TODAY. in Concept and Practice Profitability and Effectiveness CROP. Volatility Factor. of the Federal Crop Insurance Program VOL. 44, NO.

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1 PUBLICATION OF NATIONAL CROP INSURANCE SERVICES VOL. 44, NO. 2 MAY 2011 CROP INSURANCE TODAY Volatility Factor in Concept and Practice Profitability and Effectiveness of the Federal Crop Insurance Program

2 Technology on the move. RCIS CIMax software was the first automated system for crop insurance agents. RCIS Quest was the first automated claim software. In 2010, we introduced the first mobile claim inquiry with RCIS Mobile Portal. Our technology and infrastructure give RCIS agents speed and convenience, even in peak acreage reporting season. Visit rcis.com. More than a promise a partnership you can rely on. Rural Community Insurance Agency, Inc., D/B/A RCIS. RCIS is an equal opportunity provider Rural Community Insurance Agency, Inc. All rights reserved.

3 TODAYPRESIDENT S MESSAGE What s the Message? So Simple and So Important Even An Adult Can Explain It! Laurie Langstraat, Editor TODAY IS PROVIDED AS A SERVICE OF NATIONAL CROP INSURANCE SERVICES TO EDUCATE READERS ABOUT THE RISK MANAGEMENT TOOLS PRODUCERS USE TO PROTECT THEMSELVES FROM THE RISKS ASSOCIATED WITH PRODUCTION AGRICULTURE. TODAY is published quarterly February, May, August, and November by National Crop Insurance Services 8900 Indian Creek Parkway, Suite 600 Overland Park, Kansas If you move, or if your address is incorrect, please send old address label clipped from recent issue along with your new or corrected address to Laurie Langstraat, Editor, at the above address. NCIS Website: NCIS EXECUTIVE COMMITTEE Steve Rutledge, Chairman Ted Etheredge, Vice Chairman Tim Weber, Second Vice Chairman NCIS MANAGEMENT Thomas P. Zacharias, President P. John Owen, General Counsel James M. Crist, CFO/COO Frank F. Schnapp, Senior Vice President Mike Sieben, Senior Vice President Creative Layout and Design by Graphic Arts of Topeka, Inc., Kansas Winner of The Golden ARC Award Printed on recycled paper. A while back, I was running an errand with our oldest granddaughter when from the car seat comes the question...what is it you do? Hmmm...should I explain the ongoing SRA renegotiations? What about the new Common Crop Insurance Provisions? What about revenue coverage and volatility factors? With these thoughts and a host of crop insurance acronyms (MPCI, RMA, FCIC, APH, CIH, LAM, etc.) swirling in my head, I simply responded,...we help farmers grow the food we eat. That was over a year ago; now fast forward to Spring of The cover of the February issue of Bloomberg Businessweek reads Food Tom Zacharias, NCIS President Crisis: Weather, Speculation, and Politics have created a Global Food Crisis that threatens...everything. Same week, the February 26 issue of The Economist has a Special Report entitled Feeding the World-The 9 Billion People Question. Again the question...what is it you do? Can we, as the crop insurance industry, have a more important role and mission than to help farmers grow the food we eat by providing financial stability, peace of mind, and support to agriculture, both domestically, as well as internationally? I don t think so. So how important is agriculture? Let s start with the demand side. Well, the people of this world need to eat. Currently, world population is approximately 6.9 billion, expected to increase to 9.1 billion by 2050, according to the medium variant projections of the Population Division of the United Nations. With urbanization, income growth and changing diets, food demand will grow sharply. What about the supply side? That is where the crop insurance industry comes in. Last month s USDA Prospective Plantings report indicated farmers intend to plant 92.2 million acres of corn and 76.6 million acres of soybeans this spring. If realized, this would be the second-highest planted corn acreage since 1944, behind only the 93.5 million acres planted in Increases of 250,000 acres or more are expected in Iowa, Kansas, Nebraska, North Dakota, Ohio, and South Dakota. Soybean planted area for 2011 is estimated at 76.6 million acres, down one percent from last year, but if realized, the third largest on record. All wheat planted area is estimated at 58.0 million acres, up 8 percent from last year. And cotton, too, at 12.6 million acres is expected to be up 15 percent. In terms of yields and acres-that is, total production-we find ourselves at peak capacity. With stocks low and demand high, we are using up the once-excess production capacity of American agriculture. On one hand, this is very impressive, alternatively, we find ourselves questioning the agriculture s ability to meet the coming need for food, fiber and renewable fuel. Continued on page 44 CROP INSURANCE TODAY 1

4 VOL. 44, NO. 2 MAY 2011 CROP INSURANCE TODAY Table of Contents 1 What s the Message? Year in Review 15 Volatility Factor in Concept and Practice 4 22 Step 2-Human Resources: What are my interests and skills? 26 Passing the Gavel 28 Profitability and Effectiveness of the Federal Crop Insurance Program NCIS Board of Directors 35 NCIS Service and Leadership Awards 38 NCIS Leadership Regional/State Crop Insurance Committees Crop-Hail Loss Ratio by State In Memory of Oscar Deardorff and Gary Schmidt Visit 28 Copyright Notice All material distributed by National Crop Insurance Services is protected by copyright and other laws. All rights reserved. Possession of this material does not confer the right to print, reprint, publish, copy, input, transform, distribute or use same in any manner without the prior written permission of NCIS. Permission is hereby granted to Members in good standing of NCIS whose Membership Class (and service area, if membership is limited by service area) entitles them to receive copies of the enclosed or attached material to reprint, copy or distribute such NCIS copyrighted material in its present form solely for their own business use and solely to employees, adjusters or agents who are under contract with them, and as a condition to receiving such copies, such employees, adjusters and agents agree that they will not reprint, copy or distribute, or permit use of any such NCIS copyrighted material to or by any other person and/or company, or transform into another work such NCIS copyrighted material, without prior written permission of NCIS National Crop Insurance Services, Inc.

