Crop Insurance for Cotton: Premium Rates and Related Matters 

Executive Summary 

Pages 67-68 of Senate Report 105-212, state the following: 

Cotton producers participating in the Federal Crop Insurance Program 

pay significantly higher premiums and receive a lower indemnity per 

dollar of coverage when compared to other major commodities. In 

addition, cotton insurance premiums vary greatly among otherwise 

similar regions with little explanation. Within available funds, the 

Committee directs the agency to carry out a study to review current 

cotton crop insurance rates, rating practices, and compare current rates 

to other major commodities. The Committee urges the agency to use 

independent experts representing all geographic cotton-growing 

areas. (Report accompanying S. 2159 Agriculture, Rural 

Development, Food and Drug Administration, and Related Agencies 

Appropriation Bill, 1999) 

This report is a progress report to comply with that directive. The cotton crop insurance 

program in the States east of Oklahoma and Texas (except Virginia) was reviewed. Virginia is 

not included because there was insufficient information. However, the results for other States in 

the Atlantic coast region can provide guidance with regard to this State. Review of the 

remaining States is underway. Premium rates for cotton will be adjusted for the crop year 2000 

to reflect the results of this study. In many cases, premium rates will decrease. The exact 

amount of the decreases, both in terms of the overall levels and the effect within each State, is 

being determined in a separate analysis. 

Highlights of the report are summarized below. 

Three premium rating methods traditionally have been used by the insurance industry. Those 

are judgement, loss ratio, and pure premium (loss cost). A newer method, one that is not yet 

widely adopted, is mathematical or statistical modeling. RMA primarily uses the pure premium 

method because it is the most appropriate for the data, is objective, and is the simplest to 

implement operationally. RMA has used modeling in certain circumstances. 

The report describes RMA’s premium rate-making methods in detail to assure there are no 

misconceptions about the treatment of losses among States and crops. All calculations remain 

within a State and a crop. Crops and States are not cross-subsidized. Methods are intended to 

meet standard industry characteristics of a desirable premium rate, including stability, 

responsiveness, provision for contingencies, promote loss control, and fair discrimination among 

insured persons. 

Premium rates estimated from insurance experience data are valid only insofar as those data are 

valid indicators of the probable future losses of the population. This report indicates that the 
insurance experience for cotton in some of these States may not provide valid information for 

setting premium rates for most producers. However, the experience does appear valid for some 

farmers who presently insure in some of the States, and this information must be considered as 

premium rates are adjusted. In other cases, the experience seemingly is random, developed 

independently of weather conditions. 

There is no one, simple, easy solution for this situation. Much analysis of specific conditions 

must be undertaken and consultations with interested parties will be needed to identify 

appropriate changes. In particular, revisions must assure that controls are in place to minimize 

the chances that losses again will spiral out of control and lead to higher premium rates and low 

participation. 

Premium rates for cotton were compared to other major crops in these States. In general, there 

is no indication that premium rates for cotton systematically are higher than for other crops. 
2 

Crop Insurance for Cotton: Premium Rates and Related Matters 

Pages 67-68 of Senate Report 105-212, state the following:1 

Cotton producers participating in the Federal Crop Insurance Program 

pay significantly higher premiums and receive a lower indemnity per 

dollar of coverage when compared to other major commodities. In 

addition, cotton insurance premiums vary greatly among otherwise 

similar regions with little explanation. Within available funds, the 

Committee directs the agency to carry out a study to review current 

cotton crop insurance rates, rating practices, and compare current rates 

to other major commodities. The Committee urges the agency to use 

independent experts representing all geographic cotton-growing 

areas. 

This report is submitted to respond to this directive. It is organized in the following manner: 

I. Insurance rate-making principles and methods

 II. Rate-making methods and practices of RMA 

III. Review of current cotton insurance premium rates in selected States 

IV Comparison of premium rates for cotton to other major commodities

 V. Summary 

I. Insurance rate-making principles and methods 

Rate-making Principles 

Rate-making techniques in the commercial insurance industry are intended to develop a price 

structure adequate to cover claims and the operating costs of the insurer and provide a 

reasonable profit while not being so high as to be noncompetitive. Federal crop insurance has 

similar objectives in its rate-making processes, except that the price structure need only be 

adequate to pay expected losses and build a reasonable reserve. Expenses are not included in 

crop insurance premium rates, but instead are paid from the Treasury as subsidy on behalf of the 

producer. 

1

 Report accompanying S. 2159 Agriculture, Rural Development, Food and Drug 

Administration, and Related Agencies Appropriation Bill, 1999. 
Any insurance rate-making system also has secondary objectives. Commonly, these include: 

Stability. Premium rates that are highly volatile from year to year are disconcerting to 

policyholders. Most consumers prefer predictability to wildly fluctuating prices. Thus, 

rate-making techniques normally incorporate procedures to promote stability, especially 

for a product that is subject to infrequent occurrences of extremely severe events, as is 

the case for crop insurance. 

Responsiveness. Rate-making techniques are responsive if changes in the loss exposure 

are incorporated into the premium rates without undue delay. In some respects, 

responsiveness and stability are competing objectives. However, both must be pursued. 

Provide for contingencies. Premium rates are forecasts of future losses. Thus, they are 

imperfect. Circumstances change over time. New varieties of a crop may be developed. 

These new varieties may have risk characteristics that differ from older varieties. The 

data available for rate-making typically are not adequate to reflect all the circumstances 

that might result in a loss. For crop insurance, more years of data are preferred so that 

the extremes of adverse weather are included. However, as the number of years 

increases, the data become less representative of current production conditions and 

technology. Hence, rate-making procedures typically include some loading for the 

unknown and unknowable. 

