A Comprehensive Review of the RMA APH 

and COMBO Rating Methodology

Final Report

March 15, 2010

By 

Keith H. Coble

Thomas O. Knight

Barry K. Goodwin

Mary Frances Miller

Roderick M. Rejesus

Contact: 

George Duffield

Sumaria Systems

618-632-8450

gduffield@sumaria.net

Keith H. Coble

662-352-9491

khcoble11@aol.com
A Comprehensive Review of the RMA APH and COMBO Rating 

Methodology

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Table of Contents

Executive Summary......................................................................................................................................1

Introduction................................................................................................................................................... 5

1.0 Crop Insurance Program Background and the Nature of........................................................................ 6

Crop Yield and Revenue Risk....................................................................................................................... 6

1.1. Brief History of the Crop Insurance Program.................................................................................... 6

1.2. The Nature of Crop Yield Risks ........................................................................................................ 8

2.0 An Overview of the RMA Lost Cost Approach to APH Rates............................................................11

2.1. Objective of the RMA Actuarial Procedures...................................................................................11

2.2. An Overview of the RMA Actuarial Procedures.............................................................................12

2.3. Overview of the RMA APH Rating Approach ................................................................................14

3.0 Documentation of Procedures for Developing County Base Rates......................................................15

3.1. Rate Making Concepts/Methods: Insurance Experience Component .............................................15

3.1.1. Statplan Database Construction ................................................................................................15

3.1.2. Statplan Database Construction: Liability and Indemnity Adjustments...................................20

3.2. Rate Making Concepts/Methods: Reference Yield Component......................................................26

3.3. Rate Making Concepts/Methods: Coverage Level Rate Relativity Component .............................27

3.4 Target Rate Development Component.............................................................................................28

3.4.1 Capped Loss Cost.......................................................................................................................28

3.4.2 Net Acres Insured......................................................................................................................28

3.4.3 County Unloaded Rate Determination......................................................................................29

3.4.4 State Catastrophic Load ............................................................................................................30

3.4.5 County Catastrophic Load .........................................................................................................30

3.4.6 Miscellaneous Rate Loads .........................................................................................................31

3.4.7 Disaster Reserve Factor ............................................................................................................31

3.4.8 Optional Unit Factor .................................................................................................................32

3.4.9 County Target Rate ...................................................................................................................32

3.4.10 Judgment Target Rate ..............................................................................................................32
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3.5. Mandated Capping Requirements Component ................................................................................33

3.6. APH Business Process and Critical Control Component................................................................34

4.0 Adjustments from County-Level Target Rates to Individual Rates......................................................35

4.1. Introduction......................................................................................................................................35

4.2. Yield Ratio.......................................................................................................................................36

4.3. Yield Ratio Curve and Exponent .....................................................................................................37

4.4. Type-Practice Factors ......................................................................................................................38

4.5. Coverage Level Differentials...........................................................................................................41

4.6. Unit Division Factor ........................................................................................................................42

5.0 Assessment of Alternative Procedures to Develop APH Yield Rates ..................................................44

5.1 Objectives of RMA Rating ...............................................................................................................44

5.2 Context of RMA Rating ..................................................................................................................45

5.3 Assessment of Alternative Rating Approaches.................................................................................48

5.3.1 The Use of Unadjusted Loss Ratios...........................................................................................48

5.3.2 The Use of a Yield Simulation Based Rating ...........................................................................52

6.0 Evaluation of the APH Rating System and Suggestions for Consideration .........................................60

6.1 Basic Approach to APH Rating .......................................................................................................60

6.2 Reference Rate, Reference Yield and Exponent..............................................................................60

6.3 Type and Practice Factors ...............................................................................................................64

6.4 Adjustments for Unit Structure........................................................................................................71

6.5 Coverage Level Adjustments...........................................................................................................73

6.6 Catastrophic Loading.......................................................................................................................74

6.7 Use of Expert Judgment...................................................................................................................75

6.8 Additional Rating Variables............................................................................................................75

6.9 Statewide Rate Level Adequacy ......................................................................................................76

6.10Catastrophic Coverage Rates...........................................................................................................76

6.11Weighting of Experience.................................................................................................................78

6.11.1Yield Correlation and Weighting Loss Experience Data..........................................................78

6.11.2 Adjusting for Unrepresentative Weather in Short Loss Experience Data................................86

6.12 Using a Loss Ratio Approach Rather than Pure Premiums ...........................................................91
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7.0 Evaluation of Procedures to Develop COMBO Revenue Rates.........................................................94

7.1. Introduction and Overview ..............................................................................................................94

7.1.2. Brief Description of the Combo Rating Method.......................................................................94

7.1.3. Outline of the Combo Review Section......................................................................................97

7.2. Combo Rate Relativities..................................................................................................................98

7.3. Minor Comments...........................................................................................................................109

8.0 Summary of Recommendations.........................................................................................................111

8.1 Basic Approach to APH rating ......................................................................................................111

8.2 Reference Rate, Reference Yield and Exponent............................................................................111

8.3 Type and Practice Factors .............................................................................................................111

8.4 Unit Factor.....................................................................................................................................112

8.5 Catastrophic Loading.....................................................................................................................112

8.6 Catastrophic Loading.....................................................................................................................112

8.7 Use of Expert Judgment ................................................................................................................112

8.8 Additional Rating Variables..........................................................................................................112

8.9 Statewide Rate Level Adequacy....................................................................................................113

8.10Yield Correlation and Weighting Loss Experience Data ..............................................................113

8.11Study of Loss Ratio Rating System...............................................................................................113

8.12Combo Rating................................................................................................................................113

Review Team Biographies........................................................................................................................114

Keith H. Coble, Ph. D. ..........................................................................................................................114

Thomas O. Knight, Ph. D......................................................................................................................115

Barry K. Goodwin, Ph. D......................................................................................................................116

Mary Frances Miller, FCAS, MAAA, Hon FIA, CPCU, ARe, AIM....................................................117

Roderick M. Rejesus, Ph.D...................................................................................................................118

References.................................................................................................................................................119

Appendix...................................................................................................................................................123
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Executive Summary

Sumaria Systems was contracted by the USDA/ Risk Management Agency (RMA) to 

conduct a comprehensive review of the APH Rating Methodology and the COMBO 

rating procedure. Our team, including experienced crop insurance analysts and a leading 

professional actuary, has reviewed the materials provided by RMA and additional 

materials we collected. Our conclusions are based on the expert opinion of our team and 

on analysis conducted during the course of our review. 

We carefully considered the context of rating crop yield and revenue insurance. The 

underlying nature of risks insured by crop multi-peril yield and revenue insurance makes 

these products somewhat unique among property insurance products. First, the products 

themselves have expanded and changed over time. For example, the introduction of 

catastrophic coverage and revenue insurance a little over a decade ago has dramatically 

altered the insurance pool. Further, technological change has occurred rapidly in crop 

agriculture, yet infrequent but severe weather events are still primary drivers of crop yield 

insurance losses.

Our review first considered the basic approach taken by RMA in rating the APH and 

COMBO products. RMA uses historical loss experience as the framework for APH 

rating and then simulates revenue insurance risk for the COMBO by building from the 

APH rate and then simulating revenue given price-yield correlations and price volatility. 

We strongly concur with this mixed approach. The heterogeneous nature of crop yield 

risk across farms and the variety of perils insured by an APH policy strongly suggest that 

RMA should use the observed historical loss experience as the foundation for the APH 

rating system. While alternative simulation-based approaches exist, we conclude that the 

RMA should avoid such systems as they impose a different and less defensible set of 

assumptions. We find that the RMA’s experience-based approach is typical of property 

insurance rating. 

The approach taken for the COMBO add-ons for revenue coverage in effect ‘wraps’ 

revenue coverage around the APH coverage. This allows for rate consistency among the 

various products. Price volatility is derived from futures options markets and correlations 

are fairly straightforward to estimate. It is our opinion that this system is fundamentally 

sound. In general, the nature of price risk is such that very accurate price variability 

estimates can be obtained from the futures and options markets, and can be applied 

effectively across a broad set of producers. Thus, the COMBO rating system is designed 

to make best use of available information. 

While we are strongly supportive of the general approach RMA has taken in rating APH 

and COMBO insurance, we do offer several recommendations for RMA to consider. We 

have tried to balance consideration of the competing objectives placed on these crop 

insurance programs and to balance the benefits of the proposed changes relative to the 

costs to producers, insurance companies, and the USDA. In some cases we believe our 
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suggestions offer clear improvements to the program. In other cases our review leads us 

to recommend that the RMA conduct further analysis to evaluate a potential rating 

improvement. 

Basic Approach to APH Rating 

We recommend that RMA continue to use loss experience as the foundation of the rating 

system as it is the only way to assure that actual losses drive the rating results. This is 

consistent with standard property and casualty insurance rating practices. While crop 

insurance poses a unique set of actuarial challenges, alternatives to loss-experience-based 

rating would likely fail to adequately address the multiple objectives of the APH 

program. 

Unit Factor 

We recommend that RMA adopt procedures for developing target rates that incorporate 

unit factors that are consistent with the actual mix of unit structures in the historical loss 

experience. 

Reference Rate, Reference Yield and Exponent

We recommend RMA adopt updated reference yields that are congruent with reference 

rates and exponents. This is a critical step in obtaining appropriate rates for insured units 

at all yield levels. These updated reference yields would be based on APH data so that the 

reference yield and reference rate are ‘centered’ within a county’s book of business. Also, 

based on our review of the yield ratio curve and the rating exponent, we recommend that 

RMA conduct analysis to update these parameters of the rating formula. 

Type and Practice Factors

We recommend that RMA modify its procedures so that the type/practice factors are 

applied to the State Catastrophic Rate Load portion of the target rate. Further, we 

recommend that RMA rebase its rates and type/practice factors to a common 

type/practice to improve the transparency of the rating structure. We also recommend the 

RMA adjust prior experience for the current mix of types and practices insured. 

Statplan Adjustments

We recommend that RMA eliminate the coverage approximation procedure and adjust all 

experience to the 50% coverage level when low coverage levels make up a significant 

proportion of the experience base. We recognize this would place greater reliance on the 

estimated coverage level relativities. However, we believe these can be effectively 

estimated for the major crops. 
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Catastrophic Loading 

We recommend that RMA reevaluate the catastrophic loading procedure and reduce the 

degree to which catastrophic loading influences rates in low risk regions. Having said 

this, we generally support maintaining state/crop catastrophic loading boundaries, except 

in the case of a crop with geographically-sparse participation. 

Use of Expert Judgment

We recommend that if local conditions have changed such that an existing credible time 

series of data is not appropriate for rating, an explicit discussion of the changes should 

accompany a plan to either adjust the prior data or to set a rate through judgment that will 

then be adjusted using standard methods as new data become available. Regional offices 

of the RMA should play an important role in this process. The decision and results 

should be documented, transparent, and reviewable by outside parties. 

Additional Rating Variables 

We recommend a comprehensive study to evaluate utilizing soil and other site-specific 

information as factors that could be used to individualize rates. We also suggest RMA 

consider defining and collecting additional type and practice data for characteristics that 

likely affect insurance risk levels.

Statewide Rate Level Adequacy

We recommend that RMA evaluate the extent to which statewide rate levels may be 

inadequate due to capping and, if significant, consider the use of an inadequacy offbalance. We recommend that RMA consider reevaluating whether the minimum 

catastrophe load is appropriate in light of the disaster reserve factor. 

Yield Correlation and Weighting Loss Experience Data

We recommend that RMA evaluate alternative loss cost experience weighting 

procedures. Our analysis suggests it is feasible to incorporate additional weather 

information into the rating system and to allow additional weight to be placed on more 

credible annual observations. However, we do not offer specific recommendations for 

changing the manner in which experience is weighted over time in current rating 

methods. We believe a detailed study of this issue should investigate both optimal 

weights and implementation issues.
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Study of Loss Ratio Rating System 

We recommend that RMA undertake a comprehensive study of the loss ratio method for 

determining future rate changes, perhaps in conjunction with a study aimed at a Statelevel, top-down approach as recommended by Milliman.

Combo Rating

In general we find that the COMBO rating methodology does an appropriate job of 

combining the various aspects of yield and price risk into a revenue product that provides 

both a Harvest Price Revenue Rate (HP Rate) and the Harvest Price Exclusion Option 

Revenue Rate (HPEO Rate). Maintaining consistency between these revenue products 

and the underlying APH product is not simple. However, we generally concur with the 

way RMA determines price-yield correlations and price variability. We recommend that 

RMA consider revising the COMBO rating method to eliminate potential inconsistency 

in the yield rate relativities applied. Though the differences may be modest in many 

cases, we believe that a more conceptually sound result would be obtained from applying 

a consistent set of yield rate relativities across the entire rating process. 
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Introduction

Sumaria Systems was contracted by the USDA/ Risk Management Agency (RMA) to 

conduct a comprehensive review of the APH Rating Methodology. RMA designs and 

rates the Approved Production History (APH) crop yield insurance products which are 

federally subsidized. Private insurance companies market and deliver these products to 

U.S. crop producers. These companies are compensated with an administrative and 

operating reimbursement and provided with reinsurance for their risk exposure. 

The APH product provides a yield risk protection guarantee that protects the producer 

against shortfalls in yield. The yield guarantee is obtained by multiplying the insured 

unit’s APH yield times the coverage level chosen by the producer. If a yield shortfall 

occurs, losses are valued using a predefined price (value). While various modifications 

have occurred over time, yield insurance dates back to the inception of the U.S. Multiperil Crop Insurance Program in 1938. 

After this project began, it was modified to also include a rating evaluation of the 

COMBO policy – a crop insurance product that, in addition to crop yield insurance, also 

includes a simple revenue (expected price × APH yield) insurance option, as well as 

revenue upside price protection which bases the guarantee on the higher of the expected 

or harvest period price. Revenue insurance was first introduced in 1995 and various 

forms of revenue insurance have been developed. In particular, Revenue Assurance (RA) 

and Crop Revenue Coverage (CRC) were developed and in some cases provided quite 

similar coverage. In more recent years, RMA has moved to reduce the redundancy and, 

with introduction of the COMBO policy, to collapse the revenue products into two 

variants: revenue insurance and revenue insurance with up-side price protection. The 

COMBO product in many ways builds upon the yield risk rates of the APH product, 

which provides the rationale for evaluating the actuarial methodology of the entire 

COMBO set of products simultaneously. 

This report provides the comprehensive review requested by RMA. The Sumaria 

Systems team approached this project with the intent to provide a detailed, third-party 

review of the current RMA APH and COMBO rating procedures. In this report we 

provide an overview of the goals and legislative mandates placed on RMA, describe the 

RMA procedures, consider alternative procedures, and finally provide recommendations 

to the Agency. The organization of the report is as follows. In the first chapter we 

summarize the history of the crop insurance program and provide a background regarding 

yield and revenue risk. The second chapter provides an overview of the RMA approach 

to rating the APH product and then Chapter Three provides details of the APH base 

county rate process. Chapter Four describes mechanisms by which RMA adjusts the base 

county rate to individual farms in a county. Chapter Five begins our assessment of the 

RMA rating system by addressing the merits of the RMA system relative to alternatives. 

Chapter Six addresses suggestions that the review team proposes for rating the APH 

product. Our review then turns to the COMBO products. Here we review the procedures 

used and then make suggestions for RMA consideration. 
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1.0 Crop Insurance Program Background and the Nature of

Crop Yield and Revenue Risk

1.1. Brief History of the Crop Insurance Program 

The United States Congress first authorized creation of Federal Crop Insurance in the 

1930s along with other initiatives to help agriculture recover from the combined effects 

of the Great Depression and the Dust Bowl. The Federal Crop Insurance Corporation 

(FCIC) was created in 1938 to carry out the program with three objectives in mind: (a) to 

protect the income of farmers against crop failure or price collapse; (b) to protect 

consumers against shortage of food supplies and extreme prices; and (c) to assist business 

and employment by providing an even flow of farm supplies and establishing stable farm 

buying power. 

In the early years of the program, insurance coverage was limited to major crops (e.g. 

wheat and cotton) in main producing areas of the U.S. Program participation was low 

during this period and net losses and loss ratios were high. In fact, losses were so heavy 

in the early 1940s that the program was discontinued in 1944; but due to political 

pressure was reintroduced in 1945 (Kramer, 1983; Goodwin and Smith, 1995; Harms, 

2005). In 1947, Congress reduced the scope of the program in order to mitigate losses, 

which prompted the characterization of the program from 1947-1980 as largely being 

“experimental.” During this period participation remained low but loss ratios were much 

lower than in the early years of the program. Losses were brought under control through 

the introduction of underwriting and loss adjustment controls. The program also reacted 

to unfavorable experience by raising premiums, reducing coverage or closing sales. 

Passage of the Federal Crop Insurance Act of 1980 marked a transition of the program 

out of the “experimental” phase into the “modern era” of crop insurance. Developments 

in the 1980s included expansion of the crop insurance program to cover many more crops 

and regions of the country and introduction of policy modifications to base coverage on 

the historical average yield for an insured unit, with individualized rates that decrease as 

the average yield increases. The 1980 Act also laid the foundation for creation of a 

public-private partnership between the US government and private insurance companies. 

Congress expected that the private sector would play a major role in the marketing and 

delivery of the program to farmers in order to increase participation. 

Though the 1980 Act helped expand the program by increasing the number of 

commodities insured and the coverage provided, farmer participation fell far short of 

Congress’ expectations throughout the 1980s and into the 1990s. In the early 1990s, 

participation rates were still in the 30% range (significantly below the 50% plus range 

expected by Congress) and in many years the government provided considerably more in 

disaster relief expenditures than in crop insurance indemnity payments. Many members 

of Congress were also frustrated by recurrent requests for ad hoc disaster assistance in the 

late 1980s and early 1990s that served to undermine the crop insurance program. 
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Given the situation in the early 1990s, Congress passed the Crop Insurance Reform Act 

of 1994 that dramatically restructured the program. The 1994 Act made participation in 

the crop insurance program mandatory for farmers to be eligible for deficiency payments, 

certain loans, and other farm program benefits. Because participation was effectively 

mandatory, catastrophic (CAT) coverage was created. CAT coverage compensated 

farmers for losses exceeding 50 percent of an average yield paid at 60 percent of the price 

established for the crop for that year. The premium for CAT coverage was fully

subsidized. Participants paid a $50 fee per crop per county, subject to maximum amounts 

for multiple crops and counties insured by the same individual. Farmers could also “buy 

up” to higher coverage levels offered under the standard multi-peril insurance available at 

that time.

In 1996, Congress repealed the mandatory participation requirement. However, farmers 

who accepted certain other benefits were required to purchase crop insurance or 

otherwise waive their eligibility for any disaster benefits that might be made available for 

the crop year. These provisions remain in effect today. In the same year (1996), the Risk 

Management Agency (RMA) was created to administer FCIC programs and other noninsurance-related risk management and education programs for agricultural producers. 

The 1994 Act also increased subsidies for insurance coverage levels above the 

catastrophic level to further encourage participation in the program. By the late 1990s 

participation rates were approximately double those at the start of the decade. However, 

despite these increased crop insurance participation rates, supplemental disaster relief 

legislation was still passed in 1998 and 1999. 

Along with the increased subsidies and subsequent growth in participation that has 

occurred since the 1994 Act, crop insurance has also broadened through the creation of 

new crop insurance products that are different from the traditional individual yield-based, 

multi-peril insurance coverage (See Glauber and Collins, 2002). In 1993, an area yield 

insurance policy called the Group Risk Plan (GRP) was introduced. Under this policy, 

producers receive indemnity payments based on shortfalls in county average yields rather 

than individual yields. In 1996 and 1997, the Crop Revenue Coverage (CRC) and 

Revenue Assurance (RA) policies were introduced. These two products provide coverage 

for losses based on revenue. A third revenue product called Income Protection (IP) was 

also introduced in 1996, but this product was piloted on a very limited basis compared to 

CRC and RA. An area-based revenue insurance design, called the Group Risk Insurance 

Plan, was introduced in 1999. Note that in 1998, the traditional yield-based insurance 

product accounted for 82% of insured acres, while CRC and RA accounted for only 14% 

of the insured acres. By 2008, CRC and RA accounted for 52% of total insured acres, 

while the APH/MPCI produce only accounted for 22%. 

Despite the increase in participation and in the breadth of products offered subsequent to 

the 1994 Crop Insurance Reform Act, Congress enacted additional legislation in 2000 

called the Agricultural Risk Protection Act (ARPA). This legislation increased premium 
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subsidies for coverage levels above CAT and encouraged development of new types of 

insurance products. ARPA also included provisions designed to reduce fraud, waste, and 

abuse in the program. In the early 2000s, refinements including continuous rating and 

variable coverage-level rate differentials were also implemented to better tailor rates to 

individual farm risks. Additional changes to the crop insurance program were contained 

in the 2008 Farm Bill, which further encouraged expansion of the program to cover more 

agricultural commodities (i.e. organic crops, aquaculture, energy crops, etc.) and 

increased subsidies for insuring at the more aggregate enterprise unit level.1

With the increasing number of insurance products being offered and program changes 

over time, there is a natural concern about maintaining fair and actuarially sound 

premium rates. As will be described in the sections to follow, the current RMA 

ratemaking procedure for most individual-level insurance products (e.g. APH, CRC, and 

RA) relies on historical loss experience data from the program. The advantage of this 

approach is clear: rates are based on actual loss experience for the program. The 

disadvantage of this historical loss experience approach is the limited length of time for 

which historical experience is available as well as the significant program changes that 

may complicate comparisons over time. Since many crop losses are driven by infrequent 

but extreme weather events, a 20-30 year time series of loss data may not be sufficient to 

appropriately reflect loss probabilities (Glauber, 2004). Unfortunately, having a longer 

experience data base may be problematic due to program changes over time such as 

increased participation, a shift to revenue products, a shift to higher coverage levels, and 

rating adjustments made through the years. In addition, underlying yield risks of the 

crops themselves may evolve and the relative riskiness may not be constant over time – 

which is suggestive of non-stationary yield risks (Harri et al, 2009).2 Hence, historical 

loss experience may not be indicative of future loss expectations given the evolution of 

changes in the program.

1.2. The Nature of Crop Yield Risks 

As touched on above, an understanding of the nature or evolution of yield risks over time 

is needed so that a particular crop insurance rating approach can be appropriately 

evaluated. In particular, an understanding of the nature of crop yield distributions is 

important in order to appreciate the complexity of crop yield risks and, consequently, the 

difficulty in rating crop yield and revenue insurance products. In this section, we provide 

a brief description of the crop yield distribution literature that focuses on trends in mean 

yield, yield variability evolution, the potential influence of climate change on yield risks, 

and spatial differences in yield risk. 

 

1 Enterprise units include all of a producer’s interest in a crop in a county.

2 The advantages/disadvantages of the historical loss cost approach and issues related to non-stationary 

yield risks are discussed in more detail below.
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Numerous studies have examined the issue of identifying appropriate crop yield 

distributions for risk analysis and crop insurance premium rate development (see 

Norwood et al. 2004; Lu et al., 2008; Harri et al, 2009b for recent studies with brief 

descriptions of this literature). One consistent thread in this literature has been to 

recognize that yields in many regions tend to be upward trending over time due to 

technological advances. Hence, there is general consensus that deterministic yield trends 

due to technological change have to be taken out prior to risk analysis and insurance 

rating (Goodwin and Ker, 1998). The typical approach is to first detrend the time series 

yield data by regressing yields against time and then using the detrended yields for risk 

analysis and rate making. However, there is no consensus on how to choose the 

appropriate trend specification and a study by Zhu et al. (2008) even argues that 

simultaneously estimating the time trend and parameters of the yield distribution may be 

more appropriate. 

In contrast to the widely accepted notion of upward trending mean yields in many crops 

and regions, there is no general consensus in the literature as to the evolution of yield 

variability over time.3 The majority of studies have found a relationship between mean 

yield and yield variability over time. Many studies, such as those by Hazell (1984), 

Gallagher (1987), Yang et al., (1992), Traxler (1995), Atwood et al. (2002, 2003), and 

Harri et al. (2009b), have found evidence of yield heteroskedasticity over time but no 

strong evidence exists for assuming a constant yield variability over time (see Skees and 

Reed, 1986; Just and Weninger, 1999). Even though most of the literature has found 

some relationship between mean yield and yield variability over time, the particular 

relationship between mean yield and yield variability (i.e. magnitude and signs) 

significantly varies by region, crop, and practice (among other factors). Furthermore, the 

nature of this relationship is also strongly influenced by climate and individual farm level 

risk characteristics. 

One important aspect to understanding the nature of crop yield risk is the influence of 

weather and climate on crop yields. As mentioned in the previous sub-section, crop yield 

loss events (and consequently yield risks) are driven by extreme but infrequent events. 

Therefore, a long time-series of yields is needed to adequately represent the true 

probabilities of yield losses (i.e. the true yield risks). This tends to be an important issue 

in agricultural risk analysis and crop insurance rating because most yield data sets are not 

“long” enough to fully characterize crop yield risks. However, consistent with the 

literature that found non-constant yield variability over time, there are studies that found 

that yield risks (and yield distributions) are significantly different depending on certain 

climatic conditions. For example, Ker and McGowan (2000) and Nadolnyak et al. (2008) 

show that crop yield distributions and risks systematically vary with the different phases 

of the El Niño Southern Oscillation (ENSO) – a well-known predictor of future weather 

and climate patterns that can be observed months in advance through oceanic and 

atmospheric anomalies. A related study by McCarl et al. (2008) showed that crop yield 

 

3 When there is a relationship between yield variability and mean yield evolution over time (e.g. increasing 

variability as mean yields increase over time) this is known in econometrics as heteroskedasticity.
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variability will generally be higher in the future (2030) based on the climate scenario 

forecasted by the Hadley and Canadian General Circulation Models (CGMs). This led 

them to conclude that crop yield variability is not stationary – climate change will 

increase crop yield variability – and to recommend that risk analysis using historical yield 

distributions should take this into account. This implies that premium rating procedures 

that rely on historical yield distributions to set rates for the future may need to 

incorporate the yield risk-increasing effect of climate change. However it appears that 

climate change onset would be quite gradual relative to the time period typically used for 

insurance rating.

Another important issue in understanding the nature of crop yield risk is the spatial 

heterogeneity of these risks. As touched on above, crop yield risk evolution over time 

differs by region, crop, and practice. This spatial heterogeneity can be partly explained by 

the different weather/climate conditions in different regions of the US, but varying 

resource endowments (i.e. soil quality, topography, etc.) also play a role in the spatial 

distribution of crop yield risks. The spatial heterogeneity in crop yield risks is evidenced 

by spatial disparity in loss performance across the different regions of the US (See 

Glauber, 2004; Babcock, 2008). Generally, the Midwest, California, and parts of the 

Northwest and Florida have had good loss performance for the period 1981-2003 (loss 

ratio < 1.0), while the Plains states and the Southeast have had generally poor 

performance (loss ratio > 1.5) (Glauber, 2004). 
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2.0 An Overview of the RMA Lost Cost Approach to APH Rates

2.1. Objective of the RMA Actuarial Procedures 

As a Federal agency, the USDA/RMA has objectives that differ from those of a private 

insurance company in which profit in a competitive market is the driving force behind 

actuarial procedures. In the case of RMA, legislative language found in the Federal Crop 

Insurance Act contains the following provisions pertinent to rate making: 

Sec. 508(i) (2) states “Review of rating methodologies. To 

maximize participation in the Federal crop insurance 

program and to ensure equity for producers, the 

Corporation shall periodically review the methodologies 

employed for rating plans of insurance under this subtitle 

consistent with section 507(c)(2).” 

Sec. 508(i) (3) states “Analysis of rating and loss history. 

The Corporation shall analyze the rating and loss history of 

approved policies and plans of insurance for agricultural 

commodities by area.” 

Sec. 508(d) (2) states “the amount of the premium shall be 

sufficient to cover anticipated losses and a reasonable 

reserve.”

Thus, RMA is directed to conduct its rating in a manner such that its rates remain 

actuarially sound. The rates must be sufficient to cover the expected costs, but RMA 

should not over-price the product such that it drives all but the most risk averse out of the 

crop insurance market. Further, equity of rates must be taken into consideration. We 

interpret equity to imply that rates will, to the extent possible, be tailored to be consistent 

with the risk level of each producer. This implies that loss experience by area is to be 

evaluated for the purpose of determining whether a sufficient amount of premium will be 

collected. 

Several aspects of Sec. 508 merit note. First, not unlike private insurance there is a 

balancing of actuarial soundness against program participation. However, this legislation 

is interpreted to exclude the cost of sales, loss adjustment, underwriting and other 

activities that a private insurance firm would have to cover. The operating costs of the 

USDA/RMA are not included in premium rates, nor are administrative and overhead 

(A&O) reimbursement provided to approved insurance providers (AIP) that deliver the 

insurance program to producers. RMA rates also do not include a return on investment as 

would typically be added to a privately-provided insurance product. The reserve 
A Comprehensive Review of the RMA APH and COMBO 

Rating Methodology

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provision included in the rates can be compared to a private insurer’s provision for profit 

and contingencies. 

Second, we interpret the term “anticipated losses” to imply the mathematical expectations 

as used in the Statement of Principles Regarding Property and Casualty Insurance 

Ratemaking. There it identifies a fundamental principle of insurance ratemaking as: “A 

rate is an estimate of the expected value of future costs.” 

Typically, the largest component of the rate is the provision for losses. While there are 

other important considerations in rate development, most of the actuarial foundations of 

ratemaking are intended to provide a framework for estimating the expected loss 

component of the rate. Because other expenses and costs are provided for in the A&O 

agreements, the ratemaking procedure deals strictly with deriving the expected loss 

component. This component is represented by the “loss cost ratio” (LCR), which is 

derived by dividing expected indemnity by liability. The LCR is a measure of loss per 

unit of exposure. Thus, one of the objectives of APH ratemaking is to derive LCRs that 

are representative of the expected losses for a given unit of exposure. If the premium rate 

is based on an accurately derived expected LCR then the expected loss ratio 

(indemnity/premium) will equal 1.0. Prior to the Food, Conservation, and Energy Act of 

2008, RMA was statutorily mandated to achieve a target loss ratio of 1.075. However, 

the 2008 Farm Bill lowered that target to the actuarially-fair 1.0 level.

2.2. An Overview of the RMA Actuarial Procedures 

Because different crops are subject to different perils and, therefore, varying LCRs, the 

APH procedure establishes rates for each crop separately. It is rare that a single insured, 

for any insurance coverage, will have sufficient insurance experience such that expected 

losses can be estimated solely from the insured’s own loss history. In the context of crop 

yields, ten to fifteen years of experience is often considered an unusually long time-series 

of available farm-level crop yields. However, as is shown later in this report, that amount 

of data (i.e. that number of yield observations), is often still inadequate for rating. Thus, 

it is a common and appropriate insurance practice to aggregate experience of a group of 

similar risks in developing rates.

For APH, the aggregation is done by crop and geography. Rates are developed by 

geographic area, primarily at the county level. Thus, for each crop, the APH ratemaking 

process typically derives LCRs (and consequently rates) by county. There are other 

determinants used to tailor the rate to an individual producer, depending on utilization of 

certain farming practices, coverage choices, and rate yield.4

 

4 The rate yield is the yield upon which the rate for an insured unit is based. In many cases the rate yield is 

equal to the APH yield for the unit. However, when a substitute yield is used in computing the APH yield

or a yield floor is applied the yield used in rating (the rate yield) does not incorporate these substitute yields 

or yield floor. The rules governing use substitute yields and yield floors can be found in the Crop Insurance 

Handbook on the RMA web site.
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An important consideration as well is that RMA insurance programs are nondiscriminatory in that when insurance is offered for a crop in a county it must be broadly 

available to all producers as long as they do not violate an underwriting provision such as 

farming in floodplains or using practices not deemed consistent with good management 

in the area. Thus, producers with little or no yield experience may come into the 

program. Further, farms in a county may vary dramatically in terms of risk related 

characteristics such as soil types, slope, production intensity, and irrigation. As a result 

RMA actuaries are challenged to correctly rate individuals with very little production 

experience of their own and who may operate in a manner quite dissimilar from nearby 

producers.

This challenge is also complicated by some of the unique aspects that make crop 

insurance deviate from standard characteristics of property and casualty insurance. Crop 

yield risk in most crops and locations is strongly tied to weather variability. The nature 

of weather is such that there tends to be a relatively high degree of spatial correlation in 

extreme weather events. For example, a drought may cover an area of hundreds of 

square miles. Within that region all crops may be affected. This violates the 

independence of risks that may be observed in lines of insurance such as automobile and 

life insurance. Weather risk also is quite difficult to accurately assess without many 

years of observation. This makes it difficult to determine, for example, whether a loss 

event is a 1-in-100 year event or if it is a 1-in-25 year event. For actuarial purposes this 

is a critical issue. However, it is not unlike the actuarial challenge confronted by 

property actuaries rating in hurricane-prone coastal zones. 

Another challenge to the APH rating system is the potential for moral hazard and adverse 

selection to affect losses and rates. These two well-known phenomena are contractual 

problems arising from asymmetric information between the insurer and the insured. 

Adverse selection in crop insurance arises when RMA sets rates with incomplete 

information about the inherent riskiness of the insured and the insured may opt into or out 

of the program based on better knowledge of his level of risk. This is a common problem 

in property insurance and is largely addressed by finding observable characteristics of the 

insured that allow classification of individuals into pools of similar risk. No insurer, 

private or public, can do this perfectly. But, in the extreme, adverse selection precludes 

the existence of a private insurance market (Akerlof, 1970). Adverse selection still may 

be present in the crop insurance program but may be masked by the subsidies applied to 

the program. As described later in this report, RMA rates vary by a number of 

observable characteristics of the insured unit. We will assess whether we believe these 

observed characteristics are sufficient or may be improved.

Moral hazard is a term used to describe an increase in risk-taking behavior due to the 

producer responding to economic incentives of the insurance contract (Coble et al. 1996). 

An example would be a crop producer who exerts less effort to protect a crop from 

insects once insured. In some instances moral hazard implies actionable fraudulent 

behavior while in other cases it is less clear that moral hazard is anything other than a 
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rational economic response to the terms of the insurance contract. For RMA actuaries, 

using a historical loss cost approach, moral hazard, if consistent across time and 

participants, will be captured and built into the rates. The challenge arises when the 

degree of moral hazard may evolve over time or vary across individuals. One of the most 

common insurance strategies for reducing moral hazard is the insurance deductible. In 

the case of APH insurance, the maximum coverage level allowed is 85% which implies a 

15% deductible (100% - 85%). Prior to 2000, the maximum coverage level was 75%. 