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6 TODAYcrop insurance 2010 Year in Review By Keith Collins and Harun Bulut, NCIS Year in Review is an annual feature of Crop Insurance TODAY and is intended to provide a historical record of the performance of crop insurance during the previous crop year. Overview The 2010 crop year started with a sharp decline in winter wheat planted area during the fall of 2009, as low wheat prices, wet weather and late harvests of 2009 row crops delayed and reduced plantings and slowed crop development. The winter of featured a persistent high pressure system that continually fed cold air into the central and southern states and an El Nino that helped produce stormy weather patterns across these regions. However, drier spring weather permitted rapid planting of major crops. Winter wheat production turned out only slightly below 2009 despite the acreage decline, and spring wheat production was up, with record-high yields. Warm temperatures in the major crop production areas of the Plains and Midwest states spurred rapid crop development during the summer and continued with timely harvests, although cool, wet weather affected the Northern Plains, Pacific Northwest and California. The 2010 corn harvest was down from 2009 s record high, as high temperatures late in the summer cut into corn yields in the Central Corn Belt, although yields were up from a year earlier in the northern and southern tier of states. Soybean production was the second highest ever and cotton production was up over 50 percent. With the generally favorable weather and large harvests, crop insurance losses as a percent of premium under the Federal crop insurance program were the lowest since the modern program began in Regarding crop-hail business, paid losses as a percent of premium written were lower as compared with 2008 and 2009, yet remained higher than in earlier years going back to Key market and policy developments also highlighted Despite generally strong U.S. crop production, crop prices soared as the year unfolded, sharply raising U.S. farm income. World food and biofuel use spurred crop demand and, combined with a series of crop production shortfalls in foreign countries, put stress on already low global crop supplies, raising food price concerns in developing countries. U.S. policy focused on taming a record Federal budget deficit, which influenced the negotiations for the 2011 Standard Reinsurance Agreement (SRA). Begun in the fall of 2009, the negotiations were completed by July 2010 with the result being a reduction of funding for crop insurance of $6 billion over 10 years. An estimated $4 billion of that total was allocated to deficit reduction. Further funding reductions for agriculture appear likely to be made in the 2012 Farm Bill, and initial Congressional hearings on the legislation were conducted during the summer of In keeping with past annual reviews, this article expands on this overview by reporting on the 2010 crop insurance season and highlighting the significant events that affected the program. A brief discussion of weather conditions and their impacts on crop production is followed by a review of commodity market prices. Crop-hail experience for the U.S. and Canada is presented, followed by overall results of the Federal crop insurance program. Finally, issues for the crop insurance industry from the 2011 SRA and the prospective 2012 Farm Bill are examined. 4 MAy 2011

7 Weather and Production The area planted for harvest of winter wheat was 37.1 million acres during the fall of 2009, down 14 percent from 2008, the lowest since Seeding started slow, delayed by late harvests of spring crops and wet weather. Most states in hard red wheat areas reduced planted acreage, with Kansas having its lowest since 1957, Oklahoma the lowest since 1971, and Texas the lowest since In soft red states, acreages were at a record low for Illinois, Indiana, Missouri and Ohio. In the Pacific Northwest, seeding started early, and acreage was down only one percent from a year earlier. By the end of November, the winter wheat crop was rated 64 percent good to excellent, about the same as the year earlier. The National Climatic Data Center (NCDC) reported the winter of was the seventeenth coldest and fifteenth wettest ever. Winter precipitation averaged 114 percent of normal as shown in Fig. 1. It was among the ten driest winters in Wyoming and Idaho, but among the top ten wettest in South Dakota, Alabama, and seven coastal states from Florida to New Jersey. Notable events included two December blizzards in the Plains and upper Midwest, a severe January freeze in Florida, and record February snowfall in the Mid-Atlantic States. California received more normal winter snowfall after three years of drought. With generally favorable winter and spring weather, U.S. winter wheat production totaled 1.49 billion bushels, only three percent below 2009, despite the large drop in planted area. U.S. yield was 46.8 bushels per acre, the fourth highest ever. Excellent weather in Oklahoma and Texas resulted in greater harvested acres than in Production fell in all soft red states, while production in white wheat states was up 14 percent. Spring was warm and fairly dry for much of the nation, see Fig. 2, although there was some late spring rain in the western Corn Belt that ruined just-seeded crops. Spring rain alleviated the winter dryness in the Northwest, while California remained cool. As the spring proceeded, dryness emerged in Great Lake states and drought appeared in Gulf Coast states. Figure 1. Winter 2009/2010 National Climatic Data Center/NESDIS/NOAA The excellent spring weather for much of the nation facilitated rapid planting for all major crops, as illustrated in Fig. 3. This contrasted sharply with 2009, when wet, cool weather caused significant planting delays. In 2010, by April 25 (week 16), 50 percent of the U.S. corn acreage was planted, the earliest date that planting has ever reached 50 percent. At that point, corn planting progress was 127 percent of the previous five-year average pace. Corn plantings finished at 88.2 million acres, up two percent from Similarly, planting for soybeans and spring wheat advanced rapidly. Soybean planted area at 77.4 million acres was nearly identical with the 2009 level. Barley and oats planting was ahead of normal, and sorghum was near normal. Acreage fell 2.2 Figure 2. Spring 2010 National Climatic Data Center/NESDIS/NOAA CROP INSURANCE TODAY 5