Loss control and loss reduction. Insured persons should have incentives to control the 

occurrence of and the amount of losses. Since premium rates are driven by expected 

losses, they can be made more affordable only if insured persons retain incentives to avoid 

losses when possible or, when a loss does occur, to minimize the amount of the loss. The 

first incentive is called loss control. In crop insurance, loss control is stimulated by such 

actions as requiring that a deductible be met before any loss becomes payable. The 

second incentive is called loss reduction. In crop insurance, loss reduction is the goal of 

policy provisions such as those that require all production of a crop be counted against 

the guarantee, even if unharvested. 

Discriminate risk appropriately. Premium rates must recognize the risk that is presented 

by a particular class of insured persons. For example, younger persons are charged 

higher premiums for automobile insurance because such persons as a class present a 

greater risk of loss. Rates must discriminate among classes of insured persons in an 

objective manner. In crop insurance, premium rates discriminate among crop production 

practices, among producers at different average yields, and in other ways. However, 

rates cannot arbitrarily discriminate, or the effectiveness of the rating system will be lost. 

2 
Other objectives could be stipulated, but these five are adequate to provide a basis for describing 

the rationale of the premium rate-making processes employed by the RMA. 

Insurance industry rate-making methods 

Most insurance textbooks identify three generic methods for rate-making: judgement, loss ratio, 

and pure premium (loss cost). 

Judgement. This is the oldest of the rate-making methods used in the insurance industry. 

As the term implies, the intuition of the rate-maker plays a major role in setting the rate. 

Its major use in contemporary insurance is for unusual or unique situations. In Federal 

crop insurance, the initial premium rates for a new crop may be influenced by judgement 

because reliable data for an extended historical period rarely exist. 

Loss ratio. This method appears simple but often is misleading. Simply put, the losses 

paid to policyholders are summed and then divided by the sum of the premiums paid by 

those policyholders expressed on current rate level. A ratio greater than 1.00 indicates 

that more losses have been paid than premiums that would have been earned at current 

rate level, suggesting that an increase in the premium rate is needed. The converse also 

applies. The reason that a loss ratio can be misleading as to rate adequacy is discussed 

below. This method is most appropriate when the units exposed to loss are homogenous 

and do not change substantially over time. It is a method for adjusting an existing 

premium rate. 

Pure premium method (loss cost). The term loss cost describes a ratio of the losses paid 

to policyholders divided by the insurance provided to those policyholders. It is called the 

pure premium method because the result is the premium rate that should have been 

charged to exactly pay those losses. For example, if $100 of losses are paid and the 

insurance provided to policyholders is $2,000, the loss cost is 5.0 percent. In other 

words, if a premium rate of 5.0 percent before allowances for expenses and profit had 

been charged, the insurer would have received enough premium income to pay the losses 

realized. The pure premium method calculates a new premium rate each time it is used 

whereas the loss ratio method adjusts a previously established premium rate. 

These are the traditional insurance rate-making methods. A newer method is mathematical or 

statistical modeling. It is very appropriate when losses occur very infrequently and the 

magnitude of the loss is extremely unpredictable. Examples are potential damage due to 

hurricanes or earthquakes. Both happen rarely and the magnitude of the event is unpredictable. 

A premium rate typically is estimated by Monte Carlo simulation. Modeling is a form of the pure 

premium methodology in that it estimates a rate rather than changing an existing rate. 

As an example of modeling, the expected frequency of hurricanes might be described by a 

specific probability distribution. The force of a hurricane, once one occurs, might be described 

3 
by a second probability distribution. The angle at which the hurricane approaches the coast and 

the point of landfall might be described by other probability distributions. The total damage 

resulting from a particular hurricane is described by the force of the hurricane, the angle at which 

it hits land, and the point of landfall. 

Once all the probability distributions and damage models are estimated, Monte Carlo simulation 

proceeds as follows: first, a random number is drawn and applied to the hurricane frequency 

distribution. Most such random draws result in no hurricane. However, if a hurricane does 

result, a second random draw is taken to determine its force. Other random draws are made to 

establish the angle of approach and point of landfall. Once these values are established, the 

amount of damage is determined. After thousands of these random draws, the total damage is 

summed and divided by the total value of the property at risk during those thousands of random 

draws. The result is a pure premium rate, but one estimated through simulation rather than from 

insurance experience. 

Modeling is very data intensive and requires skilled professionals from several disciplines when 

the exposures are complicated. It may not perform well for some coverages offered under the 

Federal crop insurance program because damage predictions may not be reliable. Since crop 

yields may be reduced by numerous factors, isolating the amount of damage due to a certain 

event (such as a prolonged dry spell that is less pervasive than a drought) may be difficult. 

Weather data may permit predictions of the probability that the dry spell will occur, but lack of 

reliable predictors of the resulting damage may preclude reliable estimates of required premium 

rates. In other cases, data may be insufficient. However, as will be discussed later, there are 

situations in which modeling of crop insurance coverage is possible. 

Earlier, it was stated that the loss ratio method appears simple but may be misleading. The 

reasons for this statement are demonstrated with an example. Assume that a new crop is insured 

with an initial premium rate of 5.0 percent set by judgement (for example, by comparing its 

production characteristics and risk factors to a crop that has been insured in that area). Suppose 

experience in the first 3 years is: 

Year Liability Premium Losses Loss cost Loss ratio

 1 $2,000 $100 $150 0.075 1.500

 2 2,500 125 50 0.020 0.400

 3 3,000 150 375 0.125 2.500

 Total $7,500 $375 $575 0.073 1.533 

This experience is extremely limited, but suppose the management is concerned that the initial 

rate of 5.0 percent is not adequate, and decides to increase it to 6.5 percent in year 4, again 

relying on judgement. Assume that after the increase, the experience is (years 1 - 3 are repeated 

for convenience): 

4 
Year Liability Premium Losses Loss cost Loss ratio

 1 $ 2,000 $100.00 $150.00 0.075 1.500

 2 2,500 125.00 50.00 0.020 0.400

 3 3,000 150.00 375.00 0.125 2.500

 4 2,500 162.50 50.00 0.020 0.308

 5 3,500 227.50 100.00 0.029 0.439

 Total $13,500 $765.00 $725.00 0.054 0.948 

At this point, management might ask: should this premium rate be changed once again, and if so, 

by how much? The average of the loss costs is 0.054 (5.4 percent); hence, the indicated pure 

premium rate using this method is 5.4 percent. Many persons would incorrectly assume that the 

rate calculated by the loss ratio method should be 0.948 times 6.5 percent, or 6.2 percent. Why 

is there a difference in the 2 estimates and why would the loss ratio method be applied incorrectly 

to arrive at a premium rate of 6.2 percent? 