The deductible reduces moral hazard incentives as the insured must absorb the value of 

the first losses incurred. However, price guarantees or APH yields that are set too high 

can counter the deductible. The problem of moral hazard illustrates why good actuarial 

analysis must be married with proper policy design (underwriting) for a successful 

insurance product. 

2.3. Overview of the RMA APH Rating Approach 

The RMA actuarial process used to generate APH rates primarily uses historical loss 

experience for a crop in a county to derive the rates for an insured unit within that county. 

The process begins by collecting the observed insurance and loss data for that 

county/crop combination and using it to derive a base county rate. The RMA begins by 

removing or adjusting the individual loss experience to construct the Statplan data, which 

for most crops, begins in 1975. For example, replanting losses are separated out from the 

base county rate calculation. Conversely, revenue insurance experience is recomputed as 

if it were a yield insurance policy to be able to use that data in the base county rate 

calculation. The objective of this process is to normalize loss experience with various 

characteristics to a common base. In the next section the process used to derive the base 

county rate is described. 
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3.0 Documentation of Procedures for Developing County Base Rates

As indicated above, the RMA rating procedures use historical loss cost experience for a 

crop in a county in developing county base rates. These county base rates are then 

adjusted for factors such as coverage level, unit format, crop type, and crop practice to 

obtain the rate for an insured unit. In this chapter we present a description of the 

procedures followed in developing county base rates. The summary provided here draws 

heavily from detailed descriptions contained in an RMA internal document titled “Rate 

Methodology Handbook: Actual Production History” which is applicable for 2009 and 

subsequent years. Our objective is to provide a condensed summary of approximately 

75% of the information contained in the underlying document, at a level of detail that will 

allow the reader to understand the RMA’s rating process without having to absorb all of 

the technical and operational details needed for the RMA staff to implement the process. 

Given that objective, the organizational structure and some of the wording of this chapter 

closely follows that of large sections of the underlying document. We use quotes where 

significant passages are taken directly from the Handbook. However, in order to improve 

the readability of this chapter we do not repeatedly cite the document in the conventional 

manner. Therefore, this chapter is not represented as original work that does not draw 

heavily from another source. 

3.1. Rate Making Concepts/Methods: Insurance Experience Component 

3.1.1. Statplan Database Construction

The Statplan database forms the foundation for the APH rating process. The purpose of 

Statplan is to provide a reliable database to support sound actuarial decisions. The stated 

objectives of this database are: “(1) Standardize the multiple policy databases into a 

single database with multiple years of data under a single standard format, (2) Filter the 

data to include only data that is relevant to the risk analysis, (3) Stabilize the database so 

multiple analysts will be evaluating identical historical data, and (4) Summarize the 

producer experience whereby it is friendlier and provides quicker data access.” The steps 

in Statplan development are: 

1. Merge the historical insurance records from 1948 forward to create a simplified 

database with identical data fields across time.

2. Remove information for policies that are not rated on the basis of actual 

production history rating procedures. 

3. Apply updating procedures that allow reconciliation of computations across time 

as the underlying insurance experience databases are being continuously updated.

4. Develop data tables containing only the information needed and at the 

appropriate level of aggregation for rating purposes. 
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The result of these procedures is the construction of a set of data tables. Two of these 

tables, the production ratio table and the county summary table, contain the essential data 

that support the actual production history rating process. The production ratio table 

contains the data used in computing production ratios, which are discussed in subsequent 

sections, and the county summary table contains information summarized at the county 

level and used in evaluating specific risks such as prevented planting. 

Below we discuss the treatment of several specific issues in the construction of the 

Statplan database and its tabular outputs. 

Winter Kill Experience

Optional coverage offered for winter wheat and barley poses a challenge in maintaining 

consistency in the Statplan output tables. These two crops can be planted in either the fall 

or spring in some regions. In the primary regions where both fall and spring planting are 

viable production practices, an endorsement with two options (Option A and Option B) is 

offered. These options provide coverage for losses on fall planted wheat that occur 

between the fall final planting date and the spring planting date. An additional premium 

charge is applicable when one of these options is chosen. To maintain data consistency, 

Statplan separates data for policies choosing this optional coverage from other policies 

and does not include those data in the computation of production ratios for the crop in the 

county. 

High Risk Experience

The RMA classifies some land as high risk. This includes “acreage with identifiable 

physical limitations to crop production that may increase the potential frequency and/or 

severity of losses; or expose a planted or intended crop to perils not generally 

encountered by most insureds.” The RMA states that “such acreage may consist of flood 

plains, poorly drained areas, high sand content soils, high aluminum toxicity soils, high 

sodium content soils, high alkali soils, peat soils, soils with high or low pH, soils that are 

highly erodible, etc.” Adjustments are made to base rates for crops planted on high-risk 

land. Because high-risk experience is not considered to be consistent with other land in a 

county, this insurance experience is excluded from the production tables upon which base 

rates are determined and saved in other databases for use in rating high-risk experience.

Whole Farm Units 

Most of the insurance coverage based on actual production history procedures is insured 

at the county/crop level or at a more disaggregated level based on irrigated versus dryland 

production practices or geographical location of the land. However, the Revenue 

Assurance product is unique in offering whole farm units which combine the coverage 

for two or more crops in a county. Insurance experience for whole farm units poses 

special challenges because this experience cannot be segregated by crop. Therefore, 

experience based on whole farm coverage is not useful for developing base rates for a 

crop in a county and is excluded from all Statplan data tables. 
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Prevented Planting 

The RMA defines prevented planting as “a failure to plant the insured crop with proper 

equipment by the final planting date designated in the Special Provisions for the insured 

crop in the county.” In order to qualify for a prevented planting payment, failure to plant 

must have been “due to an insured cause of loss that is general in the surrounding area 

and that prevents other producers from planting acreage with similar characteristics.” 

Prevented planting coverage was first made available for a set of crops insured under the 

APH insurance design in 1994, with additional crops added later. The amount of base 

prevented planting coverage differs from 25% to 60% on covered crops with additional 

coverage options of 5% and 10% on many crops. Prevented planting is not considered a 

production loss and so prevented planting indemnities and associated liability are 

excluded from the production ratio tables for a crop in a county. These indemnities and 

liability are captured in other Statplan databases for use in prevented planting reviews. If 

planting is prevented on only a part of an insured unit while the remaining acreage is 

planted, then the experience associated with the planted acreage is included in production 

ratio tables for use in calculating the county base rate.

Written Agreements

The RMA’s definition of a written agreement is: “a document that alters designated terms 

of a crop policy as authorized under the basic provisions, the crop provisions, or the 

special provisions for the insured crop.” Written agreements are also used to provide 

coverage for an insurable crop in a county where coverage is not otherwise offered. 

Insurance experience established under a written agreement is excluded from the standard 

Statplan rating data because the covered risks are not generally consistent with the risks 

insured in the county and reflected in published county rates. 

Late Planted/Planting Adjustments 

The RMA defines late planted acreage as “acreage initially planted to the insured crop 

after the final planting date designated in the Special Provisions for the insured crop in 

the county.” A late planting period is defined following the final planting date and 

reduced coverage is offered on acreage that is planted during this late planting period. 

Thus, the late planting insurance experience is first adjusted to reflect the correct 

liability/coverage (if it was not late planted) and still included in the Statplan database. 

Replants 

The RMA defines replanted as: “performing the cultural practices necessary to prepare 

the land to replace the seed or plants of the damaged or destroyed insured crop and then 

replanting the seed or plants of the same crop in the insured acreage with the expectation 

of producing at least the yield used to determine the production guarantee.” Thus, 

replanting occurs when acreage is planted, a viable stand is not obtained, and the acreage 

is prepared and replanted to the same crop in a timeframe that would not be expected to 

result in a yield reduction compared with the insured yield. In order for coverage to 

continue on a crop, the APH based plans of insurance require an insured producer to 
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replant acreage that was damaged prior to the final planting date if the severity of damage 

is such that: “a majority of growers in the area would not normally further care for the 

crop...” [Note that this requirement can be waived if the insurance provider agrees that it 

is not practical to replant.] Indemnities that are paid to insured producers to cover the cost 

of replanting are not included in the base rate calculations and thus are not stored in the 

yield ratio or county summary tables. However, the liability and any indemnities paid on 

replanted acreage are included in the Statplan tables and in base rate development 

because the acreage is planted under conditions that are expected to produce at least the 

guaranteed yield.

Revenue Adjustments

Three revenue insurance products were introduced by the RMA in the mid 1990s. Two of 

these plans of insurance, Revenue Assurance (RA) and Crop Revenue Coverage (CRC), 

were privately developed and submitted for approval by the Board of Directors of the 

FCIC. The third product, Income Protection (IP), was developed by the RMA. All of 

these products insure producers against shortfalls of gross revenue below a guaranteed 

level and in all three the yield risk component of the coverage is based on APH

procedures. The expected or base price used in determining the revenue guarantee is 

established using futures market prices for a harvest-period contract during a specified set 

of days in the weeks prior to the sales closing date. Harvest period prices for the same 

futures market contract at harvest time are used in determining the value of production to 

count and in calculating indemnities. 

These three plans of insurance are similar but not identical. Some differences are:

• CRC bases the insurance guarantee on the higher of the base price or the harvest 

period price. 

• IP and standard RA guarantees are determined using the base price, with no 

adjustment in coverage if the price increases between the times when the base and 

harvest prices are established. 

• RA offers up-side price protection like that of CRC as an option but IP does not. 

• IP limits unit formats to basic units, which include all interest in a crop in a 

county held under identical ownership. 

• RA is unique in offering coverage on whole farm units, which integrates the 

coverage on from two to three crops. 

Because these revenue plans of insurance (especially CRC and RA) have grown in 

popularity and now account for a large proportion of coverage for some crops, it is 

essential to use the experience associated with these products in setting the actual 

production history rates. The RMA states that in order to do this “revenue adjustments 

must occur and are accomplished in an automated routine by converting these records to 

equivalent APH records for use in APH rate evaluations.”5

 

5 IP experience is not used in deriving actual production history rates because the product uses a separate 

yield rating process and offers only enterprise units.

The adjusted data are included 
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in Statplan, with identifiers for the insurance plan. The following are the Statplan 

adjustments to revenue product liability and indemnity as described in the “Rate 

Methodology Handbook: Actual Production History” discussed earlier.

Liability Adjustment:

 . 

Indemnity Adjustments:

Step 1A: 

Step 1B: 

Step 2: 

Step 3: 

Step 4: 

What the above calculations do is transform indemnities for revenue insurance products 

to be equal to what they would have been if the coverage were based on the fixed APH 

Price Election rather than the revenue plan base price and harvest price. The result is a 

calculated indemnity, for insured units that are indemnified, that is equal to what the 

indemnity would have been under APH yield insurance. This achieves consistency within 

the Statplan data across the APH yield insurance product, CRC and RA, with or without a 

harvest price feature or option. 

Revenue Adjustments for Replanted Acreage 

The process described in the previous section is used to convert revenue product loss 

experience to equivalent yield losses. A similar process is followed for replant losses. 

The liability conversion is the same as described earlier and remains in the production 

ratio tables. The formula used in converting replant indemnities on revenue products to 

yield equivalents in Statplan is as follows:

 . 

Production Ratio Calculations 

The “Rate Methodology Handbook: Actual Production History” uses the following set of 

mathematical expressions to define the production ratio. 
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which implies,

 . 

The production ratio is defined as: 

 

which, following from above, can be computed as: 

 . 

The minimum production ratio of zero is associated with no production to count on an 

insured unit. The maximum computed production ratio is equal to the coverage level. For 

example, if expected production per acre (in dollars) is $100 and the 75% coverage level 

is selected then an indemnity is recorded only if the value of production per acre, based 

on the APH price election, is below $75 per acre. Thus, the maximum computed 

production ratio approaches . This maximum production ratio also is 

assigned to all cases where production to count exceeds liability and thus is not recorded 

because no loss is reported. 

3.1.2. Statplan Database Construction: Liability and Indemnity Adjustments

The section of the internal RMA report “Rate Methodology Handbook: Actual 

Production History” summarized in this section contains detailed descriptions of the 

structure of the Statplan database. The summary of that information provided here is 

limited. Presumably, anyone who is given access to the Statplan database will also have 

the “Handbook” available for reference. Without access to the Statplan database, the 

database descriptions contained in the “Handbook” are of limited value except in 

understanding how the data support the rating process and in evaluating changes in the 

database that would be required to support proposed modifications to the rating methods. 

Here we describe the information in the database only to the extent necessary to explain 

the rating process. 

Historically, the RMA’s rating process has involved development of target and base rates 

at the county/crop level. A fundamental step in this process is summarizing loss 

experience for each year in the historical rating period across all relevant insurance plans, 

crop types, crop practices, and coverage levels. Liability and indemnity for revenue 
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insurance plans are converted to a yield basis as described above. A second critical step 

in the rating process is converting the experience for a crop in a county to a common 

coverage level. The common coverage level used as the base for APH rating is the 65% 

coverage level. Therefore, loss experience for units insured at levels above 65% must be 

adjusted down to reflect what it would have been at the 65% coverage level and loss 

experience for coverage levels below 65% must be adjusted upward to what it would 

have been at the 65% coverage level. Here we describe the process used by the RMA in 

making these coverage level adjustments. In doing so, we rely heavily upon examples 

provided in the “Rate Methodology Handbook: Actual Production History”. 

Adjustments to Liability

Tables 3.1 and 3.2 show simplified examples of 70% and 60% insurance experience for a 

crop in a county. For each recorded production ratio, these tables show the following 

information: discrete indemnity, discrete liability, cumulative indemnity, and cumulative 

liability. Discrete indemnity and discrete liability are the amounts of indemnity and 

liability on insured units that have the indicated production ratio. For example, Table 3.1 

shows that insured units with a production ratio of 0.62 accounted for $4,540 in 

indemnities and $40,076 in liability, when insured at the 70% coverage level. Cumulative 

indemnity and cumulative liability are the amounts of indemnity and liability on insured 

units that have recorded production ratios at the indicated level or below. For example, 

Table 3.1 shows that insured units with a production ratio of 0.62 or below accounted for 

$567,310 in indemnities and $1,515,350 in liability. It should be noted that for 

convenience Table 3.1 does not display information for production ratios less than 0.60. 

However, this production experience is reflected in the cumulative indemnity and 

cumulative liability amounts. Also, since the maximum production ratio of 0.70 is 

recorded for all cases where production to count exceeds liability, discrete liability 

recorded at a production ratio of 0.70 includes liability for that production ratio and all 

non-observable production ratios above that level. Cumulative liability recorded at a 

production ratio of 0.70 includes liability for that production ratio, for production ratios 

below that level and for all non-observable production ratios above that level. 

Adjusting liability from higher or lower coverage levels to the common coverage level of 

65% is straightforward. All that is required is multiplication of total (cumulative) liability 

at the actual coverage level by the ratio of the common coverage level to the actual 

coverage level. For example, Table 3.1 shows cumulative liability of $4,681,802 at the 

0.70 production ratio. This is the total liability insured at the 70% coverage level. This 

liability is multiplied by the ratio 65/70 to obtain liability of $4,347,388 at the 65% 

common coverage level. Table 3.2 shows cumulative liability of $41,418 at the 60% 

coverage level. This is multiplied by the ratio 65/60 to obtain liability of $44,870 at the 

65% common coverage level. 
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Table 3.1. Example Loss Experience at the 70% Coverage Level

Table 3.2. Example Loss Experience at the 60% Coverage Level 

Coverage Level

70 Percent

Production Discrete Discrete Cumulative Cumulative

Ratio Indem Liability Indem Liability

0.60 6146 41951 552681 1397956

0.61 10089 77318 562770 1475274

0.62 4540 40076 567310 1515350

0.63 630 6584 567940 1521934

0.64 2465 30436 570405 1552370

 0.65 481 6320  570886  1558690

0.66 188 3641 571074 1562331

0.67 1061 27527 572135 1589858

0.68 1144 37072 573279 1626930

0.69 875 46935 574154 1673865

0.70 49 3007937 574203 4681802

Coverage Level

60 Percent

Production Discrete Discrete Cumulative Cumulative

Ratio Indem Liability Indem Liability

0.04 2330 2516 2330 2516

0.25 5083 8812 7413 11328

0.37 1530 4069 8943 15397

0.47 854 3978 9797 19375

0.58 174 4293 9971 23668

0.60 0 17750 9971 41418
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Adjustments to Indemnities 

The process of adjusting indemnities to a common coverage level depends on whether the 

actual coverage level is higher or lower than the common coverage level. Here we 

explain each of these cases.

Case 1 – Adjusting higher coverage levels down to the common coverage level 

The process of adjusting indemnities for higher coverage levels to the common coverage 

level is straightforward. Here we use the information in Table 3.1 to illustrate this 

process. At the 65% common coverage level, indemnities are paid only if the production 

ratio is 0.65 (with rounding) or below. Therefore, recorded indemnities for production 

ratios above 0.65 can be ignored. The adjustment of indemnities in this case is described 

by the RMA as the following five step process using the information in Table 3.1. 

 Step 1: Cumulative Indemnity (I@65) = $570,886 

Step 2: Cumulative Liability (L@65) = $1,558,690 

Step 3: Cumulative Liability re-stated at 65% (L65) = L@65 × 65/70 

= $1,447,355 

Step 4: Reduction in Liability = L@65 – L65 = $1,558,690 - $1,447,355 

= $111,335 

Step 5: Adjusted Indemnity (I65) = I@65 – Reduction in Liability

= $570,886 - $111,335 

= $459,551 

Steps 1 and 2 are just a statement of cumulative indemnities and cumulative liability at a 

production ratio of 0.65 (associated with the 65% common coverage level). Step 3 

converts the 70% coverage level cumulative liability at a production ratio of 0.65 to what 

that liability would be at the 65% common coverage level. In step 4, the reduction in 0.65 

production ratio cumulative liability resulting from conversion from the 70% to 65% 

coverage level is computed. In step 5 the adjusted 65% coverage level indemnity is 

computed by subtracting the reduction in liability (Step 4) from 0.65 production ratio 

cumulative indemnities (for the 70% coverage level) from step 1. 

Case 2 – Adjusting lower coverage levels up to the common coverage level 
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The process of adjusting indemnities for lower coverage levels to the common coverage 

level is complicated by the fact that detailed information on indemnities and liability for 

production ratios between the actual coverage level and the common coverage level is not 

available. Here we use the information in Table 3.2 to illustrate the process RMA follows 

in making these adjustments. 

A lower bound for the estimate of indemnities at the 65% common coverage level can be 

calculated as follows.

Min I65 = [L<60 × 65/60] – L<60 + I60 

Min I65 = [$23,668 × 65/60] - $23,668 + $9,971 

Min I65 = $1,972.33 + $9,971 

Min I65 = $11,943 

Here, L<60 is cumulative liability for the highest recorded production ratio that is less than

the actual coverage level (in our example from Table 3.2 a production ratio of 0.58). This 

is multiplied by the ratio of the common coverage level to the actual coverage level to 

obtain the liability for policies with production ratios less than the actual coverage level, 

when adjusted to the 65% common coverage level. The actual cumulative liability (L<60) 

is subtracted from this to obtain the change in liability associated with insuring at the 

65% common coverage level versus the actual coverage level of 60%. This change in 

liability is added to the cumulative indemnity at the 60% coverage level to obtain the 

lower-bound or minimum indemnity at the 65% coverage level. Note that this lowerbound or minimum estimate of indemnities at the common coverage would accurately 

reflect indemnities at this coverage level if there were no (non-observable) indemnities at 

production ratios between 0.60 and 0.65. 

An upper bound for the estimate of indemnities at the 65% common coverage level can 

be calculated as follows. 

Max I65 = [L60× 65/60] – L60 + I60 

Max I65 = [$41,418 × 65/60] - $41,418 + $9,971 

Max I65 = $3,451.50 + $9,971 

Max I65 = $13,423 
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In this case it is important to recognize that L60 is total insured liability (including that 

associated with production ratios below, at, or above the 60% coverage level). 

Multiplying this by the ratio of the common coverage level to the actual coverage level 

(65/60) obtains total liability at the common coverage level. Subtracting cumulative 

liability at the 60% coverage level (L60) obtains the change in total liability if all units had 

been insured at the 65% common coverage level rather than the actual 60% coverage 

level. Adding this change in total liability to cumulative indemnities at the 60% coverage 

level (I60) provides the maximum indemnity that could have resulted at the common 65% 

coverage level. Note that the implicit assumption underlying this calculation process is 

that all policies have production ratios less than the common coverage level. If this were 

true then this would be an exact conversion of indemnities from the 60% to 65% common 

coverage level. However, if some policies have production ratios greater than the 

common coverage level, then this process over estimates indemnities at the 65% common 

coverage level.

 

The adjusted indemnity estimate is obtained as follows.

)/()]()60/65()[( 65 65 60 LLMinII <60 60 <60 ×−−×−+= LILL 6060

I65 = $11,943 + [(($41,418 - $23,668) x (65/60)) – ($41,418 - $23,668)] x 

($9,971/$41,418) 

I65 = $11,943 + [$1,479 x 0.24074] 

I65 = $11,943 + $356 

I65 = $12,299 

The first term in this expression (MinI65) is the lower-bound or minimum indemnity 

calculated above. Remember that this indemnity estimate made adjustments for policies 

with production ratios less than the actual coverage level and no adjustments for policies 

with production ratios at or above the actual coverage level. The remainder of the 

expression estimates the indemnity adjustment for policies with production ratios at or 

above the actual coverage level. We believe this is best understood when the part of the 

expression to the right of MinI65 is rewritten as follows. 

 = 

The first term in the numerator of the fraction above is total liability at or above the 60% 

coverage level. This is multiplied by the adjustment factor for the 

difference in 60% coverage level liability and 65% coverage level liability. Thus, the 
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numerator is the difference between 60% coverage level liability and 65% common 

coverage level liability for policies that are not accounted for in the minimum indemnity 

adjustment MinI65. This is divided by total 60% coverage level liability (L60) to obtain the 

percentage change in total liability when liability for 60% or higher production ratios is

converted from the 60% coverage level to the 65% coverage level. This percentage 

change in total liability is multiplied by total 60% coverage level indemnities and then, as 

indicated above, added to MinI65 to obtain the adjusted indemnity estimate. What this 

process does is to directly compute the change in indemnity for units with production 

ratios below the actual coverage level, estimate the change in indemnity on units with 

production ratios at or above the actual coverage level, and sum the two to get the 

adjusted indemnity. The assumption underlying the estimate for policies with production 

ratios above the actual coverage level is that the percentage change in indemnity is equal 

to the computable percentage change in liability. 

 

3.2. Rate Making Concepts/Methods: Reference Yield Component

The RMA introduced the APH plan of insurance for corn, grain sorghum, peanuts and 

tobacco in 1986. Subsequently, individual yield coverage for a large number of crops, as 

well as the related CRC and RA revenue insurance products, have incorporated this basic 

product design. The RMA has identified two distinguishing features of this insurance 

product design: “1) the growers guarantee was based on their actual production history 

(APH) [and], 2) the growers premium rate was based on their actual production history 

(APH yield) relative to other growers in the same geographic area.” The latter 

relationship, between grower and regional yield, is a function of the ratio , where 

is the grower’s APH yield and is intended to represent the “center point of the yield 

range” for growers of the same crop type and practice in the rating area. This is referred 

to as the reference yield for the crop type and practice in the area. As will be seen in the 

next chapter, the reference yield plays an important role in determining the premium rate 

for an insured unit. Since 1986, a number of methods have been used to calculate and 

update reference yields. Currently, target reference yields are established using the 

following procedures as laid out in the “Rate Methodology Handbook: Actual Production 

History.” 

1. The transitional yield (t-yield) is multiplied by two factors to create limits of 

movement. 

2. If the reference yield was within the boundaries calculated in step 1, no change 

was made to the reference yield.

3. If the reference yield was outside the boundaries calculated in step 1, the 

reference yield was updated to equal the t-yield boundary. 
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Thus, when updated, target reference yields for a crop are calibrated to be within a 

specified range of the t-yield for the crop.6 

3.3. Rate Making Concepts/Methods: Coverage Level Rate Relativity Component 

Because of program changes over time, coverage levels available to producers have 

expanded dramatically over the loss experience used in rating by RMA. Prior to 1980 

only one coverage level was available – 65% coverage. The Federal Crop Insurance Act 

of 1980 added two additional coverage levels at 50% and 75% coverage. At that time 

RMA developed coverage level differentials that adjusted rates from the base 65% 

coverage to the other two coverage levels. By 1985, these relativities were largely fixed 

across crops and regions with 65% coverage rates set at 65% of the 75% coverage rate 

and the 50% coverage rate set to 47% of the 75% coverage rate. 

The 1996 Federal Agricultural Improvement and Reform (FAIR) Act modified crop 

insurance by specifying coverage levels would be offered from 50% to 75% coverage in 

5% increments. Then in 1998, 80% and 85% coverage levels were added for selected 

crops and regions. With these additions rate relativities were set as:

Table 3.3. Fixed Coverage Relativities Used Prior to the Current 

Variable Relativity Approach 

Coverage Level Rate Relativity

85% 1.60

80% 1.22

75% 1.00

70% 0.79

65% 0.65

60% 0.51

55% 0.47

 

Beginning in 1996 several crop revenue products were introduced. These new products 

were rated with alternative methodologies. As these products became increasingly 

popular differences in the way the alternative rating methodologies handled coverage 

relativities became obvious. This led RMA to reevaluate coverage level relativities. 

Through a series of studies beginning in 2002, RMA began implementing variable 

coverage relativities that are conditioned on the riskiness of the crop and endogenous risk 

factors that account for behavioral changes as the coverage level increases (deductible 

decreases). More detail on this process is provided in section 4.5. 

 

6 Transitional Yield (or t-yield) is defined as “An estimated yield provided in the Actuarial Table which is 

used in calculating average/approved APH yields when less than four years of actual, temporary, and/or 

assigned yields are available on a crop by county basis” (2007 Crop Insurance Handbook, p. 16). For 

additional information on Transitional Yields see Section 6E of the 2007 Crop Insurance Handbook.
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3.4 Target Rate Development Component

Once RMA has adjusted existing loss experience in the Statplan data development 

process, the actuarial branch begins a multi-step process to develop a target rate for a 

county/crop program. In effect, the target rate is the rate RMA believes should serve as 

the base upon which rates in a county are anchored. 

3.4.1 Capped Loss Cost 

RMA uses a catastrophic loading procedure to reduce the influence of outliers in the 

experience of a county/crop program. Because crop losses are often characterized by 

infrequent but severe losses, even several decades of county loss experience may be 

subject to sampling error. Catastrophic loading is an actuarial technique to mitigate the 

effect of sampling error when the true magnitude of sampling error is not known. 

Catastrophic loading is intended to remove anomalous experience from the county/crop 

data but not remove normal loss experience. In general, losses deemed catastrophic are 

spread across all counties for a crop in a state. Thus, the capping of loss cost experience 

in a county/crop program is not a load in the sense that it is an additional factor added to 

rates, but rather it redistributes loss experience within a state/crop program. 

The current RMA procedure censors the county loss experience at the 80th percentile of 

the historical county experience. No distributional assumptions are required for the 

procedure. To illustrate this, assume 30 years of data are available for the county/crop 

program. Then the 80th percentile of the loss cost is the 24th highest observed loss cost 

ratio (note when the percentile does not fall on a discrete observation, a linear 

interpolation is used). 

All observations with indemnities above the truncation point are aggregated to the 

state/crop program level. For a county, the catastrophic (CAT) indemnity is calculated as 

follows: 

The CAT indemnities are used in the catastrophic loading calculations described below 

and losses not removed into the cat indemnity are used in the calculating the county 

unloaded rate. 

3.4.2 Net Acres Insured 
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RMA uses credibility weighting to smooth rates among adjoining counties. Credibility in 

the RMA process is a function of net acres insured so a historical record of net acres 

insured is retained in the Statplan process.

3.4.3 County Unloaded Rate Determination

The county unloaded rate is a weighted combination of the county LCR after removal of 

the catastrophic (CAT) indemnity and the LCR of surrounding counties. Both LCRs are 

calculated after removal of the CAT indemnity. For many years RMA used weights of 

60% for the target county and 40% for the circle LCR. Milliman USA recommended 

refinements of this process that were adopted in 2006 which replaced the historical 

weights with a procedure which credibility weights the target county and surrounding 

counties based on a function of the acres underlying the target county’s data and the 

circle’s data. RMA now refers to the surrounding counties as the county group or 

credibility complement. A surrounding county is defined to be a county which corners or 

borders the target county. The exact process used by RMA follows a decision tree as 

follows: 

Step 1: 

Does the target county have at least six consecutive most recent years of data, at 

least 5 exposure units, and at least one non-zero indemnity observation after 

applying capping?

If the answer is yes, calculate the credibility weight using the Bühlmann method. 

If the answer is no, then ask; 

Step 2: 

Does the county group have at least six consecutive most recent years of data, at 

least 5 exposure units, and at least one non-zero indemnity observation after 

applying capping?

If the answer is yes then rate the target county using 0% credibility. 

If the answer is no rate the county subjectively. 

The following outlines the procedure for calculating the target rate using the Bühlmann

weights:

The variables in this equation are defined as: 
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R = County Unloaded Target Rate 

Z = Bühlmann credibility factor, ranging from 0 to 1 

X = The sample mean of an individual county to which credibility is applied (average of 

the adjusted capped LCRs for all available years of data for the county)

μ = The underlying mean (average of the adjusted capped LCRs for all available years 

of data for the entire county group) 

P = Exposure units (Total number of net acres for a given crop in the target county 

summed over all available crop years divided by the appropriateα) 

K = v/a where: v = Sample variance of the adjusted capped LCRs for all available years 

of data for the target county; and a = Sample variance of the Xs from the county group

The target rate is a weighted average of the county’s capped experience (X in the 

formula) and the experience of the county group as a whole (μ). A county’s “credibility” 

(Z) increases with the exposure underlying a county’s experience (P). It also increases if 

the variance in the year-to-year experience for the county (v) is low and/or if there is a lot 

of variance in the average experience among the counties in the county group (a). Thus 

credibility weighting adds additional information from the surrounding counties in cases 

where the individual county’s experience is either sparse (few exposure units) or highly 

variable. It also serves as a smoothing mechanism which reduces discrepancies between 

the rates of adjoining counties. 

3.4.4 State Catastrophic Load

Given the county CAT indemnities described in section 3.4.1, the state CAT load for a 

crop is calculated as:

However the state CAT load is limited to a maximum of .0325 and a minimum of .0065 

for all states and crops. 

3.4.5 County Catastrophic Load 

Because the state CAT load is capped at 0.0325 some state/crop programs have a county 

CAT load that reallocates any state excess above the 0.0325 cap back to each county 

proportional to each county’s adjusted liability. The purpose for the caps is to limit the 

amount of rate being shared across all counties when the counties may or may not be 

homogenous. Because the state CAT loading, up to 0.0325, is applied evenly to all 

counties regardless of the county’s indicated unloaded target rate, the state CAT load 

represents a larger proportion of the total rate in counties with better experience. 
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If the state CAT load is greater than 0.0325, the following calculations are performed to 

establish the target county’s County Cat Load. 

Then 

Finally 

Note this procedure spreads indemnities in proportion to the counties’ contribution to the 

total CAT indemnity (including the non-excess portion). Thus, counties with more CAT

losses receive a relatively greater portion of the load for excess catastrophe losses (if 

there is one). 

3.4.6 Miscellaneous Rate Loads 

As noted earlier losses due to certain causes are removed from the Statplan data and 

treated separately. There are three rate loads incorporated in the ratemaking process: 1) 

Prevented Planting rate load; 2) Replant rate load; and 3) Quality Adjustment rate load. 

However, not all crop policies include these coverage options. These rate loads are 

associated with losses that are in some fashion less directly related to yield loss. In the 

case of prevented planting the crop is never seeded. In the case of replant coverage one is 

really covered against the additional cost incurred to replant the crop. Quality losses 

reflect a reduced crop value rather than production. Also, some of the coverage options 

have been offered for a limited period. Prevented planting coverage was first offered for 

a limited set of crops in 1994. Quality adjustment language was added to a number of 

crop policies beginning with spring crops of crop year 2000. The procedures used to 

develop these loads are not well defined due to the inability to accurately identify quality 

losses in RMA data and the limited experience in other cases.

3.4.7 Disaster Reserve Factor

Legislative mandates require RMA to set rates that “cover anticipated losses and a 

reasonable reserve.” The disaster reserve is intended to meet the reasonable reserve 
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requirement of the program. RMA’s current disaster reserve factor is 0.88 for all crops 

and insurance plans. This factor is incorporated into rates by dividing the unloaded 

county rate and the county CAT load by 0.88. This results in a 13.6% increase in this 

portion of rates. RMA recently contracted with an outside actuarial firm to review the 

adequacy of the disaster reserve factor load. Based on that review, RMA has continued to 

use the 0.88 disaster reserve factor. 

3.4.8 Optional Unit Factor

Various aggregations of crop acreage are allowed in the APH program – optional, basic, 

enterprise, and whole-farm units. Spatial aggregation of crop acreage will have the 

tendency to reduce risk as less than perfectly positive correlation is typically observed. 

The data for Statplan rating are a mixture of these unit structures. However loss 

experience prior to 2009 is dominated by basic and optional unit experience. The 2008 

Farm Bill resulted in a change in subsidy that encourages enterprise unit coverage. Thus, 

future experience may include more of this experience. 

The basic unit structure is the reference unit format for rating. RMA loads the historical 

optional unit experience by the discount for selecting basic units over optional units. For 

many years the multiplicative discount factor for basic units has been set at 10%. RMA 

recently contracted for an external review of the appropriate discount and unit structure. 

The study concluded that the current unit structure does not need to be changed. 

However, the current basic unit discount of 10% could be improved by tailoring it to vary 

across crops, regions, and unit structures (including enterprise units). The FCIC Board 

has approved adoption of the study recommendations. RMA planned to implement the 

variable unit discount but implementation has been suspended while awaiting the RMA’s 

eWA system redesign. More details of unit rate adjustments are provided in section 4.6. 

3.4.9 County Target Rate 

The county target rates bring together the various computations discussed in this chapter. 

In general there are two primary components of the county target rate: 1) the variable rate 

portion and 2) the fixed rate portion. The distinction is that the variable rate portions can 

differ based on the insured unit’s approved yield while the fixed portion of the rate 

remains constant across all approved yields. In chapter 4, the formulas and process by 

which rates are quoted for a particular unit given the county target rate will be described 

in detail. 