8 Figure 3. Planting Progress: Share of Crop Planted Compared with million from 2009 for these three feed grains. Upland cotton started slowly in California but quickly caught up to normal progress and U.S. planted area was 11.0 million acres, up 20 percent, reflecting improved price prospects relative to other crops. Rice progressed rapidly except in cool, wet California. U.S. rice acreage planted totaled 3.6 million, up 16 percent. During the summer, the Plains and the Midwest states experienced above-normal temperatures and ample rain causing most crops to develop and mature quickly, enabling a rapid harvest. High temperatures and drought in the eastern Corn Belt and South reduced prospective yields. Some northern and northwestern areas faced cool, damp weather that delayed small grain development and harvesting. California also had delayed development and harvest for crops such as rice and cotton. Overall major crop conditions remained favorable throughout the growing season and yields of most major crops turned out high or record high. Fig. 4 indicates the portion of key crops rated good or excellent in the weekly survey conducted by the National Agricultural Statistics Service. All major crops had 50 percent or more of the crop rated good or excellent throughout the growing season. Spring wheat had the highest ratings, and 2010 production of 616 million Figure 4. U.S. Crop Conditions: Share of Crop Rated Good to Excellent bushels was five percent more than 2009 and the third highest ever. U.S. yield was a record-high 46.1 bushels per acre, breaking the prior record set in U.S. corn production was 12.4 billion bushels, five percent below the recordhigh of 13.1 billion produced in Yield was bushels per acre, 11.9 bushels below 2009 s record bushels. Yields were down across much of the Corn Belt, Central Great Plains, Ohio Valley, and Mid-Atlantic States compared with 2009, with high temperatures a contributing factor. However, yields were up in the Southern Great Plains, Delta and Southeast regions and were at record highs in Michigan, Minnesota, North Dakota and Wisconsin. U.S. soybean production was 3.33 billion bushels, down one percent from 2009 and the second largest ever. Average yield was 43.5 bushels, 0.5 bushels below 2009 s record high. Hot, dry weather during blooming and pod development reduced yield potential. Upland cotton production was 17.8 million bales, 51 percent over The U.S. average yield was estimated at 814 pounds per acre, up 48 pounds from With expanded acreage, rice production in 2010 was a record-high 243 million hundredweight (cwt), 11 percent above 2009, despite an average yield of 6,725 pounds per acre that was 360 pounds below the 2009 yield. As calendar 2010 closed, a developing La Niña contributed to drought across the South, dry conditions in the Southern Plains and Southwest and cold, stormy conditions in the Pacific Northwest to the Upper Midwest. [Information for this section was obtained from the publication of the National Climatic Data Center at the National Oceanic and Atmospheric Administration State of the Climate National Overview for Annual 2010, published online, December 2010, and retrieved on March 18, 2011 from /13 and USDA publications, including Global Crop Production Review, 2009, Prospective Plantings March 2010, Crop Production 2010 Summary, and various issues of the Weekly Weather and Crop Bulletin and World Agricultural Supply and Demand Estimates Report.] 6 MAy 2011

9 Commodity Markets and Prices Global commodity markets tightened significantly during 2010, and midway through the marketing year, season-average prices received by farmers were forecast by USDA to set new record highs, surpassing the previous records set in With growth returning to the global economy after the financial crisis of , particularly in emerging markets, global food consumption has continued to surge ahead. Demand growth has been especially strong in Developing Asia, including China, as well as Latin America. Fig. 5 shows the pickup in global use of all grains and oilseeds since U.S. ethanol producers are forecast to purchase 40 percent of 2010 U.S. corn production. In the face of the strong global demand for food, fiber and renewable fuel, a series of weather problems caused global grain and oilseed production to decline below total use, cutting into global reserves. Drought in Russia, excess moisture in South Asia, floods in Australia, dry weather in Argentina and the decline in the U.S. corn crop all contributed to the reduction in global grain and oilseed production in Global consumers looked to the United States to help offset their reduced production during Increased U.S. production enabled large increases in U.S. wheat and cotton exports. However, low stocks and reduced U.S. production of corn and soybeans resulted in large price increases needed to ration tight supplies among uses. Fig. 6 illustrates U.S. carryover stocks of corn and soybeans as a percent of total use, a measure of the tightness of crop markets. Corn carryover stocks on September 1, 2011 are forecast by USDA to be only five percent of total use of corn, tied with , and the lowest since at least Soybean stocks are expected to be only 4.2 percent of use, the lowest ever. Season-average farm prices, which are well correlated with stocks-to-use ratios, are expected to reflect this historic tightness. Corn farm prices for are forecast to average a record high Figure 5. World Production and Use of Grain and Oilseeds $5.40 per bushel, compared with the previous record of $4.09 in Soybean prices are forecast at a record $11.60 per bushel, compared to the prior record of $10.10 in Similarly, cotton prices are expected to be 81.5 cents per pound, the highest price since at least The all-wheat farm price, forecast at $5.70 per bushel, is up sharply but remains below the record of $6.78 per bushel. Rice, which is in ample supply, is expected to average $12.50 per cwt, down from and well below s average farm price of $16.80 per cwt. Figure 6. U.S. Stocks/Use and Prices The overall effect on farm prices due to 2010 s strong export demand, rising oil prices and farm production costs, biofuel demand, weaker foreign currency value of the dollar and reduced global crop production is summarized by the index of prices received by farmers for all crops, Table 1. The index for calendar year 2010 was up slightly from 2009 and below However, the rapid increase in 2010 crop farm prices occurred during the second half of the year, the main harvest period, and by December 2010, the monthly index had reached 175. CROP INSURANCE TODAY 7