The loss ratio method is applied incorrectly because all the premiums must be stated at the 

current rate level. That is, the premiums for years 1, 2, and 3 must be recalculated using the 

premium rate of 6.5 percent that was in effect in years 4 and 5. That is what is meant by “current 

rate level.” The actual premium rate in the first 3 years was 5.0 percent. The data restated to 

current rate level are shown below. 

Year Liability Premium Losses Loss cost Loss ratio

 1 $ 2,000 $130.00 $150.00 0.075 1.154

 2 2,500 162.50 50.00 0.020 0.308

 3 3,000 195.00 375.00 0.125 1.923

 4 2,500 162.50 50.00 0.020 0.308

 5 3,500 227.50 100.00 0.029 0.439

 Total $13,500 $877.50 $725.00 0.054 0.826 

With premiums stated on the current rate level of 6.5 percent, the loss ratio is 0.826. When 

multiplied by the existing premium rate of 6.5 percent, the result is 5.4 percent – the same as the 

loss cost or pure premium method. 

5 
Standardizing premiums to current premium rate level is difficult, time-consuming, and may 

result in inaccuracies when exposure units are not homogeneous. Because the two methods 

result in the identical premium rate when applied to the same data, RMA uses the loss cost 

method to calculate premium rates. 

There is another important point regarding the estimation of premium rates using either the loss 

cost or the loss ratio method. In this example, both are nearly identical if calculated as the sum 

of the losses for the 5 years divided by the sum of the liability for the 5 years (or the sum of the 

premiums for the loss ratio) or if calculated as the simple average of the annual loss costs or loss 

ratios. This occurs because the liability each year is similar in amount. However, suppose for 

whatever reason the liability in year 3 had been $500 (perhaps a Payment in Kind program was 

offered as did occur in 1983) rather than $3,000. The result of this change is shown below. 

Year Liability Premium Losses Loss cost Loss ratio

 1 $ 2,000 $130.00 $150.00 0.075 1.154

 2 2,500 162.50 50.00 0.020 0.308

 3 500 32.50 62.50 0.125 1.923

 4 2,500 162.50 50.00 0.020 0.308

 5 3,500 227.50 100.00 0.029 0.439

 Total $11,000 $715.00 $412.50 0.054 0.826 

The dollar weighted loss cost with this change is 3.8 percent ($412.50 divided by $11,000). The 

dollar weighted loss ratio declines similarly, to 0.577 ($412.50 divided by $715). However, the 

simple averages remain at 0.054 and 0.826 for the loss cost and loss ratio, respectively. The 

difference between these data and the previous data is critical. Should the premium rate be 3.8 

or 5.4 percent? This question can be answered only by examining the reasons for the decline in 

business in year 3 and taking future expectations into account. Year 3 was a year of high losses. 

If it can be expected that business always will decline sharply in a year with high losses, the dollar 

weighted average of 3.8 percent would be appropriate. Since weather occurs randomly and there 

are no reasons to anticipate that crop insurance business levels will fluctuate in proportion to the 

severity of losses., RMA uses the average of the loss costs in its rate-making. 

As an aside to this discussion, it should be clear that the historical loss ratio of the Federal crop 

insurance program provides no guidance as to adequacy of current premium rates. Historical 

premiums never have been corrected to current rate level in the reports of insurance experience. 

Hence, loss ratios calculated on the basis of the actual premiums for prior years are incorrect and 

misleading if one attempts to use these as an indicator of the adequacy of current premium rates. 

II. Rate-making methods and practices of RMA 

6 
As stated above, the primary rate-making technique followed by RMA for crops that have been 

insured for several years is the loss cost or pure premium method. The steps to analyze the loss 

costs and the ways that the previously described characteristics of a desirable rate-making system 

are incorporated into the methods are described below. Two sample counties are used. One 

represents the experience for corn in Iowa and the other represents experience for cotton in 

Texas. 

Charts 1A and B2 depict insured acres as a percent of planted acres. The counties are similar in 

terms of the level of participation. Note that participation apparently has declined in the corn 

county during 1996 and 1997. This is not the case: the apparent decline consists of acres insured 

under the Crop Revenue Coverage and the Revenue Assurance plans of insurance. These data 

will be converted to yield based coverage and included in the analysis when premium rates are 

calculated for the 2000 crop year. However, losses in this county in those years were extremely 

low. Omission of the data from these 2 insurance plans would have no effect on the premium 

rate since RMA uses a simple average of loss costs, not the dollar weighted value. The loss costs 

for these 2 years will not change materially when the data from the other insurance plans are 

converted and included in the rate base. 

Charts 2A and B contain several key pieces of information. First, consider the corn county. 

Yields in this county demonstrate a trend as exhibited by the straight, upwardly sloping line in the 

upper part of the chart.3 The county average yield as reported by the National Agricultural 

Statistics Service (NASS) for each year is charted around this trend line. In most years, the 

annual yield is fairly close to the trend line. However, in 1977, 1988, and 1993, the annual 

average yield is sharply lower than the trend line. 

Additional data are depicted at the bottom of the chart. These points represent the loss cost for 

crop insurance for each year. The average of the loss costs is 5.4 percent. Note that the loss 

cost is less than 0.5 percent in most years. However, there are 3 years in which it is substantially 

greater. As one might expect, those 3 years are 1977, 1988, and 1993 – the same 3 years that 

the yield was below the trend. 

