Importance of historical weather information in setting premiums – AgFax

Corn damaged by hail. Photo: Wes Nelson, USDA-FSA

A new study by agricultural economics experts from Texas A&M AgriLife and Virginia Tech makes the case for using historical weather information in crop insurance programs for even more accurate policy pricing.

The study “Integration of Historical Weather Information in Crop Insurance Pricing”, written by Yong LiuPh.D., Texas A&M AgriLife Research agricultural economist and assistant professor at the Texas A&M Department of Agricultural Economics, Bryan-College Station, was recently published online in the American Journal of Agricultural Economics. It was co-authored by Ford RamseyDepartment of Agricultural and Applied Economics, Virginia Tech.

About Crop Insurance Rates

Crop insurance is the most expensive agricultural policy in the United States, with more than $110 billion in liability in 2020. Agricultural producers and others purchase crop insurance to protect against crop loss due to natural disasters or loss of income due to falling prices. agricultural raw materials.

In the United States federal crop insurance program, a key principle in the design of crop insurance policies is that they must be actuarially fair, which means that the indemnity provided under the policy must be equal to the premium.

“Achieving this goal requires accurate policy pricing, and accurate pricing depends on accurate modeling of all loss-causing variables,” Liu said.

Traditionally, he said, known or fixed historical return data or historical loss cost data have been used to estimate returns or loss costs.

“For example, ground information is fixed or known at the time of font sale,” he said. “Integrating this type of known information is conceptually similar to managing time trends and other fixed determinants of returns or loss costs.”

Liu said loss probabilities and expected losses are then used to calculate premiums. Many rating procedures exclusively use fixed or deterministic variables to determine expected losses.

“But it is widely recognized that much of the observed variation in yields and loss costs is due to climate change and other variables,” Liu said. “The current loss variables used to determine crop insurance rates can be modified to incorporate other applicable variables like weather.”

Stochastic variables, like time, have a random probability distribution or pattern that can be analyzed statistically but not accurately predicted. Unlike fixed variables, stochastic variables are unknown when the policy is sold.

“Inclusion of these variables, especially long-term meteorological data, would allow for more thorough and accurate estimation of the distribution over time,” Liu said.

The case for using historical weather information

Liu noted that in the federal crop insurance program, historical weather information is already integrated to some extent through post-event rate adjustments. He also noted that reinsurers frequently use weather information when evaluating crop and risk insurance portfolios.

“The distribution of yields related to weather conditions has been shown to approximate the distribution of yields based on observed yields,” he said. “And several previous studies have discussed the potential benefits of using weather or climate information in crop insurance pricing.”

He also noted that weather data is often available over a longer period of time than yield data or loss cost data.

“This is especially the case at the farm level where yield records are notoriously short, in counties where production is sporadic, or for crops with limited historical production,” he said.

Liu said while weather data is useful for making predictive assumptions about yields and loss costs, incorporating historical weather information into setting crop insurance rates should provide additional accuracy.

“Our approach uses observations where loss variables are missing,” he said. “The inclusion of historical meteorological data necessarily involves observations with missing dependent variables.”

About the study

In this study, Liu and Ramsey implemented a Bayesian approach to incorporate historical weather information into crop insurance pricing. The Bayesian paradigm has the advantage of reflecting the uncertainty of all unknowns instead of only known information.

“We treated weather information instances as a stochastic predictor of crop yields and loss cost ratios,” Liu said. “In the case of yields, we used county-level corn yields from seven Midwestern states. For loss cost ratios, we used county-level corn and soybean loss cost ratios in Illinois and Iowa for the federal crop insurance program.

The models were integrated into a Bayesian algorithm that used historical weather information to estimate the actuarial factors needed to determine crop insurance premiums, he said.

Liu said that in the case of yields, the study was able to demonstrate that:

  • A private insurer incorporating weather information can develop rates that give it a competitive advantage over government-set crop insurance rates.
  • This advantage is enhanced when there is additional historical weather information. Using more informative data covering a longer period will improve overall accuracy.
  • This benefit is slightly stronger at lower cover levels.

He said that in the case of loss costs, the study was able to demonstrate that:

  • Historical distributions of weather-related loss costs differ slightly from those without historical information.
  • Weather weighting can be integrated through a simplified one-step process.

Liu said the study makes two main contributions to the crop insurance discussion. The first was to implement a theoretically consistent Bayesian approach to incorporate historical weather data into the estimation of conditional predictive yield distributions.

“In this, we show that incorporating historical weather information leads to economic gains for private insurers by demonstrating the effectiveness of the proposed approach,” Liu said.

He said the second contribution implemented the same approach for loss cost distributions.

“It involves a unique algorithm for constrained loss costs, and we find that historical weather-conditioned distributions differ slightly from empirical distributions based on observed loss costs,” he said.

Liu said the study’s findings have implications for the design of crop insurance programs in the United States and around the world.

“This study suggests that increasingly large and often disparate data sets can be combined and used to improve agricultural policy,” he said. “As the measurement and modeling of weather and agricultural production continue to evolve, so will crop insurance products and actuarial methodologies.”

He said that by developing rates that reflect heterogeneous risk exposure from place to place, the methods developed in the study can encourage increased program participation and minimize adverse selection.

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