10 Table 1. Index of Farm Prices Received by Producers, All Crops = Dec Index Source: NASS Agricultural Prices Table 2. Revenue Policy Base Prices 1/ Crop Change Wheat, winter ($/bu) (KCBOT) % Wheat, spring ($/bu) % Corn ($/bu) % Soybeans ($/bu) , % Cotton ($/bu) % Rice ($/cwt) % 1/ For Revenue Assurance plans. Source: Various RMA Manager s Bulletins Table 3. Revenue Policy Implied Price Volatilities 1/ Crop Change Wheat, winter (KCBOT) % Wheat, spring % Corn % Soybeans % Cotton % Rice % 1/ For Revenue Assurance plans. Source: Various RMA Manager s Bulletins Figure 7. Weekly Corn Futures Prices Life of Dec. Contracts Fig. 7 shows the general pattern of 2010 crop year prices for major crops, using as illustration the December futures contract prices for corn on a weekly basis for 2006 through In contrast to the sharp increase in the first half of 2008 that dissipated as the large harvest became known, December futures prices in 2010 began rising midway through the year as global crop production problems began to surface. Table 2 provides the base prices for revenue policies for major crops over the past several years. In 2008, all base prices for spring-planted crops were record highs and declined in In 2009, the base price for winter wheat set a record high. Expectations were that market prices for major spring-planted crops would decline in 2010, except for cotton, and base prices for spring wheat and corn did decline, while soybean base prices were up slightly. Cotton base prices increased markedly in response to expected tighter markets in The 2010 winter wheat base price fell from the 2009 record as a large build up in wheat stocks was expected for the 2010 wheat marketing year at the time the base price was determined. Implied volatility factors, which are determined from options contracts, are used in the calculation of premium rates for revenue policies. Table 3 shows implied price volatilities for major crops. After an increase in 2009 for most crops, volatilities declined for all major crops in 2010, which contributed to premium rate reductions. Fig. 8 shows the effects of price movements on the base and harvest prices for the 2010 CRC and RA plans of insurance for the major crops (corn, soybean, winter and spring wheat, cotton and rice). During 2008, the last year of a large runup in crop prices, harvest prices turned out mostly below base prices, which triggered indemnities for many revenue policies. However, consistent with the increase in market prices that occurred during the second half of 2010, and illustrated by the behavior of corn prices in Fig. 7, harvest prices for major spring-planted crops exceeded base prices in MAy 2011

11 In 2008, harvest prices for corn were 24 percent below base prices for CRC plans of insurance and 31 percent lower for RA plans, and again in 2009, corn harvest prices were below base prices. In contrast, 2010 corn harvest prices exceed base prices by 37 percent for CRC and 38 percent for RA. For soybeans, harvest prices were below base prices in 2008, unlike 2009 when harvest prices exceeded base prices as a reduced South American soybean harvest strengthened fall prices. In 2010, soybean CRC harvest prices were 16 percent above base prices and RA harvest prices were 26 percent above base prices. Spring wheat and cotton harvest prices in 2010 also exceeded base prices, while rice harvest prices, with ample stocks, were three percent below base prices. Cotton was a remarkable story, with harvest prices 85 percent higher than base prices in 2010 as global cotton use is exceeding production for the fourth consecutive year. With U.S. cotton exports up sharply, U.S. ending stocks for are forecast at only 1.9 million bales, one million bales below last season and far below s level of 10.1 million. The expected stock level would be the lowest since 1924, and the stocks-to-use ratio of 10 percent would be a record-low. The 2010 harvest price for winter wheat (Southern Plains) for CRC was 12 percent below, and RA was one percent below, the CRC and RA base price of $5.42 per bushel. The base price was established in the summer of 2009, well before the price increases in The harvest prices were determined before the global production problems that began with the drought in Russian and Kazakhstan and the excessive rain in Canada were fully determined and global wheat prices took off. The 2009 winter wheat harvest prices for the CRC and RA plans were also lower than base prices. Harvest prices in both 2009 and 2010 reflected expected increases in wheat carryover stocks in and again in at the time the harvest prices were being determined. [Information for this section of the article was obtained from the Risk Management Agency, National Agricultural Statistics Service, USDA s World Agricultural Supply and Demand Estimates Report, the commodity outlook reports of USDA s Economic Research Service, and data from Barchart.com.] Figure 8. Prices for Major 2010 Crop Revenue Policies Federal Crop Insurance Program Experience At the time this article was written, the crop insurance program loss ratio (gross indemnities divided by gross premium) for the 2010 crop year was estimated at 0.52, the lowest since the public-private program began in This experience continued the unusually low loss experience evident since 2004, except for 2008 which had a more expected loss ratio of 0.88, see Table 4. The total liability, premium written, and indemnities paid in 2010 were below the levels of 2009 and the records set in The acres insured were also below those in 2008 and 2009 and mainly reflected a decline in acreage planted to all crops, the termination of the Rangeland plan of insurance and lower participation in the Pasture, Range and Forage plan of insurance. Results differed for the various insurance plans (final reinsurance data for the group risk plans, GRP and GRIP, were not available at the time this article was written). The U.S. loss ratio for individual farm revenue protection (including the CRC, RA, IP and IIP plans of insurance) was 0.54, similar to the U.S. loss ratio for all plans. The rainfall and vegetative area plans for pasture, range and forage had loss ratios of 0.69 and 0.04, respectively. Actual Production History (APH) yield plans had a loss ratio of 0.62, while all remaining plans of insurance (excluding GRP and GRIP) had an overall loss ratio of The loss ratio on revenue plans was below that on APH plans, similar to 2009, but unlike 2008 when harvest prices fell below base prices and triggered large indemnities on revenue plans. In 2010, Table 4. Federal Crop Insurance Program, Gross Basis (Mil. $) Crop Year Liability Premium Indemnity Acres Loss Ratio ,600 4,186 3, ,259 3,949 2, ,919 4,580 3, ,340 6,562 3, ,897 9,851 8, ,567 8,949 5, ,003 7,582 3, / 1/ As of March 18, 2011; data not complete Source: RMA Summary of Business Reports, March 15, 2011 CROP INSURANCE TODAY 9