3.4.10 Judgment Target Rate

The formula-driven rating process described in this chapter is applied to thousands of 

county/crop programs. To maintain rating accuracy, RMA conducts a review of and 
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sometimes intervenes in setting the rates. This is most likely in cases where county 

experience is limited or the county experience does not reflect expected yields. This is 

most likely when county data lack credibility. County target rates that are judged to be 

not credible undergo a mandatory review by the AB and Regional Underwriting Office. 

3.5. Mandated Capping Requirements Component

By legislative mandate, RMA rate changes are capped at 20% compared to what the 

insured would have paid the previous year for the same coverage. However, premium 

decreases can exceed 20% to allow the base rate to reach the target base premium rate 

within three years. To summarize the capping procedure, consider first the formula of the 

Base Producer Rate (BPR).

Note that ratio of APH yield to the reference yield is bounded between 0.5 and 1.5. To 

assess the rate change RMA compares two producer rates – an initial rate and a target 

rate. Four factors may induce a rate change in this calculation: 1) the reference yield, 2) 

reference rate, 3) fixed loads, and 4) coverage differential. The total effects of these 

factors are compared through the calculation of these two equations. 

Where the subscript “Int” reflects the base period value and the subscript “Tgt” indicates 

the target value. Caps on rate changes are then applied using the following equations. 

Where ΔBRP65 is the percent change in the base premium rate at the 65% coverage level.
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3.6. APH Business Process and Critical Control Component 

RMA produces rates for several thousand county/crop programs each year. In many 

cases there are multiple insurance designs for a particular county/crop program. To 

maintain quality, RMA defines a strict filing schedule and maintains critical control 

points as well. Specifically, there are a series of reviews and multiple inspections. 

RMA’s actuarial branch periodically reviews and updates documented support for all 

factors in the rate determination process. Typically rating factors are reviewed every 3 

years unless the underlying study suggests a longer interval for review. 
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4.0 Adjustments from County-Level Target Rates to Individual Rates

4.1. Introduction

The discussion in the previous sections describes the process by which target rates at the 

crop/county level are derived. At the county level and without adjusting for crop type and 

cropping practice, target rates can be characterized as follows:

(Eq. 4.1) Target Rate = 

ULR+CntyCAT

ResFac PP+RP+QA+StCAT UnitFac UnitFac

            +    

 

 

where: ULR = County Unloaded Rate, CntyCAT = County Catastrophic Rate Load, 

ResFac = Disaster Reserve Factor (0.88), PP = Prevented Planting Rate Load, RP = 

Replant Rate Load, QA = Quality Adjustment Rate Load, StCAT = State Catastrophic 

Rate Load, and UnitFac = Unit Division Factor (here a factor of 0.90 is used).

In order to “individualize” the target rate above, the county unloaded rate (ULR) is first 

multiplied by the yield ratio (Ry) raised to a negative exponent (-E). The yield ratio is 

defined as the ratio of an individual farmer’s rate yield to the reference yield for the crop 

type and practice in the county (Ry= individual rate yield/reference yield). The rate is 

further “individualized” to the particular crop type and practice of the farmer by 

multiplying the left hand term by a type/practice factor (TpFactor). Lastly, the individual 

producer’s choice of coverage level is taken into account by utilizing a coverage level 

differential (CLD) and the rate is adjusted by the appropriate unit structure factor 

(UnitFac). The preceding adjustments can be expressed as follows:

(Eq. 4.2) Target Rate for Individual = 

( ( ) ) -E ULR Ry +CntyCAT

ResFac

PP+RP+QA+StCAT TpFactor CLD

UnitFac UnitFac

        ×        

        × +   ×    

     

. 

Given the formulation in equation 2, the rationale for each adjustment used to 

individualize rates (i.e. the yield ratio (Ry), the exponent (E), the type/practice factor 

(TpFactor), the coverage level differential (CLD) and the unit division factor (UnitFac)) 
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are discussed below. The current methods for calculating these adjustment factors are 

described as well.

4.2. Yield Ratio

The yield ratio (Ry) is a mechanism to reflect the heterogeneity of risks at the insured 

unit level. The use of Ry implies that an individual’s premium rate can be reasonably 

based on the magnitude of his/her own historical rate yield relative to the county 

reference yield. The individual rate yield is calculated based on a 4-10 year individual 

yield history reported by the producer when he/she signs up for crop insurance. The 

reference yield is based on the average of county yield estimates from the National 

Agricultural Statistics Service (NASS). 

Because the ULR is based on county-level data, the implicit assumption when using Ry 

in equation 4.2 is that the county unloaded rate reflects the rate for an individual producer 

at or near the historical county average yield. The yield ratio allows for having different 

premium rates that depend on whether the individual yield experience is near or farther 

away from the mean county yield experience. As will be explained further below, using 

the yield ratio with a negative exponent means that individual farmers with rate yield 

higher than the NASS county mean yield will have lower rates (and vice-versa).

The implicit assumption about the “congruence” between reference yield and the county 

unloaded rate is important to the accuracy of the estimated premium rates. However, the 

reference yield is calculated based on county-level NASS data that include experience 

from both insured and uninsured farmers. On the other hand, the county unloaded rate is 

primarily based on county-level loss cost experience of only the insured pool of 

producers. This means that two separate bodies of data are used to calculate the reference 

yield and the reference rate. This practice is only valid when the following implicit 

assumption holds – the NASS county average yield (calculated from both insured and 

uninsured producers) is roughly equivalent to the long term average yield of the insured 

pool of producers from which the reference rate is derived. However, if there is an 

inconsistency in this assumption (i.e. the average yield of the insured pool is different 

from the producers in the NASS insured/non-insured pool), then the risk of loss is not 

accurately reflected in the premium rates.

Furthermore, it should be noted that reference yields used in the past have not been 

regularly updated, although in recent years the RMA has implemented a process to 

update reference yields.7

 

7 According to the Rate Methodology Handbook, reference yields for a particular crop (in all counties) are 

only updated when there is a target rate review for the crop. In this updating procedure, the reference yield 

is updated if the current reference yield is outside a specified range of the transitional yield.

It is also important to note that NASS data are not available for 

many crops, crop types, and cropping practices for which crop insurance contracts are 

offered. This necessitates use of proxy measures to obtain the reference yield parameter. 
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Using dated and proxy reference yields may also adversely impact the extent to which 

premium rates accurately reflect the risk of loss.

In light of these issues, RMA-commissioned a study, completed in 2006, which 

thoroughly examined the reference yield methodology and recommended a reference 

yield calculation methodology that uses rate yields from RMA experience data. The study 

emphasized the importance of regularly updating reference yields to avoid actuarial 

shortcomings. In addition, methods for updating reference yields in counties with thin 

data were developed.

4.3. Yield Ratio Curve and Exponent 

The yield ratio curve, based on the negative exponent in equation 4.2, is part of a 

mechanism to individualize the county-level unloaded rate to reflect differences in 

expected loss costs for insured units depending on the relationship between the individual 

rate yield and the reference yield. As discussed above, this approach essentially implies 

that premium rates should be inversely related to individual average (rate) yields. That is, 

farmers with higher yields relative to the county have lower rates and those with yields 

lower than the county have higher rates.

As Milliman and Robertson (2000, p. 33) point out, the rationale for using this approach 

stems from RMA research that demonstrated that “on average, the probability of a loss is 

greater for producers with a yield lower than the average for an area and vice versa [for 

producers with yields higher than the area average].” This finding indicates that as an 

individual’s mean yield increases relative to the county average; their proportional yield 

variability decreases such that it lowers the likelihood of an indemnified loss.8 Hence, 

premium rates are structured to decline with increases in individual rate yields.

Currently, however, RMA has not established a method to update the exponents that 

determine the shape of the yield ratio curve. Based on an internal 2008 memo referring to 

a commissioned study about the exponents, the exponents currently in use appear to have 

been created to fit the yield spans used prior to implementation of the continuous rating 

system in 2001.9

 

8 These findings were also supported by work of Skees and Reed (1986) and Goodwin (1994). Although it 

should be noted that Goodwin (1994) found the relationship between mean yield and relative yield 

variability to be “tenuous”, with considerable variation among farms. Based on this, Goodwin (1994) 

recommended incorporation of observed farm-level variation or other observable risk characteristics of the 

farm into the rating process.

 In that same memo, it was determined that sampling variability in the 

limited sample (4-10 years) used to determine the individual rate yield also plays a major 

role in the relationship between average yields and premium rates. This sampling 

variability makes it empirically possible to have a positive estimated exponent parameter. 

This finding was used to explain the extreme values of exponents estimated in an earlier 

9

 Prior to the continuous rating system, discrete “yield span” classifications were defined for specified 

ranges of Ry and premium rates discretely declined for higher yield span classifications.
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2007 RMA-contracted study. This study estimated the exponents using OLS regression of 

a loss cost equation with a “linear in parameters” specification (i.e. log-transformed). 

Given the perceived shortcomings of the 2007 study the aforementioned 2008 memo 

developed and recommended an alternative estimation approach using nonlinear least 

squares (NLS) regression. This approach was found to have a narrower range of 

estimated exponents than the estimates from the earlier 2007 study. And the resulting 

exponents from the NLS regression approach were significantly lower (resulting in a 

flatter yield ratio curve) than the current exponents and the exponents from the 2007 

study. It was noted in the memo that the smaller exponent estimates may be due to the 

preponderance of zero loss cost ratios in the data. 

4.4. Type-Practice Factors

The target rate in equation 4.1 uses insurance experience data aggregated for all crop 

types and cultural farming practices at the county level. However, different crop 

type/practice combinations often have different risk characteristics. These differences

need to be addressed in order to develop appropriate rates for each crop type/practice 

combination. 

The current RMA rating procedure for type/practice is described in an internal document 

titled “RMA Type/Practice Rating Methodology Interim Underwriting Guidelines”. In 

the current system, crop type/practice is accounted for by multiplying the variable rate 

component of the county target rate by a type/practice factor (TpFactor) for each 

type/practice combination. In constructing these type/practice factors the RMA uses 

experience at a multi-county level, at the state level, or at a multi-state level. Deciding on 

the proper level of aggregation for any crop and region is a matter of balancing two 

primary considerations: homogeneity of risks and volume of data. The risks associated 

with each crop type/practice are more homogeneous at a disaggregate level; however, 

aggregation provides a greater volume of data. The approach taken to address this 

problem varies by region. For example, in deriving the TpFactors for irrigated and nonirrigated practices in the Western States, grouping a smaller number of geographically 

clustered counties within the state is more typical since the average rainfall (and the 

importance of irrigation) changes significantly over shorter distances. In contrast, the 

Eastern States have rainfall patterns that are more stable across greater distances and 

grouping more counties, whole states or multiple states may be appropriate. 

Determination of the county groupings for use in developing type/practice factors is 

largely left to the subjective judgment of the RMA regional offices. A credibility 

procedure similar to that used to determine the unloaded county target rate could be used 

to weight the experience of smaller aggregations against larger group data. Such a 

procedure would automatically give more weight to an indicated differential that is 

different from the neighboring experience provided that it is consistent across time and 

supported by a sufficient volume of experience data. We also note that, in many cases 

where one practice is rare, there may be no difference in the rates, perhaps because the 
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local experience is of insufficient volume to be credible. In that case, it would be 

appropriate to use more aggregate data to determine an appropriate differential. 

Once the level of aggregation for a particular region has been determined, the TpFactors 

are calculated by dividing the type/practice-specific loss cost ratios (LCR) for the region 

by the aggregated regional LCRs derived over all crop type/practices. Units with “mixed” 

types or practices are not used in the TpFactor calculation. The reason for this is that 

when an insured unit contains multiple crop type/practices it is not possible to segregate 

losses by type/practice. While this procedure involves discarding data it also avoids the 

introduction of data on which the type/practice effect cannot be accurately measured.

The regional factor calculated in the previous step is referred to as the “raw” factor. The 

final step in calculating the TpFactors for a county is adjusting the raw regional factor for 

the historical liability proportion for each type/practice combination in the county. Here 

we provide a description of this process for developing TpFactors to support our 

recommendations in chapter 6. To be clear about when regional versus county data are 

used, we subscript variables with R when they are measured at the regional level and C

when measured at the county level. Further, in this example we consider a region and 

county with two type/practice combinations, subscripted by 1 and 2. 

Step 1: Calculate regional simple average loss cost ratio over a period of N years for each 

type/practice and for all type/practices combined (here we use the subscript T for total).

(Eq. 4.3) , , 

 . 

Step 2: Calculate raw regional type/practice factors.

(Eq. 4.4) , . 

Step 3: Calculate , which is the county liability-weighted average of 

 

 and . Here for county C and 

type/practices . 

(Eq. 4.5) 

 . 

Step 4: Calculate the final county TpFactors for type/practices . 
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(Eq. 4.6) = 

 = . 

It is instructive to consider the total amount of premium that would be charged in a 

county using these TpFactors. Here we assume that the TpFactors are applied to the 

simple average loss cost ratio for the county ( ), which is calculated by averaging 

the individual loss cost ratios, across all practices, for the county for the N year time 

period. 

(Eq. 4.7) 

 . 

(Eq. 4.8) 

 

 

 

 

 

 . 

Equations 4.7 and 4.8 show that the process followed by the RMA in deriving the 

TpFactors for a county involves use of regional loss cost ratio relationships and county 

liability weights to derive county type/practice factors which collect the same amount of 

premium as would be collected if the simple average loss cost ratio for the county 

(combining all types/practices) were multiplied by total county insured liability. In our 

opinion the RMA’s process for using the derived TpFactors to distribute the premium 

among types and practices is reasonable. However, as discussed above and shown in 

equation 4.2 these TpFactors are only applied to a portion of the county premium rate 

(i.e., only to a proportion of the historical county loss cost ratio). The derivation above 

demonstrates that in order to maintain the indicated type/factor differentials the 

TpFactors would need to be applied to other portions of the premium rate. This issue is 

discussed further in Chapter 6. 
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4.5. Coverage Level Differentials

The county-level rate in equation 4.1 is derived at the 65% coverage level. The individual 

rates at different coverage levels in equation 2 are then calculated by scaling the 65% rate 

using coverage level differentials (or coverage level rate relativities). Prior to 2004, fixed 

coverage level differentials were used to adjust the county level rate that is assumed to be 

accurate at the 65% coverage level. This means that the coverage rate differentials were 

the same regardless whether the county level rate in equation 4.1 was 0.01 or 0.50; or 

whether the crop was corn or soybeans. 

However, the findings and recommendations of a RMA-commissioned study in 2002 led 

to the adoption of variable coverage level differentials which depend on the crop and 

county-level base rate.10 Specifically, this study found that the coverage level 

differentials should decrease as the base rate increases. The logic is as follows. Consider 

two regions: one a high risk region where the probability of an indemnity at the 65% 

coverage level is high and one a low risk region where the probability of an indemnity is 

low at the 65% coverage level. Because there is more probability of a yield below the 

guarantee for the high risk region there must be less probability of getting a yield above

the guarantee. As a result, when we move from the 65% coverage level to the 85% 

coverage level, the rate will tend to increase more for the low risk region because the 

probability of a yield between 65% and the 85% coverage level will tend to be higher 

than that of the high risk region. Therefore, the ratio of the 85% premium rate to the 65% 

premium rate for the low risk region will tend to be higher than that for the high risk 

region. 

The variable coverage level differentials are derived using a robust median regression 

method to estimate a model specification where the implied coverage level differential 

(from unit level historical loss cost data) is a function of the following: coverage level 

(and its squared term), the county-level base rate at the 65% coverage level (and its 

squared term), and an interaction between the coverage level and the county-level rate at 

the 65% coverage level: 

(Eq. 4.9) 2 Coverage Level Differential = coverage level+ coverage level β β 0 1 + × β1 ×

 2

4 65 5 65 6 65 βββ × +× +× × rate rate coverage level rate . 

Estimated parameters from this model, with actual coverage level differentials calculated 

from the historical unit level yield experience, underpin the development of coverage 

level differentials that vary with base rates and that are tailored to the loss experience for 

different crops.

 

10 The recommendation of moving to a variable coverage level differential was also supported by the article 

of Babcock, Hart, and Hayes (2004).
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Another insight emerging from the RMA-commissioned study in 2002 is the need to 

account for endogenous risk changes associated with insuring at higher coverage levels 

(i.e. lower deductibles). It is a widely accepted that economic incentives to produce are 

reduced at higher coverage levels. The challenge is to determine the magnitude of this 

effect at different coverage levels and incorporate this in the coverage level differential 

above (i.e. the endogenous risk is not factored in the estimation of equation 4.9). The 

endogenous risk behavior is incorporated in the coverage level differential by calculating 

an endogenous risk factor using historical loss cost data at a specific coverage level and 

comparing it to an “implied” loss cost used in estimating equation 4.9.

Note that the variable coverage level differentials, including the endogenous risk factor, 

have been incorporated in the rates for corn, soybeans, and wheat and some other crops. 

By 2009, it is expected that most continuously rated crops will move to the variable 

coverage level differential approach.

4.6. Unit Division Factor

The federal crop insurance program provides coverage for different “unit” formats or 

structures. Each parcel of land that is insured independently of other parcels is called a 

“unit”. Currently, there are four unit structure options available: optional, basic, 

enterprise, and whole-farm. The first three unit structures are available for APH yield 

insurance, CRC, and RA, while the whole-farm unit is only available for RA. Basic units 

consist of all acreage of the crop in a county held by the insured under identical 

ownership. Optional units are subdivided basic units, with the subdivision based on 

location (typically by separate sections) and production practices. Enterprise units reflect 

a higher level of aggregation, combining all of a producer's financial interest in a crop in 

a county. A whole-farm unit is at an even higher level of aggregation, combining all of 

the insured acreage for two or three crops in a county. 

A unit division factor (UnitFac) is used to reflect the differences in risk (or loss 

experience) among the different unit formats. Loss cost experience in a county is 

implicitly assumed to reflect basic unit experience. These loss cost ratios are factored up 

by a UnitFac of 0.9 in equations 4.1 above to convert the county target rates to the 

optional unit level. A 10% discount is then given for units insured at the basic unit level. 

Where enterprise units are available under APH and CRC, the discount depends on the 

total acres in the aggregated enterprise unit. For RA, enterprise unit discounts are based 

on the number of separate sections or section equivalents contained in the unit. The 

multiplicative unit division factors contained in the actuarial documents can be viewed as 

discount factors for insuring at a higher level of aggregation than the optional unit level. 

The actuarial logic for the unit division factor (or unit discount) is based on the risk 

reducing effect of insuring at a higher level of aggregation. The reduction in overall risk 

associated with units that are combined rather than having those units separately insured 
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arises from an aggregation effect that is similar to the risk reduction implied by standard 

portfolio theory. Aggregation of individual risks (insurance units in this case) that are not 

perfectly correlated reduces overall (portfolio) risk. This notion is related to the theory 

underlying farm portfolio selection, where the number of farm enterprises affects overall 

farm level risk by reducing non-systemic or diversifiable risk (see Turvey, Driver, and 

Baker 1988, for example). In farm portfolio selection, the producer allocates portions of 

acreage to different enterprises with less than perfectly positively correlated yields, which 

results in lower overall risk. As more subunits are aggregated to form the “unit portfolio” 

to be insured, the level of risk and the premium associated with the aggregated unit 

should decline. 

However, the use of a constant proportional discount of 0.9 when insuring basic units 

implicitly assumes that the difference in risk (or loss experience) between these two unit 

formats is fixed across different unit characteristics (e.g. unit size), across crops, and 

across regions. Previous studies have shown that, in general, loss experience for optional 

units is higher than for basic units, and that the overall difference was not significantly 

different from the current 10% fixed differential (See Knight and Coble, 1999; Schurle, 

1996). If a fixed discount is to be used, then on average the current 10% basic unit 

discount is reasonably consistent with loss experience. 

Notwithstanding the finding above, a 2004 RMA commissioned unit structure study 

recommended that a variable (rather than fixed) unit discount approach would be a more 

appropriate procedure when rating different unit formats. The study results indicated that 

the magnitude of appropriate discounts differs among crops and is strongly affected by 

the coverage level chosen by the producer. The appropriate discount also depends upon 

other characteristics of the insurance policy including the total number of acres in the 

aggregated unit and the average premium rate for the aggregated unit. These factors are 

easily derived from information that is currently collected on every insurance policy. In 

light of these findings, the Board of Directors of the FCIC has approved adoption of the 

variable unit discount approach but the discounts have not yet been implemented. 
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5.0 Assessment of Alternative Procedures to Develop APH Yield Rates

5.1 Objectives of RMA Rating

As we begin our critique of the RMA actuarial system, it is helpful to revisit the objective 

of the rating system. Legally, RMA has a clear actuarial objective defined in terms of a 

loss ratio. The Federal Crop Insurance Act (as amended February 17, 2009) defines the 

loss ratio as follows: 

LOSS RATIO.—The term ‘‘loss ratio’’ means the ratio of all 

sums paid by the Corporation as indemnities under any 

eligible crop insurance policy to that portion of the 

premium designated for anticipated losses and a 

reasonable reserve, other than that portion of the premium 

designated for operating and administrative expenses.

This legal definition speaks of anticipated losses rather than past or current losses. It also 

defines the premium as the premium designated for loss expenses and a reasonable 

reserve. However, operating expenses and administrative expenses are not included in the 

loss ratio calculation. Also, while not explicitly stated, crop insurance subsidies are 

separately computed and the premium used in this loss ratio calculation is the total 

premium associated with the policies sold. Thus, this definition implies RMA must assess 

actuarial soundness by comparing expected future losses to premium dollars that will be 

collected. 

The Federal Crop Insurance Act goes on to specify an explicit target for the projected 

loss ratio. 

PROJECTED LOSS RATIO.—The Corporation shall take 

such actions, including the establishment of 

adequate premiums, as are necessary to improve 

the actuarial soundness of Federal multiperil crop 

insurance made available under this subtitle to 

achieve an overall projected loss ratio of not 

greater than 1.0. 

As mentioned earlier, for many years RMA was mandated to achieve an overall loss 

ratio of 1.075. However, the Food, Conservation, and Energy Act of 2008 revised the 

target loss ratio to 1.0, which created a budget savings but also put increased pressure on 

RMA to ensure actuarial soundness. The implication of these targets for RMA actuaries 

is to mandate that the program should collect premiums sufficient to cover indemnities. 

However, RMA is also directed in the Federal Crop Insurance Act to, “...promote the 

national welfare by improving the economic stability of agriculture through a sound 

system of crop insurance ...” Thus, RMA must balance making a program broadly 
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available to producers, which improves national welfare, while still maintaining 

actuarial soundness. 

Note also that the legislative mandates do not specify the level at which the targets must 

be achieved. It is a valid question whether the mandates must be maintained at the 

national level, crop level, or at a less aggregate level. RMA is to make a compelling 

case that rates charged will be sufficient to cover future losses. Ideally, this challenge 

would be achieved by meeting actuarial targets at every level of the program down to 

the policy level. However, when the legislation speaks of “anticipated losses” or 

“projected loss ratios” a context of mathematical expectations is implied. Therefore, it 

is an increasingly demanding goal to achieve actuarial targets at more disaggregate

levels and it is more difficult to assess whether targets are met. This is true of all 

insurance designs and not unique to crop insurance. To put this in context, RMA 

insured over 2.4 million APH, RA, and CRC insured units in 2009 (September 2009 

RMA Summary of Business Reports). Since the rate for each of these units is in effect a 

mathematical expectation of the loss cost ratio for that unit, then one has the opportunity 

to evaluate the accuracy of rates on each insured unit. However, only one random 

outcome for each unit is observed in 2009. This is valuable information, but completely 

inadequate to draw a statistically valid conclusion. Statistical confidence in assessing 

rates can only be achieved with a sufficiently large sample. 

Another way of looking at the target achievement level is to note that if it were possible 

to accurately predict the experience for every producer every year then every producer 

could be charged premiums exactly equal to the producer’s losses – completely 

eliminating the insurance. Insurance must always be provided on a pooled basis, where 

total premiums match collected losses, recognizing that most insureds in most years will 

not experience a loss greater than the premiums paid. Rate adequacy can and should be 

determined for the system as a whole. Adequacy at this level ensures that the system is 

financially sound. In addition, in order to avoid having one group subsidize coverage 

for another, it is necessary to identify groups of sufficiently similar risks so that the 

long-term average (“expected”) losses for everyone in each group are about the same. 

5.2 Context of RMA Rating 

Given the objective mandated for the Federal crop insurance program, our review team 

has evaluated the specific nature of the rating challenges confronting RMA. We find 

seven features of the portfolio of crop yields that RMA insures to be critical determinants 

of how RMA should approach rating 

Nature of the Risks Insured 

Crop yields are subject to a variety of risks: frost, floods, drought, extreme temperatures, 

hail, disease, insects, and other perils. The products reviewed in this report are multiperil coverage designs such that a reduction in yield (or revenue) is measured without 
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specifically determining how much each peril contributed to the loss. This is in part due 

to the fact that crop yields are the result of the interactions among several factors. For 

example, this makes it quite difficult to sort out how much of a yield loss is due to high 

temperatures versus inadequate rainfall. 

Because so many of the risks driving yield losses are related to environmental conditions, 

assessing crop insurance yield risk probabilities is predicated on an accurate assessment 

of the probability of these events. In effect, at least with respect to the systemic 

component of crop yield risk, it may be argued that we only obtain one empirical 

observation for each crop in a county each year. This creates an inherent tension in 

rating. Actuaries would clearly like to have a long time series of experience for rating 

purposes in order to accurately determine the probability of historic loss events. For 

example, a long time series of data would be required to reliably estimate the frequency 

of occurrence of a flood such as the one observed in the Midwest in 1993. Conversely, 

because crop production systems have changed and the crop insurance program itself has 

changed, older loss experience is likely to be less reflective of the current program than 

more recent experience. 

Correlation of Losses

Many loss events are geographically correlated across broad regions. A drought often 

spreads across several states. A flood may affect low-lying farms in several states. 

Conversely, hail losses tend to be localized – a characteristic amenable to insurance 

which is a reason private hail-insurance coverage has been successfully offered for 

decades. The multiple-peril yield insurance policy shares at least some of the 

characteristics of coastal property insurance where weather is a driving factor and a high 

percentage of liability may be indemnified in the same period. This characteristic makes 

rating more difficult than if losses were truly independent. Much more would be learned 

from a short time-series of experience with uncorrelated risks. For example, automobile 

collision rates might be credible if based on only a few years of data because losses are 

effectively independent. 

Broad Availability

In administering a government-funded insurance program, RMA is tasked to make 

insurance broadly available. This may take on various forms and has a number of 

implications. By making insurance broadly available, RMA is at times asked to insure 

crops in regions where production is quite thin. For example, 25 percent of counties 

where RMA sold corn insurance (APH, CRC, and RA) had less than 1100 acres insured 

in 2008. Further, while the preponderance of the RMA program business (83% of 

insured acres) is in the “big four” crops (corn, soybeans, wheat, and cotton) RMA also 

offers yield insurance on 105 other crop programs. In many cases these crops are widely 

dispersed with heterogeneous growing conditions such that aggregation of experience 

across regions is questionable. Thin data regions and crops pose particular actuarial 

challenges. Private companies would likely avoid the most difficult of these cases 

because the potential volume of business would not support the cost of program 
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administration and due to the difficulties in developing valid rates. As a government 

agency, RMA does not attempt to maximize profit. In fact, the agency operates under a 

political imperative to extend coverage to regions where the potential volume of business 

is low and accurate rate development is challenging. 

Heterogeneity of Risks 

For a variety of reasons producers within a very small region or county may or may not 

have similar risk levels. While weather events in a county may be highly correlated, 

other factors may influence losses as well. Soil quality, slope, and elevation can strongly 

affect production risk. Management choices and production systems unique to the farm 

may also influence yield risk. For example, investment in costly irrigation systems may 

greatly reduce yield risk. Similarly, producers with superior management skills may be 

significantly less risky than a neighbor. Thus, while there is often a benefit to combining 

experience from nearby producers, there is often a significant influence of individualspecific risk characteristics on actuarially fair rates. 

Available Producer Experience

Another aspect of making crop insurance broadly available is the amount of data 

available for an individual insured unit. From an actuarial perspective it would be 

desirable to have a long time series of yields from an insured unit to assess risk. 

However, due to crop rotations, changes in cropping patterns, and other factors, RMA 

frequently insures units with 4 or fewer historical yields. This challenges the rating 

system even when a significant amount of information from surrounding farms is 

available. 

Perils Covered

We also note that the terms of coverage Congress has mandated for RMA include several 

protections that go beyond what would normally be considered as a part of the yield risk 

on a crop. For example, RMA coverage includes protection against prevented planting, 

replanting cost, and quality losses – none of which is usually reflected in yields that are 

measured and reported on a per planted acre basis.

Voluntary Coverage

The purchase of crop insurance is a decision made by each producer. There is no 

mandate, and producers must weigh the cost of coverage against their perception of the 

potential for loss. Rates that do not accurately reflect relative risk result in adverse 

selection. That is, potential insureds who are offered rates that are too high opt out of the 

program while potential insureds whose rates are too low purchase coverage at prices that 

do not cover their losses. The result is an entire system where total premiums collected 

are lower than the covered losses. It is essential, then, that rates reflect expected 

experience. 
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5.3 Assessment of Alternative Rating Approaches

Given the context in which RMA must set rates, we now turn to assessing alternative 

rating systems. While the legislative mandate to RMA states that actuarial soundness is to 

be measured in terms of anticipated losses and projected loss ratios, these outcomes must 

be evaluated with data available in the current period. This is a common forecasting 

challenge and is encountered in private insurance as well as in the crop insurance 

program. Historical experience is frequently used, but often it must be adjusted to 

account for program changes or structural changes over time. Various statistical 

techniques can be used and will be discussed in our further review of RMA rates. 

5.3.1 The Use of Unadjusted Loss Ratios

Because the RMA actuarial standard is defined in terms of a projected loss ratio, program 

actuarial soundness is often assessed by computing a historical loss ratio. While 

sometime suggestive of performance, a simple loss ratio is a crude way to assess program 

performance with historical data. By unadjusted loss ratios we refer to computed values 

based on aggregated data (such as those available from the summary of business reports 

available from RMA). Typically the indemnities and premiums for some crop/geographic 

area are summed across some period of years and the loss ratio computed. 

Mathematically this may be written as

(Eq. 5.1) 

Where the subscripts R reflects region (e.g. state or nation); C indicates county; and the 

subscript T indicates time period. For a variety of reasons the use of unadjusted historical 

loss ratios can lead to erroneous conclusions. Some of the most obvious reasons are as 

follows. 

The unadjusted loss ratio approach implicitly applies the premium rates of the past to the 

future. In many cases we know that rates have evolved through time and may now be 

substantially lower or higher than they were at the time of a historical loss. It is clear that 

loss ratio experience does not predict future losses if rate changes over time are not 

recognized. 

Because of the nature of the risks involved, short time periods are likely to be misleading. 

Yield and price risk both tend to be characterized by infrequent but large shocks which 

drive the actuarially fair rate. If crop insurance insured a risk like automobile collision a 

short historical experience base might be sufficient. However, crop insurance losses in 

many cases are much more similar to coastal property insurance which requires 

sophisticated modeling of weather probabilities. This is especially true for low risk 

regions/crops or low coverage levels. Thus, a loss ratio computed over a few years can 

lead to grossly erroneous conclusions regarding actuarial soundness and should not be 

relied upon. 
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Crop insurance program participation has grown dramatically over time. This results in 

relatively more weight to recent years when loss ratios are computed as in equation 5.1, 

which implicitly weights each year’s loss ratio by premiums paid in the year. It could be 

argued that recent experience is more relevant and that recent experience may be more 

credible because more farms are participating. For example, the Iowa corn program 

earned $90.4 million of premium in 1997 but almost $575 million in 2008. This means 

the 2008 experience is associated with 6.35 times more premium. However, we doubt 

that anyone would argue that the weather in 2008 should be given 6.35 times more 

weight than the weather in 1997. 

A number of policy attributes can profoundly alter the risk distribution of crop insurance 

experience. This can imply the unadjusted loss experience is no longer representative of 

the current or future loss expectations. Some county/crop programs have experienced a 

significant shift in the mix of types or practices. An example would be a county where 

irrigation has increased or decreased. Similarly, coverage levels have tended to increase 

in recent years. Also, the crop insurance program has experienced significant expansion 

of insurance plans since the advent of revenue insurance in 1996. 

The issues raised here clearly show that simple historical loss ratios cannot be used to 

support strong conclusions about future loss expectations. Fundamentally, short loss 

ratio series can grossly misrepresent expected loss ratios as they suffer from the error 

resulting from a limited sample of weather and price shocks. Conversely a longer loss 

ratio series will increasingly be subject to criticism for failing to account for program 

changes over time. Ultimately, valid actuarial assessment of loss experience will 

combine a sufficiently large sample to accurately reflect random events with reasonable 

adjustments to account for program modifications. 

To illustrate the effect of using a short time series of loss experience to evaluate the crop 

insurance program, we conducted a stochastic simulation which allowed us to define a 

typical crop insurance program where the true underlying probabilities, and thus the 

actuarially fair premium rate, are known. We then evaluate the observed loss ratios 

realized with premium rates that are known to be actuarially fair. We conduct this 

simulation using the Anderson, Harri, and Coble (2009) multivariate random simulation 

technique. This technique allows for multiple random variables to be produced with 

known correlation between the random variables and mixed marginal distributions. 

This simulation is quite simple in that we generate a yield distribution from a beta 

parametric distribution with shape parameter alpha equal to 3 and shape parameter beta 

equal to 2. The upper bound and range of the beta distribution is from zero to 120 

bushels per acre. In this simulation analysis we also specify the error term or the random 

variability of this crop to be centered on a mean of zero, but have a negatively skewed tail 

as one would expect in a highly productive cropping region which suffers infrequent but 

severe losses. The simulation also assumes that there is a trend in yield of 1.5 bushels per 

acre per year and that the starting expected yield at the beginning of the time series is 75 

bushels per acres. The simulation is carried out for a number of years and assumes that 
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the yield variability for this crop is directly proportional to the mean yield. What results 

is a random deviation that increases over time as the mean increases as well. This will 

result in an exact loss cost and exact rate for the program. To further specify the 

stochastic simulations, the coverage level is set to 0.65 and we then calculate the 

actuarially fair premium rate for this stochastic specification by doing 100,000 random 

draws of these data. 

