ENSO Index-Based Insurance for Agricultural Protection in Southern Peru

Abstract

Agricultural operations in southern Peru are particularly vulnerable to climate variability due to water resource scarcity. In general, the response to drier than normal conditions in this region is reactive and fairly limited due to challenges associated with climate forecasting and administrative capacity to distribute resources. To shift this paradigm, we investigate the potential for an El Niño-Southern Oscillation index-based insurance product. The article presents a demonstration of methodology and application for one specific crop in a department of southern Peru. The purpose of this product is to streamline the ability of decision makers to provide financial relief to affected farmers during, and perhaps before, drought; extending the lead-time of the index that is used to trigger product payouts produces results of similar skill to a product trained on concurrent conditions. Issues explored in this work include basis risk, initial endowment requirements, product longevity, and the potential crossover from index-based insurance to forecast-based financing. The ability of such products to mitigate losses during and after drought may be advantageous in Peru and other regions with notable interannual climate variability.

Review article: Design and Evaluation of Weather Index Insurance for Multi-Hazard Resilience and Food Insecurity

“…The loss expectation can be determined using historical burn rate method (HBR), which is the mean historical losses (Guerrero-Baena and Gómez-Limón, 2019; Hohl et al, 2020;Mortensen and Block, 2018;Shirsath et al, 2019).This method is widely applied in the insurance industry, however, requires sufficient data in order to be accurate. For smaller datasets considering uncertainty, expected values can be evaluated by fitting loss data to a probability density function (Aizaki et al, 2021;Bokusheva, 2018;Bucheli et al, 2021;Eze et al, 2020;Kath et al, 2019;Salgueiro, 2019;Sacchelli et al, 2018;Vroege et al, 2021;Ward et al, 2020).…”

“…The surface sea temperature of the Pacific Ocean has a close relationship with droughts in many parts of the globe, such as Central America, South Africa, Australia, and Southeast Asia (Vicente-Serrano et al, 2011). El Niño South Oscillation (ENSO) has demonstrated the potential to predict extreme drought conditions (Vicente-Serrano et al, 2011), therefore being used as a customized index for insurance design in Peru (Mortensen and Block, 2018). Lastly, customized indices can be used in situations where the relationships between droughts and losses and damages are very specific.…”

“…Deterministic models were applied for income reduction impacts, especially for crop insurance. Additionally, some models were related only with one explanatory variable (one index) was the most common solution found in the in-depth analysis (Aizaki et al, 2021;Bokusheva, 2018;Bucheli et al, 2021;Hohl et al, 2020;Kath et al, 2019;Mortensen and Block, 2018;F et al, 2020;Vroege and Finger, 2020;Ward and Makhija, 2018). There is evidence that machine learning techniques improve loss modeling from different sources and present different time and spatial scales (Eze et al, 2020).…”

“…El Niño-Southern Oscillation (ENSO) triggers extreme precipitation events (Lyon and Barnston, 2005), which makes the hypothesis of a compound event plausible in this case. Mortensen and Block (2018) proposed an insurance policy for droughts using ENSO as an index and obtained good crop losses prediction accuracy. Nonetheless, in a multi-hazard risk schema, this variable should be considered to improve the model's ability to evaluate the probability of extreme precipitation events related to ENSO or other larger-scale meteorological phenomena that impact a region.…”

From Stream Flows to Cash Flows: Leveraging Evolutionary Multi‐Objective Direct Policy Search to Manage Hydrologic Financial Risks

“…For example, modeling of financial reserves is not common in the water resources literature, and the limited examples tend to assume that the utility will contribute either a fixed amount or a fixed fraction of revenues to the reserve fund each year (Rehan et al., 2013, 2015; Zeff et al., 2014). Similarly, there is a growing interest in using hydrology‐based financial hedging contracts in applications such as hydropower (Foster et al., 2015; Hamilton et al., 2020; Meyer et al., 2016), water supply (Brown & Carriquiry, 2007; Maestro et al., 2016; Zeff & Characklis, 2013), and agriculture (Denaro et al., 2020; Mortensen & Block, 2018; Turvey, 2001), but researchers have generally assumed that the same contract is purchased each year, not allowing for risk management to be adjusted over time as conditions change.…”