall have been shown to be ineffective in some cases because they do not incorporate different water requirements across each phenological stage of crop growth. One of the challenges in incorporating crop phenology in insurance design is to determine the water requirement in crop growth stages. Borrowing from agronomy, crop science, and agrometeorology, we adopt evapotranspiration methods in determining water requirements for a crop to survive in each stage that can be used as a trigger level for a WII product. Using daily rainfall and evapotranspiration data, we illustrate the use of Monte Carlo risk modeling to price an operational WII and WII-linked credit product. The risk modeling approach that we develop includes incorporation of correlation between rainfall and evapotranspiration indices that can minimize significant intertemporal basis risk in WII.

Nudge to insure: Can informational nudges change enrollment decisions in pasture, rangeland, and forage rainfall index insurance?

AbstractThrough a framed field experiment with livestock farmers in the Northeast and Southeast United States, this research explores whether an informational nudge changes producers' selection of two‐month intervals and/or increases the likelihood of enrollment in pasture, rangeland, and forage (PRF) insurance. We find no evidence that a nudge influences interval choices; however, producers are more likely to enroll when PRF is framed as a risk management decision regarding forage loss. Risk aversion, familiarity with other United States Department of Agriculture livestock programs, and higher risk exposure increase the likelihood of enrollment. Past PRF and crop insurance participation decrease the amount insured during growing‐season months.

Parametric and non-parametric estimates of willingness to pay for weather index insurance in Malawi

As climate-related shocks on agriculture production intensify, weather index crop insurance has emerged as one potential risk management strategy for farmers. Nevertheless, sustainable uptake of this risk management strategy will largely depend on farmers’ willingness to pay for the associated premiums. This study identifies various socio-economic factors that influence farmers' purchase of index insurance for staple food in rural Malawi. It further establishes an associated willingness to pay per hectare per cropping season. We use a hypothetical rainfall index insurance program to estimate insurance demand. The study highlights several determinants of willingness to pay for an index insurance product. Results suggest that gender, the previous record of climate shocks, extension contact, and access to remittances significantly influence willingness-to-pay for weather index insurance for a staple food crop. Both parametric and non-parametric estimates of willingness-to-pay are in the range of US$5.5 to US$15 per hectare per cropping season. At the infancy stage, government subsidies for insurance premium and linkage of premium payments to Village Savings Groups will be crucial.

Designing Rainfall Index Insurance for Rubber Plantation in Balikpapan

Rubber commodity is one of the essential agricultural sectors in the City of Balikpapan. Rubber plantation has become a leading product to boost the economic growth in Balikpapan from the agricultural sector. However, this commodity is vulnerable through the changes of rainfall index since less rainfall can decrease the amount of latex in a rubber tree. This unexpected event will make a financial loss to farmers because of delaying the cultivation time. Therefore, the crop insurance for rubber farmers should be discovered. This study applied the Burn Analysis method to determine some classes of rainfall index and calculating the crop insurance premiums based on Black Scholes methods. The lowest class of rainfall index is 1264.36 mm with the crop insurance premiums at this class is Rp. 521,482.73. Otherwise, the highest rainfall index is 1761,36 mm whereas the premium level is Rp.568,592.93.

Field comparisons of incentive-compatible preference elicitation techniques

Knowledge of consumer demand is important for firms, policy-makers, and economists. One common tool for incentive-compatible demand elicitation, the Becker-DeGroot-Marschak (BDM) mechanism, has been widely used in laboratory settings but rarely evaluated for reliability at large scale in the field. In two field experiments (for a new agricultural information service and rainfall index insurance) we compare demand curves estimated from BDM implementations with demand curves estimated from choices at individually randomized fixed prices. In the test (for an agricultural information service) we obtain demand curves from BDM and fixed prices that align closely and are unable to reject equivalence of the demand curves. For the second test (of rainfall index insurance) the results are mixed, with the distributions lining up well at certain points of the demand curve and deviating at others. Overall, we find no evidence for systematic bias. Our evidence suggests that a ‘reframed’ version of BDM that uses discrete prices may minimize non-standard bidding behavior and is well suited to future experimental work in low-literacy environments.