12 Figure 9. Numbers of States with Loss Ratios in the Indicated Range Figure 10. State Loss Ratios by State, 2010 also a factor in revenue policy losses in New Jersey (soybeans) and Virginia (cotton and corn). Mississippi (corn) and South Dakota (corn and soybeans) faced some losses due to price declines but excess moisture and rain appear to have been large factors. The map in Fig. 11 shows gross loss ratios by state. States where the loss ratio exceeded the U.S. statutory target (1.00) are shown in blue or red. The states are Kentucky, Nevada, North Carolina, West Virginia and Virginia, which together accounted for $262 million in gross premium in 2010, only 3.4 percent of the U.S. total. The effect of the late season hot and dry weather in the Southeast is evident on the map. [Information for this section of the article was obtained from Summary of Business reports released by the Risk Management Agency.] million units were indemnified, compared with million in 2009 and million in Fig. 9 shows how widespread the excellent loss ratios were for revenue and yield-based plans of insurance. Loss ratios for the revenue-based plans in the majority of states (31 out of 47) were below Loss ratios for yield plans were somewhat higher, but 36 of 50 states had loss ratios below Fig. 10 illustrates the loss ratios by state. Of the 50 states, a majority (30) had a higher loss ratio for yield plans than revenue plans. Some states had loss ratios for revenue plans that were much higher than yield plans, including California, Georgia, Mississippi, New Jersey, South Dakota and Virginia. Revenue policies are only a small fraction of plans sold in California and the relatively higher loss ratio was due to losses on cotton, which developed slowly all summer. Georgia had higher loss ratios on revenue policies due to cotton and soybeans, which were affected by hot, dry weather in late summer. The high temperatures and dry weather were U.S. Crop-Hail Experience For the U.S., crop-hail insurance generally refers to policies in which direct damage to hail is the primary cause of loss. In addition to hail damage, many policy forms carry endorsements for additional perils. For the most part, the added perils include wind and fire, although there are exceptions. For the purpose of this article, results will be reported for all losses on hail policies, including the experience of non-member companies not included in NCIS Annual Statistical Summary reports. Premium for 2010 was $681 million (much higher than 2009, up a bit from 2008, and the highest in the last seven years), providing more than $27 billion in privately insured crop-hail insurance coverage for U.S. farmers, Table 5. From a profitability standpoint, 2010 was a better year for the industry compared with 2008 and Nevertheless, losses of $459.3 million exceeded the earlier years going back to 2003, and particularly were more than twice the amount paid in The country-wide loss ratio of 0.67 (paid losses divided by premium written) improved compared with ratios in 10 MAy 2011

13 2008 and 2009 and yet remained above those going back to Large storms contributed importantly to losses for the year. The largest one-day storm in 2010 occurred in Minnesota on June 25, resulting in more than $14.4 million dollars paid out to farmers. The top 10 storm events for the year, measured in terms of losses, occurred in Minnesota, Kansas, Iowa, South Dakota, Montana, North Dakota, Texas, with over $59 million being paid out in these states. Of the top 50 most damaging storms, 17 occurred in the month of July, 16 in June, six in August, and four in October. On a county by county basis, Nebraska counties took the top four spots for the largest payouts: $12.96 million in Holt County, $9.4 million in Kearney, $5.97 million in Dawson County, $5.4 million in Lincoln County. The fifth largest payout was $5.2 million in Blue Earth County, Minnesota. The top five losses on a county basis came down by nearly 45 percent compared to the top five in 2009 and became slightly lower than those in Crop-hail loss ratios by state are shown in Fig. 12. Colors identify states with similar loss ratios, and shading is used to identify states with similar premium volume. Crop-hail insurance was written in 43 states in Of these, nine states had a loss ratio in excess of Arizona, with premium over $2.25 million, had the highest lost ratio of 2.5 and is in red on the map. In addition, Montana ($22.4 million premium), Nebraska ($103.6 million premium), Minnesota (near $60 million premium), and Wyoming ($1.72 million premium) had loss ratios ranging from 1.15 to 1.02, respectively. The loss ratio for the remaining states was less than California, with a premium exceeding $1.27 million, had a loss ratio of Utah had a loss ratio of 0.85 albeit with a very small premium of just over $53,000. Finally, Idaho, with a premium over $10.5 million and New Mexico with a premium close to $1.9 million, had loss ratios 0.74 and 0.73, respectively. [Information for this section was obtained from NCIS Insured Crop Summary and claim files.] Table 5. U.S. Crop-Hail Results, all Perils (Mil. $) Crop Year Liability Premium Losses Loss Ratio 2004 $13,942 $414.0 $ , , , , , , Figure MPCI Premium and Loss Ratios - All Plans Combined Canadian Crop-Hail Experience Crop-hail business in Canada is primarily written in the prairie provinces of Alberta, Manitoba and Saskatchewan. After a low-loss year in 2009, crop-hail losses increased sharply in Payouts totaled C$155 million (Canadian), over double the level of 2009 but still less than half the record losses of Total premium for 2010 for all three provinces was C$264 million which resulted in a loss ratio of 0.59, compared with 0.29 in 2009 and the severe loss ratio of 1.18 experienced in Manitoba had premiums of C$37.6 million, down 12 percent from 2009 and payouts of C$14.8 million. The loss ratio of 0.39 exceeded 2009 s About 2,200 claims were filed, below 2009 and the five-year average. While Alberta had frequent storms all summer, results were fairly favorable. Losses totaled C$38 million, down 22 percent from 2009 but similar to the 10-year average. Premiums were C$62 million, up from 2009, and the loss ratio was 0.61 down from 0.83 in Saskatchewan, the largest province in terms of hail business, had about C$166 million in premium for the year, down from C$172 million in Losses in 2010 were C$103 million and the loss ratio was This experience was much different from 2009 when payouts were a record low of $23.4 million and the loss ratio was only The number of claims rose from 4,075 in 2009 to 11,600 in 2010 with storms generally lighter but more widespread. [Information for this section of the article was taken from the The Hail Report, a publication CROP INSURANCE TODAY 11

