Search results

An economic analysis is presented of the Standard Reinsurance Agreement (SRA), the contract governing the relationship between the Federal Crop Insurance Corporation and the private insurance companies that deliver crop insurance products to farmers. The paper outlines provisions of the SRA and describes the modeling methodology behind the SRA simulator, a computer program developed to assist crop insurers and policy makers in assessing the economic impact of the Agreement. The simulator is then used to analyze how the SRA affects returns from underwriting crop insurance. The results are presented in aggregate and also at the regional and individual company levels.

The purpose of this paper is to provide empirical information about the past loss experience in major US crop insurance programs, and documents the impacts of ratings changes through time on the premiums and exposure to participants. The losses are also examined within the structure of the current SRA to identify impacts on insurance companies and the government by fund designation.

- The study uses RMA Summary of Business data and methods consistent with the use of loss-cost ratemaking to analyze loss performance across years with different starting prices and volatilities. Additionally, the RMA premium quoting system was replicated across years with the ability to adjust only one feature at a time to isolate the impacts of changes in individual rating elements from changes in market conditions. Tabulations are provided in map and table form to present the loss ratios through time, in aggregate across time, and within each of the possible funds in which exposures are held. Additionally, the tools developed allow a direct tabulation of the farmer-level premium impacts of individual changes in the policy premium system, and of changing conditions over time.

Corn and soybeans represent dominant shares of aggregate policy premiums and liability, and also are the crops that underwent the greatest degree of revision in rates over the recent past both due to rate study implications, and to loss rate experience. Despite commonly made arguments that payments associated with the drought of 2012 “more than wiped out all historic gains,” it appears that insurance worked very much as intended and that the loss ratios through time are within reasonable ranges of targets. Fund designation, and the separation under the most recent SRA of Group 1 and Group 2 states substantially dampened the loss sharing and ability to capture gains by private companies, and leads to fairly low rates of return on a pure fund-loss sharing basis for insurance companies. Finally, despite the extreme losses of 2012, the aggregate performance of corn relative to the remainder of the program exhibits lower than average loss rates both in aggregate and on a scale-adjusted basis.

The study provides an important means to isolate and assess implications of rate changes, and to associate causes of losses with rate charges. Additionally, the structure of the SRA, and possible future versions of the SRA are informed by both the aggregate, and the normalized performance results provided. And, the relative performance of major row, crops even with recent extreme losses, appears appropriate or positive to insurance companies after considering the impacts of the SRA on company exposure. In total, the evidence points toward appropriate movement toward target overall loss ratios in the US crop insurance program.

This paper provides an extensive empirical evaluation of ratings for major crop insurance policies and provides a unique means to decompose sources of changes in premiums and rates across locations and through time. It also provides an evaluation of the performance of crop insurance post-SRA in a manner that allows both totals and scale-adjusted performance to be assessed.

The purpose of this research is to investigate the degree to which trends and structural change may have altered crop insurance expected loss cost ratios across time. Because loss experience is used to set rates for the program, these changes can impact the premiums paid by producers and cost to the government.

County level adjusted loss cost data was merged with climate division weather data for the 1980‐2009 period. Crop‐specific regional‐level regression models were estimated to test for trends and structural changes in the loss experience for major crops (corn, soybeans, sorghum, cotton, winter wheat, and spring wheat). Climate data was used to control for the effect of weather.

For several crops and regions, a significant break point in the loss cost data is found at 1995. This is consistent with the policy changes that occurred in in the program due to the 1994 legislative change. In most instances loss experience prior to 1995 is higher than more recent years even when controlling for the effect of weather. The exception is in winter wheat where it appears recent experience may be worse rather than older experience.

This paper provides a large‐scale assessment of the magnitude of improved crop insurance loss experience across time.

The purpose of this paper is to be an academic inquiry into rating issues confronted by the US Federal Crop Insurance program stemming from changes in participation rates as well as the weighting of data to reflect longer‐run weather patterns.

The authors investigate two specific approaches that differ from those adopted by the Risk Management Agency, building upon standard maximum likelihood and Bayesian estimation techniques that consider parametric densities for the loss‐cost ratio.

Both approaches indicate that incorporating weights into the priors for Bayesian estimation can inform the distribution.

In most cases, the authors' results indicate that including weighting into priors for Bayesian estimation implied lower premium rates than found using standard methods.

Federal crop insurance programs are the primary risk management programs of the US farm programs. Currently, these programs have been criticized for being disproportionally in favor of the riskier areas. Despite previous researchers having widely speculated its existence, a formal study of the scale, spatial pattern and fiscal impacts of such misrating phenomenon is still missing in the literature.

This paper first purposes an empirically testable definition of misrating, and then detects the scale of the misrating phenomenon by using over two million actuarial records collected by United States Department of Agriculture (USDA's) risk management agency since 1989. Furthermore, multiple spatial statistics methods have been adopted to study the spatial patterns of the misrating statuses. Finally, the paper builds a simple theoretical model to study the potential fiscal impacts of any policy attempts to mitigate the misrating issue.

The result reveals that roughly 40% of the counties display some degree of misrating. Furthermore, the distribution of misrating displays a significant pattern of positive global spatial autocorrelation, which reflects the existence of regional clusters of premium rate mispricing. Last but not least, the paper concludes that whether an attempt toward fair rating decreases the total program outlay or not relies on the demand elasticity of crop insurance in both overrated and underrated regions.

This paper offers the first attempt to quantify the scale, identify the spatial pattern and evaluate the fiscal impact of the premium misrating in federal crop insurance programs.

This paper examines the relationship between farm-level variables related to cash flow and premium rates on federal crop insurance coverage selection.