The information in the chart for the cotton county is identical. In this case, however, no 

statistically significant trend line could be estimated.4 Thus, the fluctuations in the annual NASS 

yield are depicted relative to the average yield for 1976-1997. The NASS average yield is 

sharply lower than average in four years -- 1975, 1982, 1986, and 1992. Again, loss costs are 

much higher than average in those 4 years. In comparison to the corn county, the fluctuations of 

the loss cost are much more variable in the other years, indicating that this county perhaps has a 

2

 All charts are contained in the Appendix. 

3

 The trend line is estimated for the years 1956-1997. 

4

 The trend coefficient for 1956-1997 was negative but not statistically significant. 

7 
higher variation in yield among farmers, among farming practices, or for other reasons. The 

simple average of the loss costs in the cotton county is 15.2 percent for the period. 

Charts 3A and B also contain several key pieces of information. The leftmost vertical bars in 

each set depicts the average of the coverage levels purchased by producers each year. It is the 

weighted average of the number of acres insured at each of the coverage levels -- e.g., at 65 

percent, 70 percent, or 75 percent.5 Coverage averaged about 70 percent in the corn county and 

slightly less than 65 percent in the cotton county. Since the average coverage level changes 

during these years, RMA makes the loss costs comparable over time by mathematically restating 

the average loss costs to the 65 percent coverage level. The 65 percent coverage level was 

chosen for this purpose because this is the level of coverage most commonly purchased since the 

late 1980s. Also, the experience at higher coverage levels can be recalculated at 65 percent very 

readily. The raw loss cost is shown in the charts as the second bar in each set while the third bar 

depicts the loss cost after the adjustment to the 65 percent coverage level. After this adjustment, 

the average loss cost for the corn county declines from 5.4 to 4.8 percent. In the cotton county, 

the adjusted loss cost is greater than the raw value in some years, about the same in others, and 

less in still others. This is because the average coverage level was under 65 percent in some 

years, over 65 percent in others, and at about 65 percent in a few years. The adjusted average 

for the cotton county is 15.0 percent compared to the original value of 15.2 percent. 

Recall that stability in the premium rates is a major objective of an insurance rate-making system. 

The next step of RMA’s rate-making methods is intended to achieve this objective. Refer once 

again to Chart 2A. This county had 3 major loss years. However, many counties in Iowa 

incurred major losses in 1983 whereas this county had no abnormal loss that year. This county’s 

losses in 1988 and 1993 are not as severe as in other parts of the State. The cotton county has 4 

major loss years, which again may or may not exceed long-run expectations. When confronted 

with such highly infrequent and variable events, insurance rate-makers frequently spread losses 

associated with those events over a larger geographic territory to gain a better estimate of their 

average effect. The method used by RMA is called “80th percentile capping.” The method ranks 

the annual loss costs from smallest to largest. Then the year corresponding to the 80th percentile 

is determined. For example, if there are 20 years of data, the 80th percentile is the 16th largest 

loss cost. The loss cost for that year is substituted for the actual loss cost for the remaining 4 

years. The excess of the losses and the associated liability over this cap is pooled at the State 

level. This process is illustrated below with the same example as used earlier. 

5

 Most crop insurance business is heavily concentrated in 1 or 2 of the coverage level 

choices. The levels differ by crop and county, but 65 percent is the most common in recent years. 

8 
The 80th percentile for this example is the loss cost for year 1, which is 7.5 percent. This value is 

substituted for the 5th largest loss cost, which is 12.5 percent. The average of the capped loss 

costs is 4.4 percent. The losses over the cap ($150, which is (0.125 - 0.075) times $3,000) is 

allocated to a pool with the excess of losses for all other counties in the State. 

Year Liability Premium Losses Loss cost Capped loss 

cost

 1 $ 2,000 $130.00 $150.00 0.075 0.075

 2 2,500 162.50 50.00 0.020 0.020

 3 3,000 195.00 375.00 0.125 0.075

 4 2,500 162.50 50.00 0.020 0.020

 5 3,500 227.50 100.00 0.029 0.029

 Total $13,500 $877.50 $725.00 0.054 0.044 

Charts 4A and B depict the effects of this capping of loss costs. The raw and adjusted loss costs 

from Charts 3A and B are repeated in these charts. After capping, the average loss cost is about 

0.60 for corn and 8.5 for cotton. This reflects the differences between the two counties in terms 

of the “normal” levels of losses that occur in a typical year. 

Next, RMA’s method compares the capped average loss cost in each county to its immediate 

neighbors (a concentric circle, i.e., all counties bordering the host county). Extreme changes in 

premium rates from county to county often simply reflect chance events. Thus, RMA smooths 

the county’s rate by averaging it with those neighbors. In counties that have significant acreage, 

the weighting is 60 percent to the host county and 40 percent to the neighbors. This corn county 

is very similar to its immediate neighbors. Its capped loss cost is 0.57 percent whereas its 

neighbors have a capped loss cost of 0.68 percent. The weighted average (0.57 times 0.60 plus 

0.68 times 0.40) is 0.62 percent. The cotton county is less similar to its neighbors. Its capped 

loss cost is 8.5 percent whereas the neighbors have a capped average loss cost of 11.2 percent. 

The weighted average is 9.6 percent. 

Next, to recognize the provision of the law requiring a reasonable reserve, and to incorporate the 

insurance rate-making objective of providing for contingency, the weighted average loss cost is 

divided by 0.88. This value was based on a set of assumptions about the probability distribution 

of losses at the national level. It is set at the national level because that results in the smallest 

amount of loading. If it were set at a lower level of aggregation (e.g., a county), the amount 

needed to achieve an expected loss ratio would be much larger. 

The next step adds the average of the pooled loss costs at the State level (pooled losses divided 

by pooled liability) to the capped and smoothed loss costs. Losses due to prevented planting, 

replanting, and other coverages not directly related to yield loss that were removed from the loss 

9 
 

cost data before these calculations began are analyzed separately to estimate the appropriate 

charge for those coverages. These estimates are added to the previous results. 