14 sponsored by the Canadian Crop Hail Association. The Hail Report is produced every two weeks during the hail season.] Figure U.S. Crop Hail Premium & Loss Ratios All Crops, Losses, Plans Combined Program and Policy Developments The first half of 2010 featured the conclusion of the renegotiation of the Standard Reinsurance Agreement (SRA). In late 2009, RMA announced its intention to terminate the 2005 SRA and negotiate a new SRA for the 2011 reinsurance year. In December 2009, RMA released the first draft of the 2011 SRA seeking to reduce program funding by $8.4 billion over 10 years. After industry objections, two additional drafts and technical corrections, the SRA negotiations were completed in mid- 2010, and the 2011 SRA was signed by the then-16 companies. The final version was estimated by RMA to reduce program funding by $6 billion over 10 years. Key features of the final SRA include two state groups for reinsurance terms, with potential underwriting gains reduced for five Corn Belt states (Group 1) and increased for all other states (Group 2 and 3), a net book quota share set at 6.5 percent, with 1.5 points of underwriting gains returned to the companies operating in underserved states (Group 3), a cap on administrative and operating expense (A&O) payments and a cap on agent compensation. Implementation of the SRA occupied the remainder of A key issue was the application of the caps on A&O payments and agent compensation. Under the SRA, companies cannot compensate agents in excess of their total A&O payments. However, these payments are not known until far after premiums are collected, forcing companies to estimate total industry A&O and their share of total A&O and then only pay out a prudent level of compensation that would avoid exceeding the unknown dollar level of the agent cap. In addition to the difficulty of addressing the amount and timing of agent compensation, the definition of compensation had to be developed. Industry provided recommendations to RMA, and RMA ultimately issued a Managers Bulletin defining compensation subject to, and exempt from, the agent cap. In a move related to the SRA, the administration announced at the end of 2010 that part of the $6 billion SRA savings would be used to provide a good performance refund of premiums paid to producers who had a limited number of losses in past years. The final rule for the program had not been issued at the time this article was written. The 2008 Farm Bill expires in September 2012, and 2010 marked the beginning of the discussion in Congress on the successor legislation. The House 12 MAy 2011

15 and Senate Agricultural Committees held hearings on the prospective Farm Bill during the summer of 2010 on a wide range of issues. While witnesses representing farm and commodity groups, academics, farmers and others raised general issues of concern about the affordability and coverage of crop insurance, few specific recommendations were made and no conclusions were reached by Congress. With a change of leadership of the House Agriculture Committee as a result of the November 2010 elections, the 2012 Farm Bill is now expected to be developed during 2012 rather than during The large, continuing Federal budget deficit is expected to place very tight budget constraints on program spending authorized in the next Farm Bill. Conclusion The crop insurance industry again delivered essential services and benefits to U.S. agricultural producers in a timely way in While liability, gross premium, and insured acres all declined for a second straight year in the Federal crop insurance program, participation remained high. Lower wheat and corn base prices and reduced volatility factors for all major crops contributed to the reduction in liability and gross premiums. A large reduction in insured pasture, range and forage land accounted for the decline in insured acres since However, insured acres, excluding pasture, range and forage land, was the highest ever in Rising prices and generally good yields reduced the frequency of claims and losses in The estimated loss ratio of 0.54 was the lowest in the history of the modern program, which dates to the Federal Crop Insurance Act of The companies approved to provide crop insurance coverage continue to be financially sound, with the strength to meet the financial obligations that stem from a range of natural disasters. The Crop-hail program, which provides protection against localized damages that might otherwise be noninsured losses for producers under the Multiple Peril Crop Insurance program again provided essential protection to producers during The Federal crop insurance program has emerged as the most essential component of the farm safety net. As the Congress approaches the 2012 Farm Bill, while grappling with record-high Federal deficits and debt, the fundamental contribution of crop insurance in protecting farm production and farmers from risk must be recognized and maintained. Crop insurance provides individualized risk protection, requires producers to offset part of the costs of the program, pays claims promptly, facilitates pre-harvest marketing, ensures access to credit and has a very strong loss adjustment process that ensures producers receive only the payments they merit. Moreover, crop insurance companies have created a delivery system that features substantial invested assets in people, places and technology that should be leveraged to the fullest extent possible by the government as a means to increase the efficiency of delivering risk management opportunities to producers. come grow with us... Hudson Crop is a unit of Hudson Insurance Group, the US Insurance Division of Odyssey Re Holdings Corp. OdysseyRe operates through 18 offices worldwide with $3.5 billion in policyholders surplus. Hudson Insurance Company is rated A (Excellent) XV by A.M. Best and is widely licensed. With an exclusive strategic relationship with Growers National Cooperative*, Hudson Crop is a rapidly growing company, committed to providing our farmers and their agents with the best service our industry can offer. *GNC is not available in all states Hudson Crop Hudson Insurance Group is an equal opportunity provider. For career opportunities, contact: Dan Gasser, National Sales Manager Doug Nelson, National Claims Manager CROP INSURANCE TODAY 13

16 Get Real Mapping Solutions Online and offl ine, we ve got the tools you need to increase productivity and get on with life after crop insurance. For real solutions to your mapping problems, contact NAU Country today to learn more NAU Country Insurance Company. All Rights Reserved. NAU Country Insurance Company is an Equal Opportunity Provider NAU.MPCI

