1. Practical Procedures For Evaluating Crop Insurance Policies That Trigger On County Yield Ben Chaffin, Graduate Research Assistant J. Roy Black, Professor Department of Agricultural Economics Xiaobin Cao, Graduate Research Assistant, Agricultural Economics/Statistics Michigan State University

2. Introduction Personal history –Experience with crop insurance county and unit trigger policies –Raise sugar beets, corn, soybeans, wheat, & cucumbers. Yield is a 1 st order proxy for revenue If a county yield trigger policy does not transfer risk, then a county revenue policy will not either Corn yields and revenues used through the presentation Feel free to ask questions about clarification anytime

3. Research Objectives To increase knowledge of county trigger crop insurance policies To provide better tools to support farmers in insurance purchase decision

4. Target Audience FarmersLenders Insurance agents Extension staff Trade association representatives

5. Crop Insurance Policy Overview No Insurance GRP (Group Risk Policy) –County yield insurance Triggers on county yield index Coverage levels 70% - 90%, in 5% increments APH (Actual Production History) –Insurance triggering on farm yield Triggers on actual production history –Units and optional units Coverage levels 50% - 85%, in 5% increments

6. Case Study Results Some farms preferred GRP to APH Some farms preferred GRP to no insurance Some farms preferred no insurance to APH

7. Research Approaches Used Measures –Net worth –Net returns or net cash flow Evaluation criteria –Mean - Variance –Expected utility / willingness to pay Measures are adequate, but are not farmer friendly

8. Outreach Approaches Measures –Net worth –Net returns or net cash flow Evaluation criteria –Scenario analysis Assume perfect correlation between farm and county yield Farmer friendly, but criteria does not go far enough

9. Another Step: Build on previous approaches Scenario analysis Show tracking between farm yield and county yield Show tracking between farm yield and county yield Outreach publications and presentations Insurance agency and extension services software Use of cumulative probability distributions –ARMS software to evaluate crop insurance, pre- harvest pricing, and enterprise portfolio (King, Black: 1987) and applications (ND 1993) –Price probability distribution forecast WWW (Hilker, 1996) Producers have seen this technique before

10. Case Studies are used to Display Tracking Case Farm #1

11. Cumulative Probability Distribution

12. Where to? Should a county index trigger product be considered? –Will it transfer significant downside risk? How do we get our arms around this decision process? –How good is good enough? –Factors influencing performance County index products vs. no crop insurance County index products vs. unit trigger products

13. Factors Influencing Tracking Homogeneity of farm’s: soils, drainage, irrigation, and micro climates when compared to the county Farm location within the county –Spread across county –Center of county –Edge of county –Corner of county

14. Standardized Mean Farm #1 and County Yield 1984 to 2003 Correlation 0.85, 1994 to 2003 Correlation 0.96

15. Yield Insurance Comparison Farm Yield + Insurance Indemnity Farm #1

16. Standardized Mean Farm #3 and County Yield 1994 to 2003 Correlation 0.78

17. Farm #3, Farm Yield vs. Farm Yield + GRP Indemnities

18. Farm With High Correlation Farm to County Yield ≈ 0.95

19. Farm With Low Correlation Farm to County Yield ≈ 0.80

20. Simulation Setup Hypothetical county –16 locations Pairwise yield correlations across locations Yield CDF –Shape based upon county and farm data –Calibrate to farm insurance unit mean & standard deviation Hypothetical farms –The 16 locations that make up the county are treated as individual insurance units.

21. Hypothetical County NASS sampling error added to county yield 1234 5678 9101112 13141516

22. Location Correlations Observed unit correlations from case study –Actual farm unit correlations High 0.96 Low 0.46 Simulation used distance to determine correlation –High 0.85 (Nearest neighbor) –Low 0.50 (Corner to corner: 1 to 16)

23. Unit Yield Probability Distribution The yield CDF is a composite of 3 counties –Yield data is from 1970 – 2003 Mean yield used was: –140 Standard deviation used was: –40 Mean and standard deviation are representative of the county where the case farms are.

24. Generation of Yield Guaranties and Indemnities Model draws 31 outcomes for each of the 16 units The first 30 outcomes are used to calculate ECY Draws 21 – 30 calculate the expected yield of each APH unit Draw 31 determines if there is an insurance payment for county and unit insurance policies. Working model typically takes 10,000 sample draws.

25. Farm Location with ≈0.95 correlation Farm included locations –6,7,10, and 11 with equal weight 1234 5678 9101112 13141516

26. County Trigger Yield Insurance “GRP” Used 90% coverage Used maximum protection 100% –Scale 1.5 Farms are made up of 1 to 16 of the units –If county insurance pays an indemnity add it the average farm yield.

27. Unit Yield Insurance “APH” Used 75% coverage Each unit in the county is treated as an insurance unit –Optional unit approach Farms are made up of 1 to 16 of the units

28. Calculating Insurance Premiums County premium charged –Averaged the indemnities paid (pure premium) –Multiplied pure premium by (1 – subsidy) Unit premium charged –Average the indemnities paid (pure premium) –Multiplied by a wedge (1.3, 1.6) Moral hazard + adverse selection –Multiplied pure premium by (1 – subsidy)

29. Model Tests and Double Checks Experimented with a correlation matrix based on soil types in the county –Varied means and standard deviations based on soil type –Results were relatively the same. Rates generated are relatively the same as insurance rates charged for county and unit policies.

30. Farm With High Correlation Farm to County Yield ≈ 0.95

31. Model Results Corr. No Ins APH Ins GRP Ins Downside Variance ≈ 0.95 904205142 ≈ 0.87 1015240360 ≈ 0.80 1017237441

32. Farm With Low Correlation Farm to County Yield ≈ 0.80

33. Model Results Corr. No Ins APH Ins GRP Ins Downside Variance ≈ 0.95 904205142 ≈ 0.87 1015240360 ≈ 0.80 1017237441

34. Increased Knowledge Spatial diversity helps GRP Center of county helps GRP As wedge on APH insurance ↑, the relative performance of GRP to APH ↑

35. Research and Extension GRP Evaluation spread sheet –http://www.aec.msu.edu/agecon/blackj/grp.htm http://www.aec.msu.edu/agecon/blackj/grp.htm GRP Staff Paper –http://www.aec.msu.edu/agecon/blackj/grp.htm http://www.aec.msu.edu/agecon/blackj/grp.htm GRP and GRIP MATLAB program code –http://www.aec.msu.edu/agecon/blackj/grp.htm http://www.aec.msu.edu/agecon/blackj/grp.htm

36. An Improved Model Would Use more units in the example county The model used did not have enough detail in the example county –Used 16 Units Actual county has 500+ square miles Perhaps 36 units

37. Continued Research M.S. Plan B Paper –Insurance Policies that Trigger on County Indexes GRP, GRIP, GRIP HRO Unit policies will be compared to county policies APH, RA and RA HRO –http://www.aec.msu.edu/agecon/blackj/grp.htm http://www.aec.msu.edu/agecon/blackj/grp.htm

38. Acknowledgements Insurance agents for input and review –Special thanks to Lisa Tuggle Farmers for case study information and review

39. Practical Procedures For Evaluating Crop Insurance Policies That Trigger On County Yield Ben Chaffin, Graduate Research Assistant J. Roy Black, Professor Department of Agricultural Economics Xiaobin Cao, Graduate Research Assistant, Agricultural Economics/Statistics Michigan State University