The Impact of Commercial Rainfall Index Insurance: Experimental Evidence from Ethiopia

We present the results of an experiment introducing commercial rainfall index insurance into drought‐prone farming cooperatives in Amhara Region, Ethiopia. We provided a market‐priced rainfall deficit insurance product through producer cooperatives and tested a number of potential ways to kick start private demand. Take up of the insurance at market prices is very low, between 0.5% and 3% across seasons. When we use a randomized experiment to distribute small free insurance contracts to farmers, 39% of subsidized individuals enroll but this fails to stimulate input use, yields, or income, nor does it enhance demand in subsequent seasons. A training and promotion on the product improves uptake and willingness to pay but also does not improve farming outcomes. We conclude with a case study of our efforts to interlink index insurance with credit for agricultural inputs.

Will urban migrants formally insure their rural relatives? Family networks and rainfall index insurance in Burkina Faso

Abstract Large segments of the population in developing countries, especially in rural areas, have a high level of vulnerability to weather-related shocks but have limited means to insure themselves against them. In recent years, microfinance institutions have experimented with micro-insurance products, including rainfall index insurance, but the uptake of these products has generally been very low. We present findings from a pilot study exploring whether and how existing ties between urban migrants and rural farmers may be used to provide the latter improved access to formal insurance. Urban migrants in Ouagadougou (the capital of Burkina Faso) originating from nearby villages were offered, at the prevailing market price, a rainfall index insurance product that can potentially protect their rural relatives from adverse weather shocks. The product had an uptake of 22% during the two-week subscription window. Half the urban migrants in the study were randomly offered an insurance policy in which payouts would be made, not to the subscriber, but directly to the intended beneficiary. This feature increased uptake rates by 17–22 percentage points. We argue that rainfall index insurance can complement informal risk-sharing networks by mitigating problems of informational asymmetry and self-control issues.

Participation patterns of the rainfall index insurance for pasture, rangeland and forage programme

Using a novel policyholder-level data set, we analyse participants’ choices of 2-month index intervals in the Rainfall Index for Pasture, Rangeland and Forage (RI-PRF) insurance programme. We first provide a conceptual model that illustrates participation patterns of the rainfall index insurance. We then connect these predicted patterns to some empirical evidence from the policyholder-level data set, which is a subset of data provided by the USDA Risk Management Agency for all RI-PRF participants in Nebraska and Kansas during the years 2013–2017. Because the correlations between forage yield and precipitation and the expected premium subsidy vary by month, different degrees of risk aversion may predict distinctively different choices of the 2-month intervals. Using cluster analysis, we group the participants with similar allocation patterns across the 2-month intervals. We observe that the number of participants displaying relatively low levels of risk aversion increase over time. We connect this to the fact that premium subsidies and producer returns associated with non-growing season (risk-increasing) months are often greater than those for growing season (risk-reducing) months, and this has important implications for policy design. Our findings suggest that more research in this area could assist policymakers in keeping the RI-PRF programme in line with its objective of reducing risk for livestock producers.

Loss aversion and the demand for index insurance

Abstract This work analyzes if reference dependence and loss aversion can explain the puzzling low adoption rates of rainfall index insurance. We present a model that predicts the impact of loss aversion on index insurance demand to vary with different levels of insurance understanding. Index insurance demand of farmers who are unaware of the loss-hedging benefit that insurance provides decreases with loss aversion. In contrast, insurance demand of farmers who are aware of the loss-hedging benefit increases with loss aversion. The model further predicts that farmers who are unaware of the loss-hedging benefit will not demand an even highly subsidized index insurance. Using data from a randomized controlled trial involving a sample of Indian farmers we provide empirical support for our core conjecture that insurance understanding mitigates the negative impact of loss aversion on index insurance adoption.

Field Comparisons of Incentive-Compatible Preference Elicitation Techniques

Knowledge of consumer demand is important for firms, policy-makers, and economists. One common tool for incentive-compatible demand elicitation, the Becker-DeGroot- Marschak (BDM) mechanism, has been widely used in laboratory settings but rarely evaluated for reliability at large scale in the field. In two field experiments (for a new agricultural information service and rainfall index insurance) we compare demand curves estimated from BDM implementations with demand curves estimated from choices at individually randomized fixed prices. In the test (for an agricultural information service) we obtain demand curves from BDM and fixed prices that align closely and are unable to reject equivalence of the demand curves. For the second test (of rainfall index insurance) the results are mixed, with the distributions lining up well at certain points of the demand curve and deviating at others. Overall, we find no evidence for systematic bias. Our evidence suggests that a ‘reframed’ version of BDM that uses discrete prices may minimize non-standard bidding behavior and is well suited to future experimental work in low-literacy environments.