We then evaluate what is observed when various moving averages of the loss ratio are 

used to rate the program. We use a 40-year, 24-year, and 8-year moving average to set 

rates and then evaluate what happens to the observed loss ratio when the moving average 

rate is applied rather than the known true rate. In Figure 1 the results from an eight-year, 

a 24-year, and a 40-year moving average rate are reported. This is just one time path 

through the data and we use the first 40 years of the series to begin the 40-year moving 

average and then we model an additional 60 years of the crop insurance program. The 

true loss ratio in this figure is 1.0 by construction. The observed loss ratios vary 

dramatically from the 1.0 level. The most extreme deviation is that for the eight-year 

moving average loss ratio. The 24-year moving average is more stable and the 40-year 

moving average is more stable still. However, even with a 40-year moving average of a 

loss ratio, there are instances where the observed loss ratio is more than 50 percent 

different than the true loss ratio for this series of data. We use this illustration to point 

out that with negatively skewed and relatively low probability events, one is likely to 

observe significant periods where the loss ratio is quite different than the true loss ratio. 

We also conducted 1,000 simulations of these alternative loss ratios and find that the 

standard deviation of the loss ratio increases as the number of years used to calculate the 

loss ratio decreases. The standard deviation for a 40-year moving average loss ratio is 

0.25 whereas the standard deviation for an eight-year loss ratio is 0.29. This is indicative 

of the greater uncertainty of the loss ratios based on data for short time periods. 
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Figure 5.1

As mentioned earlier, RMA is mandated to set rates such that the projected loss ratio is 

1.0. For reasons we have already discussed, this is difficult to assess and simplistic 

approaches to assessing program loss ratios can be misleading. Frequently, when 

aggregate loss ratios are observed, especially over relatively short periods, the loss ratio 

for some crop/region programs will be much higher than for another region. It is often 

perceived that losses in one region are used to compensate for losses in another region. 

Our review of the RMA rating system leads us to conclude the following regarding these 

concerns with regard to actuarial soundness. 

The rating system described in Section 4 shows the loss experience of a policy for a 

particular crop in a particular state will not affect the base rate for a policy in any other 

state11

 

11 Actually, the experience for a policy in a border county in one state may have some effect on the rates in 

a neighboring county in another state through the credibility system, but the reach out of state is never 

beyond a contiguous county

, nor will it affect the rate for another crop in the same state. Clearly, loss 

experience for a crop in a given county has a strong effect on the rates within that county. 

Through the credibility system, losses for a crop may influence the rate for the same crop 

in an adjoining county. In cases where the data by type/practice is insufficient to 

determine the appropriate differentials, RMA aggregates data across a wider area, but 

only for the purpose of determining the appropriate discount or surcharge. Finally, 

through the catastrophic loading procedure there is potential for losses in one part of a 

state to significantly influence the rate in another part of the state for the same crop. 

Beyond these relationships, we find no evidence that losses for one policy can affect the 

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

Simulated Loss Ratios Using Short Histories

8 Year Aveage 24 Year Average 40 Year Average
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rates for another policy for the major crops12. Further, since RMA draws upon CCC 

funds for program costs rather than from firm equity as a private firm would, any excess 

losses are absorbed by the U.S. Treasury. 

5.3.2 The Use of a Yield Simulation Based Rating

Dealing with Risk Heterogeneity

The RMA rating system cannot simply reflect expected losses for a representative farm 

within a county. Rather the RMA rating system is designed to develop a schedule of rates 

that accurately represent the risk of various farms within the county. For example, a 

county may have different crop types and practices, a county may also have farms with 

greatly differing yield potential because of soil, topography, or producer management 

skills. RMA must develop a rating system that is valid across all these farms and, 

importantly, that has the flexibility to reflect the heterogeneous risk characteristics. 

If yield a distribution or yield simulation approach were used, there would need to be a 

great variety of yield distributions even within a single county. Most of the yield 

distribution approaches we have observed fit a univariate farm-level distribution or use 

combined information from distributions of farm and county yields. However, this 

approach does little to address within county risk heterogeneity and does not utilize data 

from adjoining or similar farms to augment the information used to rate a particular unit. 

Multivariate or conditional distribution fitting is possible but complex, and this approach 

typically relies upon strong assumptions regarding the appropriate parametric distribution 

and other specification issues. In contrast, loss experience rating typically categorizes 

insured units into pools of similar risk and uses experience from the pool to derive a 

premium rate. This is a well-accepted insurance rating approach and widely used in 

property and casualty insurance. 

Convergence of Rates with Losses

A valid insurance rating system needs a mechanism by which rates and the observed 

experience are brought together and expected to converge. In property and casualty 

insurance, loss experience often serves as the primary basis for rating. Sometimes this 

experience is augmented with simulations, but typically the only time that you would see 

a simulation based approach for property and casualty insurance in the private sector is 

when the available historical experience provides an inadequate basis for determining the 

appropriate rate. Simulation based approaches are used almost universally in rating 

property catastrophe exposures such as earthquakes and hurricanes because the observed 

experience does not reflect either the full range of potential outcomes or the current 

 

12 Minor crops are sometimes rated based on aggregate experience over a larger area due to lack of 

credibility at a finer level of geographic division, but the ratemaking for even minor crops has no effect on 

the rates for other crops.
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distribution of exposures. At considerable expense, a handful of comprehensive 

catastrophe models have been developed that can overlay several hundred thousand 

potential weather scenarios or earthquake events on an insurance company’s current set 

of insureds and determine the insurance losses under each scenario. The insurance losses 

are influenced by the severity of the weather or earthquake event and also by the 

individual construction characteristics and exact geographic location of each insured 

building. Expected losses for a given combination of storm, location and construction are 

determined by a team of experts including meteorologists, engineers and actuaries. The 

expected losses are the average insured losses over all of the hundreds of thousands of 

scenarios. Typically, insurers in the United States purchase the results of property 

catastrophe models (or the models themselves) from modeling companies rather than 

developing in-house models due to cost constraints and lack of expertise. While it is 

theoretically possible to construct a similar model for crop insurance, the number of 

required variables is staggering, and we believe that the cost of constructing such a 

system would be prohibitive. Technological change would necessitate frequent updating 

or extensive revision of such models at a cost that would be even more prohibitive. 

 

On a related note, within a county RMA is required to provide rates for individuals with a 

variety of risk characteristics. In many cases this implies that there are production 

practices or yield expectations of farms within a county where the supporting data for that 

particular farm or type of farm is relatively thin. It is essential for the RMA rating system 

to be structured so that information from similar farms in a county can be used to infer 

the appropriate rate for a particular farm unit. But that would also take into account the 

differences in expected yield and expected variability of those farms. This is a particular 

challenge that RMA confronts in its rating system. It is our opinion that the lose 

experience based system used by the RMA is more robust than simulation approaches in 

meeting this challenge. 

Losses Not Captured in Yield Data 

A reason that loss experience base rating has a strong advantage relative to a yield 

simulation based approach is that the crop insurance program indemnifies losses that are 

not normally reflected in planted acre yields. In particular, prevented planting provisions 

may indemnify a producer and then allow the land to be fallowed or released to another 

crop. In either case, a farm yield series would not normally reflect this indemnity. 

Rather it would likely show zero acres planted. Similarly, the replant payment provision, 

indemnifies producers for the cost of replanting when an adequate stand is not established 

on the first planting. Often a crop is grown and harvested after replanting occurs. Farm 

yield data would typically consider the replanted acres as the crop acres. Suppose a unit 

receives a replanting payment, is then replanted, and a normal yield is achieved on the 

replanted acreage. Yield per planted acre data would reflect zero loss, but a crop 

insurance indemnity would have been paid. A third problem is quantifying quality loss. 

Quality loss occurs when the production to count on the insured unit has low value due to 

below average quality attributes.

 
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These three sources of losses are important examples of where crop insurance 

indemnities are incurred but yield loss may not be recorded, or the magnitude of the 

indemnity is not accurately reflected in a realized yield loss. Therefore, we have reason 

to believe that there is a component of the indemnity that is not well represented by the 

standard yield distribution. Loss experience provides a measure of these indemnities that 

is consistent with what is actually triggering and causes losses as opposed to a yield 

simulation based approach which is likely to omit these relevant causes of loss. 

Challenges for Using Yield Simulation Based Rating 

The estimation of parametric, semi-parametric or even non-parametric yield distributions 

has been shown to be empirically feasible. However, a long series of data is required to 

obtain credible estimates of even the mean yield, much less the distribution. In addition, 

as noted in the paper by Harri et al (2009b), there is a great deal of disagreement among 

researchers regarding the appropriate specification of yield distributions. This lack of 

consensus is largely driven by the fact that observed yield distributions tend to be 

inconsistent across region, production potential, soil types, etc. This makes the modeling 

of yield variability extremely difficult in the context of insurance rating because either a 

large number of distributions must be estimated or assumptions must be made about the 

underlying distributions that may not be reflected well in the data for many subsets of 

experience. Since insured losses occur toward the tail of the distribution rather than near 

its median, parameter and model error in the estimation of the yield distribution are 

compounded when the yield distribution is translated into insured losses. 

We also note that the heterogeneity of yield distributions and the disagreement among 

researchers regarding appropriate yield distribution estimation is in stark contrast of what 

one observes in the literature on price distributions. That literature is quite consistent, 

that the log-normal distribution is generally accepted, and importantly prices are quite 

homogenous across regions. In other words, a corn price distribution in western Kansas 

is not expected to be radically different than the corn price distribution in Ohio or North 

Carolina. Price variability measured in one location is quite helpful in estimating price 

variability in another region. With yield distributions there is relatively little that we 

learn as we move from one crop to another or from one location to another. This 

suggests that yield simulation based rating would have to estimate a multitude of yield 

distributions using approaches that are frequently disputed, perhaps also indicating that a 

model that fits the available data well might not be a very good predictor of future 

experience.

In-Sample Fit

Another aspect of rating with a yield simulation approach is the problem of in-sample fit 

or, in other words parameter uncertainty. The standard approach when using a yield 

distribution for an actuarial study is to estimate the appropriate yield distribution using a 

chosen estimator to find the shape of the distribution. The parameters are estimated with 

a degree of uncertainty and in relatively short yield series the degree of uncertainty is 
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often quite large. Typically after the parameters of the yield distribution are estimated 

the distribution is integrated, either analytically or numerically, to the coverage level to 

obtain expected crop insurance indemnities. This is a fairly straight forward process. 

However, the results obtained depend on the parameter values. It has been shown by 

Norwood, Roberts and Lusk (2004), and others, that this should be done using an out-ofsample approach. 

In terms of model fitting and forecasting ability, it is our opinion that from an actuarial 

standpoint it is necessary to recognize that rating of crop insurance is inherently an outof-sample process. By that we mean that each APH yield and rate offered to a producer 

is in effect a forecast of the expected yield and indemnity for that particular insured unit. 

When actual indemnities from the program are measured, one inherently captures the 

forecasting error of the rating system. In other words, if the APH does not accurately 

reflect the expected yield in every case, and that has an influence on rates, that 

uncertainty is built into the observed losses. Therefore, we recommend that any approach 

used to rate crop yield insurance should be based on out-of-sample evaluation. 

Conversely, within-sample fitting of parametric distributions will tend to underestimate 

the losses that would be observed in a crop yield insurance program. 

Available Data 

Another challenge for the yield simulation based approach to crop insurance rating is that 

it is extremely difficult to obtain a long enough yield series at the appropriate level to 

estimate distributions with a sufficient degree of confidence. In other words, the more 

observations that are available to use in estimating a parametric distribution, the greater 

the confidence in the estimates obtained. To be credible one would need a long time 

series of yields, which is available in only a very few cases. For example, the ten years 

of yield history that is associated with an APH record would be far below the minimum 

that we would consider sufficient to estimate a statistically valid yield distribution. The 

RMA rating system uses the APH yield history to estimate the expected yield, but not the 

entire yield distribution. 

In short, there are few cases where high quality farm yield series are available to support 

yield simulation based rating. Further, the best of yield series is almost always too short 

to adequately capture the effect of weather. Therefore, we are of the opinion that there 

are few, if any, instances where the data are adequate to support reliable yield simulation 

based rating. 

Assumptions Regarding Non-Constant Yield Variability

Another important assumption that is troubling in the area of simulation based crop 

insurance rating is the issue of heteroskedasticity of the yield distribution. 

Heteroskedasticity is simply a statistical term that describes the possibility that the 

residuals in the regression context or the yield variability in the context of crop yields are 

not stable across time (or some other dimension). The work that has been done to date 
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clearly suggests that assumptions about heteroskedasticity have a large effect on the rates 

derived from a simulation based rating approach (Woodard et al., 2009 and Harri et al., 

2009a). Secondly, the evidence to support consistent assumptions about 

heteroskedasticity is lacking. In other words, the Harri, et al (2009a) paper clearly shows 

that it is possible to fail to reject heteroskedasticity and also fail to reject 

homoskedasticity in many data series. In some data series it is possible to draw statistical 

inferences about whether heteroskedasticity exists only to find out that with a few 

additional observations added to the time series the results change. It is our opinion that 

this is due to the fact that we are often examining heteroskedasticity in yield distributions 

without sufficient data. Ultimately we believe that in the context of simulation based 

rating reliable estimates would require the examination of heteroskedasticity on a case by 

case basis. And given sample sizes (time series length) of even 50, we would lack 

confidence that the appropriate assumption has been made. 

Woodard et al (2009) examine the loss cost rating approach and conclude that a loss cost 

based system is exact only when risk increases proportionally with mean yields. They 

use farm-level data from the Illinois farm management record system to evaluate a 

simplified representation of the RMA procedures. The data used were for the 1980-2006 

time period. They conclude the RMA assumptions are not likely to hold for Illinois corn. 

The Woodard et al (2009) analysis and the Harri et al (2009a) both consider how yield 

variability may evolve with time or with changes in expected yield. However, neither 

controls for weather effects. As shown in the discussion of catastrophic loads, infrequent 

extreme losses often drive program rates in low risk regions. Thus, properly accounting 

for extreme weather events is essential for an accurate assessment of the RMA 

assumptions. Otherwise, a small sample can provide misleading results. We also note 

that various adjustments that RMA makes in the rating process alter the proportional risk 

assumption. For example, the catastrophic load procedure and the more recent Biotech 

Endorsement both modify rates from the simple historical loss cost approach. 

To examine these issues we obtained the monthly total rainfall, mean temperature, and 

mean Palmer Drought Index for most states for the years 1950 to 2008. Similarly, we 

obtained state corn planted acre yields from 1950 to 2008 from the USDA/NASS 

database. With these data we investigate the RMA assumptions with a model that can 

control for weather. 

Yield is modeled as a function of time to capture technology-driven changes in yields. 

Two alternative specifications are used. The first approach simply examines the residuals 

of a model with time trend where, yt is the yield at time t as shown in equation 5.2. The 

second model (equation 5.3) controls for weather with the August Palmer Drought Index 

(PDI) and then tests for heteroskedasticity in the remaining unexplained variation in 

yield.

(Eq. 5.2) t t tty +++= εγγγ 2

310 
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(Eq. 5.3) t PDI t y tt ++++= εγγγγ 4

2

310

Harri et al.(2009b) show that alternative forms of heteroskedasticity can be represented 

by the following relationship:

The case of β = 0 indicates homoskedastic errors. When β = 1, then the variance of 

yields is proportional to the predicted (trending) yield. Finally, the case of β = 2 suggests 

that the standard deviation of yields moves in proportion to the predicted (trending) yield 

(i.e., that the coefficient of variation is constant). Given, 

(Eq. 5.4) 

where the parameters γ of the variance equation are different from the parameters α of the 

mean equation. Then the following functional relationship holds: 

(Eq. 5.5) 

2 ln( ) ln( ) ˆ ˆ t e y = + α β 

 

where t eˆ is the error term and yˆ is the predicted value from a trend line. 

The results of heteroskedasticity analysis are provided in Table 5.1. The values in this 

table reflect the β coefficient from the heteroskedasticity test (equation 5.5) estimated for 

corn and soybeans for various major production states. Corn data ends in 2005 to avoid 

the recent data affected by Bt technology for which RMA has now created a rate 

adjustment. The results are reported for each state/crop combination estimate, and with 

and without the PDI included in the equation to control for weather. While there is 

considerable variation in the estimated β across states, in 36 of the 38 models 

homoskedasticity is rejected. Further, if one statistically tests the β=2 assumption, it is 

not rejected in 32 cases. In the six cases where proportional risk is rejected only one 

(Wisconsin corn) is found to have a β less than 2. 

These results are for a sample of crops and states that use data back to 1950. Ultimately, 

the question of proportional heteroskedasticity and RMA rating assumptions is an 

empirical one. In general, we conclude one should control for weather and use as many 

years of available data as possible to analyze the issue. 

Table 5.1. Examination of Proportional Heteroskedasticity for Select 

Major Corn and Soybean States 

)]([ ˆ . 22 β 2 β

t σσ t σ t = = yyE

2 22 2

)x'()]y(E[ t t σ = γσ

t t = α x')y(E
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Soybean Corn

State Without 

PDI

With PDI State Without 

PDI

With 

PDI

IL 1.94 2.81 IL 2.98 1.62

IN 5.28 2.49 IN 3.40 2.71

IA 1.99 2.07 IA 1.46 1.64

KY 1.91 1.33 KY 3.29 1.13

MI 1.80 2.64 MI 3.16 2.09

MN 2.21 2.48 MN 1.94 1.82

ND 1.61 1.64 MO 1.70 1.61

OH 3.51 2.50 OH 5.02 1.87

WI 1.64 2.47 SD 1.07 1.89

(Bold indicates statistically significant at the 

10% level

WI 1.38 1.01

Yield Data Challenges 

In considering the possibility of using a relatively long yield series for a yield simulation 

based rating system, we think there are some very important caveats regarding the data. 

For example, we believe that it is very important to recognize whether the yields are 

taken from observations where the acreage was insured in each time period. The reason 

for this concern is that crop insurance may have an effect on producer behavior and yield 

outcomes. Unfortunately, it is unlikely that a long yield series will contain information 

on whether the acreage was insured and, if insured, at what coverage level. Thus, even 

with a long time series it is likely that essential information affecting yield and insurance 

outcomes will not be available. 

It is also important to note that the level of aggregation reflected in the yield series is 

crucial. For RMA rating purposes what is needed is the ability to rate at the insured unit 

level, whatever level of aggregation may exist in that unit. So, we would strongly 

recommend that level of aggregation be taken into consideration. On a related note, the 

RMA does have a system in place which validates the accuracy of yield data when it is 

reported into the APH data system, and more so when an indemnity occurs and loss 

adjustment takes place. This gives a level of confidence in the crop insurance observed 

yields and loss experience that is greater than with alternative sources of yield data. 

Behavioral Effects and Yield Data 

While we do not attempt to quantify the degree to which adverse selection and moral 

hazard occur in the crop insurance program. It is well known that adverse selection (the 
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fact that one will not always correctly categorize insured individuals) and moral hazard 

(the behavioral effect that results from having insurance coverage) are phenomena 

common to all property and casualty insurance. Crop insurance has safeguards such as 

deductibles which are deterrents to this type of behavior. However, to the extent that 

adverse selection and moral hazard occur in the crop insurance program, those behaviors 

are captured in the loss experience. That is, for example, if moral hazard behavior occurs 

its effects are reflected in the historical loss experience and will be a factor that 

influences future rates. To the extent that adverse selection and moral hazard have stable 

effects in the program, then they are priced into the crop insurance premium rates as they 

would be in any property and casualty line that uses loss experience. It would be very 

difficult to incorporate behavioral effects of this type in a yield simulation model unless 

one has explicit information about behavior which is seldom if ever available. 
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6.0 Evaluation of the APH Rating System and Suggestions for 

Consideration

In the course of our review of RMA’s current rating procedures we identified several 

issues warranting further analysis. In this chapter we discuss those issues and the results 

of the analysis that was done within the scope of the current review. Our analysis of some 

of these issues alleviated our concerns. On other issues it led to suggestions for potential 

refinements to the rating procedures. Most of the suggested refinements would require 

some modifications of the Statplan database and all would require at least modest 

changes to computation procedures. Some suggested changes would require in-depth 

studies to develop rating factors for specific crops and regions. Finally, one of the 

refinements we recommend for further evaluation constitutes a significant departure from 

the current loss cost ratio based rating procedures. Given the scope and overall 

programmatic implications of this change we believe that extensive analysis would be 

needed prior to implementation. As we discuss each potential refinement we also offer 

our assessment of challenges the change would pose in implementation. 

6.1 Basic Approach to APH Rating 

Based on our review of the fundamentally challenges of crop insurance rating that were 

discussed in Chapter 5 we believe that it is clear that RMA should continue to use loss 

experience rather than simulation-based models. We recommend that RMA continue to 

use loss experience as the foundation of the rating system as it is the only way to assure 

that actual losses drive the rating results. This is consistent with standard property and 

casualty insurance rating practices. While crop insurance poses a unique set of 

actuarial challenges, alternatives to loss-experience-based rating would likely fail to 

adequately address the multiple objectives imposed on the APH program. 

6.2 Reference Rate, Reference Yield and Exponent

The reference rate, reference yield and exponential yield ratio curve are closely 

connected. The reference rate (county ULR with reserve factor load applied) is the 

variable portion of the premium rate charged for an insured unit with rate yield equal to 

the reference yield (i.e., yield ratio of 1.0). As discussed in section 4.2 above, the variable 

portion of rates for insured units with rate yields below the reference yield are higher than 

the reference rate while rates for units with rate yields above the reference yield are lower 

than the reference rate. These rate differentials are determined by the yield ratio rating 

curve, the shape of which is determined by the exponent discussed in section 4.3. We find 

two concerns about the validity of reference rates as applied in conjunction with the 

reference yield. The first is whether the reference rate and reference yield are developed 

in a congruent manner and the second is whether the reference yield is biased upward due 
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to convexity of the yield ratio curve. Here we discuss these concerns and also examine 

issues relating to the yield ratio curve and exponent. 

Connecting Reference Rates and Reference Yields 

As discussed in section 4.2, two different bodies of data are used in calculating the 

county reference yield and reference rate. Reference yields were initially based on NASS 

county yields. They are now periodically adjusted based on updated county T-yields.

County reference rates are based on average capped loss cost experience for insured units 

with varying yield ratios. Given these procedures, there is no assurance that the reference 

rate for a county is appropriately centered on the reference yield. This potential problem 

is illustrated in Examples 1 and 2 in table 6.1. These examples make use of the 2009 

rating parameters for corn in Boone County, Iowa. These parameters are: reference 

yield=150, reference rate=0.015, exponent=-2.051, fixed rate load=0.008. 

Table 6.1. Example Reference Rate Calculations for Insured Units with

Different Patterns of Rate Yields Relative to the Reference Yield

Example 1 Example 2 Example 3

Rate 

Yields

Implied Loss 

Cost Ratio

Rate 

Yields

Implied Loss 

Cost Ratio

Rate 

Yields

Implied Loss 

Cost Ratio

100 0.042 150 0.023 100 0.042

105 0.039 155 0.022 110 0.036

110 0.036 160 0.021 120 0.032

115 0.034 165 0.020 130 0.028

120 0.032 170 0.020 140 0.025

125 0.030 175 0.019 150 0.023

130 0.028 180 0.018 160 0.021

135 0.027 185 0.018 170 0.020

140 0.025 190 0.017 180 0.018

145 0.024 195 0.017 190 0.017

150 0.023 200 0.016 200 0.016

Average 0.031 0.019 0.025

Example 6.1 illustrates what would occur if all of the expected insured rate yields in a 

county in a given year were at or below the reference yield. Here the eleven yields used 

range from the reference yield of 150 bushels per acre down to 100 bushels per acre, in 

five bushel increments. The loss cost ratio for each unit is calculated under the 

assumption that the realized loss experience is exactly equal to the insured’s expected 
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yield and therefore consistent with what is implied by the rates. For example, the loss 

cost ratio for a unit with a particular rate yield is assumed to be exactly equal to the 

associated premium rate. For simplicity, we also assume that equal liability is insured at 

each yield level. Given these assumptions, the average loss cost ratio is 0.031. This is 

35% higher than the county’s implied loss cost ratio of 0.023 at the reference yield. What 

this means is that if the loss experience in a county is weighted toward yields that are 

below the reference yield the average loss cost ratio, in this case 0.031, will be 

substantially higher than the appropriate premium rate for an insured unit with rate yield 

equal to the reference yield of 150 bushels per acre. If this loss cost ratio is applied to 

units with rate yields equal to the reference yield then the rates are biased upward. [Note: 

To simplify this illustration we have not censored the loss cost ratios at the 80th

percentile.]

Example 2 illustrates the opposite situation—all of the expected insured yields are at or 

above the reference yield. In this case the actual expected loss cost ratio of 0.019 is 

substantially (16%) below the expected loss cost ratio (0.023) and fair premium rate for 

an insured unit with rate yield equal to the reference yield. The implication of this is that 

if the loss experience in a county is weighted toward yields that are above the reference 

yield the average loss cost ratio, in this case 0.019 will be substantially lower than the 

appropriate premium rate for an insured unit with rate yield equal to the reference yield. 

If this loss cost ratio is applied to units with rate yields equal to the reference yield then 

the rates are biased downward.

Examples 1 and 2 above clearly show the importance of proper alignment of reference 

rates with reference yields. The 2003 Reference Yield Update Methodology report cited 

earlier in section 4.2 suggested that at that time reference yields were biased downward 

compared with APH yields of insured units. Specifically, the study showed that for the 

following major crops average APH yields of insured units exceeded associated county 

reference yields by the following average percentages: wheat 19%, cotton 13%, sugar 

beets 15%, corn 25%, sweet corn 15%, soybeans 21%, and potatoes 11%.13

 

13 The reported values reflect national averages. Significant regional differences in these values were 

identified in the report.

 Based on the 

relationships illustrated in table 6.1 and discussed above, this pattern of APH yields 

substantially higher than county reference yields would bias reference rates downward 

compared to the “correct” rate for the reference yield. It should be acknowledged that 

updates to reference yields since 2003 may have substantially changed these 

relationships. However, the Reference Yield Updating Methodology report recommended 

that the RMA adopt procedures for using recent rate yields of insured units in a county 

and, where needed to satisfy data credibility standards, for surrounding counties, in 

computing county reference yields. We recommend RMA adopt updated reference 

yields which are congruent with reference rates and exponents, a critical step in 

obtaining appropriate rates for insured units at all yield levels. These updated 
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reference yields would be based upon APH data so that the reference yield and 

reference rate are ‘centered’ within a county’s book of business.

Another issue regarding reference rates and reference yields that warrants consideration 

is illustrated in Example 3. In this example, the insured rate yields are uniformly 

distributed around the reference yield. Thus, there is no weighting of liability either 

above or below the reference yield. Even in these circumstances, with the reference yield 

centered on the rate yield to which the reference rate applies, we find a potential bias in 

the loss-cost ratio based reference rate. This bias arises due to the convexity of the yield 

ratio curve. Given a convex function relating expected loss costs to reference rates it is 

well known that due to Jensen’s inequality the average loss cost in Example 3 will be 

higher than the expected loss cost ratio for a unit with rate yield equal to the average for 

the county. The reason for this is that, with a convex yield ratio function, “surcharges” for 

units with rate yields above the reference yield are larger than “discounts” for units with 

rate yields that are an equal distance below the reference yield. The point is that the 

average loss-cost-ratio based premium rate may be biased upward due to convexity of the 

yield ratio relationship, even if insured liability is uniformly spread around the reference 

yield. The average loss cost ratio in Example 3 is 0.025, which is 10% higher than the 

appropriate rate for an insured unit with rate yield equal to the reference yield.

The 2003 Reference Yield Methodology report addressed the potential problem of rate 

bias due to the convexity of the yield ratio curve. Two approaches were used to analyze 

the issue. One approach assumed that the current “exponential rate curve for each county 

is accurate (i.e., that it accurately reflects differences in expected loss cost ratios for 

insured units with different yield ratios). This curve and other rating parameters were 

applied to the actual rate yields for units insured in 2003 to obtain the implied loss cost 

ratios for each unit. The second approach involved calculation of “the 13-year loss costs 

for each crop/type/practice in each county in two ways: (1) using data for all insured 

units; and (2) using just data for units with updated yield ratios (yield ratios based on 

updated reference yields) that are ―near 1.0 (specifically from 0.9 to 1.1).” The all-data 

county loss cost ratio was divided by the restricted-data loss cost ratio “to determine how 

well averaging across all data represents the loss cost experience for insured units with 

yields near the reference yield.” This analysis was conducted for corn, cotton, soybeans 

and wheat. The results of this analysis led the authors to conclude that the convexity 

effect is modest in magnitude (less than 5% to 7%). It is our opinion that correcting for 

this modest potential bias would substantially increase the Statplan data and analysis 

requirements. We do not believe the rate accuracy gained would justify the costs 

associated with these additional data and analysis requirements. Therefore, we do not 

recommend changes to the reference rate development procedures to address this small 

potential bias. 
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Yield Ratio Curve and Exponent 

Assuming that the reference rate and reference yield are derived in a consistent manner 

the yield ratio curve is used to adjust rates for producers with different rate yields. As 

discussed in section 4.3, it is our understanding that the exponents currently used to 

determine the shape of the yield ratio curve are based on relationships that were in use 

prior to implementation of the continuous rating system in 2001. Given that, we 

recommend that RMA update these exponents. The 2008 internal memo discussed earlier 

identifies statistical relationships, related to sampling error in average (rate) yields and 

regression to the mean, which could potentially give rise to yield ratio relationships that 

are “flatter” than those produced by the current exponents. The memo proposes updating 

the exponents using nonlinear least squares (NLS) to estimate insured unit level loss cost 

ratios (LCRs) as follows:

(Eq. 6.1) . 

We agree with the explanation provided in the memo regarding a conceptual basis that 

could lead to flatter yield ratio relationships. We would restate the estimation equation as 

follows to clarify that the rate yield rather than the APH yield for an insured unit should 

be used in the numerator and the reference yield in the denominator: 

(Eq. 6.2) . 

We also believe there is merit in using a censored regression model due to the nature of 

the dependant variable. (See, for example, Stute, 1999 and Chay and Powell, 2001). 

Based on our review of the yield ratio curve and the rating exponent we recommend 

that RMA conduct analysis to update these parameters of the rating formula. Given 

the heavy censoring of loss cost ratios at zero we suggest that the RMA investigate the 

use of a censored regression model for the estimation. 

6.3 Type and Practice Factors

Procedures for developing type/practice factors were summarized in section 4.4. It is our 

opinion that the approach used by the RMA in developing these factors is reasonable. 

The non-censored loss cost ratio for each type/practice combination in a region is divided 

by the non-censored, combined loss cost ratio for all types and practices in the region to 

obtain a raw TpFactor. These raw factors are weighted by county type/practice liability 

proportions over the full rating period and normalized to obtain final county TpFactors. 

These final factors have the property of collecting the same amount of premium as would 

be collected if the simple average loss cost ratio for the county (combining all 

types/practices) were multiplied by total county insured liability. That is, with no 
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censoring of the data, use of these final type/practice rating factors distributes premium 

rates to types/practices combinations in a way that collects the same amount of premium 

as would be collected if the combined (across all type/practices) loss cost ratio were used 

as the premium rate and multiplied by total liability for the rating period. 

Although we consider the general type/practice factor development procedures 

reasonable, we have one major concern about an inconsistency between how the factors 

are developed and how they are applied. As indicated above and shown in section 4.4, the 

type/practice factors are constructed in a way that distributes total premium to 

type/practice combinations while collecting an amount of total premium consistent with 

expected losses, as measured by the raw combined type/practice loss cost ratio for the 

county. This balance between premium collected and expected losses is achieved using 

the county non-capped loss cost ratio as an implicit “all type/practice” rate to which the 

TpFactors are applied. However, as shown in equation 6.3 below (repeated from section 

4.1 (equation 4.2)), when rating an insured unit the TpFactors are only applied to a 

portion of the rate. Importantly, the State Catastrophic Rate Load is not included in the 

part of the rate that is multiplied by the TpFactors. It is our opinion that this is 

inconsistent with the way the TpFactors are developed and that the result is type/practice 

rates that are likely biased downward for higher cost types/practices and upward for 

lower cost types/practices. Whether the TpFactors should be applied to the prevented 

planting (PP), replant, and quality adjustment (QA) rate components is less clear. For 

example, prevented planting indemnities and associated liability are excluded from the 

production ratio calculations underlying the target county rates. The same is true of 

indemnities that are paid to insured producers to cover the cost of replanting. Further, it is 

not clear that rates associated with these causes of loss should be distributed to crop 

types/practice combinations in the same way as production losses.14

(Eq. 6.3) Target Rate for Individual = 

( ( ) ) -E ULR Ry +CntyCAT

ResFac

PP+RP+QA+StCAT TpFactor CLD

UnitFac UnitFac

        ×        

        × +   ×    

     

. 

 

14 We should also note that proper allocation of these rate factors to crop types and practices likely varies 

significantly by crop and region. Here we use prevented planting as an example. In some regions, such as 

the Midwest, a primary cause of prevented planting losses is excess moisture. This would likely have 

similar effects on both corn types (corn for grain and corn for silage). However, in an area where 

inadequate moisture is a significant cause of prevented planting losses the effect could vary greatly between 

irrigated and non-irrigated planting practices. 
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Our main point is that type/practice factors are developed in a way that is structured to 

balance premiums collected with expected losses (as measured by the combined 

type/practice loss cost ratio for the rating period). However, inconsistencies between the 

way these factors are developed and how they are applied create a risk of premium 

inaccuracies between types and practices. 

Although the current process results in a balanced collection of revenue, it also results in 

separate type/practice factors for every county, making it appear that the type/practice 

differentials are somehow different by county. If the same calculations currently used to 

adjust regional type/practice factors were instead used to adjust the county base rate to a 

single practice level, then a single set of regional type/practice factors could be applied in 

every county. Moreover, if one type/practice were selected as the base and assigned a 

factor of 1.00, then type/practice differentials would be more easily comparable across 

time and across regions. 

