Can Experiential Games and Improved Risk Coverage Raise Demand for Index Insurance? Evidence from Kenya

2020 ◽  
Vol 103 (1) ◽  
pp. 338-361
Author(s):  
Sarah Janzen ◽  
Nicholas Magnan ◽  
Conner Mullally ◽  
Soye Shin ◽  
I. Bailey Palmer ◽  
...  
Keyword(s):  
Index Insurance ◽  
Risk Coverage

Teresa Mannion and the Emergence of Extreme Weather Culture in Ireland

2019 ◽  
Vol 49 (1) ◽  
pp. 135-150
Author(s):  
Diane Negra
Keyword(s):  
Climate Change ◽  
At Risk ◽  
Popular Culture ◽  
Extreme Weather ◽  
Cross Cultural ◽  
Viral Video ◽  
Cultural Information ◽  
Strong Winds ◽  
Risk Coverage

In this article I consider how registers of weather media carry/convey cultural information, specifically how texts about extreme weather articulate with investment in a supposed post-recession restored normality marked by the Irish government's commitment to deregulated transnational capitalism. I maintain that, in a process of cross-cultural remediation, sensationalist codes of US weather media that discursively manage awareness of systemic climate problems are just starting to infiltrate the Irish broadcasting environment. In early December 2015 RTÉ’s Teresa Mannion covered a strong gale, Storm Desmond, amidst inclement conditions in Salthill, Co Galway. Modelling the kind of ‘body at risk’ coverage consummately performed by US Weather Channel personnel, Mannion could barely speak over the lashing rain and strong winds in a dramatic broadcast that quickly became a viral video. This article analyses the fascination with Mannion's piece and its memetic, and attends to the nature of the pleasure taken in her on-camera discomfiture and the breach of gendered territory committed by Mannion at a time when national popular culture in Ireland is under increased obligation to identify and explain climate change-related extreme weather.

scholarly journals Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design

2021 ◽  
Vol 13 (9) ◽  
pp. 5207
Author(s):  
Zed Zulkafli ◽  
Farrah Melissa Muharam ◽  
Nurfarhana Raffar ◽  
Amirparsa Jajarmizadeh ◽  
Mukhtar Jibril Abdi ◽  
...  
Keyword(s):  
Pearson Correlation ◽  
Extreme Rainfall ◽  
Peninsular Malaysia ◽  
Coefficient Of Determination ◽  
Index Insurance ◽  
Weather Index ◽  
Basis Risk ◽  
Insurance Contracts ◽  
Weather Index Insurance ◽  
Streamflow Statistics

Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.

scholarly journals Designing Rainfall Index Insurance for Rubber Plantation in Balikpapan

2021 ◽  
Vol 1863 (1) ◽  
pp. 012018
Author(s):  
Muhammad Azka ◽  
Fauziyyah ◽  
Primadina Hasanah ◽  
Syalam Ali Wira Dinata
Keyword(s):  
Index Insurance ◽  
Rubber Plantation ◽  
Rainfall Index

scholarly journals Farmers’ Demand for Informal Risk Management Strategy and Weather Index Insurance: Evidence from China

2021 ◽  
Author(s):  
Yingmei Tang ◽  
Huifang Cai ◽  
Rongmao Liu
Keyword(s):  
Risk Management ◽  
Field Experiments ◽  
Management Strategies ◽  
Insurance Companies ◽  
Index Insurance ◽  
Insurance Demand ◽  
Weather Index ◽  
Weather Index Insurance ◽  
Risk Management Strategies ◽  
The Government

AbstractIn the absence of formal risk management strategies, agricultural production in China is highly vulnerable to climate change. In this study, field experiments were conducted with 344 households in Heilongjiang (Northeast China) and Jiangsu (East China) Provinces. Probit and logistic models and independent sample T-test were used to explore farmers’ demand for weather index insurance, in contrast to informal risk management strategies, and the main factors that affect demand. The results show that the farmers prefer weather index insurance to informal risk management strategies, and farmers’ characteristics have significant impacts on their adoption of risk management strategies. The variables non-agricultural labor ratio, farmers’ risk perception, education, and agricultural insurance purchase experience significantly affect farmers’ weather index insurance demand. The regression results show that the farmers’ weather index insurance demand and the influencing factors in the two provinces are different. Farmers in Heilongjiang Province have a higher participation rate than those in Jiangsu Province. The government should conduct more weather index insurance pilot programs to help farmers understand the mechanism, and insurance companies should provide more types of weather index insurance to meet farmers’ diversified needs.

scholarly journals Index Insurance Quality and Basis Risk: Evidence from Northern Kenya

2016 ◽  
Vol 98 (5) ◽  
pp. 1450-1469 ◽  
Author(s):  
Nathaniel D. Jensen ◽  
Christopher B. Barrett ◽  
Andrew G. Mude
Keyword(s):  
Index Insurance ◽  
Basis Risk ◽  
Northern Kenya

Overcoming basis risk in agricultural index insurance using crop simulation modeling and satellite crop phenology

2021 ◽  
Author(s):  
Mehdi H. Afshar ◽  
Timothy Foster ◽  
Thomas P. Higginbottom ◽  
Ben Parkes ◽  
Koen Hufkens ◽  
...  
Keyword(s):  
Smallholder Farmers ◽  
Simulation Models ◽  
Extreme Weather ◽  
Index Insurance ◽  
Yield Estimation ◽  
Crop Phenology ◽  
Yield Data ◽  
Basis Risk ◽  
Crop Simulation ◽  
Crop Simulation Models

<p>Extreme weather causes substantial damage to livelihoods of smallholder farmers globally and are projected to become more frequent in the coming decades as a result of climate change. Index insurance can theoretically help farmers to adapt and mitigate the risks posed by extreme weather events, providing a financial safety net in the event of crop damage or harvest failure. However, uptake of index insurance in practice has lagged far behind expectations. A key reason is that many existing index insurance products suffer from high levels of basis risk, where insurance payouts correlate poorly with actual crop losses due to deficiencies in the underlying index relationship, contract structure or data used to trigger insurance payouts to farmers. </p><p>In this study, we analyse to what extent the use of crop simulation models and crop phenology monitoring from satellite remote sensing can reduce basis risk in index insurance. Our approach uses a calibrated biophysical process-based crop model (APSIM) to generate a large synthetic crop yield training dataset in order to overcome lack of detailed in-situ observational yield datasets – a common limitation and source of uncertainty in traditional index insurance product design. We use this synthetic yield dataset to train a simple statistical model of crop yields as a function of meteorological and crop growth conditions that can be quantified using open-access earth observation imagery, radiative transfer models, and gridded weather products. Our approach thus provides a scalable tool for yield estimation in smallholder environments, which leverages multiple complementary sources of data that to date have largely been used in isolation in the design and implementation of index insurance</p><p>We apply our yield estimation framework to a case study of rice production in Odisha state in eastern India, an area where agriculture is exposed to significant production risks from monsoonal rainfall variability. Our results demonstrate that yield estimation accuracy improves when using meteorological and crop growth data in combination as predictors, and when accounting for the timing of critical crop development stages using satellite phenological monitoring. Validating against observed yield data from crop cutting experiments, our framework is able to explain around 54% of the variance in rice yields at the village cluster (Gram Panchayat) level that is the key spatial unit for area-yield index insurance products covering millions of smallholder farmers in India. Crucially, our modelling approach significantly outperforms vegetation index-based models that were trained directly on the observed yield data, highlighting the added value obtained from use of crop simulation models in combination with other data sources commonly used in index design.</p>

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