Loss Aversion and the Demand for Index Insurance

This work analyzes if reference dependence and loss aversion can explain the puzzling low adoption rates of rainfall index insurance. We present a model that predicts the impact of loss aversion on index insurance demand to vary with different levels of insurance understanding. Index insurance demand of farmers who are unaware of the loss-hedging benefit that insurance provides decreases with loss aversion. In contrast, insurance demand of farmers who are aware of the loss-hedging benefit increases with loss aversion. The model further predicts that farmers who are unaware of the loss-hedging benefit will not demand an even highly subsidized index insurance. Using data from a randomized controlled trial involving a sample of Indian farmers we provide empirical support for our core conjecture that insurance understanding mitigates the negative impact of loss aversion on index insurance adoption.

Multi-Site Bundling of Drought Tolerant Maize Varieties and Index Insurance.

Drought Tolerant Maize Varieties (DTMV) and Rainfall Index Insurance (RII) are potential complements, though with limited empirical basis. We employ a multivariate spatial framework to investigate the potential for bundling DTMV with a simulated multi-site and multi-environment RII, designed to insure against mild, moderate and severe drought risk. We use yield data from on-farm trials conducted by the International Maize and Wheat Improvement Center (CIMMYT) and partners over 49 locations in Eastern and Southern Africa spanning 8 countries and 5 mega-environments (dry lowland, dry mid altitude, wet lower mid altitude, low wetland and wet upper mid altitude) in which 19 different improved maize varieties including DTMV were tested at each location. Spatially correlated daily rainfall data are generated from a first-order two-state Markov chain process and used to calibrate the index and predict yields with a hierarchical Bayes multivariate spatial model. Results show high variation in the performance and benefits of different bundles which depend on the maize variety, the risk layer insured, and the type of environment, with high chances of selecting a sub-optimal and unattractive contract. We find that complementing RII with a specific DTMV produces contracts with lower premiums and higher guaranteed returns especially in dry lowland increasing the chances of scaling up RII within this environment.

Demand for temperature and rainfall index insurance in India

AbstractWeather index insurance has been attracting considerable attention from academics and policymakers. This study investigates the demand for two types of index insurance sold in India: temperature index insurance for dry season and rainfall index insurance for subsequent monsoon season. Using data from randomized subsidy experiments, we separate purchase and quantity decisions and investigate how demand for rainfall insurance is correlated with demand for temperature insurance sold in the previous season. We find that the price (premium) does not influence purchase decisions per se but does significantly influence quantity decisions. The quantity demanded is less price‐sensitive for subsequent rainfall insurance than it is for temperature insurance. We also find that purchasers of temperature insurance tend to buy rainfall insurance more often than nonpurchasers do. However, a one‐time subsidy does not influence on subsequent demand, suggesting no price‐anchoring effect.

Spatial variability in regional scale drought index insurance viability across Australia’s wheat growing regions

Wheat is key global food crop that is heavily influenced by climatic variability. There has been extensive research on improving forecasts and management practices to minimise climate related yield losses, but less on how to handle yield losses caused by climate variability. We investigated whether index insurance could be used to manage climate related losses, specifically from winter rainfall drought for wheat crops in Australia. We utilised 31 years of yield data from 15 of Australia’s key wheat producing regions. The winter rainfall index was developed and tested using generalised additive regression models, allowing for non-linear effects. Models with the winter rainfall index explained significant variation in wheat yields in each of the regions assessed. Wheat yield models had cross-validated R2 values > 0.5 for two-thirds of the 15 regions modelled and best explained wheat yields in the Mallee, Western Australia (cross-validated R2 of 0.70). Calculated fair premiums ranged from $8.62 to $77.1 AUD/ha, while maximum liability was $59.25 to $212.12 AUD/ha. Throughout the eastern most wheat growing regions the winter rainfall index was consistently inefficient (i.e. not beneficial). In contrast, the winter rainfall index was financially efficient (i.e. beneficial) in the western wheat regions of eastern Australia and parts of Western Australia, with benefits of up to $97 AUD/ha and loss reductions of $9 AUD/ha. The spatial variability in insurance efficiency was explained by rainfall variance. As rainfall variance increased the efficiency of the winter rainfall index insurance for wheat decreased. Our findings have two important policy implications; (1) in areas where climate change is anticipated to increase rainfall variability risk-transfer options, such as index insurance, may become less viable and as such policies that support the development of index insurance without acknowledging or adjusting for variability in its benefit could lead to inefficient outcomes for both government and agricultural producers; and (2) where index rainfall insurance is not efficient then greater emphasise may need to be placed on developing alternate types of index insurance (e.g. using satellites) and/or on risk-management and climate adaptation strategies that minimise losses.