Beginning with Step 2 in our example from Section 4.4: 

(4.4alt) 

1

2

2

1 00.1

R

R

R

R

SALC

SALC RF

RF

=

=

Step 3: (4.5alt) Extension C C CR 1 22 =+× LW LW RF

Step 4: Calculate the adjusted base rate for the county, which is the rate for type/practice 1: 

(4.6alt) 

C

CT

C Extension

SALC ABR =

The total premium collected still balances:

(4.7alt) Premium RF ABR Liab Cj Cj C Cj =× ×

(4.8alt) Premium Premium Premium CT = + C1 2 C

 = × +× × ABR Liab RF ABR Liab C CR C C 1 2 2

 12 2

1 22

( ) ( )

CT

CR C

C CR

SALC Liab RF Liab

LW LW RF = × +×+ ×

 12 2

1 2

2

( )

( )

CT

CR C

C C

R

CT CT

SALC Liab RF Liab Liab Liab RF

Liab Liab

= × +×

+ ×

 12 2

12 2

( )

( )

CR C

CT CT CT CT

CR C

Liab RF Liab SALC Liab SALC Liab

Liab RF Liab

+ ×

= ×× = ×

+ ×
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Based on our analysis we recommend that the RMA modify its procedures so that the 

TpFactors are applied to the State Catastrophic Rate Load portion of the target rate. 

Further, we recommend that RMA rebase its rates and type/practice factors to a 

common type/practice to improve the transparency of the rating structure.

Using Liability Weights Based on More Recent Experience 

In its internal type/practice guidelines document the RMA recognized that a problem may 

exist in its type/practice rating procedures when the type/practice mix changes 

significantly over the rating period. In this case the county type/practice liability weights 

and the county simple average loss cost for the rating period may not be reflective of 

current liability proportions and future expected losses. 

The method currently used by RMA to set and apply type/practice factors, even if 

adjusted to simplify the resulting rate structure as suggested above, is still dependent on 

the assumption that the distribution of liability across types/practices has not changed 

materially throughout the experience. Clearly, this is not always the case and assuming 

that producers shift to more productive/less risky types and practices over time results in 

an upward bias on the base rates. To illustrate how changes in mix affect the indicated 

base rates, we extend the previous example. Consider a crop with two production 

practices, with a constant differential in experience between the two practices. Practice 

2’s expected losses are twice those of practice 1, and in our example, the actual 

experience exactly equals the expected. The proportion of producers electing practice 1 

has been steadily increasing:

Table 6.2. Example Experience for a County with a Shifting Mix by Practice 

Year Practice 1 

Liability

Practice 2 

Liability

Practice 1 

Indemnity 

Practice 2 

Indemnity

1 300,000 700,000 30,000 140,000

2 400,000 600,000 40,000 120,000

3 500,000 500,000 50,000 100,000

4 600,000 400,000 60,000 80,000

5 700,000 300,000 70,000 60,000

Total 2,500,000 2,500,000 250,000 500,000 

SALCR1 = %10 and SALCR2 = %20

Note that the SALC by practice has not changed over time.

The SALC for the county is then: 
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Table 6.3. Example County Experience: All Type and Practices Combined

Year Liability Indemnity Loss Cost Ratio

1 1,000,000 170,000 17%

2 1,000,000 160,000 16%

3 1,000,000 150,000 15%

4 1,000,000 140,000 14%

5 1,000,000 130,000 13%

SALC for the county = All year average 15%

The indicated type/practice differentials and total premium are then:

RFR1 = 00.1 and 00.2

%10

%20 RFR2 ==

%70 LWC1 = and %30 LWC2 =

 70% 30% 2.00 1.30 ExtensionC = +×=

%5385.11

3.1

%15 ABRC ==

Assuming that the liability in year 6 is the same as in year 5, then the premium collected 

will be: 

700,000 11.5385% 300,000 11.5385% 2.00 $150,000 PremiumC = × + × ×=

Expected indemnity, however, is $130,000. 

Where the distribution across practices in the history is known, the appropriate 

adjustment is straightforward, and simply requires substituting the current liability 

distribution: 

Table 6.4. Adjusting the Historical Weighting by Practice to the Current 

Distribution

Year Prac 1 L/R Prac 2 L/R Weight to Prac1 Adjusted L/R

1 10% 20% 70% 13%

2 10% 20% 70% 13%

3 10% 20% 70% 13%

4 10% 20% 70% 13%

5 10% 20% 70% 13%

SALC 13% 
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%10

3.1

%13 ABRC ==

Assuming that the liability in year 6 is the same as in year 5, then the premium collected 

will be

700,000 10% 300,000 10% 2.00 $130,000 PremiumC = ×+ ×× =

In cases where the liability distribution by type/practice cannot be determined for the 

whole history, a reasonable approximation would be to assume that the oldest available 

distribution holds for the prior years. If it is clear that there was significant evolution in 

the distribution even in the oldest time period for which data are available, earlier 

distributions could be extrapolated, using appropriate judgment. If a particular 

type/practice was introduced after the start of the APH time series, the distribution 

percentage for the new type/practice is set to 0% for prior years. We also must make an 

assumption about the relative experience of the various types/practices, and again it is 

reasonable to assume that the relative experience in the oldest few years with available 

data can be extrapolated backwards. Extending our example a bit further, suppose that 

there is actually 10 years of experience for our county, but only total county experience is 

available prior to year 1, and moreover that there were some years with dramatically 

different experience in the prior period: 

Table 6.5. Extending the Example County Experience to 10 Years 

Year Liability Indemnity Loss Cost Ratio

-4 1,000,000 500,000 50%

-3 1,000,000 200,000 20%

-2 1,000,000 400,000 40%

-1 1,000,000 190,000 19%

0 1,000,000 180,000 18%

1 1,000,000 170,000 17%

2 1,000,000 160,000 16%

3 1,000,000 150,000 15%

4 1,000,000 140,000 14%

5 1,000,000 130,000 13%

SALC for the county = All year average 22.2% 

Given the observed shift in liability weights, it is appropriate to assume that the mix was 

shifting prior to the observed data, but perhaps somewhat more slowly (a conservative 

assumption). We assume that the 2:1 experience ratio applied for the prior years. 
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Table 6.6. A Reasonable Estimate of the Split by Practice for the Older Years

Year Practice 1 

Liability

Practice 

2 

Liability

Practice 1 

Indemnity 

Practice 2 

Indemnity

Practice 1

L/R

Practice 2

L/R

-4 50,000 950,000 12821 487,179 25.6% 51.3%

-3 100,000 900,000 10526 189,474 10.5% 21.1%

-2 150,000 850,000 32432 367,568 21.6% 43.2%

-1 200,000 800,000 21111 168,889 10.6% 21.1%

0 250,000 750,000 25714 154,286 10.3% 20.6%

1 300,000 700,000 30,000 140,000 10.0% 20.0%

2 400,000 600,000 40,000 120,000 10.0% 20.0%

3 500,000 500,000 50,000 100,000 10.0% 20.0%

4 600,000 400,000 60,000 80,000 10.0% 20.0%

5 700,000 300,000 70,000 60,000 10.0% 20.0%

SALC by practice 12.9% 25.7%

Restating the old experience under the current weights gives us: 

Table 6.7. Adjusting the 10 Years of Experience to Reflect the Current Distribution 

by Practice

Year Prac 1 L/R Prac 2 L/R Weight to Prac1 Adjusted L/R

-4 25.6% 51.3% 70% 33.3%

-3 10.5% 21.1% 70% 13.7%

-2 21.6% 43.2% 70% 28.1%

-1 10.6% 21.1% 70% 13.7%

0 10.3% 20.6% 70% 13.4%

1 10% 20% 70% 13%

2 10% 20% 70% 13%

3 10% 20% 70% 13%

4 10% 20% 70% 13%

5 10% 20% 70% 13%

SALC 16.7%

We have accounted for the change in the mix of business while continuing to capture the 

fact that, even with different type/practice weighting, the experience in prior years was 

significantly poorer than in more recent years. 

Premiums for year 6 are then: 
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%863.12

3.1

%7.16 ABRC ==

700,000 12.863% 300,000 12.863% 2.00 $167,000 PremiumC = × + × ×=

We recommend that RMA adjust prior experience for the actual or estimated mix by 

type and practice.

6.4 Adjustments for Unit Structure

In Chapter 3 we discussed how the historical loss experience is adjusted in developing the 

county target rate. Revenue product liability and indemnity are adjusted to reflect yieldbased loss experience. Additional adjustments approximately normalize the experience to 

a common 65% coverage level. These adjustments are made to obtain a consistent dataset 

for rate computation. However, there are no similar adjustments to the data to reflect 

differences in unit structure. In this section we examine the implications of not adjusting 

for unit structure in developing county target rates. 

Equation 6.5 below, is repeated from section 4.1 (equation 4.1) for convenience. It is the 

expression for the county target rate. The unit factor (UnitFac) used in this equation is 

0.9. The implication of this is that all of the rating parameters in the numerator are treated 

as though they represent basic unit loss experience. Dividing by UnitFac=0.9 effectively 

surcharges these rates up to the level treated as appropriate for optional units. When the 

rates are published in the actuarial documents the multiplicative rating factor for optional 

units is 1.0, indicating that the published reference rate and fixed rate load are for 

optional units. A multiplicative unit factor of 0.9 is used to provide a 10% discount for 

basic units. 

(Eq. 6.5) Target Rate = 

ULR+CntyCAT

ResFac PP+RP+QA+StCAT UnitFac UnitFac

            +    

 

 

It is our opinion that this treatment of unit structure in the rating process is problematic 

because loss experience for all unit formats (optional, basic, and enterprise) is treated as 

though it were for basic units. In reality, a significant part of the experience since the late 

1980s has been for optional units and a relatively small amount of experience, primarily 

on CRC and RA, is for enterprise units. Treating the experience for optional units as 

though it were for basic units and surcharging it up in obtaining a county target rate 

assumed applicable for optional units has the effect of adding an additional load to the 

optional unit loss experience contained in the loss history (the opposite is true for the 

relatively small proportion of enterprise units with rating factors less than 0.9). If all of 

the historical experience was for basic units, and the current 10% discount factor was 
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appropriate for basic versus optional units, then this process would be correct. The 

experience would be surcharged up to the optional unit level and then discounted by 10% 

for units insured as basic units. Conversely, if all of the experience was for optional units 

the target rate, applicable for optional units, would be 11.1% higher than appropriate. 

This would result in rates that are too high for both optional and basic units. A possible 

correction to this process reflecting mixed basic and optional unit experience in a county 

would be: 

where proportion optional and proportion basic are the proportions of total county 

insured liability on optional and basic units, respectively. This adjustment should be 

correct when applied to the ULR, CntyCAT, and StCAT portions of the target rate. It is 

less clear whether the adjustment is correct for the PP, RP, and QA portions of the rate 

because it is not clear whether unit structure rate differentials are appropriate for these 

rate components. Further, even if unit structure rate differentials are appropriate for these 

rate components, the appropriate differentials may vary by component. However, the PP, 

RP, and QA rating components are generally small in magnitude compared with the 

ULR, CntyCAT, and StCat. Thus, with just basic and optional units in a county, the 

above process should closely approximate the appropriate target rate for the county.

The above process addresses the problem of counties with mixed basic and optional unit 

experience. However, it does not address the variable discounts for enterprise units which 

are becoming more important due to larger subsidies for this unit structure provided in 

the 2008 Farm Bill and the significant shift to enterprise units that has occurred. Given a 

substantial shift in the book of business to enterprise units, an appropriate adjustment 

process for the variable discounts associated with this unit format would be needed. A 

factor analogous to the above factor for basic and optional units is: 

 , 

where is one of a range of proportionate discount factors and is the 

proportion of liability in the county to which the discount factor applies. For example, 

if a county had 50% of liability in optional units with a discount factor of 1.0, 30% of 

liability in basic units with a discount factor of 0.9, and 20% of liability in enterprise 

units with a discount factor of 0.8, then UnitFac used in equation 7.5 above would be: 

 . 

The above process is straightforward. However, we recognize that the primary difficulty 

posed is that a large number of enterprise unit discount factors may be used for different 

insured units in a county (the same will be true of basic units when the results of the Unit 

Structure study are applied). In order to implement the process it would be necessary to 

maintain in Statplan records of liability in each county to which each discount factor 
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applies in each year. The rounding of discounts to a manageable set of discrete factors 

might be useful in simplifying this process. Even if the actual discounts were not 

rounded, it might be judged acceptable to round the factors maintained in Statplan at say 

5% increments so that the number stored would be relatively modest. This compromise in 

the interest of ease of data management would introduce approximation error but would 

be superior to the current process which is subject to substantial error when larger 

enterprise (or basic) unit discounts are applicable for a substantial proportion of liability 

in a county. We recommend that RMA adopt procedures for developing target rates 

that incorporate unit factors that are consistent with the mix of unit structures in 

the historical loss experience. 

6.5 Coverage Level Adjustments 

Rates are currently set at the 65% coverage level. Other coverage levels are rated by 

applying a factor to the 65% rate. Coverage level factors are periodically adjusted 

through special studies that are not part of the base rate setting process.

Data for other coverage levels are adjusted to a 65% equivalent. The adjustment from 

higher coverage levels (e.g. from a 75% level to a 65% level) is appropriate and 

accurately captures the change because all indemnity under a 65% coverage level is

captured within the 75% indemnity. This is not the case, however, when the actual 

coverage level in the data is less than the target 65% level. The missing data are 

indemnity for producers with production ratios between the actual coverage (e.g. 50%) 

and the base 65% level. Because no coverage was provided at the lower level, actual 

production is not captured in the database, making it impossible to calculate what the 

actual losses would have been had the producer elected 65% coverage. RMA estimates 

the missing indemnity by selecting an average between the maximum and minimum 

possible outcomes, where the minimum assumes that no producers had production 

percentages between the lower coverage level and 65% and the maximum assumes that 

all producers without indemnity losses had actual production just above the actual 

coverage level. When this estimation approach was adopted a number of years ago, the 

percentage of business at coverage levels below 65% was very small, and the averaging 

method in most cases would not have had a material effect on the result. However, some 

crop/region combinations have a significant proportion of producers below 65% 

coverage, resulting in more weight given to an ad hoc adjustment procedure. 

Neither adjustment (from higher coverage to 65% or from lower coverage to 65%) 

captures differences in insured population or insured behavior at different coverage 

levels. As has been discussed, there is evidence that, as the coverage level increases, the 

opportunity for moral hazard effects increases. Thus, adjusting from a higher coverage 

level to a lower coverage level may overstate the adjusted experience. It is difficult to 

assess whether the adjustment from lower coverage levels to the base level overstates or 

understates the appropriate adjustment for producer behavior. 
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If the base coverage level were, instead, set at the 50% level, it would not be necessary to 

manufacture missing indemnity data. It is typical in property/casualty ratemaking either 

to set the base coverage level at the largest deductible (lowest coverage level) with a 

significant percentage of insureds or to set separate rates by deductible. Setting separate 

rates by coverage level requires credible volumes of data at each coverage level, which is 

not practical for the RMA system. However, changing the base deductible for pure 

premium calculation purposes to the 50% coverage level would be a simple modification 

that would eliminate the need to guess at a potentially large volume of missing indemnity 

data. We believe the error due to approximation outweighs the statistical error associated 

with lower coverages identified by Ker and Coble (1998). 

We are left, then, with the possible need to reflect the effect of variances in producer 

behavior across coverage levels in the base rate. Coverage level adjustment factors are 

appropriately estimated through studies that compare actual experience at different 

coverage levels for recent experience over a wider aggregation of data than the county 

level. It would be possible, using the same set of data, to calculate coverage level 

adjustment factors based on the effect of adjusting the experience to the lowest coverage 

level, which captures only the difference in liability and indemnity assuming consistent 

producer behavior. It is likely that the results of the latter calculation (the same 

calculation used in setting the base rate) will produce smaller coverage level differentials 

than the actual experience indicates. The difference in the two sets of differentials is a 

reasonable estimate of the behavior effect and if significant could be reflected through a 

reduction in the base rate 

We recommend that RMA eliminate the coverage approximation procedure and adjust 

all experience to the 50% coverage level when low coverage levels make up a 

significant proportion of experience. Published base rates could still be maintained at 

the 65% coverage level, simply by dividing the 50% pure premium by the 50% coverage 

level adjustment factor. We recognize this would place greater reliance on the 

estimated coverage level relativities. However, we believe these can be effectively 

estimated in the major crops. 

6.6 Catastrophic Loading

The current catastrophe loading methodology removes indemnity above the 80th

percentile from the county experience and spreads it across the entire state. Most of the 

catastrophic load is spread through an additive of up to .0325, while any remaining 

catastrophe load is spread in proportion to each county’s excess indemnity. This 

methodology assumes that the potential for catastrophe (or at least the potential for 

moderate catastrophes up to the cap) is not affected by the actual county experience. The 

result is, on a percentage basis, a much bigger loading for catastrophes in counties with 

better non-CAT experience than in counties with poorer non-CAT experience. Milliman 

has performed a comprehensive evaluation of the RMA CAT loading process (Milliman, 

2008), concluding that there are several potential changes to the process that would 
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improve the efficiency ratio. We concur with the Milliman and Robertson review. We 

recommend that RMA re-evaluate the catastrophic loading procedure and reduce the 

degree to which CAT loading influences rates in low risk regions. Having said this, we 

generally support maintaining state/crop catastrophic loading boundaries, unless one 

is addressing a crop with geographically-sparse participation. 

6.7 Use of Expert Judgment 

Where data are sparse, expert judgment is inserted into the ratemaking process to ensure 

that the resulting rates are logical. The ratemaking process also allows for expert 

judgment intervention where there is significant credible data. It is not clear to us under 

what circumstances such an intervention is permitted, and even allowing for such 

adjustments may lead to the perception that the ratemaking process unfairly selects 

certain crops or areas for special treatment. We recommend that if, in fact, local 

conditions have changed such that an existing credible time series of data is not 

appropriate for rating, an explicit discussion of the changes should accompany a plan 

to either adjust the prior data or to set a rate through judgment that will then be 

adjusted using standard methods as new data accumulate. Regional offices of the 

RMA should play a role in any such process. The decision and results should be 

documented, transparent, and reviewable by outside parties. 

6.8 Additional Rating Variables 

Actual producer experience is likely to be significantly influenced by both physical farm 

characteristics and production methods. The producer’s actual production history, if 

available, serves as a proxy for the collective effect of all such characteristics, filtered 

through the overlay of weather and other environmental effects and producer choices 

(moral hazard effects). In the days when data gathering and storage posed significant 

cost issues, it was appropriate to limit the number of rating variables considered. In 

today’s environment, however, such limitations on data should no longer impose 

significant barriers. We note that recent advances in property/casualty ratemaking 

include a significant increase of the number of potential explanatory variables considered. 

It may be the case that a significant amount of the county-to-county variation in rates 

may be explained by differences in soil types, elevation, slope, production systems 

employed, and other data that should be readily collectible and either stable across time 

or with changes that can be documented. It is reasonable to assume that the number of 

coverage options will only continue to increase, resulting in the need for finer and finer 

rating variables. We recognize that site-specific information is a significant advancement 

from where the program is today. However, a pilot project could examine the logistics of 

such a system. We recommend a comprehensive study evaluate utilizing soil and other 

site specific information. We also suggest RMA consider defining and collecting 

additional type and practice data for characteristics that likely affect insurance risk 

levels.
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6.9 Statewide Rate Level Adequacy 

The methods employed to limit the effect of rate changes on individual producer rates are 

asymmetrical. That is, they move actual rates toward indicated levels much more quickly 

on the downside than on the upside. If a significant number of producers are affected by 

rate capping, the result can be overall rate inadequacy even when the published base rate 

is actuarially sound. There is no mechanism in the rating process to test whether the 

actual premiums collected meet the mandate that premium shall be sufficient to cover 

anticipated losses and a reasonable reserve. In cases where the statewide premiums prior 

to the application of the reserve factor are significantly short, it may be appropriate to 

increase the reserve factor and/or add a rating off-balance load to uncapped rates. In the 

other direction, the catastrophe minimum load of 0.0065 may in some instances produce 

a statewide premium that is in excess of the mandated level. We recommend that RMA 

evaluate the extent to which statewide rate levels may be inadequate due to capping 

and, if significant, consider the use of an inadequacy off-balance. We recommend that 

RMA consider re-evaluating whether the minimum load is appropriate in light of the 

additional reserve loading. 

6.10 Catastrophic Coverage Rates

RMA currently treats the catastrophic coverage policy as a comparable unit of insurance 

to any other policy, and the premium rates that are offered on catastrophic coverage are 

identical to the premium rates that are offered on a 50/100 policy. We evaluated whether 

the premium rates for the catastrophic coverage policy appeared to be consistent with 

those for other coverages. Note the following language applied to the catastrophic 

coverage policy: 

Unit Division

(a) This section is in lieu of the unit provisions specified in the applicable 

crop policy. 

(b) For catastrophic risk protection coverage, a unit will be all insurable 

acreage of the insured crop in the county on the date coverage begins for 

the crop year:

(1) In which you have one hundred percent (100%) crop share; or 

(2) Which is owned by one person and operated by another person on a 

share basis. (Example: If, in addition to the land you own, you rent land 

from five landlords, three on a crop share basis and two on a cash basis, 

you would be entitled to four units; one for each crop share lease and one 

that combines the two cash leases and the land you own.) 

(c) Further division of the units described in paragraph (b) above is not 

allowed under this Endorsement. 
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Thus, the catastrophic coverage policy is defined essentially as a basic unit. However the 

reduced price election serves as a co-payment on this policy. To analyze the potential 

differences between the pool of catastrophic coverage losses versus non-catastrophic 

coverage policies, we utilized the RMA summary of business data from 1995 through 

2008. These data sets report the loss experience on a county-by-county basis for various 

coverage levels and allow one to identify both the coverage level and whether it was a 

catastrophic coverage policy or a buy-up policy. We aggregated these data across the 

1995-2008 period and looked at the observed loss cost ratios for policies that were the 

APH insurance plan and at the 50 percent coverage level. Thus, the experience that we 

examined is experience for both the 50 percent coverage buy-up policies and 50 percent 

coverage catastrophic policies. 

To evaluate the relative loss experience, we aggregated the data by crop, state, and year. 

So, for example, we conducted a comparison of 50 percent coverage corn APH policies 

in Indiana in 2005. We compare the loss cost ratios observed for catastrophic coverage 

policies versus the loss cost ratios observed for the non-CAT 50 percent coverage 

policies and computed a ratio of the catastrophic loss cost ratio to the buy-up loss cost 

ratio. In the case where the actuarially fair rates for CAT policies and buy-up policies 

were identical, the expected ratio would be 1.0. In our analysis we also limited a 

minimum level of liability to be $1,000 in a county to avoid thin data counties. We 

conducted the analysis and then aggregated the average values that we observed to the 

national level for seven crops. These are reported in Table 6.8. For example, for wheat 

we had 599 state year combinations. The average ratio that we observed was 0.638. This 

indicates that wheat catastrophic coverage in the same year, in the same state, had a loss 

cost ratio that was roughly 64 percent of the loss cost ratio for buy-up coverage. Results 

of similar analysis for other crops all produced ratios below 1.0. The highest observed 

ratio was for oats and the lowest was for soybeans. The weighted average across all the 

programs was just under 60 percent. 

These results suggest that there is a difference between the appropriate premium rates for 

catastrophic coverage and buy-up policies, and that the differences are fairly large in 

some instances. Therefore, we believe that it is appropriate for RMA to offer a discount 

in the catastrophic coverage rates relative to buy-up rates. We think this is partially 

justified because the reduced price election that is applied to a catastrophic coverage 

policy is a co-payment. This suggests to us that there is a greater disincentive for moral 

hazard behavior on the part of producers with the catastrophic coverage policy. We also 

think that there is likely to be a different pool of participants purchasing CAT than the 

buy-up policies. We recommend that RMA evaluate adjusting the rates for catastrophic 

coverage to reflect the lower risk associated with those policies. Further we recommend 

that catastrophic coverage experience be treated differently than other coverage levels 

in the Statplan process. 

Table 6.8. Summary of 50/100 Loss Experience Relative to Catastrophic Coverage 

Policies
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Crop State/years Ratio of Cat Loss Cost relative to Buy up 50% coverage Loss 

Cost

Wheat 599 63.8%

Oats 394 90.9%

Cotton 237 47.0%

Sugar Beets 164 33.9%

Corn 658 46.4%

Sorghum 325 55.7%

Soybeans 445 29.0%

6.11 Weighting of Experience

We considered how RMA can deal with the need to capture the effects of infrequent 

catastrophic weather events and give them the proper probability weights. The dilemma 

is that as the insurance experience time series increases in length, program changes 

reduce the relevance of more distant experience to the current program. We conducted 

sample analyses which we recommend that RMA consider as a means to improve the 

statistical validity of their rates by incorporating a longer series of weather experience 

with a relatively shorter times series of loss cost experience. While sophisticated 

approaches to modeling the effect of weather could be utilized, we suggest two relatively 

straightforward approaches to address whether experience observed in the Statplan data 

history reflects unusually good or poor growing conditions. The first approach considers 

both reweighting historical weather and accounting for acreage changes over time. 

Weather is addressed through a single variable, the Palmer Drought Index. We are not 

suggesting that this is the optimal index to use in such an exercise and recommend that 

additional analysis consider alternative indexes that would measure historical weather 

stresses. The second approach examines use of multiple weather variables to compare the 

weather observed in the short recent history with a longer weather data series.

6.11.1 Yield Correlation and Weighting Loss Experience Data 

An issue related to the representativeness of historical weather observations is the relative 

weight given to historical experience. Current rating procedures used by RMA within the 

Statplan framework essentially assume that each year of data contributes the same 

amount of information to estimating expected losses. The assumption may be reasonable 

in cases where the underlying programs, technology, participation, and agronomic 

structures are relatively stable over time. However, in the case of the Federal crop 

insurance program, a number of factors have changed over time. In considering how to 

best use data collected across time, there is a need to balance concerns regarding the 

comparability of loss information collected over different periods with corresponding 
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concerns relating to the relatively small samples that are available and the need to 

observe the effects of loss events that may be rare but large in magnitude. 

A first important point to consider regarding the treatment of loss data over time involves 

the degree to which participation has changed. Figures 6.1 and 6.2 below illustrate 

participation patterns, in terms of net insured acreage, for corn and soybeans, 

respectively, in the three-state region that forms the heart of the U.S. Corn Belt—Illinois, 

Indiana, and Iowa. It is clear that participation patterns in recent years are different from 

those in the earlier years of the program. In particular, the 3-state total of insured corn 

acreage was less than 2 million acres in 1983. By 2008, this total exceeded 20 million 

insured acres—a 10-fold increase in participation. Similar changes are observed for other 

crops. 

Figure 6.1. Net Corn Acreage Insured by Year in IA, IL, IN

Total Insured Acreage in Corn

In APH-Based Plans (APH, CRC, RA, IP)

Acres (millions)

Year
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Figure 6.2. Net Soybean Acreage Insured by Year in IA, IL, IN

If all acres have similar risk characteristics or if the acres (or farmers) insured in 1981 are 

comparable to those insured in 2008, then the loss-experience data can be consistently 

compared across time, regardless of the fact that many more acres (and thus potentially 

more information) is available in recent years. However, the fact is that participation 

patterns are very different in recent years. This arises in part due to differences between 

current crop insurance program and the program of the early 1980s. The result of these 

program differences can be associated with differences in the characteristics of an insured 

acre in 1981 versus 2008. Program characteristics have changed, with significant 

increases in premium subsidies, the introduction of revenue insurance, and a number of 

other changes to the terms of coverage and to crop insurance offerings. 

In earlier years when participation rates were low and insurance premium subsidy rates 

were lower, it is likely that a self-selected subset of farmers purchased insurance. In 

particular, it might be expected that higher risk farmers were more likely to participate in 

crop insurance program In addition to changes in crop insurance programs, other 

fundamental changes in the underlying structure of agricultural production have occurred 

for many crops. Perhaps most relevant are agronomic changes involving improved 

germplasm and biotechnological traits that have improved yield performance and reduced 

yield risk. These innovations have been explicitly recognized by the RMA in the recent 

Biotech Endorsement for corn crop insurance, which provides a premium rate adjustment 

for growers planting certain biotech hybrids. “Stacked” biotech hybrids, which have 

multiple biotech traits that improve yield performance, were introduced in the early 

2000s and have been widely adopted in recent years. The most recent NASS statistics 

indicate that, for the U.S. as a whole, 46% of all corn planted in 2009 was of the stacked 

Total Insured Acreage in Soybeans

In APH-Based Plans (APH, CRC, RA, IP)

Acres (millions)

Year
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trait variety. Further, within the main Corn Belt states, stacked trait varieties accounted 

for 55-59% of all corn acres in 2009.15 Other technological advancements may have also 

changed production risks: precision farming, seed treatments, equipment innovations, 

new herbicides and pesticides, and many other technological advances are constantly 

shaping agriculture. 

The shortcomings of a relatively short span of data available for rating must be balanced 

against the aforementioned issues associated with changes in technology and crop 

insurance programs. One approach would be to estimate losses solely on the basis of 

more recent loss experience (say since 1997). In fact, some critics of current RMA rating 

methods take this approach, basing arguments about inaccurate or excessive rates on a 

few years of recent experience. This is equivalent to giving the recent years full weight 

and assigning zero weights to earlier years. Such an approach will inherently focus on a 

limited range of weather and loss experiences. In particular, significant systemic loss 

events occurred in 1988, with a widespread drought, and in 1993 with significant 

Midwest flooding. Any analysis that ignores such data has the potential to understate 

actual loss risks since data from 1997 forward tend to reflect more favorable loss 

experience. 

One approach to incorporate these fundamental changes in the underlying structure of 

insurance programs and agricultural production is to choose weights for use in calculating 

expected or average loss costs or other measures of loss performance using data observed 

over time. Under the present system, RMA essentially gives each year of experience 

equal weight. So, if there are N years of experience, each year receives 1/N weight in 

calculating an average loss cost to establish rates. 

An alternative is to give greater weight to recent experience. Perhaps the simplest 

approach would be to use some form of polynomial distributed lag model, such as Koyck 

or Almon lags. A simple approach is to weight data over time using declining weights of 

the form j wt j − = λ , where 0 < λ < 1 and where wt j − represents the weight assigned to 

observation t-j. Such a specification does raise questions about its ad hoc nature and the 

specification of appropriate values of λ. 

Weighting by Acreage or Other Measures of Participation

Weights based on net acres insured would also concentrate experience in more recent 

years. An analogous approach would involve weighting experience by liability, once 

appropriate normalization of liability totals over years is undertaken to adjust for 

changing commodity prices or by other measures intended to capture participation 

changes, such as units or policies insured. A key consideration in forming weights on the 

basis of acreage or other measures of participation involves the extent to which the 

experience on individual acres (or units, policies, or dollars of liability) is independent 

 

15 See http://www.ers.usda.gov/Data/BiotechCrops/ExtentofAdoptionTable1.htm .
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within a given year. If loss experience is perfectly correlated across acres (or other units 

reflecting participation), one acre provides as much information as ten thousand acres. 

However, as this correlation falls, the amount of information conveyed in varying levels 

of participation or insurance experience may differ significantly. RMA’s current rating 

methods essentially assume perfect correlation across individual acres or units in that no 

adjustment is made to reflect different levels of participation across years. Again, each 

year out of an available sample of N years is given 1/N weight in determining the average 

loss-cost. 

This correlation relationship plays a prominent role in the area of statistics dealing with 

case-control genetic association studies. In such studies, characteristics measured across 

multiple members taken from a common group (e.g., a family) are recognized to be nonindependent. These related members are often termed “sib-pairs” (or sibling-pairs). For 

example, for a sample taken across N individuals, the effective number of observations 

may be expressed as λN, where 0 < λ ≤ 1. As the correlation among individuals 

approaches zero, λ approaches 1. However, as this degree of correlation increases, λ 

decreases. The effective number of observations is defined as the equivalent number of 

independent observations that lead to the same variance for the variable of interest. 

A common measure of the number of effective observations in a correlated sample can be 

derived by considering the number of independent groups and then the number of 

individuals within the groups. For example, consider a case of 100 observations made up 

of 10 groups of 10. Across the groups, observations are independent. However, within 

the groups, observations are correlated with a Pearson (linear) correlation coefficient of ρ. 

The effective number of observations for this sample of 100 observations will be less 

than 100 if the correlation is greater than zero. In the case of m equally sized independent 

groups of sib-pairs, the effective number of observations is given by: e N = N/(1+(m - 1) ρ) . 

This concept is illustrated below in Figure 6.3 for samples of 100 observations, 

comprised of certain numbers of groups of correlated individuals. The first case 

considers 2 groups of 50 individuals, where individuals are independent across groups 

but correlated within groups. Likewise, the second, third, and fourth examples are 

comprised of 4 groups of 25, 5 groups of 20, and 10 groups of 10. Note that, as long as 

the correlation coefficient is zero, the effective number of observations is 100. However, 

as the correlation rises, the effective number of observations drops off considerably until, 

when observations within groups are perfectly correlated, it reaches the number of 

independent groups. Similar patterns are observed for the alternative samples that are 

comprised of differing combinations of groups. The larger the number of independent 

groups, the more information that is conveyed by the sample and the less the penalty for 

correlation within groups that is reflected in the effective number of observations. 
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Figure 6.3. Correlation and Effective Number of Observations 

Deriving an actual correction to be made to acreage or policy counts in estimating 

weighted averages is much more complex than this simple example illustrates. The exact 

patterns of correlation and definitions of independent groups is a complex issue. It is also 

likely that correlation patterns vary significantly over years. A form of state-dependence 

where correlation tends to be stronger during extreme weather events has often been 

noted. Directly using acreage or policy counts to derive weighted averages of loss-costs 

may give too much weight to recent experience in light of the significant concentration of 

participation in more recent years. We believe that the issue of weighting remains an 

important topic for future research. 

Historical Weather

Balanced against any consideration of differential weighting of experience over time is 

the issue of representativeness of the weather captured by a relatively short span of the 

RMA experience data for the longer-run typical weather. For example, 1988 and 1993 

are benchmark loss years but may represent much rarer weather events than would be 

implied by assigning a 1/34 weighting in a simple average of 34 years of loss data. One 

method of adjusting experience for the representativeness of data observed over the span 

of observable loss experience data relative to the longer run weather experience is to use 

some other long-run weather index. An abundance of weather data is available. For 

example, the National Climate Data Center of NOAA reports a wide range of weather 

statistics back to 1895. 