17 TODAYcrop insurance Volatility Factor in Concept and Practice By Harun Bulut, Frank Schnapp and Keith Collins, NCIS Starting in crop year 2011, the Risk Management Agency (RMA) introduced the Common Crop Insurance Policy (Combo policy) with the goal of unifying and simplifying the crop insurance program. 1 With the Combo policy, Crop Revenue Coverage (CRC), Revenue Assurance (RA), Income Protection (IP), and Indexed Income Protection (IIP) are replaced by a single uniform policy: Revenue Protection with or without harvest price exclusion (RP-HPE and RP, respectively). The APH plan of insurance is replaced by Yield Protection (YP) for crops with Commodity Exchange price discovery. In Combo policy rating, RMA integrates the main elements of the major crop insurance plans and develops a single rating and pricing procedure in order to make insurance coverage, protection and cost consistent across the board. A single projected price will be used in all three (YP, RP, and RP-HPE) plans of insurance, helping to simplify and streamline the program. In order to account for price risk, volatility factors are used in calculating premium rates for revenue protection coverage. Prior to 2011, price volatility factors were used for rating the Revenue Assurance (RA) program only. Despite the industry s familiarity with the RA program, the use of volatility factors in the calculation of the revenue add-on component of the premium rate in the Combo policy has created some confusion and operational issues. The objective of this article is to examine how the price and volatility factors are determined in Combo rating and assess the reasonableness of the process. Price Component of Combo Rating The Commodity Exchange Price Provisions (CEPP) endorsement defines the price component in Combo rating. CEPP uses exchange prices because these are well-studied and established as unbiased and efficient in utilizing all the information available to market participants. The exchanges used are the Chicago Board of 1 CROP INSURANCE TODAY 15

18 Trade (CBOT), the Kansas City Board of Trade (KCBOT), the Minneapolis Grain Exchange, Inc. (MGEX), and Intercontinental Exchange (ICE). Combo rating applies for the following crops: corn, cotton, grain sorghum, rice, soybeans, sunflowers, wheat, barley, malting barley, and canola/rapeseed. These crops constitute the bulk of the crop insurance business, about 73% of the total liability. Exchange prices are available for corn (CBOT), wheat (CBOT, MGE, or KCBT depending on the state and type of wheat), cotton (ICE), soybeans (CBOT), rice (CBOT), and canola/rapeseed (ICE). Because the price of corn is highly correlated with the prices of barley and grain sorghum, the latter are based on the corn exchange price (CBOT). Similarly, soybean oil futures from CBOT are used for the price of oil-type sunflowers. For a given crop, different futures contract delivery months can be applicable for determining the projected price (base price). For example, the CBOT September futures contract is used for determining the price for Texas corn (whose sales closing date is January 31). For counties with a March 15 sales closing date, corn policies use the harvest year s CBOT December corn futures contract. Using the latter example, daily settlement prices for the harvest year s December corn futures contract are averaged in February, which is the base price discovery month as defined by the CEPP. The reason the base price discovery month is so close to the sales closing date is to establish the policy price using the most current market information. For the harvest price, the December futures contract s daily settlement prices are averaged in October, which is the harvest price discovery month in most counties. Furthermore, RMA uses the average price over the entire price discovery period (base or harvest) with the purpose of smoothing out day-to-day variations. RMA provides an on-line price discovery tool in their website. 2 The tool reports the price election for the RP plan (therefore for the YP plan) along with volatility figures as the discovery month proceeds. The tool lists month-to-date values for the average price and the average volatility over the most recent five days (prior to the end of the price discovery period). Averages are listed at the top of the exhibit. Volatility Component of Combo Rating The CEPP describes how prices are determined for the Combo program. However, it doesn t explain how volatility factors are to be determined. RMA determines the volatility factors based on the last five days of the price discovery period. Using the last five days of volatility seems to have the purpose of estimating the most current volatility possible prior to sales closing date. RMA recently posted a onepage summary on their website describing the volatility procedure. 3 RMA calculates implied volatility using data from barchart.com. 4 Anyone at any time can acquire the same information by paying a subscription fee to access the website. Once the implied volatility estimates are available, they are further adjusted for the time difference specific to crop insurance. RMA provides an example for 2010 Iowa corn in their one-pager. The following discussion updates that example for For Iowa corn, the harvest price discovery period is October, and the relevant futures contract is the December 2011 contract (CZ11 in barchart.com notation). Using Excel, the number of days from each Table 1. day during February (the projected price discovery month) to the 16th day of October is calculated and divided by 365. The square root of the quotient is then taken. The result is a time adjustment which is then multiplied by the implied volatility corresponding to the same date. The far right column of Table 1 presents the resulting time-adjusted volatilities for the last five days of February. The factor from the first row of Table 1 is calculated below. For 2/22/2011, using the following Excel formula, (((DATE (2011,10,16) DATE(2011,2,22))/365)^0.5 the quotient is calculated as From barchart.com, the implied volatility for the December contract is for February 22, Multiplying by yields Similar calculations are done for the remaining days of February. Finally, a simple average of timeadjusted volatility factors is obtained and rounded to two decimals. The result is 0.29 in this example. Note that for corn, the harvest price discovery month is October for the majority of states. For some states such as Idaho, Michigan, Oregon and Washington, the harvest price is determined in November. That would result in a different time adjustment; the average would be calculated as 0.31, implying slightly higher risk and higher premium for those states. Determining the Volatility Factor for 2011 Iowa Corn Contract Date Implied Time Adjusted Implied Volatility Volatility (RMA s Volatility Factor) CZ11 2/22/ CZ11 2/23/ CZ11 2/24/ CZ11 2/25/ CZ11 2/28/ Simple Average To view active discovery periods: ActiveDiscoveryPeriods.aspx. By the time this article was written, there were no crops in discovery. To view commodities recently in discovery: For location specific daily prices: MAy 2011