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.

Efficiency of region-wide catastrophic weather risk pools: Implications for African Risk Capacity insurance program

In an attempt to better manage extreme weather risk using (re)insurance and securitizing devices, the African Union recently established the Africa Risk Capacity (ARC) to pool extreme drought risk across Africa. We investigate the prevalence of systemic risk within potential ARC risk pools and its effect on diversifying drought risk using rainfall index-insurance. To develop an index that captures the tail behavior of losses, we employ a two-stage process that involves a vector autoregressive model and nested Archimedean copulas. Finally, using the loss distributions, we derive and compare buffer load and pooling effects across risk pools. Overall, we find strong benefits in pooling drought risk in Africa. However, the benefits are highly heterogeneous across risk pools, some of which are very inefficient. The heterogeneity was found to be driven by non-linear dependence among losses that tends to be stronger in the tails of the distribution.

Does experience with natural disasters affect willingness-to-pay for weather index insurance? Evidence from China

Managing the risk associated with natural disasters is crucial for agricultural development in developing countries. In this study, we estimate farmers’ willingness to pay (WTP) for a hypothetical excess rainfall index insurance contract, with a special interest in whether recent experience with a catastrophic flooding influences farmers’ attitude towards the insurance product. The study is based on a survey of small farm households in China's Heilongjiang Province, which suffered a catastrophic flood in the summer of 2013. Employing a natural experiment framework, we find that, other things being equal, farmers from flooded areas have a higher WTP for index insurance than farmers from non-flooded areas.

Evaluating effectiveness of rainfall index insurance

PurposeThe purpose of this paper is to examine the reduction of crop yield uncertainty using rainfall index insurances. The insurance payouts are determined by a transparent rainfall index rather than actual crop yield of any producer, thereby circumventing problems of adverse selection and moral hazard. The authors consider insurances on rainfall indexes of various months and derive an optimal insurance portfolio that minimizes the income variance for a crop producer.Design/methodology/approachVarious regression models are considered to relate crop yield to monthly mean temperature and monthly cumulative precipitation. A bootstrapping method is used to simulate weather indexes and corn yield in a future year with the correlation between precipitation and temperature incorporated. Based on the simulated scenarios, the optimal insurance portfolio that minimizes the income variance for a crop producer is obtained. In addition, the impact of correlation between temperature and precipitation, availability of temperature index insurance and geographical basis risk on the effectiveness of rainfall index insurance is examined.FindingsThe authors illustrate the approach with the corn yield in Illinois east crop reporting district and weather data of a city in the same district. The analysis shows that corn yield in this district is negatively influenced by excessive precipitation in May and drought in June–August. Rainfall index insurance portfolio can reduce the income variance by up to 51.84 percent. Failing to incorporate the correlation between temperature and precipitation decreases variance reduction by 11.6 percent. The presence of geographical basis risk decreases variance reduction by a striking 24.11 percent. Allowing for the purchase of both rainfall and temperature index insurances increases variance reduction by 13.67 percent.Originality/valueBy including precipitation shortfall into explanatory variables, the extended crop yield model explains more fluctuation in crop yield than existing models. The authors use a bootstrapping method instead of complex parametric models to simulate weather indexes and crop yield for a future year and assess the effectiveness of rainfall index insurance. The optimal insurance portfolio obtained provides insights on the practical development of rainfall insurance for corn producers, from the selection of triggering index to the demand of the insurance.