A preliminary question pertains to the degree of weather variability that has been 

observed in recent years. As noted, 1988 and 1993 are often considered to be benchmark 

0

10

20

30

40

50

60

70

80

90

100

0 0.2 0.4 0.6 0.8 1

Effective Number of Observations

Correlation Coefficient 

50 Groups of 2

25 Groups of 4

10 Groups of 10

5 Groups of 20
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years but one must consider the extent to which other years in the 1975-2008 period 

realized significant weather stresses that may be comparable to 1988 and 1993. Of 

course, any direct recognition of weather experience requires a specific index or set of 

indices that adequately reflect weather stresses. As an example, we consider Palmer’s Z 

index of soil moisture. Figure 6.4 presents historical values of Palmer’s Z index in July 

from 1970-2008. The drought of 1988 and extreme flooding of 1993 are obvious in the 

diagram. However, it is also apparent that weather stresses have been realized in other 

years as well. Perhaps most important is the fact that recent years have realized 

significant weather stresses in individual states. For example, Illinois realized significant 

drought in 1999, 2002, and 2005. Indiana also realized significant drought in 1999 and 

2002. It must be acknowledged that different implications would emerge from a 

consideration of different indicators of weather and we are not suggesting Palmer’s Z 

index is the best indicator of growing stresses. Further study would be required to choose 

the weather index or indexes that would perform best by crop and region. 

Figure 6.4. July Values of Palmer’s Z Index in Iowa, Illinois, and Indiana: 1970-

2009 

Palmer's Z Index of Soil Moisture

July Index in Illinois, Iowa, and Indiana

Z-Index

Year
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Empirical Examples of the Effects of Weighting

In order to illustrate how loss-cost experience could be weighted over time, we 

considered six crop/county combinations—cotton in Bolivar County, Mississippi and 

Scurry County, Texas; corn in Webster County, Iowa; and soybeans in Webster County, 

Iowa, Bolivar County, Mississippi, and Jasper County, Indiana. We considered six 

possible measures of the expected (average) loss cost ratio—an unweighted average, an 

average weighted by insured acres, an average weighted by the number of policies, an 

average weighted by the frequency of weather for each year in the longer history of 

weather, and weights constructed from the interaction of weather, acreage, and policies 

sold. 

In order to empirically represent weather, we used the data series for the July value of 

Palmer’s Z drought index for each state from 1895-2008. We grouped the distribution of 

the index into 11 discrete values (bins) and counted the frequency associated with values 

in each grouping over the 1895-2008 period. An example of the resulting distribution is 

presented in Figure 6.5. 

Figure 6.5. Frequency Distribution of July Value of Palmer’s Z Index in Indiana 

(1895-2008) 

Weighted average loss-costs from the six alternative weighting methods are presented in 

Table 6.9 below. It is important to again emphasize that this empirical exercise is meant 

merely to illustrate how alternative weighting schemes might be applied and how the 

resulting loss-cost estimates may be affected. The average loss-costs illustrate that the 

0.00877193

0.04386

0.070175

0.10526

0.18421

0.23684

0.11404

0.16667

0.017544 0.008772

0.04386

0

0.05

0.1

0.15

0.2

0.25

-4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5

Frequency

Palmer's Z
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estimates may be sensitive to the specific weighting scheme employed. In most cases, 

the estimates obtained by weighting by a weather index are closest to the unweighted 

averages, which may suggest that the current unweighted approach to rating may provide 

a reasonably accurate representation of long-run weather patterns, at least as such is 

reflected in our particular choice of index. In most cases, weighting by acreage or policy 

counts tends to produce lower rates. In short, there is little available information to 

recommend one weighting scheme over another and we believe that this should remain a 

topic for future research and evaluation. As it stands, the current weighting methodology 

appears to produce estimates reasonably consistent with those emerging from an 

evaluation of historical weather patterns. 

6.11.2 Adjusting for Unrepresentative Weather in Short Loss Experience Data

We also investigated an alternative approach to account for historical weather. This 

section briefly describes this alternative approach. The analysis conducted is structured as 

follows. We assumed that RMA takes the annualized summary Statplan data for a 

particular county that has had the standard adjustments made to put loss experiences on a 

comparable basis. This results in a single observation for each historical year of loss cost 

experience for a county/crop program. For our illustration we use Statplan data from 

1985 forward to illustrate a scenario where RMA wished to omit older experience. The 

weather data is then matched with weather data from a nearby weather station. In our 

analysis, we used data from the United States Historical Climatology Network (USHCN) 

web site, which gives historical data for weather locations in the contiguous United 

States. This data source has 1,218 observation stations from the 48 contiguous states. 

These weather stations are chosen to be dispersed across the United States, but also tend 

to be located in less urbanized areas for consistency and a lack of human urbanization 

effects near the weather station. For many of the locations, data are available back to 

1895. Thus one has over 100 years of available weather experience. 

In our analysis we use monthly average precipitation and mean temperature data for the 

growing season of the crop analyzed. These data are merged by year with the Statplan 

data set available from RMA. This is an admittedly simplistic set of weather variables 

for explaining variation in the observed loss cost ratios. The monthly variables used are 

an average temperature and rainfall for two-month periods beginning in May through the 

primary growing season. So, the average temperature for the May/June period and the 

July/August period are computed along with squared terms and interaction terms between 

temperature and rainfall within the two-month period. So, econometrically a model is 

specified with the loss cost ratio dependent variable regressed on a linear and squared 

term for May/June temperature, July/August temperature, May/June precipitation, 

July/August precipitation, and a May/June interaction between rainfall and temperature 

and a July/August interaction between rainfall and temperature. This is our base model, 

although in some locations we omit variables due to a lack of statistical significance of 

some of the terms. We proceed by estimating a predictive model of loss cost using the 

time period over which the weather data and Statplan data overlap. In many instances 
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between 70 and 80 years of additional weather data extend beyond the time series of the 

Statplan data base.
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Table 6.9. Alternative Weighted Measures of Loss-Ratios and Loss-Costs

Loss Loss-Cost Loss-Cost

Crop FIPS Weighting Factor Ratio from SOB from Statplan

21 28011 Unweighted 0.8763 0.0709 0.0814

21 28011 Acres 0.8180 0.0709 0.0559

21 28011 Policies 0.8288 0.0697 0.0545

21 28011 Weather Frequency 0.8230 0.0675 0.0833

21 28011 Weather Frequency*Acres 0.7878 0.0712 0.0553

21 28011 Weather Frequency*Policies 0.7845 0.0680 0.0525

21 48415 Unweighted 1.6604 0.3003 0.2867

21 48415 Acres 1.6951 0.3299 0.3142

21 48415 Policies 1.8006 0.3319 0.3171

21 48415 Weather Frequency 1.8311 0.3165 0.2986

21 48415 Weather Frequency*Acres 1.8541 0.3451 0.3242

21 48415 Weather Frequency*Policies 1.9742 0.3509 0.3320

41 19187 Unweighted 0.5026 0.0183 0.0163

41 19187 Acres 0.4356 0.0178 0.0107

41 19187 Policies 0.4507 0.0173 0.0118

41 19187 Weather Frequency 0.3502 0.0129 0.0139

41 19187 Weather Frequency*Acres 0.2984 0.0122 0.0074

41 19187 Weather Frequency*Policies 0.3132 0.0122 0.0081

81 18073 Unweighted 0.9361 0.0366 0.0254

81 18073 Acres 0.6244 0.0321 0.0220

81 18073 Policies 0.6502 0.0315 0.0222

81 18073 Weather Frequency 0.8831 0.0314 0.0222

81 18073 Weather Frequency*Acres 0.4904 0.0221 0.0146

81 18073 Weather Frequency*Policies 0.5266 0.0223 0.0155

81 19187 Unweighted 0.6671 0.0238 0.0159

81 19187 Acres 0.5868 0.0230 0.0162

81 19187 Policies 0.5947 0.0225 0.0164

81 19187 Weather Frequency 0.4658 0.0172 0.0118

81 19187 Weather Frequency*Acres 0.4049 0.0166 0.0116

81 19187 Weather Frequency*Policies 0.4028 0.0159 0.0114

81 28011 Unweighted 1.4083 0.1563 0.1163

81 28011 Acres 1.1260 0.1192 0.0913

81 28011 Policies 1.2710 0.1383 0.1035

81 28011 Weather Frequency 1.4334 0.1569 0.1248

81 28011 Weather Frequency*Acres 1.1468 0.1188 0.0918

81 28011 Weather Frequency*Policies 1.2833 0.1367 0.1046

- 

We estimate a Tobit model of loss cost because the loss cost ratio variable is censored at 

zero. This prevents our predictive model from predicting a loss cost below zero. 
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The structure of the Tobit model is:

where ). The observed y is defined by the following measurement equation

Importantly the expected loss cost is written as:

where λ is the inverse mills ratio

Once the loss cost predictive model is estimated, we calculate the average loss cost 

observed during the time period when loss cost data were available, and the predicted 

values of the tobit loss cost model given the weather data observed for the full time series 

back to 1895. This allows us to predict what the loss cost would have been given weather 

that occurred in a time period prior to the crop insurance program’s observed data. Thus, 

we are able to backcast loss cost ratios. We then take the mean predicted loss cost ratio 

over the full 108-year period between 1895 and 2008 and compare it to the predicted loss 

cost ratio in recent periods to derive a ratio of recent loss cost to the longer period. Table 

6.10 shows an example of the estimated model for Fort Dodge county, Iowa soybeans. 

The 24 observations are used to estimate the relationship of the various weather variables 

and the observed loss cost. 

Our interpretation of this comparison is to see whether the predicted loss cost ratios for 

the most recent period where RMA has available data, is greater or less than for the 

longer time period. For example, if the average predicted loss cost ratio in the most 

recent period for a particular location is five percent and the average loss cost over the 

108-year period is six percent, this would suggest that the most recent time period has 

experienced weather that is more favorable than over the longer period. Conversely, if 

the average predicted loss cost ratio over the full time period is less than that observed in 

the actual experiences of the program, this would suggest that insurance program has 

experienced worse weather events in the recent observed period than over the longer time 

span. 
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Table 6.10. Fort Dodge County Iowa Loss Cost Model 

 Number of Observations 24

 Name of Distribution Normal

 Log Likelihood 61.03659237

 Analysis of Parameter Estimates

Parameter DF Estimate Standard 

Error

95% confidence 

Limits

ChiSquarePR>Chi

sq

Intercept 1 -2.3014 0.4957 -3.2729 -1.3299 21.56 <.0001

May/June Temp 1 0.018 0.0046 0.0091 0.027 15.54 <.0001

July/Aug Temp 1 0.016 0.006 0.0042 0.0278 7.07 0.0079

May/June Interaction 1 -0.0035 0.0011 -0.0056 -0.0014 10.73 0.0011

July/Aug Temp 1 -0.0031 0.0012 -0.0054 -0.0007 6.25 0.0124

May/June Precip 1 0.232 0.0698 0.0953 0.3687 11.06 0.0009

July/Aug Precip 1 0.2055 0.089 0.0309 0.38 5.32 0.021

July/Aug Precip 

Square

1 0.0018 0.0009 0 0.0037 3.84 0.0502

Scale 1 0.019 0.0027 0.0143 0.0252

Our seven example county/crop programs are shown in Table 6.11. The predicted value 

over the actuarial period is reported first, then for the longer period and then the ratio of 

the two is reported in the final column. Note that in some cases the ratio is above 1.0 and 

in others below 1.0, suggesting differences between the actuarial period and the longer 

weather period. For example, in Bolivar County soybeans the ratio of less than 0.97 

suggests recent experience is the result of better than average weather. Conversely, Fort 

Dodge county Iowa corn and soybean appear to have incurred actuarial experience about 

14% worse than one would expect over the longer period. 

 

Table 6.11. Comparison of Actuarial Period to Long-term Predicted Loss Cost

Location and 

Crop

Actuarial 

Period Mean 

Predicted 

Loss Cost 

1895-2008 

Period Mean 

Predicted Loss 

Cost

Ratio actuarial 

Period/Longer 

Period

Audrian Co Mo Corn 0.089 0.090 0.984

Bolivar Co MS Cotton 0.061 0.060 1.014

Bolivar Co MS Soybeans 0.095 0.098 0.967

Fort Dodge Co IA Soybeans 0.024 0.021 1.147

Fort Dodge Co IA Corn 0.020 0.017 1.146

Scurry Co TX Cotton 0.284 0.282 1.005

Jasper Co IN Soybeans 0.038 0.042 0.911 
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Conclusions and Recommendations

We have reviewed the implications of changes in the crop insurance program as well as 

structural changes in the agronomic setting underlying recent experience for current 

rating procedures. At present, these procedures assign equal weight to each year’s 

experience in calculating an expected (average) loss cost ratio. We point out that crop 

insurance participation patterns have changed significantly over time, with current 

participation being ten-fold larger in terms of acreage insured or total liability than was 

the case in the 1980s. We also discuss the implications of technological innovations that 

may have altered yield performance and risk over time. These innovations include 

significant advances in biotechnology. RMA has made explicit adjustments for these 

innovations in its Biotech Endorsement (BE). 

We note the approach taken here makes several assumptions that RMA should recognize. 

First, as specified in our examples, climate change is not addressed. We have assumed 

all years of weather data are equally likely. A second caveat to our preliminary analysis 

is that weather variables do not fully explain loss cost experience and functional form is 

uncertain. We also anticipate that in some crops/regions the model fit will be relatively 

poor. It may be that estimating these relationships at a more aggregate level (e.g. 

state/crop) would be preferred. 

We recommend that RMA evaluate alternative loss cost experience weighting methods. 

Our analysis suggests it is feasible to incorporate additional weather information into 

the rating system and to allow additional weight be placed on more credible annual 

observations. However, we do not offer specific recommendations for changing the 

manner in which experience is weighted over time in current rating methods. We 

believe a detailed study of this issue should investigate both optimal weights and 

implementation issues.

6.12 Using a Loss Ratio Approach Rather than Pure Premiums

The current pure premium approach to developing a base rate requires that the data be 

massaged extensively to restate all experience on a common basis. When there were 

fewer choices in the program, this was a much easier exercise, although it has always 

included some rather ad hoc adjustments that were not necessarily actuarially sound, such 

as the interpolation adjustment to approximate unobserved experience for producers 

electing lower than 65% coverage. This adjustment made very little difference to the end 

result when the proportion of producers below 65% coverage was low, but as more and 

more producers opt into the program, the proportion of producers with coverages both 

above and below 65% coverage has increased, making the adjustment more and more 

important. This is just one example where the data adjustment is ad hoc. There are other 

cases where no adjustment is made at all, such as for the distribution by type/practice 

discussed in section 6.3. 
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One way to avoid the need for such adjustments in the development of the base rate 

would be to adopt a loss ratio rather than pure premium approach. The following is an 

outline of how that might be done. A more extensive study, with testing on several data 

sets, would be needed before we could make a strong recommendation, but we believe 

that this approach is at least worth exploring in further detail.

Consider the fact that the current base rate represents in a single value the entire time 

series of experience to date. It also provides a starting point from which all producer 

premiums are determined. If the base rate in use is equal to the indicated rate, then the 

expectation is that the experience over the long run using that base rate will produce a 

loss ratio of 100%. Let Bi be the base rate in year i. How is B1 related to B0? The 

current ratemaking methodology would suggest that they are unrelated, but in fact B1 is 

merely the result of adding one more year to the time series that produced B0. This 

suggests that B1 can be calculated without resorting to restating the entire history. If B0 

is based on N years of experience and expected to produce a loss ratio of 100% and the 

loss ratio for year 0 is X%, then the expected loss ratio for year 1, given B0 and the loss 

ratio for year 0, is 100% 1 %

1

N X Z

N

× +× = +

and B1 = Z × B0. 

If the current base rate is not equal to the indicated rate due to capping, then the a priori 

expected loss ratio, rather than 100%, is the ratio of the actual rate to the indicated rate:

Bi

B0 . The a posteriori expected loss ratio is 

0 1 %

1

i

B N X

B Z

N

× +×

= +

, and the indicated rate 

for year 1 is Bi+1 = Z × B0. 

Where the data are thin, the indicated base rate based on county-only experience gets 

credibility weighted with the circle experience. The credibility-weighted indicated rate is 

a “better” estimate of the expected 100% ratio rate than the rate based on county 

experience alone. Clearly, the credibility weighting methodology would need to be 

adjusted to determine how much weight to give to the new year’s experience for both the 

county and the circle, but the objective remains to determine how much the addition of 

another year’s experience adjusts the base rate.

A loss ratio approach study would also need to address how to manage the catastrophe 

procedure. We note, however, that Milliman (2008) has considered a loss ratio approach 

rather than a pure premium approach to identifying how much of county experience 

should be spread to the state. It would also be easier to work with if the CAT load were 

multiplicative rather than additive in cases where there is a long time series of data 

indicating that the cat exposure is not uniform across a state. 
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Rating Factor Adjustments 

All of the above has no effect on adjustments to any of the rating factors. The proper 

factor for type or practice still has to be calculated separately from the base rate, just as it 

is today. However, once prior experience has been adjusted once for type/practice mix 

changes as discussed in Section 6.3, moving to a loss ratio methodology removes the 

need to readjust the prior experience. Once an actuarially sound base rate for an 

identified base type/practice has been established, the overall rate level is adjusted by

increasing or decreasing that base rate, while the relationships among types/practices are 

adjusted by changing the type/practice relativities. Changing a type/practice relativity, 

however, also has an effect on the overall rate level, properly accounted for by an 

offsetting change in the base rate. For example, if there is an X% discount given for a 

particular type or practice and a rating study indicates that the discount should be Y%, 

then the effect on the indicated base rate is as follows:

B0 = indicated 100% loss ratio base rate with discount at X% 

p = proportion of insureds with discount 

All other things being equal, the premium with a discount of X% will be 

0 0 B pB X p ×− + ×− × (1 ) (1 %) . The total premium needs to be unchanged, so the 

adjusted base rate B0* will need to be Error! Bookmark not defined. 0

1 %

1 %

X p B

Y p

− × − . 

This is called an off balance adjustment. The calculation of the off balance is a bit more 

complicated where the rating factor is additive rather than multiplicative, but is easier 

than the requisite reweighting of the experience discussed in section 6.3 to maintain a 

balanced loss cost.

We note that virtually all property/casualty rates are made using a loss ratio approach that 

applies an indicated change based on recent experience to an existing rate structure. Loss 

ratio methods are employed in most cases because they remove most of the need to adjust 

prior experience for changes in distribution by classification. As the number of “classes” 

(types/practices/options) permitted by RMA increases, the loss cost method of setting 

rates becomes either considerably more cumbersome or less and less accurate. We 

recommend that RMA undertake a comprehensive study of the loss ratio method for 

determining future rate changes, perhaps in conjunction with a study aimed at a State 

level top-down approach as recommended by Milliman (2008).
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7.0 Evaluation of Procedures to Develop COMBO Revenue Rates

7.1. Introduction and Overview 

The combo policy will integrate existing APH-based crop insurance plans into a common 

umbrella crop insurance policy that has a number of options for yield and revenue 

coverage. This allows the common and basic provisions of the underlying crop insurance 

plans to be the same across different coverage options. The combo policy represents a 

straightforward extension of the design and methods underlying the Revenue Assurance 

(RA) policy, albeit with some important modifications that more closely resemble the 

current rating methods used for Crop Revenue Coverage (CRC). These modifications 

arise because the method involves summation of several independent components in a 

manner analogous to the piece-wise design of the CRC policy. 

The development of the combo policy was preceded by two contracted studies that 

evaluated rating methods relevant to yield and revenue insurance plans. One study 

addressed the issue of rate relativities and recommended a change to RMA’s longstanding practice of using fixed rate relativities for all crops and counties. RMA adopted 

this study’s recommendations and now bases rates at coverage levels different from 65% 

on variable rate relativities that depend on the underlying rates and other characteristics 

of the county’s experience. The motivation for this study and its conclusions reflected 

concerns over rate differences for what was essentially identical coverage under the RAHPO and CRC plans. A second study undertaken by AgRisk Management, LLC 

recommended that RMA adopt the rating methods used in establishing RA and RA-HPO 

premium rates—a recommendation that RMA has partly accepted in rating the combo 

plans. 

7.1.2. Brief Description of the Combo Rating Method16

The combo rating method is adapted from various components of the RA and CRC rating 

approaches. The combo rating procedure follows from the RA rating in the sense that 

rates are derived from a parametric yield distribution. The parameters of the yield 

distribution are essentially calculated by calibrating a specific parametric probability 

density function (PDF), a censored normal in this case (see discussion below), so that it 

corresponds to a distribution having a mean equal to the farmer’s underlying APH yield 

and a rate that is equal to the underlying APH rate at the 65% coverage level. The 

important underlying assumption in this approach is that the APH yield rates are correct. 

This assumption is consistent with the CRC rating approach. Combo rating also follows 

the RA method in assuming that the price distribution is assumed to follow a lognormal 

distribution and its parameters can be computed based on an options based volatility 

 

16 The brief description of the combo rating procedure in this section is based primarily on the RMA 

document “RMA Revenue Rating An Analysis of the Combo Rating Method.”
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measure. The parameters of the yield and price distributions, together with an assumed 

degree of yield-price correlation, are then used in a simulation procedure to calculate a 

revenue rate at various coverage levels. Consistent with the CRC rating approach, a 

“revenue load” is then calculated by taking the difference between the simulated revenue 

rate and a corresponding simulated yield rate (for yield insurance coverage). This revenue 

load is then added to the empirical APH yield rate to get the premium rate that will 

eventually be charged to an insured choosing revenue coverage under the combo policy. 

The combo rating process can be basically divided into four components: (1) Calculating 

price and yield correlations, (2) Calculating the mean and standard deviations (i.e. the 

parameters) of the yield and price distributions, (3) Deriving the correlated yield and 

price draws, and (4) Simulating losses and calculating revenue/yield rates. These 

components are succinctly described below.

The yield-price correlations used in combo rating are calculated from yield and price 

deviates from 1990 to 2005. The National Agriculture Statistics Service (NASS) county 

yield data are detrended using a linear tend and the yield deviates are calculated as the 

percentage deviation from trend. The price deviates, on the other hand, are calculated as 

the percent change in price from the expected price to the harvest price. Once the price 

and yield deviates have been calculated, the county-level yield-price correlations are 

derived and then state-level yield-price correlations are computed by taking the weighted 

average of the county-level correlations (i.e. weighted by production). The state-level 

correlations are then adjusted downward to more accurately reflect the yield-price 

correlation at the individual level. A 1990 to 2005 period was chosen to calculate the 

county-level correlations because it can be argued that significant changes in agricultural 

policy have occurred prior to this period (i.e. from the 1985 Farm Bill) that could have 

caused structural changes in the relationship between yield and prices. Choosing the 1990 

to 2005 period avoids this structural break. One limitation of the procedure used to 

calculate the yield-price correlations is that it imposes a constant yield-price correlation 

for all producers in the state. 

As mentioned above, the parameters of the price distribution (i.e. mean and standard 

deviation) are calculated primarily using an options based volatility measure and 

assuming that prices are log-normally distributed. The assumption of log-normality is 

consistent with the Black-Scholes option pricing method that is commonly used by 

traders. On the other hand, the yield distribution in the combo rating method is assumed 

to follow a censored normal distribution. This is in contrast to the beta distribution used 

in the RA rating approach.17

 

17 As will discussed later in this chapter, the main argument for using the censored normal instead of the 

beta is the perceived practical/computational difficulty in working with distribution due to the lack of a 

closed form solution. Note that we do not necessarily agree with this argument (See Minor Comment (4) in 

section 8.3 below). The AgRisk Management, LLC report also justified the use of the censored normal by 

showing that the rates derived from a censored normal and a beta are fairly similar.

The procedure to derive the parameters (i.e. the mean and 

standard deviation) of the censored normal yield distribution corresponding to the APH 

yield insurance rate at the 65% coverage level is as follows:
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(1) Normalize the APH yield to a value of 100 (i.e. μ=100), 

(2) Select a target APH rate (i.e. the target rate)

(3) Find μ and σ that ensure that the following two equations hold: 

(Eq. 7.1) 

5000

1

1 100 max( ,0) 5000 i

i

y =

= ∑

 

(Eq. 7.2) Target Rate = 

5000

1

5000

1

max(0,65 max( ,0))

65

i

i

y =

∑ −

, 

where i i y z =⋅+ σ μ and i z is the standard normal deviate. 

(4) Transform the parameters in step (3) for APH yields other than 100 by using 

the following formulas:

(Eq. 7.3) 100 y

APH μ μ

×  =

(Eq. 7.4) 

y y

σ

σ μ

μ   = ×

. 

Once these yield and price parameters are calculated, the correlated yield and price draws 

can then be determined. First, 500 draws are calculated from the inverse normal of 

Babcock’s Nearly Uniform Sequence. This is a low discrepancy sequence (a variant of 

the rectangular rule also called quasi-random sequences) that ensures that even with a 

lower number of draws, on average, these draws can still be consistent with a uniform 

distribution. In general, such quasi-random numbers converge faster than pseudo-random 

numbers and the results from quasi-random numbers tend to be more accurate than with 

pseudo-random numbers using the same number of points/draws (See Morokoff and 

Caflisch, 1994). Due to the relative accuracy of the quasi-random sequence, the number 

of draws can be reduced to improve computational speed. After the 500 draws are 

determined, the Iman and Conover (1982) method is used to impose the state-level yieldprice correlations calculated in step (1) of the combo rating process. This method allows 

arbitrary marginal distributions (the censored normal yield distribution and the lognormal price distribution in this case) to be combined into a joint distribution with a 

specified correlation. The Iman and Conover (1982) method is a computationally simple 

algorithm of imposing correlation that has already been used in rating an existing crop 

insurance product (the Livestock Gross Margin (LGM) product). 

Using the deterministic points from the 500 correlated draws, yield and revenue rates 

(e.g., the Harvest Price Revenue Rate (HP Rate) and the Harvest Price Exclusion Option 
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Revenue Rate (HPEO Rate)) can then be calculated using the following formulas (i.e. this 

process is also called a quasi-Monte Carlo simulation):

(Eq. 7.5) Yield Rate = 

500

1

max(0, max(0, ))

500

iy y

i

CY y

Y C

σ μ

=

⋅− ⋅ +

⋅ ⋅ ∑  

(Eq. 7.6) HP Rate =

500

1

max(0, min(2 , max( , )) max(0,( ) min(2 , )))

500

iy y

i

CY P Pp y P p

YCP

σ μ

=

⋅⋅ ⋅ − ⋅ + ⋅ ⋅

⋅⋅⋅ ∑   

(Eq. 7.7) HPEO Rate = 

500

1

max(0, max(0,( ) min(2 , )))

500

iy y

i

CY P y P p

YCP

σ μ

=

⋅⋅− ⋅ + ⋅ ⋅

⋅⋅⋅ ∑  

where C is the coverage level, Y is the APH yield, P is the planting time price, i y is the 

yield draw, and p is the log-normally distributed harvest time price draw (calculated 

based on the parameters of the lognormal price distribution as follows: pi p p p eσ μ ⋅ +  = ). 

The “revenue load” can then be calculated as follows:

 HP Combo Revenue Load = HP Rate – Yield Rate

 HPEO Combo Revenue Load = HPEO Rate – Yield Rate.

The resulting combo premium rates are then derived using the following formulas: 

HP Combo Premium Rate = APH Base Premium Rate + HP Combo Revenue Load 

HPEO Combo Premium Rate = APH Base Premium Rate + HPEO Combo Revenue Load.

 

Given the rate calculations above, it is important to emphasize that the actual combo 

revenue rate is not the simulated revenue rate per se, but instead it is the APH Base 

Premium rate added to the difference between the simulated revenue rate and simulated 

yield rate. This captures the additional price risk from insuring revenue and ensures that 

even as yield risk approaches zero there will still be a positive premium to account for 

price risk. In addition, this “revenue load” approach is consistent with the CRC rating 

methods. 

7.1.3. Outline of the Combo Review Section

Our review of the combo rating methods below identifies issues that we believe merit 

additional consideration. None of these issues is considered to be so serious as to inhibit 

the implementation of the combo plan. However, we do believe that these issues merit 

additional consideration as the combo plan is implemented, since the revenue options 
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available under the combo plan will likely account for the largest share of liability and 

premium in the program. Moreover, it should be noted that in this review of the combo 

policy and its associated rating methods, we specifically reviewed two documents. The 

first is the aforementioned internal RMA document entitled “RMA Revenue Rating: An 

Analysis of the Combo Rating Method” (See footnote 17). A second document, which 

appears to have preceded the RMA documentation of the combo rating, is an unpublished 

contract study entitled “Development of a New Rating Method for Multi-Peril Crop 

Insurance” 

Our review in this chapter proceeds according to the following plan. We first focus on 

what we believe to be the most significant issue associated with the design and rating of 

the combo plan—an inconsistency in treatment of rate relativities. It is important to note 

that the combo rating methods initially reject RMA’s current empirical rate relativities 

(from the current APH yield insurance rating approach) and instead impose the 

relativities associated with the parametric distribution assumed to govern the crop yields. 

This difference is later “reconciled” by adding the implied “revenue load” that is given by 

the difference between the combo revenue rate (derived from the combo rating methods) 

and the combo yield rate to the associated APH yield rate, which therefore embeds 

RMA’s current relativities. We identify important shortcomings that may be associated 

with this rating approach. Most important is that this approach involves an inconsistency 

in that two different yield distributions are implied—one based on the calibrated censored 

(discrete/continuous) normal distribution and another based upon the current rate 

relativities. As we show below, these distributions may differ substantially. In the next 

section of the review, we then identify a number of minor issues that RMA may wish to 

consider. None of these issues is considered to be critical. 

7.2. Combo Rate Relativities 

As we have noted, the combo rating approach incorporates a design element that may 

result in inaccurate or at least inconsistent rates across different insurance options. One 

distribution is used to model yield rates, revenue rates, and a revenue loading factor that 

is given by the difference in revenue and yield rates. This loading factor is then added to 

the APH yield rates at coverage levels away from 65% to obtain the final revenue rates. 

This essentially involves the use of one distribution and set of relativities to obtain the 

revenue load and another distribution and set of relativities to establish the revenue rate. 

Regardless of the extent to which the resulting revenue rates differ in practice, this 

procedure does involve an inconsistent approach to rating. 

Several alternatives that could eliminate this inconsistency are apparent. The first and 

perhaps preferred method is to calibrate the parametric yield distribution across the entire 

range of rate relativities rather than at a single rate, when calculating the revenue load. 

Of course, it may not be possible to perfectly replicate the entire distribution across all 

coverage levels but it is generally possible to achieve a very accurate calibration to the 

entire range of rates, provided an appropriate parametric distribution is used in the 
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calibration. As we demonstrate below, it is possible to calibrate a four-parameter beta 

distribution that has two free parameters to the existing RMA relativities to a high degree 

of accuracy in many cases. On this point, note that such a beta distribution may offer 

considerably more flexibility than the two-parameter beta that has been used in rating the 

RA plan. In the RA case, rather rigid assumptions are used to assign a minimum and 

maximum yield, which severely constrains the flexibility of the resulting distribution. In 

contrast, allowing these parameters to fit the entire span of rates across different coverage 

levels provides a more accurate and flexible approach. Alternatives to the use of a beta 

distribution include the recommended censored normal density and other parametric 

specifications, including the Weibull and mixtures of normals. 

A second natural approach would be for RMA to completely abandon the empirical rate 

relativities currently in use and instead use the relativities associated with the calibrated 

combo censored normal distribution. We do not recommend this approach in that the 

current rate relativities have been chosen on the basis of an empirical analysis of loss 

experience—an approach that we believe is likely to be superior to any assumed 

parametric distribution. 

As it stands, we believe consistency in rating dictates that a single distribution and set of 

relativities should be used in rating the yield and revenue components of the combo plan. 

This would involve a choice of either using the current empirical relativities or adopting 

the relativities implied by a censored normal distribution in both rating the APH yield 

plans and the revenue plans. 

A relevant question involves the extent to which this inconsistency results in important 

differences in rates and revenue loads. In order to provide some empirical evidence on 

this issue, we considered an evaluation of the 2009 APH premium rates for selected 

major growing regions for four important crops. We considered rates for all practices on 

corn and soybeans in all counties in Iowa, Illinois, and Indiana. We considered cotton 

rates in Georgia, Mississippi, and Texas. Finally, we considered wheat rates in Nebraska 

and Kansas. 

Absolute percentage differences between current relativities and the relativities inherent 

in the combo rating methodology are presented in Table 7.1. Because the combo rating 

method calibrates to the 65% rate, the difference should be zero at the 65% coverage 

level. Any modest differences reflect approximation errors in the optimization routine 

used to calibrate the censored normal distribution to the rate and APH yield.18

 

18 Note that we used a simple nonlinear optimization algorithm to calibrate the density to the 65% rate and 

the APH yield. This calibration routine may offer advantages in implementing the combo plan in that no 

simulation or random draws are needed. 

 In general, 

the relative (proportional) differences in premium rate relativities tend to be highest at the 

85% coverage level. The absolute proportional differences suggest average rate relativity 

differences of 13%, 17%, 19%, and 8% for corn, soybeans, cotton, and wheat, 

respectively. As expected, differences also increase at lower coverage levels as one 
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moves away from the 65% coverage level. Differences at the 50% coverage level tend to 

range from 6-18%. 

We selected several crop and practice combinations to illustrate differences in the rate 

relativities across the range of coverage levels. Examples of these differences are plotted 

in Figures 7.1 and 7.2. Of course, both relativities are defined to be 1.0 at the 65% 

coverage level. The patterns demonstrated in Table 7.1 are reinforced in the diagrams. 