The result of these calculations is a “target” premium rate. This is the premium rate that would 

be charged to a producer at the county average yield if there were no limits on premium rate 

changes (such as the statutory limitation on rate increases). 

The calculations described to this point have been made with data aggregated for all types and 

practices for a crop within a county. This is necessary because the crop insurance data by type 

and practice in any county can be very sketchy due to infrequent planting by producers. 

Insurance rate-making techniques seek data that are robust. This means that the data would not 

be changed substantially if only a few more observations were added. Thus, RMA aggregates 

the data for all types and practices (where these are distinguished) to make the data more robust. 

After the county target rate is established with the aggregated data, that target rate is multiplied 

by factors that reflect the relative riskiness of each type or production practice. These factors are 

based on insurance data drawn from larger areas than a county, on the relative importance of the 

production practice or type in an area, and from agronomic information published by State 

universities and other sources.6 

The procedure to this point has developed an average premium rate by type and practice at the 

65 percent coverage level. This rate is assumed to be appropriate for a producer whose Actual 

Production History (APH) average yield is similar to a longer-term average yield for the county. 

Premium rates for producers with APH yields that differ from average are determined with a 

mathematical formula. Extensive research demonstrates that, on average, the probability of a 

loss is greater for producers whose yields are below the average for the area and vice versa. 

Statistically, this condition indicates that the standard deviation of yield within an area does not 

change as rapidly as does the average yield. Thus, the coefficient of variation for a producer (the 

standard deviation of the producer’s average yield divided by that average yield) is larger for 

lower yielding producers (i.e., potential for loss is greater). The mathematical formula that 

changes the premium rate according to the producer’s APH yield recognizes this tendency. Since 

this tendency exists on average, this discrimination of premium rates among producers is fair. 

Finally, premium rates are needed for the different coverage levels that are offered. Recall that 

the loss costs were standardized to the 65 percent coverage level. Rates at the other coverage 

levels are multiples of these rates. For example, rates at the 75 percent coverage level for many 

crops are 1.538 times the rates at the 65 percent coverage level and those at the 50 percent 

coverage level are 0.723 times the 65 percent rates. These factors recognize that losses are more 

6

 For example, a factor for the irrigated practice would be substantially different in one 

county versus another if the principal production practice in one county were non-irrigated but 

irrigated in a second. This is because the bulk of the insurance experience would be based on the 

non-irrigated practice in the first case and on the irrigated practice in the second. The average 

loss costs would be more reflective of the dominant practice. 

10 
 

frequent at higher coverage levels and the severity of loss also differs. RMA has several research 

projects underway to examine the stability of these adjustment factors across crops, areas, and 

average yields. 

In summary, the rate-making methods used for crop insurance follow traditional insurance 

industry practices. The loss cost methodology is a standard methodology. Spreading large 

catastrophic events over larger areas is an accepted technique to gain a better estimate of the 

average effect of infrequent and severe losses. Premium rates are loaded for contingency. 

Premium rates charged to an individual producer are based on objective criteria. 

These rate-making methods, based in standard actuarial techniques, do not commingle losses 

among crops or States. Each crop in each State is analyzed separately. There is pooling of 

losses among counties within a State to determine the average effect of very infrequent, very 

severe loss events. The county smoothing procedure (concentric circles) is the only point in the 

rate-making procedure at which losses in any county in any other State play a role in the 

premium rates. Under this procedure, a county is as likely to be adjusted up as it is to be 

adjusted downward. 

The foregoing discussion focuses on the standard rate-making technique used by RMA. RMA 

also has used modeling in some cases, such as citrus trees in Florida. In this case, the probability 

functions were taken from a report issued by the National Hurricane Center of the National 

Oceanic and Atmospherics Administration. RMA would not have had the expertise to develop 

those probability functions by itself. Damage at various wind speeds was predicted from a model 

developed by a State university. This information was combined in a Monte Carlo simulation to 

estimate a pure premium rate for damage due to winds. A similar approach was taken for damage 

due to freeze. Since the direct effect of these perils could be estimated and there are few other 

perils that will break limbs or destroy the trees, the simulation was appropriate. 

Modeling also has been used to develop premium rates for the Income Protection (IP) plan of 

insurance. This model typically utilizes 35 to 40 or more years of county average yields as 

reported by NASS. These data provide a long-term perspective with regard to fluctuations in 

yields in an area. However, the IP plan does not insure a county average yield. Thus, the 

variation of yields for individual producers is introduced with the yields certified under the APH 

rules. The data for those producers who have certified 6 or more actual yields at the enterprise 

unit level (all acreage farmed by the producer) are used in the model. Data for at least 50 

producers are needed to be statistically robust. If data for fewer than 50 producers in a county are 

available, the information is aggregated across several counties. Monte Carlo simulation is used 

to estimate a pure premium rate. 

11 
III. Review of premium rates for cotton 

Does the insurance experience accumulated under the APH plan provide a reliable indicator of 

future losses and therefore required premium rates for cotton? Many believe it does not – that the 

pool of insured persons has been so adversely selected that it is not representative of the larger 

population of cotton growers. RMA has attempted to evaluate this issue. 

The initial analysis for this report focused on the southeastern and delta cotton growing areas --

i.e., cotton growing States east of Oklahoma and Texas. These areas were chosen for several 

reasons, among which are: cotton acreage has been increasing in recent years and insured acres at 

buy-up levels range from greater than 50 percent of planted acres in some States to near zero in 

others. It was believed that these States provided a wide range of participation and the reasons 

for those differences could be analyzed within the time and resources available. Evaluations for 

Texas and Oklahoma are nearing completion and will be reported separately. The remaining 

cotton-growing States will be completed as data become available. 

RMA used professional assistance from Mississippi State University, Auburn University, Texas 

A&M University, and the National Cotton Council of America for this review. The major project 

work was performed by professional staff at Montana State University, the developers of the IP 

rating model. RMA determined that this model or a similar model would be useful for this review. 