Using farm-level data from the Agricultural Resource Management Survey (ARMS), the authors estimate a linear fixed effects model to evaluate the relationship between farm-level and regional variables and federal crop insurance coverage selections.

The authors find evidence indicating that expected cash flow plays an important role in coverage level decisions. For example, variables directly related to cash flow, such as higher costs, are associated with significant differences in coverage level selection, though the direction of the association is dependent on the type of costs, whether fixed or variable, while higher revenue higher acreage farms insure at higher coverage levels. In addition, higher premium costs are associated with lower coverage level selections, despite subsidy incentives.

This is the first paper that identifies a potential solution to the puzzling finding that farmers do not consistently maximize coverage level. This research points to the influence of credit constraints as playing a role in limiting coverage level selections.

The USDA’s Risk Management Agency (RMA) made several changes to the crop insurance products available to organic growers for the 2014 crop year. Most notably, a 5 percent premium surcharge was removed and organic-specific transitional yields (t-yields) were issued for the first time. The purpose of this paper is to use farm-level organic crop yield data to analyze the impact of these reforms on producer insurance outcomes and compare the insurance options for new organic growers.

This study uses a unique panel data set of organic corn and soybean yields to analyze the impact of organic crop insurance reforms. Actual Production History values and premium rates are calculated for each farm and crop yield sequence. Producer loss ratios and subsidized premium wedges are compared for yield, revenue and area-risk products before and after the instituted reforms.

Results indicate that RMA succeeded in improving the actuarial soundness of the organic insurance program, though further refinement of organic t-yields may be necessary to accurately reflect the yield potential of organic producers and avoid reductions in program participation.

This paper provides insight into the effectiveness of reforms intended to improve the actuarial soundness of organic crop insurance and demonstrates the effect that the reforms are likely to have on new and existing organic farms. Because this analysis uses data collected independently of RMA and includes farms that may or may not have purchased crop insurance, it avoids the self-selection problems that might affect analyses using crop insurance program data.

This paper studies the impact of ownership structure on performance in European banking both prior and during the recent crisis. We use a panel of European banks during the period 1996–2011 and utilize random effects estimations in order to identify differences in bank performance (profitability, loan quality, and cost efficiency) due to differences in ownership structure. Both stakeholder and shareholder banks have distinct advantages, shareholder banks showing better profitability before the crisis but stakeholder banks having higher loan quality before and during the crisis. Differences in profitability and loan quality between stakeholder and shareholder banks before the crisis are especially pronounced in countries that experienced a banking crisis after 2007. There is strong a heterogeneity in performance between different stakeholder ownership groups. With the exception of private savings banks, profitability and loan quality of stakeholder banks has improved relative to that of general shareholder banks during the crisis years. The paper contributes to the previous literature by comparing pre-crisis and crisis performance and includes more refined ownership classifications. The results indicate that the survival of the stakeholder model is due to its competitive advantages. Our findings provide support for those arguing that the diversity of organizational structures is worth preserving. Ownership pluralism should become a policy objective in the banking industry.

The purpose of this paper is to analyze the optimal reinsurance contract structure from the crop insurer's perspective.

A very powerful and flexible empirical‐based reinsurance model is used to analyze the optimal form of the reinsurance treaty. The reinsurance model is calibrated to unique data sets, including private reinsurance experience for Manitoba, and loss cost ratio (LCR) experience for all of Canada, under the assumption of the standard deviation premium principle and conditional tail expectation risk measure.

The Vasicek distribution is found to provide the best statistical fit for the Canadian LCR data, and the empirical reinsurance model stipulates that a layer reinsurance contract structure is optimal, which is consistent with market practice.

While the empirical reinsurance model is able to reproduce the optimal shape of the reinsurance treaty, the model produces some inconsistencies between the implied and observed attachment points. Future research will continue to explore the reinsurance model that will best recover the observed market practice.

Private reinsurance premiums can account for a significant portion of a crop insurer's budget, therefore, this study should be useful for crop insurance companies to achieve efficiencies and improve their risk management.

To the best of the authors' knowledge, this is the first paper to show how a crop insurance firm can optimally select a reinsurance contract structure that minimizes its total risk exposure, considering the total losses retained by the insurer, as well as the reinsurance premium paid to private reinsurers.

The purpose of this paper is to develop methods for inferring if crop insurance premiums imply yield distributions that are valid according to standard laws of probability and broadly consistent with observed empirical evidence. The authors also survey current premium-implied distributions both before and after conditioning on the producer’s choice of coverage level.

Under an assumption of actuarial fairness, the authors derive expressions for upper and lower bounds for premium-implied yield cumulative distribution functions (CDFs) at loss thresholds for each coverage level. When observed premiums imply a CDF that exceeds one or is not non-decreasing, the authors conclude that premiums cannot be actuarially fair. The authors additionally specify very weak conditions for premium-implied yield CDFs to be consistent with two possible reasonable parametric distributions.

The authors evaluate premiums for the year 2018 for 19,104 county-crop-type-practice combinations, both before and after conditioning on producer’s choice of coverage level. The authors find problems in roughly one-third of cases. Problems are exhibited for all crops evaluated, and are strongly associated with areas with lower expected yields and higher yield variability. At least 40m acres are currently insured under premium schedules that cannot possibly be consistent with valid probability distributions.

The authors make two primary contributions. First, the premium-implied yield CDF bounds the authors derive requires fewer assumptions than previous similar work, while simultaneously placing more stringent conditions on premiums to be consistent with actuarial fairness. Second, the authors show that current US crop insurance premiums cannot possibly be actuarially fair for many cases, reflecting tens of millions of insured acres, which implies sub-optimal producer risk mitigation and inequitable expenditures for producers and taxpayers.