For most of the counties examined, the combo rate relativities are typically steeper, 

implying relativities that are higher than those currently in use at coverage levels above 

65% and lower than current relativities for coverage levels less than 65%. However, this 

is not always the case (as seen in Figures 7.1 and 7.2). In some cases, rather extreme 

differences in relativities are apparent. These cases tend to occur when the combo 

relativities are much flatter than those implied by the empirical relativities, implying 

lower rates at higher coverage levels. Figures 7.3 and 7.4 present a summary of the 

geographic differences in the differences in relativities.
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Table 7.1. Summary of Evaluation of Absolute Percentage Differences between 

Current RMA Empirical Yield Rate Relativities and Proposed Combo Rate 

Relativities

Coverage Level Mean Std Min Max

Wheat

50 0.0631 0.0236 0.0019 0.1508

55 0.0492 0.0184 0.0011 0.1171

60 0.0281 0.0104 0.0004 0.0647

65 0.0000 0.0000 0.0000 0.0000

70 0.0263 0.0150 0.0004 0.1283

75 0.0469 0.0291 0.0002 0.2427

80 0.0638 0.0423 0.0006 0.3527

85 0.0790 0.0550 0.0001 0.4063

Cotton

50 0.0947 0.0690 0.0001 0.3054

55 0.0636 0.0497 0.0003 0.2093

60 0.0296 0.0244 0.0002 0.1072

65 0.0001 0.0009 0.0000 0.0226

70 0.0553 0.0328 0.0003 0.1577

75 0.1030 0.0568 0.0001 0.2960

80 0.1460 0.0736 0.0011 0.4199

85 0.1897 0.0883 0.0033 0.5102

Corn

50 0.1754 0.0642 0.0727 0.4194

55 0.1236 0.0441 0.0443 0.2831

60 0.0662 0.0258 0.0238 0.1361

65 0.0000 0.0000 0.0000 0.0000

70 0.0289 0.0217 0.0004 0.1005

75 0.0608 0.0453 0.0000 0.2096

80 0.0940 0.0693 0.0003 0.3233

85 0.1266 0.0929 0.0008 0.4370

Soybeans

50 0.1422 0.0702 0.0009 0.3423

55 0.0685 0.0499 0.0008 0.1915

60 0.0306 0.0244 0.0000 0.0919

65 0.0000 0.0000 0.0000 0.0000

70 0.0440 0.0294 0.0001 0.0977

75 0.0891 0.0601 0.0003 0.1917

80 0.1321 0.0898 0.0004 0.2830

85 0.1720 0.1173 0.0003 0.3687

Note: Absolute percentage difference defined as |(Current-Combo)/Current|. 
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Figure 7.1. Examples of Differences in between Current RMA Empirical Yield Rate 

Relativities and Proposed Combo Rate Relativities 

A. Wheat 

B. Cotton

Comparison of Rate Relativities

For Crop=0011, Practice=004, and FIPS= 20057

Comparison of Rate Relativities

For Crop=0021, Practice=002, and FIPS= 28143
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Figure 7.2. Examples of Differences in between Current RMA Empirical Yield Rate 

Relativities and Proposed Combo Rate Relativities 

A. Cotton

B. Corn

Comparison of Rate Relativities

For Crop=0021, Practice=002, and FIPS= 48357

Comparison of Rate Relativities

For Crop=0041, Practice=002, and FIPS= 17115
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Figure 7.3. Geographic Dispersion of Corn Rate Relativity Differences

A. Corn

B. Soybeans 

Rate Relativity Percentage Differences

Combo vs. Current Relativities: Non-Irrigated Corn at 75% Coverage

%Difference = (Current Relativity - Combo Relativity)/Current Relativity

Rate Relativity Percentage Differences

Combo vs. Current Relativities: Non-Irrigated Soybeans at 75% Coverage

%Difference = (Current Relativity - Combo Relativity)/Current Relativity
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Figure 7.4. Geographic Dispersion of Cotton Rate Relativity Differences 

A. Cotton

B. Wheat 

Rate Relativity Percentage Differences

Combo vs. Current Relativities: Non-Irrigated Cotton at 75% Coverage in Georgia

%Difference = (Current Relativity - Combo Relativity)/Current Relativity

Rate Relativity Percentage Differences

Combo vs. Current Relativities: Non-Irrigated Wheat at 75% Coverage

%Difference = (Current Relativity - Combo Relativity)/Current Relativity
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The document entitled “Development of a New Rating Method for Multi-Peril Crop 

Insurance” argues that the truncated normal distribution proposed for the combo plan 

closely resembles a beta distribution. However, it is not apparent that the distributions 

were compared over the entire range of rate relativities inherent in RMA’s current rating 

process. In order to consider the degree to which calibration errors may characterize a 

beta density and the truncated normal that is only calibrated to the APH yield and the 

65% coverage rate, we conducted an empirical evaluation of the calibration errors for a 

selected set of crops and counties. We compare the truncated normal, calibrated to the 

65% rate and the APH yield, and a four-parameter beta distribution, calibrated across the 

entire range of rates implied by the current rate relativity structure. Figure 7.5 presents 

examples of calibration errors across the range of rate relativities for the proposed combo 

rating method and for a beta that is calibrated to rates at all coverage levels. Although the 

beta distribution may have a small degree of calibration error at the 65% rate, it tends to 

provide a much better fit to the range of rate relativities across coverage levels. The 

differences are more extreme at high coverage levels for the truncated normal. 

Finally, perhaps the most critical issue pertains to the extent that the implied revenue 

loads tend to differ across the alternative rating methods. Using the same selection of 

counties, we considered the differences in implied revenue loads (without a harvest price 

option) using the recommended combo methods and a 4-parameter beta distribution 

calibrated across the range of rate relativities. We assumed 40% volatility and adopted 

the same general simulation procedures to impose correlation, which we assumed to be -

0.40. Figure 7.6 presents examples of the alternative revenue loads. In many cases, the 

loading factors are very similar. However, differences become apparent at high coverage 

levels in every case. At the 85% coverage level, the loading factors typically differ by 

about 1 percentage point, with the combo revenue loads being smaller. 

In summary we generally find that the COMBO rating methodology does an 

appropriate job of combining the various aspects of yield and price risk into a revenue 

product that provides both a Harvest Price Revenue Rate (HP Rate) and the Harvest 

Price Exclusion Option Revenue Rate (HPEO Rate). Maintaining consistency between 

these revenue products and the underlying APH product is not simple. However, we 

generally concur with the way RMA determines price-yield correlations and price 

variability. We recommend that RMA consider revising the COMBO rating method to 

eliminate potential inconsistency in the yield rate relativities applied. Though the 

differences may be modest in many cases, we believe that a more conceptually sound 

result would be obtained from applying a consistent set of yield rate relativities across 

the entire rating process. This could involve calibrating the desired distribution (e.g., 

censored normal) to the empirical rate relativities currently used by RMA or, 

alternatively, abandoning these empirical rate relativities in favor of those that emerge 

from calibration to the APH and a single rate. 
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Figure 7.5. Examples of Calibration Errors (Defined by Current Rate Relativity 

Structure) From 4-Parameter Beta Distribution and Censored Normal (Combo 

Method)

A. Corn

B. Soybeans 

Comparison of Calibration Errors

For Crop=41, Practice=2, and FIPS=17115

Defined Using Current RMA Empirical Relativities

Comparison of Calibration Errors

For Crop=81, Practice=2, and FIPS=19127

Defined Using Current RMA Empirical Relativities
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Figure 7.6. Implied Revenue Loads for RA-HPEO Rates From Beta Distribution 

(Calibrated to Current RMA Rate Relativities) and Censored Normal (Combo 

Method)

A. Corn

B. Soybeans 

Implied Revenue Loads for Combo with HPEO

For Crop=41, Practice=2, and FIPS=17115

Defined Using Current RMA Empirical Relativities

Implied Revenue Loads for Combo with HPEO

For Crop=81, Practice=43, and FIPS=17191

Defined Using Current RMA Empirical Relativities
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7.3. Minor Comments 

Our review and evaluation also suggested a number of other minor points, which are 

identified here. In no cases are major concerns identified and we suspect that several of 

these points may simply reflect typographical errors. These are discussed in turn. 

1. Depending on the complexity associated with implementation, it may be 

preferable to adopt direct analytical methods to obtain numerical solutions rather 

than carrying forward a relatively small number of points taken from a uniform 

(or the “nearly uniform sequence”). Arguments that point out that some quantile 

functions do not have closed form solutions are not sufficient to rule out their use 

(as such is true even of the normal and log-normal quantile functions). Numerical 

solutions to these problems are readily available (see, for example, the BETAINV 

function in Excel). 

2. The methods used in rating RA involve calibrating a beta pdf to the APH yield 

and 65% rate. However, it was noted that the beta may be difficult to implement 

in practice, which is why a censored normal pdf is recommended in the combo 

rating method. While we acknowledge that it can be difficult to calibrate a beta 

distribution in some cases (primarily in cases involving very high premium rates), 

we do not necessarily agree that the beta is not appropriate for a wide range of 

county/crop programs. While it is true that the beta quantile function (inverse 

cdf) may not have a closed form solution, the same is true for the normal and lognormal distributions and thus reliance upon numerical solutions for working with 

the quantile functions does not seem to be a significant barrier to using the beta 

density. 

3. While one must be sensitive to the fact that it is difficult to measure yield-price 

correlations with relatively limited data and constantly shifting programs, a 

number of questions regarding the assumed correlations are relevant. First, it 

seems more likely that farm program changes (e.g., planting flexibility) that 

occurred with the 1996 FAIR Act would have significantly shifted correlations. 

Thus, using data since 1990 may also raise questions. Perceived changes in 

correlation over time which serve to support arguments for changing these 

correlations in the rating model are based upon very short series and are unlikely 

to provide statistically significant differences. 

4. We suggest that RMA evaluate estimating price-yield correlations at a level 

below the state level as there may be clear reason to allow correlation to vary 

across production regions in a state. For example, irrigated corn in western 

Kansas may have a very different price-yield correlation than in the eastern 

portion of the state.

5. In cases where an excessive degree of censoring/truncation of the normal 

distribution is needed to adequately calibrate the rate and yield, the correlation 

structure can be significantly altered when considering the y=max(0,yi) correlation 

with price. Put differently, a lower degree of correlation will exist for the 

censored variable with price than was the case for the uncensored variable. How 
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this is addressed in the rating simulation is unclear. Figure 7.7 below illustrates 

this phenomenon (where the blue line is for the censored case and the red line for 

the uncensored case). At low premium rates, no distortion occurs. However, at 

very high premium rates, a much lower degree of correlation is obtained for the 

truncated yield. We do not think this is a significant issue in that rates high 

enough to induce such distortions are unlikely to be observed in areas that have 

substantial price-yield correlation. 

Figure 7.7. Distortion in Correlation from Using Truncation of Normal Distribution 

(True Correlation = -0.40) 

-0.382889

-0.371825

-0.358503

-0.340888

-0.321095

-0.4 -0.4 -0.4 -0.4 -0.4

-0.41

-0.39

-0.37

-0.35

-0.33

-0.31 0 0.1 0.2 0.3 0.4 0.5 0.6

Correlation

Premium Rate
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8.0 Summary of Recommendations

This chapter summarizes and consolidates our recommendations for APH and COMBO 

rating. For additional details see chapter six for a detailed explanation of 

recommendations for the APH product. Chapter seven provides the explanation of our 

recommendations for COMBO rating. 

8.1 Basic Approach to APH rating 

We recommend that RMA continue to use loss experience as the foundation of the rating 

system as it is the only way to assure that actual losses drive the rating results. This is 

consistent with standard property and casualty insurance rating practices. While crop 

insurance poses a unique set of actuarial challenges, alternatives to loss-experience-based 

rating would likely fail to adequately address the multiple objectives imposed on the 

APH program. 

8.2 Reference Rate, Reference Yield and Exponent

We recommend that RMA adopt updated reference yields which are congruent with 

reference rates and exponents. This is a critical step in obtaining appropriate rates for 

insured units at all yield levels. These updated reference yields would be based upon 

APH data so that the reference yield and reference rate are ‘centered’ within a county’s 

book of business. 

Based on our review of the yield ratio curve and the rating exponent, we recommend that 

RMA conduct analysis to update these parameters of the rating formula. Given the heavy 

censoring of loss cost ratios at zero, we suggest that the RMA investigate the usefulness 

of nonlinear censored regression approaches or other parametric or semiparametric 

censored regression models, rather than NLS, for use in this estimation process. 

8.3 Type and Practice Factors

Based on our analysis, we recommend that the RMA modify its procedures so that the 

type and practice factors are applied to the State Catastrophic Rate Load portion of the 

target rate. Further, we recommend that RMA rebase its rates and type/practice factors to 

a common type/practice to improve the transparency of the rating structure.

We recommend that RMA adjust prior experience for the actual or estimated mix by type 

and practice.
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8.4 Unit Factor 

We recommend that RMA adopt procedures for developing target rates that incorporate 

unit factors that are consistent with the actual mix of unit structures in the historical loss 

experience. 

8.5 Catastrophic Loading

We recommend that RMA eliminate the coverage approximation procedure and adjust all 

experience to the 50% coverage level when low coverage levels make up a significant 

proportion of experience. Published base rates could still be maintained at the 65% 

coverage level simply by dividing the 50% pure premium by the 50% coverage level 

adjustment factor. We recognize this would place greater reliance on the estimated 

coverage level relativities. However, we believe these can be effectively estimated for

the major crops. 

8.6 Catastrophic Loading

We recommend that RMA re-evaluate the catastrophic loading procedure and reduce the 

degree to which cat loading influences rates in low risk regions. Having said this, we 

generally support maintaining state/crop catastrophic loading boundaries, unless one is 

addressing a crop with geographically-sparse participation. 

8.7 Use of Expert Judgment 

We recommend that if, in fact, local conditions have changed such that an existing, 

credible time series of data is not appropriate for rating, an explicit discussion of the 

changes should accompany a plan to either adjust the prior data or to set a rate through 

judgment that will then be adjusted using standard methods as new data become 

available. Regional offices of the RMA should play an important role in any such 

process. The decision and results should be documented, transparent, and reviewable by 

outside parties. 

8.8 Additional Rating Variables

We recommend a comprehensive study to evaluate utilizing soil and other site specific 

information for the purposes of refining and individualizing rates. We also suggest RMA 

consider defining and collecting additional type and practice data for characteristics that 

likely affect insurance risk levels.
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8.9 Statewide Rate Level Adequacy 

We recommend that RMA evaluate the extent to which statewide rate levels may be 

inadequate due to capping and, if such inadequacies are significant, consider the use of an 

inadequacy off-balance. We recommend that RMA consider re-evaluating whether the 

minimum load is appropriate in light of the additional reserve loading.

8.10 Yield Correlation and Weighting Loss Experience Data 

We recommend that RMA evaluate alternative loss cost experience weighting methods. 

Our analysis suggests it is feasible to incorporate additional weather information into the 

rating system and to allow additional weight be placed on more credible annual 

observations. However, we do not offer specific recommendations for changing the 

manner in which experience is weighted over time in current rating methods. We believe 

a detailed study of this issue should investigate optimal weights and that implementation

issues should be assessed. 

8.11 Study of Loss Ratio Rating System

We recommend that RMA undertake a comprehensive study of the loss ratio method for 

determining future rate changes, perhaps in conjunction with a study aimed at a State 

level, top-down approach as recommended by Milliman (2008). 

8.12 Combo Rating 

We generally find that the COMBO rating methodology does an appropriate job of 

combining the various aspects of yield and price risk into a revenue product that provides 

both a Harvest Price Revenue Rate (HP Rate) and the Harvest Price Exclusion Option 

Revenue Rate (HPEO Rate). Maintaining consistency between these revenue products 

and the underlying APH product is not simple. However, we generally concur with the 

way RMA determines price-yield correlations and price variability. We recommend that 

RMA consider revising the COMBO rating method to eliminate potential inconsistency 

in the yield rate relativities applied. Though the differences may be modest in many 

cases, we believe that a more conceptually sound result would be obtained from applying 

a consistent set of yield rate relativities across the entire rating process. This could 

involve calibrating the desired distribution (e.g., censored normal) to the empirical rate 

relativities currently used by RMA or, alternatively, abandoning these empirical rate 

relativities in favor of those that emerge from calibration to the APH and a single rate. 
A Comprehensive Review of the RMA APH and COMBO 

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Review Team Biographies

Keith H. Coble, Ph. D.

Dr. Keith H. Coble is W.L. Giles Distinguished Professor in the Agricultural 

Economics Department at Mississippi State University. He received his Ph.D. 

in Agricultural Economics from Texas A&M University in 1993. Coble's 

background in the area of risk management has encompassed both academic 

and government experience. Dr. Coble came to Mississippi State after serving 

as leader of the Crop Risk Management Team at the Economic Research 

Service of the U.S. Department of Agriculture. 

Coble’s crop insurance research has included investigation of adverse 

selection, moral hazard, and crop insurance demand. Coble has also 

conducted research examining the modeling of price and yield risk, which is 

fundamental to insurance rating. Coble’s work also addressed farmer 

behavior in response to risk, commodity program and farm bill analysis and 

in-depth studies the interaction between risk management tools. Dr. Coble is 

also co-author of a chapter on crop insurance in a book (editors R. Just and 

R.D. Pope) entitled: A Comprehensive Assessment of the Role of Risk in U.S. 

Agriculture. He was also commissioned by the OECD in 2008 to co-author An 

Assessment of Risk Exposure in Agriculture. Coble has testified before 

Congressional Committees regarding risk policy on three occasions. His 

research has covered a broad set of risk and crop insurance issues and has 

authored or co-authored 56 journal articles, 4 book chapters, 45 reports for 

government agencies, and 90 scientific meetings papers. 

Coble’s professional activities include having served as the Chair of the Food 

and Agricultural Marketing Section. He is also the current Chair of the new 

Applied Risk Analysis Section of the Agricultural and Applied Economics 

Association and is an Associate Editor of the American Journal of 

Agricultural Economics. 

Dr. Coble has performed numerous analyses for the Federal Crop Insurance 

Corporation and Risk Management Agency, including serving as an 

underwriting reviewer and technical expert for the Board of Directors of the 

Federal Crop Insurance Corporation. Among the other issues he has 

examined for the RMA include a comparisons of CRC and RA crop insurance 

rates, Alternative catastrophic loading procedures, Evaluation of cotton crop 

insurance on cotton acreage, Actuarially fair premium rate adjustments for 

optional versus basic units, Rate adjustments for farms with different mean 

(APH) yields, Performance-based discounts for crop insurance, Unit rate 

factors, An evaluation of county yield trend estimation procedures, and a 

review of the cotton program. 
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Thomas O. Knight, Ph. D.

Thomas O. Knight is a Professor in the Department of Agricultural and 

Applied Economics at Texas Tech University. Prior to joining the Texas Tech 

faculty in 2002, he was an assistant, associate, and full professor in the 

Department of Agricultural Economics at Texas A&M University. Dr. Knight 

was educated at Oklahoma State University, where he completed B.S. (1975) 

and M.S. (1977) degrees in Agricultural Economics, and at the University of 

Missouri, where he earned a Ph.D. in Agricultural Economics in 1984. 

Throughout his academic career, Dr. Knight’s research and teaching programs 

have focused on agricultural risk and policy analysis. His work has been 

published widely in scholarly journals, book chapters, Experiment Station, 

and the popular press. Since 1990, Dr. Knight’s research has focused on a 

wide range of issues relating to the Federal Crop Insurance Program. Among 

his journal publications are studies on crop insurance demand (American 

Journal of Agricultural Economics, 1996; Journal of Agricultural and Applied 

Economics, 2008), moral hazard in crop insurance programs (American 

Journal of Agricultural Economics, 1997), unit structure premium rate 

differentials (Journal of Agricultural and Applied Economics, 1999; American 

Journal of Agricultural Economics, forthcoming 2010), and experience-based 

premium rate discounts (American Journal of Agricultural Economics, 2006). 

Dr. Knight has served as an instructor for a seminar on crop insurance issues 

presented for U.S. House of Representatives staff members and testified 

before the U.S. House of Representatives Agriculture Committee’s 

Subcommittee on General Farm Commodities and Risk Management. 

While on faculty at Texas A&M University, Dr. Knight received the Deputy 

Chancellor’s Award for Excellence in Team Research and the Department of 

Agricultural Economics, Graduate Student Association’s Award for Graduate 

Teaching. At Texas Tech University, he has received the College of 

Agricultural Sciences and Natural Resources Research Award and the 

university-wide Faculty Recognition Award presented jointly by the Mortar 

Board and Omicron Delta Kappa senior honor societies. 

Dr. Knight also serves as an advisor to the Board of Directors of the Federal 

Crop Insurance Corporation and to the Risk Management Agency. He has 

conducted underwriting reviews of eighteen plans of insurance under 

consideration by the FCIC Board. He has also led or collaborated in six major 

program reviews for the FCIC Board and the RMA. These reviews have 

focused on: revenue insurance product premium rates and coverage level 

differentials, cotton acreage effects of crop insurance programs; actuarial 

soundness and product design of cotton insurance programs; a comprehensive 

review of the Dollar Plan insurance product design; a comprehensive review 

of the GRP pilot insurance program; and a comprehensive review of the AGR 

pilot insurance program. 
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Barry K. Goodwin, Ph. D.

Barry K. Goodwin is currently William Neal Reynolds Distinguished 

Professor with joint appointments in the Department of Agricultural and 

Resource Economics and the Department of Economics at North Carolina 

State University. He previously held the Andersons Endowed Chair in the 

Department of Agricultural, Environmental, and Resource Economics at the 

Ohio State University. He also was assistant and associate professor in the 

Department of Agricultural Economics at Kansas State University. He holds a 

Ph.D. degree in Economics and a minor in Statistics from North Carolina 

State University. 

Goodwin has received numerous professional achievement awards. He is a 

Fellow of the Applied and Agricultural Economics Association (AAEA), 

which is the highest professional award in agricultural economics. He has 

received "Best Published Paper" awards for papers published in the American 

Journal of Agricultural Economics, the Journal of Agricultural and Resource 

Economics, and the Canadian Journal of Agricultural Economics. He has 

published approximately 115 refereed journal articles and book chapters and 

has co-authored three books, including the widely cited text "The Economics 

of Crop Insurance and Disaster Assistance." He was named to Who's Who in 

Economics, and was named one of the "Top 1000 Most Cited Economists, 

1990-2000 (#644)," one of the "Top Published Economists by Article Counts, 

1990-2000 (#118, #156)," and one of the "Top Published Economists by Page 

Counts, 1990-2000 (#378)." He has done extensive research and consulting 

on issues related to crop insurance and risk management. 
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Mary Frances Miller, FCAS, MAAA, Hon FIA, CPCU, ARe, AIM

Mrs. Miller is a founder and the senior consulting actuary with Select Actuarial 

Services. With more than 20 years of property and casualty actuarial experience, 

she provides actuarial consulting services on several major accounts and is 

additionally responsible for the professional development of the other members. 

Her expertise is frequently called upon to assist clients in making decisions 

regarding the maintenance and design of their risk management programs. 

Prior to the formation of Select Actuarial Services, Mrs. Miller was the Senior 

Vice President and Chief Actuary for five years at Sedgwick Actuarial Services. 

As Chief Actuary she performed a wide spectrum of actuarial studies and also 

managed the actuarial staff. Before joining Sedgwick in 1993, Mrs. Miller was 

reinsurance actuary with American States Insurance Companies, where her duties 

included pricing within the Reinsurance Division, as well as the design and 

development of specialized software targeting property catastrophe exposures, 

case reserving for automobile and workers' compensation long-term disability 

claims, and evaluating treaty commutation proposals.

Mrs. Miller graduated with highest honor from the Honors College at Michigan 

State University with Bachelor of Arts degrees in Mathematics and Linguistics. 

She is a Fellow of the Casualty Actuarial Society, a member of the American 

Academy of Actuaries and a Chartered Property and Casualty Underwriter. She 

was elected an Honorary Fellow of the Institute of Actuaries (UK) in 2005. She 

has been an active contributor to the actuarial profession since achieving 

fellowship in 1988, and has chaired the CAS Professionalism Education 

Committee, the Education Policy Committee, and task forces on mutual 

recognition and future education planning. She was Vice-President for 

Admissions of the Casualty Actuarial Society from 2000 to 2002, President-Elect 

in 2003, President in 2004, and she chaired the CAS Board in 2005. She has been 

a member of the Boards of Directors of the American Academy of Actuaries and 

the Conference of Consulting Actuaries and currently serves on the Academy’s 

Committee on Qualifications and its Casualty Practice and Professionalism 

Councils. As a member of the Actuarial Standards Board subcommittee on 

reserves, Mrs. Miller was a drafter of the United States standards of practice for 

reserve opinions (#36) and unpaid claim estimates (#43). She currently chairs the 

International Actuarial Association’s Education Committee. 

Mrs. Miller has been elected President-Elect of the American Academy of 

Actuaries for 2010 and will serve as the Academy’s President in 2011. 
A Comprehensive Review of the RMA APH and COMBO 

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Roderick M. Rejesus, Ph.D. 

Dr. Roderick M. Rejesus is an assistant professor of agricultural and resource 

economics at North Carolina State University. Prior to his current position, 

Dr. Rejesus was an assistant professor in the agricultural and applied 

economics department at Texas Tech University. Dr. Rejesus received his 

M.S. degree at Clemson University and his Ph.D. degree at the University of 

Illinois at Urbana-Champaign, both in Agricultural Economics. He has an 

active research and extension program that focuses on crop insurance and 

applied production economics. 

Dr. Rejesus has published research findings on ex-post moral hazard in crop 

insurance, determinants of anomalous prevented planting claims in crop 

insurance, farmer preferences for risk management information sources, and 

agricultural risk management tool utilization. He has been involved in several 

RMA funded projects, namely: Unit Division Structure Review, Premium 

Rate Discount Project, and the Reference Yield Update Methodology Project. 

In addition, he has participated in several reviews of proposed pilot insurance 

products and underwriting procedures submitted for consideration to the 

Board of Directors of the FCIC. 
A Comprehensive Review of the RMA APH and COMBO 

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Rating Methodology

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Appendix

Data for Figures and Maps in the Report
A Comprehensive Review of the RMA APH and COMBO 

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Appendix Table 1

Data for Table 5.1 

LR_10 LR_24 LR_40 TRUE

1.574358 0.709215 0.87794 1

1.574358 0.709215 0.87794 1

1.574358 0.709215 0.678011 1

1.574358 0.709215 0.678011 1

1.574358 0.709215 0.678011 1

0 0.709215 0.678011 1

0 0.709215 0.678011 1

0 0.709215 0.595098 1

0 0.686896 0.5856 1

0 0.686896 0.5856 1

0 0.686896 0.5856 1

0 0.524817 0.5856 1

0.029932 0.534787 0.591563 1

0.029932 0.534787 0.591563 1

0.029932 0.534787 0.431487 1

0.029932 0.534787 0.431487 1

0.029932 0.534787 0.431487 1

0.029932 0.534787 0.431487 1

0.029932 0.534787 0.431487 1

1.815225 1.129885 0.788569 1

1.785293 1.129885 0.788569 1

1.785293 0.605067 0.788569 1

1.785293 0.605067 0.788569 1

2.841621 0.957177 0.999835 1

2.841621 0.957177 0.986448 1

2.841621 0.957177 0.986448 1

2.841621 0.957177 0.986448 1

1.056328 0.957177 0.889206 1

1.056328 0.957177 0.889206 1

1.056328 0.957177 0.889206 1

1.056328 0.957177 0.889206 1

0 0.957177 0.889206 1

0 0.957177 0.889206 1

0 0.957177 0.889206 1

0.269621 1.047089 0.943153 1

0.269621 1.047089 0.943153 1

0.269621 1.037144 0.943153 1 
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0.269621 1.037144 0.628258 1

1.602404 1.481334 0.894768 1

1.602404 1.481334 0.894768 1

1.602404 1.481334 0.894768 1

1.602404 1.481334 0.894768 1

1.332548 1.481334 0.894768 1

1.332548 0.886213 0.894768 1

1.332548 0.886213 0.894768 1

1.332548 0.886213 0.894768 1

0 0.886213 0.894768 1

0 0.534103 0.894768 1

0 0.534103 0.894768 1

0 0.534103 0.894768 1

0 0.534103 0.894768 1

0 0.534103 0.894768 1

0 0.534103 0.888805 1

0 0.534103 0.888805 1

0 0.534103 0.888805 1

1.8428 1.148362 1.257365 1

3.212821 1.605044 1.531346 1

3.212821 1.605044 1.531346 1

3.212821 1.515131 1.531346 1

3.212821 1.515131 1.174287 1
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Appendix Table 2. Soybean and Corn Acreage Insured by Year

(Data for Figures 6.1 and 6.2) 

Corn Soybeans

Total IL IN IA Total IL IN IA

1981 4.707 0.599 0.308 3.800 2.285 0.420 0.195 1.670

1982 3.330 0.491 0.330 2.509 1.998 0.375 0.254 1.368

1983 1.655 0.252 0.158 1.245 1.524 0.280 0.150 1.094

1984 4.971 0.946 0.717 3.308 2.843 0.632 0.437 1.774

1985 5.827 1.126 0.832 3.869 3.045 0.691 0.403 1.952

1986 5.648 1.249 0.689 3.709 3.328 0.783 0.356 2.189

1987 5.235 1.217 0.613 3.405 3.594 0.884 0.364 2.347

1988 5.847 1.282 0.656 3.910 3.708 0.846 0.383 2.480

1989 15.511 4.996 1.332 9.183 9.136 2.986 1.089 5.061

1990 12.429 3.513 1.122 7.794 6.730 1.988 0.720 4.022

1991 9.731 2.868 1.031 5.832 5.889 1.710 0.668 3.511

1992 11.010 3.548 1.370 6.092 6.081 2.063 0.785 3.232

1993 9.917 3.247 1.244 5.425 5.948 1.985 0.758 3.205

1994 12.203 3.672 1.355 7.177 7.145 2.276 0.797 4.071

1995 23.385 8.727 3.926 10.732 19.849 8.080 3.382 8.386

1996 16.281 7.370 2.710 6.201 14.500 6.465 2.579 5.456

1997 18.243 6.467 2.423 9.353 15.892 5.668 2.090 8.134

1998 18.178 6.294 2.318 9.566 16.208 5.833 2.244 8.131

1999 19.002 6.855 2.616 9.530 17.478 6.304 2.451 8.723

2000 20.228 7.334 2.938 9.956 18.286 6.635 2.771 8.880

2001 19.644 7.156 2.872 9.616 18.617 6.603 2.833 9.182

2002 20.162 7.195 2.851 10.117 18.104 6.422 2.931 8.750

2003 20.537 7.397 3.035 10.106 17.874 6.244 2.775 8.855

2004 20.859 7.395 2.991 10.473 16.991 5.811 2.708 8.472

2005 20.624 7.377 2.948 10.299 16.507 5.614 2.635 8.259

2006 18.192 5.584 2.580 10.028 16.563 5.552 2.662 8.349

2007 21.651 7.076 3.152 11.424 13.837 4.534 2.182 7.121

2008 21.423 7.432 2.965 11.027 16.488 5.498 2.684 8.306

 
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Appendix Table 3. Correlation and Effective Number of Observations

(Data from Figure 6.3) 

50 25 10 5

Groups of Groups of Groups of Groups of

Correlation 2 4 10 20

0.95 52.56 26.97 10.96 5.51

0.90 55.25 29.15 12.06 6.10

0.85 58.06 31.57 13.33 6.79

0.80 60.98 34.25 14.79 7.60

0.75 64.00 37.21 16.49 8.56

0.70 67.11 40.49 18.48 9.70

0.65 70.30 44.10 20.82 11.08

0.60 73.53 48.08 23.58 12.76

0.55 76.78 52.42 26.86 14.82

0.50 80.00 57.14 30.77 17.39

0.45 83.16 62.21 35.43 20.63

0.40 86.21 67.57 40.98 24.75

0.35 89.09 73.13 47.56 30.05

0.30 91.74 78.74 55.25 36.90

0.25 94.12 84.21 64.00 45.71

0.20 96.15 89.29 73.53 56.82

0.15 97.80 93.68 83.16 70.05

0.10 99.01 97.09 91.74 84.03

0.05 99.75 99.26 97.80 95.47

0.00 100.00 100.00 100.00 100.00
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Appendix Table 4. Values of Palmer’s Z Index

(Data from Figure 6.4) 

Year IL IN IA

1970 -0.28 0.85 -0.47

1971 1.96 1.96 0.13

1972 0.46 -0.11 2.92

1973 1.38 1.99 1.85

1974 -2.34 -3.21 -2.05

1975 -0.09 -1.53 -3.38

1976 -0.39 0.11 -1.75

1977 -0.62 -0.96 -1.05

1978 1.2 1.54 2.99

1979 2.29 5.86 1.62

1980 -1.18 0.06 -1.76

1981 3.63 1.55 1.92

1982 2.94 -0.08 3.29

1983 -2.46 -2.56 -1.79

1984 -0.62 -0.04 0.85

1985 -0.01 -0.69 -1.25

1986 2.17 1.98 1.63

1987 0.06 1.6 1.12

1988 -2.47 -0.77 -3.27

1989 -0.15 2.03 -1.15

1990 1.06 1.77 3.83

1991 -2.65 -2.7 -1.68

1992 4.18 6.49 6.19

1993 3.43 0.78 9.4

1994 -1.04 -0.3 0.9

1995 -0.74 -1.26 -0.22

1996 1.59 2.16 0.11

1997 -1.57 -0.81 -0.8

1998 -0.05 1.72 -0.52

1999 -1.26 -1.92 1.96

2000 1.53 0.55 1.02

2001 0.34 2.56 -0.68

2002 -1.57 -1.89 0.15

2003 1.84 6.31 0.92

2004 0.9 2.26 1.36

2005 -1.32 0.45 -0.5

2006 0.28 1.71 -1.51 
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2007 -0.16 -0.72 -0.46

2008 2.94 0.89 3.05

Appendix Table 5. Frequency Distribution of Palmer’s Z in Indiana

(Data for Figure 6.5) 

Palmer's

Z Frequency

-4.5 0.0088

-3.5 0.0439

-2.5 0.0702

-1.5 0.1053

-0.5 0.1842

0.5 0.2368

1.5 0.1140

2.5 0.1667

3.5 0.0175

4.5 0.0088

5.5 0.0439
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Appendix Table 6. Comparison of Combo and Empirical Rate Relativities 

(Data from Figures 7.1 and 7.2) 

Crop Practice FIPS Cov Lev Combo Empirical

11 4 20057 50 0.8513 0.7880

11 4 20057 55 0.8992 0.8340

11 4 20057 60 0.9488 0.8990

11 4 20057 65 1.0000 1.0000

11 4 20057 70 1.0528 1.1932

11 4 20057 75 1.1071 1.4619

11 4 20057 80 1.1628 1.7964

11 4 20057 85 1.2198 2.0545

21 2 28143 50 0.5196 0.7480

21 2 28143 55 0.6523 0.8250

21 2 28143 60 0.8116 0.9090

21 2 28143 65 1.0000 1.0000

21 2 28143 70 1.2199 1.1584

21 2 28143 75 1.4730 1.3353

21 2 28143 80 1.7602 1.5308

21 2 28143 85 2.0818 1.7812

21 2 48357 50 0.9470 0.7850

21 2 48357 55 0.9640 0.8320

21 2 48357 60 0.9810 0.9060

21 2 48357 65 0.9981 1.0000

21 2 48357 70 1.0152 1.1900

21 2 48357 75 1.0323 1.4530

21 2 48357 80 1.0495 1.7778

21 2 48357 85 1.0667 2.1777

41 2 17115 50 0.3163 0.5160

41 2 17115 55 0.4749 0.6280

41 2 17115 60 0.6972 0.8070

41 2 17115 65 1.0000 1.0000

41 2 17115 70 1.4014 1.2734

41 2 17115 75 1.9186 1.5862

41 2 17115 80 2.5668 1.9397

41 2 17115 85 3.3573 2.3363
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Appendix Table 7. Geographic Dispersion of Rate Relativity Differences