Since the IP model had been developed, it was used rather than incur the costs and delays 

associated with developing a similar model. If price changes are not included in the simulation, 

the estimated premium rates from the IP model represent a pure premium rate for yield losses at 

an enterprise unit level of aggregation. The pure premium rates from this model do not include 

estimates of the losses due to unit division, prevented planting, quality, and other coverages. 

However, a high percentage of crop insurance premium rates is due to the risk of yield loss 

regardless of the form of the insurance. Thus, it is believed that the IP model provides an index 

against which to measure the current premium rates for the APH coverage plan. For example, if 

the premium rates from the 2 methodologies are fairly similar and there is high participation in the 

cotton insurance program, it is likely that the insurance experience used by RMA is reasonably 

reliable. If the differences are large and participation is low, the quality of the experience data is 

questionable. 

The performance of the cotton crop insurance program in these States for 1980-1998 is shown in 

charts 5 through 14. Charts 5 through 14 depict the annual loss ratios and the yield ratio, which is 

the average yield for a year as reported by NASS divided by the trend (expected) average yield for 

the year. Recall that charts 2A and B demonstrate that large crop insurance losses are directly 

associated with deviations of annual yield from the trend or the average. That is, a low yield 

compared to the expected yield is accompanied by much higher than normal loss costs. If the 

crop insurance experience data for cotton in these States is reasonable for rate-making, it should 

exhibit similar characteristics. 

12 
Consider the chart for Alabama (chart 5A). In 1980, the loss ratio was approximately 200 

percent. The yield ratio that same year was 82 percent, i.e., 18 percent lower than the expected 

trend yield for that year. As was demonstrated in charts 2A and 2B, the loss ratio should be 

greater than 100 percent when the yield ratio is below 100 percent. Further, the loss ratio should 

be larger as the annual yield decreases relative to the trend. 

Because the extent to which the changes in the loss ratio are associated with changes in the yield 

ratio is difficult to discern in Chart 5A, the data are graphed differently in Chart 5B. In this chart, 

the yield ratios are ranked from lowest to highest along the horizontal axis. The corresponding 

loss ratios are shown in the chart. The expected shape of the graph would slope from left to right, 

with a sharp decline as the yield ratio becomes larger. The data for Alabama generally conform to 

this expected shape. There are a few “bumps” for years in which the losses do not change as 

expected. For example, one cannot tell from the chart whether the losses in 1995 were lower than 

expected or whether the losses in 1983 and 1997 were greater than expected. Both 1988 and 

1998 appear to be unusual. More analysis of the data are needed to determine the causes of these 

departures from the expected shape, but the crop insurance experience data for Alabama generally 

follow the expected pattern. This is not the case in some of the other States. A summary of the 

patterns is given in the following chart. 

13 
Cotton in Selected States: Loss History and 1998 

Participation at Buy-up Levels 

State Loss Pattern Summary 1998 Planted Acres 

Insured as Buy-up 

Alabama Conforms to expected pattern; a few years need 

further review; yield ratio is highly variable

 78% 

Arkansas Does not conform to expected pattern; large 

downside yield ratios but small upside values

 1% 

Florida Exhibits expected pattern but some years clearly do 

not; highly variable yield ratios

 47% 

Georgia Exhibits expected pattern with 2 or 3 years outside 

the pattern; highly variable yield ratios

 54% 

Louisiana Appears highly random with limited association of 

losses to yield ratio; yield ratios are not as variable as 

other States

 5% 

Mississippi Exhibits expected pattern with a few unusual years; 

yield ratios more variable than in Louisiana but less 

so than other States

 14% 

Missouri No insurance purchased until 1990; highly erratic 

losses since

 <1% 

North Carolina Exhibits expected pattern with a few unusual years; 

yield ratios are highly variable

 29% 

South Carolina Exhibits expected pattern with 2 or 3 unusual years; 

yield ratios are highly variable

 10% 

Tennessee Appears highly random with limited association of 

losses to yield ratio; yield ratios have wide range

 9% 

Generally, these charts indicate that the insurance experience in Arkansas, Louisiana, Missouri, 

and Tennessee is meaningless -- the indemnities had no relationship to the weather conditions that 

produced the observed yields. Buy-up participation in these States was less than 10 percent of 

planted acres in 1998. In the remaining States, the loss ratio has changed in a more predictable 

manner as the yields change. The association is stronger in some States than in others. This 

indicates that the producers in those States who are buying insurance have losses that follow 

14 
 

changes in the weather. However, there is a very wide range in the percentage of planted acres 

insured at buy-up levels, ranging from 10 percent in South Carolina to 78 percent in Alabama. 

Consequently, even though the experience of insured persons appears to track changes in yields in 

those States, other factors must be involved. 

Relating insurance experience to the yield ratio reveals no information about the segment of the 

population that chooses not to buy crop insurance. It only provides an indication as to whether or 

not the experience has any information for setting premium rates for at least a part of the 

population of cotton producers. During most of the years included in these charts, participation in 

the crop insurance program has been similar to the levels shown in the table. Since 1995, 

however, a higher percentage of acres have entered the crop insurance program at the 

catastrophic level of coverage. The experience of these producers is not sufficient to provide any 

indications of appropriate premium rates. However, the yields certified by those producers for the 

APH program can be used to provide an indicator. This was done with the IP rating method. 

Premium rates at the enterprise unit level of aggregation were estimated for all participants in 

cotton crop insurance, including the catastrophic level of coverage, who met certain criteria.7 

First, only the data for those producers who had reported 6 or more years of yields was used. 

This criterion eliminated a large number of producers in some counties. However, at least 6 years 

are needed to measure the variance of the yields of individuals relative to the variance of the 

county yield. Secondly, data were screened for statistical anomalies. For example, in one county 

in one State, the identical yield was reported for numerous producers for several years. After 

eliminating all producers with fewer than 6 yields and screening the remaining producers for 

statistical anomalies, fewer than 50 producers remained in the data base for some counties. In 

those cases, counties were aggregated until the threshold of 50 producers could be achieved. 