19 A Closer Look at the Implied Volatilities- Theoretical Roots Volatility essentially is a measure of the degree of change in futures price over a year and represents the market s view on the uncertainty (risk) associated with a futures price over a year. 5 Based on visual inspection of Fig. 1, it is apparent that December Corn futures price was more volatile (that is, it showed a higher degree of change) in 2008 as compared to In 2008, the futures price showed a dramatic surge in the first half the year and a sharp decline during the rest of that year. Along these downward and upward paths, the data exhibited sharp spikes followed by steep drops. In comparison, corn December futures prices in 2006 remained mostly stable and the changes were minor and much smoother. Of course, the preceding discussion of Fig. 1 is after the fact, that is, after the entire data was observed. At the planting time of a given year, the underlying futures price data is only partially observed and provides only limited information to infer the underlying volatility. In any case, volatility during in a given year is not directly observable and needs to be estimated. One way to estimate volatility is to utilize time-series (historical) information on the futures price which would be a backward-looking approach. A sophisticated econometric method based on this approach is discussed later in this section. Another way is to take a forward-looking approach and compute the implied volatility based on the prices of current options linked to the futures price. The latter is the approach taken by RMA. Black-Sholes formulas relate prices of put and call options to volatility and provide a convenient way to estimate the volatility factor. 6 The formulas are based Figure 1. December Corn Futures Prices 2006 versus 2008 Figure 2. Implied Volatilities in January and February * on a few readily available parameters: the prices for call and put options, the current futures price, and the strike price of the option are all available from the futures market. The interest rate for the risk-free asset and the time period before the options expire are known. The only remaining factor is the volatility factor, which can be computed using the formula for determining the price of the put or call option. 7 5 For a technical (more precise and involved) definition of volatility, the readers are referred to Appendix 1 in the Supplementary Material for this article posted at the NCIS website: 6 For Black-Sholes formulas and their derivation, the readers are referred to Appendix 2 section of the Supplementary Material. 7 Barchart.com provides the resulting implied volatilities. Otherwise, iterative search methods such as Goal Seek in Excel, can be used to calculate the implied volatilities from the Black-Sholes formulas. Alternatively, using blkimpv function (where Black-Sholes formulas are coded) in MatLab software, we are able to replicate the numbers provided by barchart.com. The Black-Sholes model assumes that volatility is constant. This assumption needs to be justified as price volatility can vary over time (Hull, page 493). If the volatility is not constant, then that requires using sophisticated econometric models, such as generalized autoregressive conditional heteroscedasticity (GARCH) models which are frequently used in estimating volatility in finance literature (Hull, Chapter 21). These methods utilize historical data to estimate the volatility, albeit most recent observations carry higher weights. The final estimated model provides weights for the following components (in a particular specification): long-term volatility value, volatility estimate in the last period, and the squared percentage change in the futures price in the last period. RMA gives equal weights to the CROP INSURANCE TODAY 17

20 implied volatilities from the last five days of the price discovery period. In doing so, RMA s method ignores the information on the futures contract prior to last five days of February (for March 15 sales closing dates), therefore, it does not fully utilize all the available information on the futures contract. Finally, the assumption of equal weights on the last five observations in the price discovery period seems to be based on practicality, and does not appear to be derived from an econometric estimation. Fig. 2 presents the implied volatilities for December corn futures in January and February from 2011 to 2006, respectively. Note that the implied volatilities in these figures are obtained from barchart.com, on an annual basis and are not time adjusted. As shown in the Figure, implied volatilities can differ from one year to the next. Based on visual inspection, volatility within a year mostly varies within five basis points (except 2008 where it shows wider variation up to 10 basis points). Nevertheless, volatility behavior appears to be changing over time. Combining Price and Volatility Components in Combo Rating RMA combines the base price with the volatility estimate in simulations to obtain the revenue-add on component in the Combo premium rating. The method draws from price and yield distributions and imposes an historical price-yield correlation to obtain the joint distribution. 8 Because the variance of the price distribution is determined by the volatility factor, the volatility factor affects price draws, which in turn affect premium rates. The revenue addon component of the Combo rating is then obtained as the difference between simulated price risk and simulated yield risk. 9 Adding the resulting revenue add-on rate to the Yield Protection rate gives the premium rates for revenue plans. Note that with the RP-HPE plan the guarantee is fixed at the base price, whereas the RP plan uses the higher of the planting and harvest prices in setting the guarantee. The simulation process takes this difference into account and produces the rates accordingly. Barnaby describes RP and RP-HPE as a yield-adjusted Asian (YAA) put option. Asian options are those options where the payoff depends on the average price of the underlying asset for a pre-determined time period (Hull). Indeed, in crop insurance, harvest price for major crops is the average of futures prices obtained over the harvest price discovery period. RMA states (in their onepager) that the premium rate to lock-in a harvest price with crop insurance is equivalent to the amount the market charges to lock in a futures price via options. This statement may need some clarification. One distinction is that a CME option uses spot price, whereas a YAA option uses an average of prices during the price discovery period. In addition, Barnaby finds that RP as a YAA put option is much cheaper than a CME put option in 2011, as the former costs less than 3 cents per bushel of corn versus the latter costing 75 cents. The main reason for this difference is that for CME contracts the yield is fixed. The value of an option solely depends on the price move. With RP, because yield factors into the farmer s revenue, RP pays less frequently and pays less than CME options, which explains the difference in value. Operational Issues with RMA s Volatility Method- Stability of Volatility Values The root of the operational issues regarding volatility lies in the fact that crop insurance agents may be using company premium quoting systems that use the prior year s volatility factor by default until the price discovery period ends. With the large changes in volatility factors from year to year as shown in Fig. 2, this has the potential to seriously distort any premium quotes. In addition, agents have only about two weeks between the end of the price discovery period and the sales closing date. The short time frame, combined with the large number of policies with sales closing dates (e.g., March 15) creates a time and workload issue. The more time they have to prepare quotes and issue policies, the easier it is for the agents to get their work done. However, the time available for agents to meet with customers is reduced due to the fact that YP prices no longer come out earlier than RP prices now that Combo establishes a single price for both plans. One issue is whether agents simply need better information or whether volatility factors needed to be determined at an earlier date. Some agents may prefer to change the period used to determine volatility to the last five days of the previous month. The government could poten- Figure 3. Implied Volatilities: Comparison of Five-Day-Averages in January/February to the Final Five-Day-Average ( )* 18 MAy 2011