(Data from Figures 7.3 and 7.4) 

Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

11 20 1 0.0653 CYAN

11 20 3 0.0640 CYAN

11 20 5 0.0460 BLUE

11 20 7 0.0450 BLUE

11 20 9 0.0519 GREEN

11 20 11 0.0536 GREEN

11 20 13 0.0524 GREEN

11 20 15 0.0618 CYAN

11 20 17 0.0488 GREEN

11 20 19 0.0636 CYAN

11 20 21 0.0645 CYAN

11 20 23 0.0582 CYAN

11 20 25 0.0900 CYAN

11 20 27 0.0221 RED

11 20 29 0.0401 BLUE

11 20 31 0.0654 CYAN

11 20 33 0.0532 GREEN

11 20 35 0.0606 CYAN

11 20 37 0.0539 GREEN

11 20 39 0.0597 CYAN

11 20 41 0.0290 RED

11 20 43 0.0542 GREEN

11 20 45 0.0574 CYAN

11 20 47 0.0672 CYAN

11 20 49 0.0534 GREEN

11 20 51 0.0460 BLUE

11 20 53 0.0412 BLUE

11 20 55 0.0512 GREEN

11 20 57 0.1467 CYAN

11 20 59 0.0627 CYAN

11 20 61 0.0455 BLUE

11 20 63 0.0770 CYAN

11 20 65 0.0588 CYAN

11 20 67 0.0643 CYAN

11 20 69 0.1199 CYAN

11 20 71 0.0789 CYAN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

11 20 73 0.0561 CYAN

11 20 75 0.0501 GREEN

11 20 77 0.0442 BLUE

11 20 79 0.0309 BLUE

11 20 81 0.0619 CYAN

11 20 83 0.0825 CYAN

11 20 85 0.0507 GREEN

11 20 87 0.0457 BLUE

11 20 89 0.0376 BLUE

11 20 91 0.0532 GREEN

11 20 93 0.0510 GREEN

11 20 95 0.0337 BLUE

11 20 97 0.0492 GREEN

11 20 99 0.0625 CYAN

11 20 101 0.1277 CYAN

11 20 103 0.0303 RED

11 20 105 0.0398 BLUE

11 20 107 0.0601 CYAN

11 20 109 0.0640 CYAN

11 20 111 0.0659 CYAN

11 20 113 0.0234 RED

11 20 115 0.0344 BLUE

11 20 117 0.0333 BLUE

11 20 119 0.0774 CYAN

11 20 121 0.0513 GREEN

11 20 123 0.0309 RED

11 20 125 0.0656 CYAN

11 20 127 0.0607 CYAN

11 20 129 0.0505 GREEN

11 20 131 0.0502 GREEN

11 20 133 0.0655 CYAN

11 20 135 0.0574 CYAN

11 20 137 0.0583 CYAN

11 20 139 0.0637 CYAN

11 20 141 0.0405 BLUE

11 20 143 0.0277 RED

11 20 145 0.0480 BLUE

11 20 147 0.0447 BLUE

11 20 149 0.0491 GREEN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

11 20 151 0.0416 BLUE

11 20 153 0.0659 CYAN

11 20 155 0.0405 BLUE

11 20 157 0.0354 BLUE

11 20 159 0.0597 CYAN

11 20 161 0.0385 BLUE

11 20 163 0.0479 BLUE

11 20 165 0.0576 CYAN

11 20 167 0.0538 GREEN

11 20 169 0.0233 RED

11 20 171 0.1160 CYAN

11 20 173 0.0389 BLUE

11 20 175 0.0512 GREEN

11 20 177 0.0517 GREEN

11 20 179 0.0580 CYAN

11 20 181 0.0656 CYAN

11 20 183 0.0309 BLUE

11 20 185 0.0447 BLUE

11 20 187 0.0473 BLUE

11 20 189 0.0668 CYAN

11 20 191 0.0528 GREEN

11 20 193 0.0663 CYAN

11 20 195 0.0615 CYAN

11 20 197 0.0581 CYAN

11 20 199 0.1088 CYAN

11 20 201 0.0290 RED

11 20 203 0.0788 CYAN

11 20 205 0.0657 CYAN

11 20 207 0.0532 GREEN

11 20 209 0.0472 BLUE

11 31 1 0.0171 RED

11 31 3 0.0576 CYAN

11 31 7 0.0698 CYAN

11 31 11 0.0537 GREEN

11 31 15 0.0530 GREEN

11 31 19 0.0495 GREEN

11 31 21 0.0464 BLUE

11 31 23 0.0434 BLUE

11 31 25 0.0204 RED
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

11 31 27 0.0545 GREEN

11 31 29 0.0429 BLUE

11 31 31 0.0323 BLUE

11 31 33 0.0594 CYAN

11 31 35 0.0186 RED

11 31 37 0.0457 BLUE

11 31 39 0.0511 GREEN

11 31 41 0.0645 CYAN

11 31 47 0.0793 CYAN

11 31 49 0.0337 BLUE

11 31 53 0.0236 RED

11 31 55 0.0547 CYAN

11 31 57 0.0636 CYAN

11 31 59 0.0175 RED

11 31 61 0.0170 RED

11 31 63 0.0408 BLUE

11 31 65 0.0261 RED

11 31 67 0.0294 RED

11 31 69 0.0565 CYAN

11 31 71 0.0749 CYAN

11 31 73 -0.0112 RED

11 31 77 0.0775 CYAN

11 31 79 0.0480 BLUE

11 31 81 0.0272 RED

11 31 83 0.0052 RED

11 31 85 0.0379 BLUE

11 31 87 0.0404 BLUE

11 31 89 0.1407 CYAN

11 31 93 0.0560 CYAN

11 31 95 0.0120 RED

11 31 97 0.0144 RED

11 31 99 -0.0218 RED

11 31 101 0.0348 BLUE

11 31 103 0.0448 BLUE

11 31 105 0.0832 CYAN

11 31 107 0.0380 BLUE

11 31 109 0.0129 RED

11 31 111 0.0433 BLUE

11 31 113 0.0913 CYAN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

11 31 115 0.0616 CYAN

11 31 117 0.0553 CYAN

11 31 119 0.0519 GREEN

11 31 121 0.1624 CYAN

11 31 123 0.1138 CYAN

11 31 125 0.0655 CYAN

11 31 127 0.0184 RED

11 31 129 0.0100 RED

11 31 131 0.0285 RED

11 31 133 0.0313 BLUE

11 31 135 0.0559 CYAN

11 31 137 0.0240 RED

11 31 139 0.0513 GREEN

11 31 141 0.0490 GREEN

11 31 143 0.0238 RED

11 31 145 0.0254 RED

11 31 147 0.0240 RED

11 31 149 0.1633 CYAN

11 31 151 0.0149 RED

11 31 153 0.0001 RED

11 31 155 0.0486 GREEN

11 31 157 0.0830 CYAN

11 31 159 0.0216 RED

11 31 163 0.0722 CYAN

11 31 165 0.0891 CYAN

11 31 167 0.0551 CYAN

11 31 169 0.0051 RED

11 31 173 0.0561 CYAN

11 31 175 0.0811 CYAN

11 31 177 0.0514 GREEN

11 31 181 -0.0095 RED

11 31 185 0.0127 RED

21 13 7 0.1057 GREEN

21 13 19 0.0943 BLUE

21 13 27 0.0840 BLUE

21 13 29 0.0782 BLUE

21 13 69 0.0831 BLUE

21 13 103 0.0799 BLUE

21 13 129 0.0771 BLUE
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

21 13 165 0.1027 GREEN

21 13 171 0.1072 GREEN

21 13 175 0.1024 GREEN

21 13 205 0.0931 BLUE

21 13 209 0.0808 BLUE

21 13 249 0.2410 CYAN

21 13 261 0.1069 GREEN

21 13 271 0.1038 GREEN

21 13 279 0.0822 BLUE

21 13 287 0.0861 BLUE

21 13 307 0.1346 CYAN

21 13 309 0.0963 BLUE

21 13 319 0.1070 GREEN

21 28 1 0.0204 RED

21 28 9 0.0403 RED

21 28 11 0.0266 RED

21 28 13 0.0123 RED

21 28 15 -0.0160 RED

21 28 35 0.0761 BLUE

21 28 43 0.0544 RED

21 28 49 0.0634 BLUE

21 28 81 0.0994 BLUE

21 28 89 -0.0806 RED

21 28 95 0.1812 CYAN

21 28 139 0.0474 RED

21 28 141 0.0314 RED

21 28 143 -0.0338 RED

21 28 151 0.0512 RED

21 28 155 0.1231 GREEN

21 28 159 0.1636 CYAN

21 48 21 0.0336 RED

21 48 23 0.1265 GREEN

21 48 33 0.1473 CYAN

21 48 41 0.0578 RED

21 48 45 0.1400 CYAN

21 48 49 0.2655 CYAN

21 48 51 0.0856 BLUE

21 48 61 0.1080 GREEN

21 48 95 0.1249 GREEN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

21 48 115 0.1559 CYAN

21 48 121 0.0874 BLUE

21 48 157 0.0703 BLUE

21 48 165 0.1361 CYAN

21 48 177 0.0985 BLUE

21 48 191 0.1042 GREEN

21 48 215 0.1088 GREEN

21 48 235 0.1207 GREEN

21 48 251 0.1051 GREEN

21 48 263 0.1302 CYAN

21 48 293 0.0662 BLUE

21 48 303 0.1113 GREEN

21 48 331 0.0867 BLUE

21 48 335 0.1330 CYAN

21 48 369 0.1421 CYAN

21 48 375 0.1220 GREEN

21 48 395 0.1016 GREEN

21 48 447 0.1165 GREEN

21 48 483 0.1062 GREEN

21 48 489 0.1074 GREEN

21 48 507 0.0744 BLUE

41 17 3 0.0487 CYAN

41 17 9 0.0003 GREEN

41 17 11 -0.1465 RED

41 17 13 0.0305 CYAN

41 17 17 -0.0277 GREEN

41 17 23 -0.0073 GREEN

41 17 29 -0.1112 RED

41 17 31 0.0007 GREEN

41 17 37 -0.1535 RED

41 17 43 -0.0503 BLUE

41 17 45 -0.1336 RED

41 17 47 0.0496 CYAN

41 17 49 -0.0149 GREEN

41 17 59 0.0380 CYAN

41 17 61 -0.0105 GREEN

41 17 63 -0.1189 RED

41 17 75 -0.0728 BLUE

41 17 85 -0.0304 GREEN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

41 17 89 -0.0387 BLUE

41 17 95 -0.0974 RED

41 17 99 -0.0986 RED

41 17 103 -0.1419 RED

41 17 109 -0.0588 BLUE

41 17 111 -0.0260 GREEN

41 17 113 -0.1665 RED

41 17 115 -0.2095 RED

41 17 119 0.0077 GREEN

41 17 123 -0.1554 RED

41 17 129 -0.1127 RED

41 17 137 -0.0938 RED

41 17 139 -0.1473 RED

41 17 143 -0.1050 RED

41 17 149 -0.0087 GREEN

41 17 155 -0.1380 RED

41 17 157 0.0348 CYAN

41 17 159 -0.0144 GREEN

41 17 169 0.0147 GREEN

41 17 175 -0.1393 RED

41 17 189 0.0259 CYAN

41 17 191 0.0419 CYAN

41 17 201 -0.0026 GREEN

41 18 3 -0.0042 GREEN

41 18 5 0.0027 GREEN

41 18 7 -0.1176 RED

41 18 9 0.0179 GREEN

41 18 13 0.0398 CYAN

41 18 17 -0.1153 RED

41 18 19 0.0302 CYAN

41 18 25 0.0519 CYAN

41 18 27 0.0161 GREEN

41 18 29 0.0488 CYAN

41 18 31 -0.0370 BLUE

41 18 37 0.0451 CYAN

41 18 39 -0.0465 BLUE

41 18 41 -0.0748 BLUE

41 18 43 0.0515 CYAN

41 18 53 -0.0472 BLUE
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

41 18 59 -0.0894 BLUE

41 18 61 0.0449 CYAN

41 18 65 -0.0612 BLUE

41 18 73 -0.0582 BLUE

41 18 77 0.0226 GREEN

41 18 79 0.0591 CYAN

41 18 83 0.0470 CYAN

41 18 87 0.0023 GREEN

41 18 91 -0.0116 GREEN

41 18 101 0.0501 CYAN

41 18 103 -0.1211 RED

41 18 107 -0.0782 BLUE

41 18 109 0.0101 GREEN

41 18 121 0.0431 CYAN

41 18 123 0.0423 CYAN

41 18 127 -0.0209 GREEN

41 18 129 0.0466 CYAN

41 18 131 -0.0469 BLUE

41 18 139 -0.0652 BLUE

41 18 147 0.0065 GREEN

41 18 151 0.0291 CYAN

41 18 153 0.0474 CYAN

41 18 169 -0.0423 BLUE

41 18 171 -0.0241 GREEN

41 18 175 0.0557 CYAN

41 18 177 -0.0168 GREEN

41 18 179 0.0000 GREEN

41 19 1 -0.0494 BLUE

41 19 5 -0.0803 BLUE

41 19 19 -0.0541 BLUE

41 19 21 -0.1353 RED

41 19 31 -0.0889 BLUE

41 19 39 0.0413 CYAN

41 19 41 -0.0854 BLUE

41 19 53 0.0482 CYAN

41 19 67 -0.0353 BLUE

41 19 71 0.0236 GREEN

41 19 75 -0.1825 RED

41 19 77 -0.0486 BLUE
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

41 19 83 -0.1475 RED

41 19 99 -0.1324 RED

41 19 101 0.0177 GREEN

41 19 103 -0.0363 BLUE

41 19 109 -0.1019 RED

41 19 113 -0.1032 RED

41 19 121 0.0285 CYAN

41 19 127 -0.1586 RED

41 19 129 -0.0130 GREEN

41 19 131 -0.0674 BLUE

41 19 147 -0.0922 RED

41 19 149 -0.1082 RED

41 19 153 -0.1039 RED

41 19 156 -0.0158 GREEN

41 19 161 -0.0441 BLUE

41 19 165 -0.0714 BLUE

41 19 171 -0.0808 BLUE

41 19 175 0.0459 CYAN

41 19 177 0.0316 CYAN

41 19 181 0.0310 CYAN

41 19 185 0.0486 CYAN

41 19 189 -0.0829 BLUE

41 19 193 -0.0437 BLUE

41 19 195 -0.0830 BLUE

41 19 197 -0.0558 BLUE

81 17 1 0.0926 GREEN

81 17 1 0.0926 GREEN

81 17 1 0.0926 GREEN

81 17 3 0.1705 CYAN

81 17 3 0.1705 CYAN

81 17 3 0.1705 CYAN

81 17 5 0.1203 CYAN

81 17 5 0.1203 CYAN

81 17 5 0.1203 CYAN

81 17 7 0.0395 BLUE

81 17 7 0.0395 BLUE

81 17 9 0.1086 CYAN

81 17 9 0.1086 CYAN

81 17 9 0.1086 CYAN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

81 17 11 0.0102 RED

81 17 13 0.1254 CYAN

81 17 13 0.1254 CYAN

81 17 13 0.1254 CYAN

81 17 15 0.0089 RED

81 17 15 0.0089 RED

81 17 17 0.0496 BLUE

81 17 19 -0.0523 RED

81 17 21 -0.0311 RED

81 17 21 -0.0311 RED

81 17 23 0.0607 BLUE

81 17 23 0.0607 BLUE

81 17 23 0.0607 BLUE

81 17 25 0.1282 CYAN

81 17 25 0.1282 CYAN

81 17 25 0.1282 CYAN

81 17 27 0.1264 CYAN

81 17 27 0.1264 CYAN

81 17 27 0.1264 CYAN

81 17 29 -0.0143 RED

81 17 31 0.1223 CYAN

81 17 31 0.1223 CYAN

81 17 31 0.1223 CYAN

81 17 33 0.1049 GREEN

81 17 33 0.1049 GREEN

81 17 33 0.1049 GREEN

81 17 35 0.0210 BLUE

81 17 35 0.0210 BLUE

81 17 37 -0.0069 RED

81 17 39 -0.0689 RED

81 17 41 -0.0326 RED

81 17 43 0.0533 BLUE

81 17 45 -0.0267 RED

81 17 45 -0.0267 RED

81 17 47 0.1440 CYAN

81 17 47 0.1440 CYAN

81 17 47 0.1440 CYAN

81 17 49 0.0713 BLUE

81 17 49 0.0713 BLUE
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

81 17 49 0.0713 BLUE

81 17 51 0.1117 CYAN

81 17 51 0.1117 CYAN

81 17 51 0.1117 CYAN

81 17 53 0.0142 BLUE

81 17 55 0.1743 CYAN

81 17 55 0.1743 CYAN

81 17 55 0.1743 CYAN

81 17 57 0.0561 BLUE

81 17 57 0.0561 BLUE

81 17 59 0.1754 CYAN

81 17 59 0.1754 CYAN

81 17 59 0.1754 CYAN

81 17 61 0.0837 GREEN

81 17 61 0.0837 GREEN

81 17 61 0.0837 GREEN

81 17 63 -0.0142 RED

81 17 63 -0.0142 RED

81 17 65 0.1455 CYAN

81 17 65 0.1455 CYAN

81 17 65 0.1455 CYAN

81 17 67 0.0728 BLUE

81 17 67 0.0728 BLUE

81 17 67 0.0728 BLUE

81 17 69 0.1227 CYAN

81 17 69 0.1227 CYAN

81 17 69 0.1227 CYAN

81 17 71 0.0812 GREEN

81 17 71 0.0812 GREEN

81 17 71 0.0812 GREEN

81 17 73 0.0231 BLUE

81 17 73 0.0231 BLUE

81 17 75 -0.0304 RED

81 17 77 0.1735 CYAN

81 17 77 0.1735 CYAN

81 17 77 0.1735 CYAN

81 17 79 0.0822 GREEN

81 17 79 0.0822 GREEN

81 17 79 0.0822 GREEN
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

81 17 81 0.1450 CYAN

81 17 81 0.1450 CYAN

81 17 81 0.1450 CYAN

81 17 83 0.0699 BLUE

81 17 83 0.0699 BLUE

81 17 83 0.0699 BLUE

81 17 85 0.0734 BLUE

81 17 85 0.0734 BLUE

81 17 85 0.0734 BLUE

81 17 87 0.1426 CYAN

81 17 87 0.1426 CYAN

81 17 87 0.1426 CYAN

81 17 89 0.0643 BLUE

81 17 89 0.0643 BLUE

81 17 89 0.0643 BLUE

81 17 91 -0.0022 RED

81 17 93 0.0144 BLUE

81 17 93 0.0144 BLUE

81 17 95 0.0093 RED

81 17 95 0.0093 RED

81 17 97 0.1388 CYAN

81 17 97 0.1388 CYAN

81 17 97 0.1388 CYAN

81 17 99 0.0123 RED

81 17 101 0.1304 CYAN

81 17 101 0.1304 CYAN

81 17 101 0.1304 CYAN

81 17 103 0.0167 BLUE

81 17 105 -0.0292 RED

81 17 105 -0.0292 RED

81 17 107 -0.0289 RED

81 17 109 0.0278 BLUE

81 17 111 0.0867 GREEN

81 17 111 0.0867 GREEN

81 17 111 0.0867 GREEN

81 17 113 -0.0311 RED

81 17 115 -0.0573 RED

81 17 117 0.0425 BLUE

81 17 117 0.0425 BLUE
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Crop State County Proportional Difference Map

Code FIPS Code at 75% Coverage Color 

81 17 119 0.1183 CYAN

81 17 119 0.1183 CYAN

81 17 119 0.1183 CYAN

81 17 121 0.1244 CYAN

81 17 121 0.1244 CYAN

81 17 121 0.1244 CYAN

81 17 123 -0.0313 RED

81 17 123 -0.0313 RED

81 17 125 0.0880 GREEN

81 17 125 0.0880 GREEN

81 17 125 0.0880 GREEN

81 17 127 0.1500 CYAN

81 17 127 0.1500 CYAN

81 17 127 0.1500 CYAN

81 17 129 0.0116 RED

81 17 131 0.0514 BLUE

81 17 131 0.0514 BLUE

81 17 133 0.1330 CYAN

81 17 133 0.1330 CYAN

81 17 133 0.1330 CYAN

81 17 135 0.0369 BLUE

81 17 137 0.0276 BLUE

81 17 137 0.0276 BLUE

81 17 139 -0.0474 RED

81 17 141 0.0368 BLUE

81 17 141 0.0368 BLUE

81 17 143 0.0046 RED

81 17 145 0.1407 CYAN

81 17 145 0.1407 CYAN

81 17 145 0.1407 CYAN

81 17 147 -0.0570 RED

81 17 149 0.0934 GREEN

81 17 149 0.0934 GREEN

81 17 149 0.0934 GREEN

81 17 151 0.1217 CYAN

81 17 151 0.1217 CYAN

81 17 151 0.1217 CYAN

81 17 153 0.1741 CYAN

81 17 153 0.1741 CYAN
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Code FIPS Code at 75% Coverage Color 

81 17 153 0.1741 CYAN

81 17 155 -0.0152 RED

81 17 157 0.1285 CYAN

81 17 157 0.1285 CYAN

81 17 157 0.1285 CYAN

81 17 159 0.1015 GREEN

81 17 159 0.1015 GREEN

81 17 159 0.1015 GREEN

81 17 161 0.0482 BLUE

81 17 161 0.0482 BLUE

81 17 163 0.1094 CYAN

81 17 163 0.1094 CYAN

81 17 163 0.1094 CYAN

81 17 165 0.1725 CYAN

81 17 165 0.1725 CYAN

81 17 165 0.1725 CYAN

81 17 167 -0.0233 RED

81 17 167 -0.0233 RED

81 17 169 0.0779 GREEN

81 17 169 0.0779 GREEN

81 17 169 0.0779 GREEN

81 17 171 0.0900 GREEN

81 17 171 0.0900 GREEN

81 17 171 0.0900 GREEN

81 17 173 -0.0023 RED

81 17 173 -0.0023 RED

81 17 175 -0.0190 RED

81 17 175 -0.0190 RED

81 17 177 0.1113 CYAN

81 17 177 0.1113 CYAN

81 17 177 0.1113 CYAN

81 17 179 0.0157 BLUE

81 17 179 0.0157 BLUE

81 17 181 0.1757 CYAN

81 17 181 0.1757 CYAN

81 17 181 0.1757 CYAN

81 17 183 -0.0180 RED

81 17 185 0.1350 CYAN

81 17 185 0.1350 CYAN
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Code FIPS Code at 75% Coverage Color 

81 17 185 0.1350 CYAN

81 17 187 0.0153 BLUE

81 17 189 0.1385 CYAN

81 17 189 0.1385 CYAN

81 17 189 0.1385 CYAN

81 17 191 0.1588 CYAN

81 17 191 0.1588 CYAN

81 17 191 0.1588 CYAN

81 17 193 0.1427 CYAN

81 17 193 0.1427 CYAN

81 17 193 0.1427 CYAN

81 17 195 0.0700 BLUE

81 17 195 0.0700 BLUE

81 17 195 0.0700 BLUE

81 17 197 0.0247 BLUE

81 17 197 0.0247 BLUE

81 17 199 0.1733 CYAN

81 17 199 0.1733 CYAN

81 17 199 0.1733 CYAN

81 17 201 0.0809 GREEN

81 17 201 0.0809 GREEN

81 17 201 0.0809 GREEN

81 17 203 -0.0398 RED

81 18 1 0.0749 BLUE

81 18 1 0.0749 BLUE

81 18 1 0.0749 BLUE

81 18 3 0.1007 GREEN

81 18 3 0.1007 GREEN

81 18 3 0.1007 GREEN

81 18 5 0.0786 GREEN

81 18 5 0.0786 GREEN

81 18 5 0.0786 GREEN

81 18 7 -0.0265 RED

81 18 9 0.1114 CYAN

81 18 9 0.1114 CYAN

81 18 9 0.1114 CYAN

81 18 11 0.0190 BLUE

81 18 13 0.1391 CYAN

81 18 13 0.1391 CYAN
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81 18 13 0.1391 CYAN

81 18 15 0.0047 RED

81 18 15 0.0047 RED

81 18 17 0.0332 BLUE

81 18 19 0.1524 CYAN

81 18 19 0.1524 CYAN

81 18 19 0.1524 CYAN

81 18 21 0.1025 GREEN

81 18 21 0.1025 GREEN

81 18 21 0.1025 GREEN

81 18 23 0.0119 RED

81 18 23 0.0119 RED

81 18 25 0.1157 CYAN

81 18 25 0.1157 CYAN

81 18 25 0.1157 CYAN

81 18 27 0.1180 CYAN

81 18 27 0.1180 CYAN

81 18 27 0.1180 CYAN

81 18 29 0.1684 CYAN

81 18 29 0.1684 CYAN

81 18 29 0.1684 CYAN

81 18 31 0.0587 BLUE

81 18 31 0.0587 BLUE

81 18 31 0.0587 BLUE

81 18 33 0.1046 GREEN

81 18 33 0.1046 GREEN

81 18 33 0.1046 GREEN

81 18 35 0.0631 BLUE

81 18 35 0.0631 BLUE

81 18 35 0.0631 BLUE

81 18 37 0.1644 CYAN

81 18 37 0.1644 CYAN

81 18 37 0.1644 CYAN

81 18 39 0.1035 GREEN

81 18 39 0.1035 GREEN

81 18 39 0.1035 GREEN

81 18 41 0.0500 BLUE

81 18 43 0.1740 CYAN

81 18 43 0.1740 CYAN
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81 18 43 0.1740 CYAN

81 18 45 0.0500 BLUE

81 18 45 0.0500 BLUE

81 18 47 0.1294 CYAN

81 18 47 0.1294 CYAN

81 18 47 0.1294 CYAN

81 18 49 0.0342 BLUE

81 18 49 0.0342 BLUE

81 18 51 0.1402 CYAN

81 18 51 0.1402 CYAN

81 18 51 0.1402 CYAN

81 18 53 0.0817 GREEN

81 18 53 0.0817 GREEN

81 18 53 0.0817 GREEN

81 18 55 0.1546 CYAN

81 18 55 0.1546 CYAN

81 18 55 0.1546 CYAN

81 18 57 0.0012 RED

81 18 59 0.0198 BLUE

81 18 61 0.1569 CYAN

81 18 61 0.1569 CYAN

81 18 61 0.1569 CYAN

81 18 63 0.0354 BLUE

81 18 65 0.0496 BLUE

81 18 65 0.0496 BLUE

81 18 67 0.0043 RED

81 18 67 0.0043 RED

81 18 69 0.0797 GREEN

81 18 69 0.0797 GREEN

81 18 69 0.0797 GREEN

81 18 71 0.1689 CYAN

81 18 71 0.1689 CYAN

81 18 71 0.1689 CYAN

81 18 73 0.0187 BLUE

81 18 73 0.0187 BLUE

81 18 75 0.1194 CYAN

81 18 75 0.1194 CYAN

81 18 75 0.1194 CYAN

81 18 77 0.1790 CYAN
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81 18 77 0.1790 CYAN

81 18 77 0.1790 CYAN

81 18 79 0.1764 CYAN

81 18 79 0.1764 CYAN

81 18 79 0.1764 CYAN

81 18 81 0.0574 BLUE

81 18 81 0.0574 BLUE

81 18 81 0.0574 BLUE

81 18 83 0.1358 CYAN

81 18 83 0.1358 CYAN

81 18 83 0.1358 CYAN

81 18 85 0.0534 BLUE

81 18 87 0.1065 GREEN

81 18 87 0.1065 GREEN

81 18 87 0.1065 GREEN

81 18 89 0.0650 BLUE

81 18 89 0.0650 BLUE

81 18 89 0.0650 BLUE

81 18 91 0.0935 GREEN

81 18 91 0.0935 GREEN

81 18 91 0.0935 GREEN

81 18 93 0.1471 CYAN

81 18 93 0.1471 CYAN

81 18 93 0.1471 CYAN

81 18 95 0.0487 BLUE

81 18 95 0.0487 BLUE

81 18 97 0.0486 BLUE

81 18 99 0.0564 BLUE

81 18 99 0.0564 BLUE

81 18 99 0.0564 BLUE

81 18 101 0.1415 CYAN

81 18 101 0.1415 CYAN

81 18 101 0.1415 CYAN

81 18 103 0.0487 BLUE

81 18 105 0.1657 CYAN

81 18 105 0.1657 CYAN

81 18 105 0.1657 CYAN

81 18 107 0.0211 BLUE

81 18 109 0.1033 GREEN
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81 18 109 0.1033 GREEN

81 18 109 0.1033 GREEN

81 18 111 -0.0060 RED

81 18 111 -0.0060 RED

81 18 113 0.0969 GREEN

81 18 113 0.0969 GREEN

81 18 113 0.0969 GREEN

81 18 115 0.1807 CYAN

81 18 115 0.1807 CYAN

81 18 115 0.1807 CYAN

81 18 117 0.1638 CYAN

81 18 117 0.1638 CYAN

81 18 117 0.1638 CYAN

81 18 119 0.1453 CYAN

81 18 119 0.1453 CYAN

81 18 119 0.1453 CYAN

81 18 121 0.1284 CYAN

81 18 121 0.1284 CYAN

81 18 121 0.1284 CYAN

81 18 123 0.1573 CYAN

81 18 123 0.1573 CYAN

81 18 123 0.1573 CYAN

81 18 125 0.1476 CYAN

81 18 125 0.1476 CYAN

81 18 125 0.1476 CYAN

81 18 127 0.0775 GREEN

81 18 127 0.0775 GREEN

81 18 127 0.0775 GREEN

81 18 129 0.1577 CYAN

81 18 129 0.1577 CYAN

81 18 129 0.1577 CYAN

81 18 131 0.0644 BLUE

81 18 131 0.0644 BLUE

81 18 131 0.0644 BLUE

81 18 135 0.0730 BLUE

81 18 135 0.0730 BLUE

81 18 135 0.0730 BLUE

81 18 137 0.1397 CYAN

81 18 137 0.1397 CYAN
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81 18 137 0.1397 CYAN

81 18 139 0.0312 BLUE

81 18 139 0.0312 BLUE

81 18 141 0.1310 CYAN

81 18 141 0.1310 CYAN

81 18 141 0.1310 CYAN

81 18 143 0.1676 CYAN

81 18 143 0.1676 CYAN

81 18 143 0.1676 CYAN

81 18 145 0.0521 BLUE

81 18 145 0.0521 BLUE

81 18 147 0.1588 CYAN

81 18 147 0.1588 CYAN

81 18 147 0.1588 CYAN

81 18 149 0.1299 CYAN

81 18 149 0.1299 CYAN

81 18 149 0.1299 CYAN

81 18 151 0.1127 CYAN

81 18 151 0.1127 CYAN

81 18 151 0.1127 CYAN

81 18 153 0.1263 CYAN

81 18 153 0.1263 CYAN

81 18 153 0.1263 CYAN

81 18 155 0.1743 CYAN

81 18 155 0.1743 CYAN

81 18 155 0.1743 CYAN

81 18 157 0.0579 BLUE

81 18 157 0.0579 BLUE

81 18 157 0.0579 BLUE

81 18 159 0.0054 RED

81 18 159 0.0054 RED

81 18 161 0.0473 BLUE

81 18 163 0.1315 CYAN

81 18 163 0.1315 CYAN

81 18 163 0.1315 CYAN

81 18 165 0.1367 CYAN

81 18 165 0.1367 CYAN

81 18 165 0.1367 CYAN

81 18 167 0.1535 CYAN
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Code FIPS Code at 75% Coverage Color 

81 18 167 0.1535 CYAN

81 18 167 0.1535 CYAN

81 18 169 0.0619 BLUE

81 18 169 0.0619 BLUE

81 18 169 0.0619 BLUE

81 18 171 0.0533 BLUE

81 18 171 0.0533 BLUE

81 18 171 0.0533 BLUE

81 18 173 0.1597 CYAN

81 18 173 0.1597 CYAN

81 18 173 0.1597 CYAN

81 18 175 0.1742 CYAN

81 18 175 0.1742 CYAN

81 18 175 0.1742 CYAN

81 18 177 0.0639 BLUE

81 18 177 0.0639 BLUE

81 18 177 0.0639 BLUE

81 18 179 0.0638 BLUE

81 18 179 0.0638 BLUE

81 18 179 0.0638 BLUE

81 18 181 0.0184 BLUE

81 18 183 0.0855 GREEN

81 18 183 0.0855 GREEN

81 18 183 0.0855 GREEN

81 19 3 0.1055 GREEN

81 19 5 0.0013 RED

81 19 7 0.1384 CYAN

81 19 11 -0.0684 RED

81 19 13 -0.0472 RED

81 19 35 -0.0700 RED

81 19 39 0.1606 CYAN

81 19 45 -0.0309 RED

81 19 49 -0.0366 RED

81 19 51 0.1132 CYAN

81 19 53 0.1618 CYAN

81 19 59 -0.0414 RED

81 19 65 -0.0251 RED

81 19 71 0.0950 GREEN

81 19 77 0.0184 BLUE
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81 19 83 -0.0590 RED

81 19 85 0.0614 BLUE

81 19 87 0.0260 BLUE

81 19 95 0.0533 BLUE

81 19 101 0.0628 BLUE

81 19 103 0.0105 RED

81 19 105 -0.0556 RED

81 19 107 0.0308 BLUE

81 19 109 -0.0651 RED

81 19 111 0.0466 BLUE

81 19 113 -0.0334 RED

81 19 117 0.1401 CYAN

81 19 121 0.0741 BLUE

81 19 123 -0.0159 RED

81 19 125 0.0326 BLUE

81 19 131 0.0197 BLUE

81 19 133 0.0822 GREEN

81 19 135 0.1348 CYAN

81 19 137 0.0627 BLUE

81 19 141 -0.0496 RED

81 19 145 0.0621 BLUE

81 19 151 -0.0146 RED

81 19 153 -0.0256 RED

81 19 155 -0.0106 RED

81 19 159 0.1384 CYAN

81 19 171 -0.0262 RED

81 19 173 0.1458 CYAN

81 19 175 0.1055 GREEN

81 19 177 0.1378 CYAN

81 19 179 0.0206 BLUE

81 19 181 0.1048 GREEN

81 19 183 0.0008 RED

81 19 185 0.1476 CYAN

81 19 189 -0.0245 RED

81 19 191 0.0139 BLUE

81 19 193 0.0812 GREEN

81 19 195 -0.0080 RED