Results of the analysis in these States is summarized in the table below. The APH premium rate is 

the average rate for the 65 percent coverage level that would have been paid by the insured 

producers in the 1997 crop year. If a producer chose catastrophic coverage in the 1997 crop 

year, this factor makes no difference: the rate that he or she would have paid at the 65 percent 

level was recovered from the actuarial tables. This premium rate is based on the average of all 

yields certified by each producer and is not an average computed unit by unit. Both the yield and 

the premium rate are based on solid planted cotton, not skip-row. It includes the surcharge for 

optional units for the reasons discussed below. The subsidized APH rate is 58.3 percent of the 

unsubsidized rate. The subsidized rate is the basis on which a producer would pay for cotton 

insurance. 

7

 There have been suggestions that the cotton growing experience of producers who 

choose not to buy insurance at any level should be considered in setting premium rates. RMA 

attempted to obtain information about such producers, but was unsuccessful. No long-term yield 

histories could be located through State universities or other sources. Since no information is 

available, estimates of the effect of such individuals on premium rates could not be made. 

15 
The IP premium rate is the average of the rates calculated with that model for all qualifying 

producers in a State. As indicated earlier, this premium rate does not include basic or optional 

units, prevented planting, quality, or any other coverage that might be included in the APH 

premium rate. The subsidized IP premium rate is 58.3 percent of the unsubsidized IP rate unless 

the IP rate is greater than the APH rate for any producer. In that case, the dollar subsidy for the 

IP coverage is limited to the amount that would be paid under the APH coverage plan. Because 

the IP premium rates exceeded the APH premium rates for some producers, the subsidized IP rate 

shown in the table averages approximately 66 percent of the unsubsidized rate. 

Producers’ decisions to purchase insurance and the level of protection purchased depend upon a 

number of factors. Among these are the individual producer’s aversion to risk; the alternative risk 

management strategies available to the individual producer; wealth; the ability to self-insure; the 

cost of the insurance; and other factors. When considering crop insurance, producers do not 

consider the unsubsidized premium rate as an indicator of cost. Most producers probably have no 

idea of that premium rate. Instead, producers consider the subsidized premium rate as the true 

indicator of the cost of the insurance. Many producers, especially those who insure the greatest 

percentage of the acres, elect coverage on the basis of optional units. Thus, the subsidized APH 

rate for optional unit protection perhaps is the single most reliable indicator of the cost of 

insurance as perceived by producers. This is the reason that the subsidized premium rate 

including optional unit coverage is included in the table. 

16 
Premium Rate Summary: APH versus IP 

Selected States for Cotton 

State Unsubsidized Unsubsidized Subsidized Subsidized Column 3 

APH rate IP rate APH rate IP rate minus 

column 2 

Alabama 10.1 7.5 5.9 5.0 -1.6 

Arkansas 12.9 4.2 7.6 2.9 +3.4 

Florida 12.8 5.1 7.5 3.6 +2.4 

Georgia 16.6 11.9 9.7 7.2 -2.2 

Louisiana 15.6 5.0 9.1 3.3 +4.1 

Mississippi 11.8 5.8 6.9 3.7 +1.1 

Missouri 10.7 6.5 6.2 4.5 -0.3 

N. Carolina 9.9 4.9 5.8 3.2 +0.9 

S. Carolina 15.2 5.1 8.9 3.2 +3.8 

Tennessee 11.4 9.0 6.6 6.1 -2.4 

For these States, the IP revenue premium rates exceeded the IP yield premium rate by about 10 

percent, or by approximately the same difference as between the basic and optional unit premium 

rates for the APH coverage.8 It is impossible to exactly compare the premium rates developed by 

the 2 methods because of the difference in the definition of units and for other reasons. But it is 

possible to use the IP premium rate as an index for comparing the APH premium rate among 

these States. If the difference between the rates is constant among States, some sort of systematic 

problem(s) might exist. If the difference is not constant, there probably are varying causes. The 

last column in the table represents the subsidized APH premium rate minus the unsubsidized IP 

premium rate. 

For the purpose of this comparison, assume that the IP premium rate represents an estimate of the 

actuarially fair premium rate for cotton. If this were an actuarially fair rate, some percentage of 

8

 Although revenue coverages sometimes are represented as less costly than yield-only 

coverage, introducing price variability increases the premium rate when the price-yield correlation 

is low. 

17 
producers would buy at that cost. Since crop insurance is subsidized, more producers should buy 

at the subsidized premium rate. A high positive difference in the last column indicates that the 

subsidized APH rate exceeds the assumed actuarially fair premium rate. If this condition truly 

does exist, low participation in APH cotton crop insurance would be expected. A small positive 

or a negative difference indicates that the current subsidized APH rate is about equal to or less 

than the assumed actuarially fair value. In such cases, participation in the APH plan should be 

greater. The purpose of this comparison is not to demonstrate the magnitude of any rate 

difference, but instead to provide an index of producers’ reactions to the current subsidized APH 

premium rates relative to their perceptions of the actuarially fair charge. 

The differences, expressed as a percentage of the subsidized APH premium rate, are graphed 

against acres insured at buy-up levels in Chart 15. Missouri is omitted since it has small acreage 

and producers in that State did not participate in the insurance program until the 1990s. Thus, it 

may be atypical of the remaining States. The premium rate “gap” is the difference from the above 

table divided by the subsidized APH premium rate, e.g., the value for Alabama is -1.6 divided by 

5.9, which equals -27 percent. It is expressed as a percentage because the difference could be 

small numerically but be a significant difference because the underlying premium rate also is small 

numerically. 

With the exceptions of Tennessee and Florida, the percentage of acres insured does track with the 

premium rate gap. Participation declines rapidly as the gap becomes less negative (it should be 

negative in all cases if the IP premium rate truly were the actuarially fair charge). This suggests 

that many producers in some of these States do not regard the current APH premium rates as 

actuarially fair even when the subsidy is considered. Thus, even though the pattern of losses may 

correspond to changes in yields, the crop insurance experience may not provide sufficient 

information to estimate an appropriate premium rate for those persons. 

These data indicate that the APH premium rates for cotton must be modified in some States. The 

reasons for the adverse insurance experience in these States must be identified so that the 

appropriate adjustments can be made. This effort now is underway and will result in lower 

premium rates for some producers for the 2000 crop year. Defining the exact amount of 

change by State and region within State is beyond the scope of this report. However, some 

possible adjustments include: 

For those States with correlation of yield and loss ratio such as Alabama, Mississippi, and 

others, the experience might be mathematically adjusted to reflect the average yields of 

producers who do not insure presently. That is, the experience that has been accumulated 

would be used to set premium rates consistent with the average yields of those persons 

who contributed to the loss history and reduced for the remaining producers according to 

the formulas used by RMA to discriminate rates among producers. 

Make appropriate adjustments to the insurance experience to eliminate events that 

occurred in the past but are not expected to reoccur in future years. For example, during 

18 
the late 1980s and early 1990s, cotton producers had a choice of 3 methods for calculating 

the average yield on which insurance was based. Producers were permitted to choose the 

largest value. This system no longer is offered. Losses that can be attributed to that 

system should be eliminated from the experience. 

Use the premium rates estimated by the IP model after adjustment to recognize optional 

units, prevented planting, and other factors in those counties that had sufficient numbers of 

producers in the sample. Some preliminary analysis indicates these premium rates should 

be increased by 25 percent or more to accommodate optional units. However, the revised 

premium rates must adequately discriminate those persons who created the adverse 

experience. 

In addition to revising premium rates, introduce statistical tools to monitor producers, 

agents, and loss adjusters. These tools would be designed to detect losses that differ in 

terms of frequency or severity from expected levels considering the yield expectation for 

the year. Once unusual situations are flagged, impose special monitoring to avoid 

degradation of the cotton crop insurance program once again. 

Other approaches may be identified in consultation with insurance providers, producers’ 

groups, and other interested parties. 

Cost of cotton crop insurance relative to other crops 

The Senate Report directs that the cost of cotton crop insurance be compared to other major 

crops. These comparisons are given in Table 1. Within each of these States, crop insurance 

experience for the 1998 crop year is compared for various crops. Corn, cotton, soybeans, and 

wheat were insured in all the States. Depending on the State, peanuts, grain sorghum, rice, or 

tobacco or some combination of these crops also were insured. Only minor acreage of several 

different crops were insured in a few of the States. Those crops are not included in the table. 

Three levels of insurance coverage are included in the table: CAT, 50 percent of yield at 100 

percent of price (50/100), and 65/100. The table includes this information: 

number of policies (a measure of the number of producers who purchased coverage for 

that crop), 

the net acres insured, 

the insurance value per acre at the coverage level sold, which is the liability of the 

insurance in the event of a total loss of the crop, 

the insurance value per dollar of farmer-paid premium as sold, which is the insurance value 

per acre divided by the farmer-paid premium, 

19 
the insurance value per acre recalculated at the 65 percent coverage level, 

the insurance value per dollar of farmer-paid premium recalculated at the 65 percent 

coverage level (the 65 percent coverage level as sold does not change), and 

Insert Table.pdf Here 

20 
the ratio of the insurance value per dollar of farmer paid premium relative to cotton. 

The ratio of the insurance value per dollar of premium relative to cotton is a measure of the 

degree to which cotton costs more or less to insure than other crops. The ratio for cotton always 

will be 1.00 since it is the reference point. For the other crops, a value greater than 1.00 indicates 

that more insurance can be purchased per dollar of farmer-paid premium than cotton. A value less 

than 1.00 indicates that the other crops are more expensive. This measure provides a basis to 

compare relative costs among crops. 

These three coverage levels were selected because these are the most likely to have significant 

acreage insured. The remaining coverage levels typically are low in participation. 

Among the States and crops, only peanuts, rice, and tobacco consistently can be insured at a 

lower relative cost than cotton. The remaining crops sometimes have a lower relative cost and 

sometimes higher. Within crops, one coverage level might offer more value per dollar than 

another. This indicates that farmers in different counties are making different choices with regard 

to coverage purchased. 

These data indicate that there is no consistent pattern that describes premium costs for cotton 

relative to other major crops grown in these States. However, cotton does not appear to 

consistently be charged higher premium rates than other crops grown in these States. 

Summary 

Premium rates for crop insurance are established with accepted and traditional insurance industry 

techniques. There are three standard techniques used by the insurance industry to set premium 

rates. RMA uses the pure premium (loss cost) methodology because it is the most appropriate for 

the data, is objective, and is the simplest to implement operationally. 

Premium rates estimated from experience data are valid only insofar as those data are valid 

indicators of the probable future losses of the population. This report indicates that the insurance 

experience for cotton in some of these States may not provide valid information for setting 

premium rates for most producers. However, the experience does appear valid for some farmers 

who presently insure in some of the States, and this information must be considered as premium 

rates are adjusted. In other cases, the experience seemingly is random, developed independently 

of weather conditions. 

21 
There is no one, simple, easy solution for this situation. Much analysis of specific conditions must 

be undertaken and consultations with interested parties will be needed to identify appropriate 

changes. In particular, revisions must assure that controls are in place to minimize the chances 

that losses again will spiral out of control and lead to higher premium rates and low participation. 

Analysis of the remaining cotton States is underway and will continue as the needed data become 

available. 

Premium rates for cotton were compared to other major crops in these States. In general, there is 

no indication that premium rates for cotton systematically are higher than for other crops. 

22 
Insert Appendix A Charts.pdf Here