The Role of Crop Insurance Participation in Farm-level Financial Leverage

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1 The Role of Crop Insurance Participation in Farm-level Financial Leverage Eric Belasco Anton Bekkerman Department of Agricultural Economics and Economics Montana State University Bozeman, MT Crop Insurance and the 2014 Farm Bill Symposium Louisville, KY Belasco and Bekkerman October 9, / 25

2 Background Introduction The 2014 Farm Bill continued to increase importance of crop insurance in farm policy Moral hazard in crop insurance is not new (briefly discuss these papers later) Crop insurance and financial decisions are likely intertwined More debt is more accessible when crop insurance is purchased Purchasing crop insurance might encourage taking on more debt Is crop insurance a cheaper (subsidized) substitute for self-insurance (e.g., savings, diversification, etc.)? Belasco and Bekkerman October 9, / 25

3 Background Insurance Participants Have Higher Debt-to-Asset Ratios, (n=25,954) Belasco and Bekkerman October 9, / 25

4 Background Outline 1 Background 2 Empirical Framework 3 Data 4 Results 5 Concluding Remarks Belasco and Bekkerman October 9, / 25

5 Background Literature Review Crop insurance participation has been shown to be associated with fewer chemical inputs (Smith and Goodwin, 1996) increased soil erosion (Goodwin and Smith, 2003) slight increases in production acreage (Goodwin, Vandeveer, and Deal, 2004) lower levels of ad-hoc disaster payments (Goodwin and Rejesus, 2012) Risk compensation can occur with well-intended safety legislation (i.e., seat belt laws) (Peltzman, 1975) Belasco and Bekkerman October 9, / 25

6 Background Main Contributions From This Study First paper to relate crop insurance participation to financial solvency Does crop insurance reduce reliance on self-insurance? Identifies the simultaneous influences of 1 expansion of crop insurance 2 reductions in other government subsidy programs Exploration of instruments to isolate the impact of insurance on other farm decisions Belasco and Bekkerman October 9, / 25

7 Empirical Framework Specification We look to examine the relationship between debt-to-asset ratios as a function of crop insurance participation farm-level cost indicators (FE, VE) acres operated operator age education cash rental rates average revenue per acre other government payments Belasco and Bekkerman October 9, / 25

8 Empirical Framework Specification We hypothesize that insurance participation and acres operated are endogenously determined Instruments are used to identify the unbiased marginal impacts Agricultural district level past (Mar-Oct)and current (Feb-May) year precipitation average insurance participation (percent of acres) average percent irrigated production County level Yield Protection insurance rates (2012) historical farmers loss ratios (6-year rolling average) A TSLS process will be used identify marginal impacts Standard errors are bootstrapped Belasco and Bekkerman October 9, / 25

9 Empirical Framework Specification (cont.) DAR it = α 0 + α 1k + α 2t + α 3 ÎNS it + α 4 Âcres it + α 5 X it + ɛ it ÎNS it = Pr(Ins it = 1 X it, InsPart dt, HFLR ic, Rates c, Irrigation dt ) Âcres it = α 6 + α 7 X it + α 8 Weather dt + α 9 HFRL ic i =farm, k =commodity, t =time, c =county, d =agricultural district X it includes EDU, AGE, FE, VE, RR, REV, GPAY Weather dt includes PYPrecip and CYPrecip CY = Feb-May, PY = Mar-Oct Belasco and Bekkerman October 9, / 25

10 Data Agricultural Resource Management Survey (ARMS) ARMS is conducted annually by NASS and ERS Independently pooled cross section Only national farm-level survey collecting financial and production information Weights are used for sample selection bias, due to stratified sampling procedure We use the top 5 states producing corn, soybeans, wheat, and cotton (15 states total) CA is excluded IA, IL, IN, and MN are assumed to be corn and soybean operations Top 1% of debt-to-asset ratios are excluded Belasco and Bekkerman October 9, / 25

11 Data Other Data Six-year rolling historical average farmers loss ratios are based on RMA data (indemnity/premium paid by farmers) Aggregated insurance participation use data from RMA and NASS (insured acres / planted acres) YP Insurance rates are from 2012 Actuarial Data Master (ADM) table Belasco and Bekkerman October 9, / 25

12 Data Weighted and Quantile Summary Statistics, (n=25,954) Weighted Quantiles Variables mean Debt-to-Asset Ratio 0.14 < Aces Operated (in 1,000s) Cash Rental Rates (in $100s) Fixed Expenses per Acre (in $1,000s) Variable Expenses per Acre (in $1,000s) Revenue per Acre (in $1,000s) Insurance Premiums per Acre Government Payments per Acre Operator Age (in years) Education: less than High School 0.08 Education: HS Diploma or Equivalent 0.46 Education: Some College 0.28 Education: 4 Yr. College Degree 0.18 Insurance Participation 0.79 Belasco and Bekkerman October 9, / 25

13 Data Summary Statistics, by Insurance, (n=25,954) Insurance? Weighted No Yes Variables mean (n=5,377) (n=20,577) Debt-to-Asset Ratio Aces Operated (in 1,000s) Cash Rental Rate (in $100s) Fixed Expenses per Acre (in $1,000s) Variable Expenses per Acre (in $1,000s) Revenue per Acre (in $1,000s) Insurance Premiums per Acre Government Payments per Acre Operator Age (in years) Education: less than High School Education: HS Diploma or Equivalent Education: Some College Education: 4 Yr. College Degree Belasco and Bekkerman October 9, / 25

14 Data Higher Educated Operators Tend to Purchase Crop Insurance Belasco and Bekkerman October 9, / 25

15 Data Insurance Participants Operate Larger Operations Belasco and Bekkerman October 9, / 25

16 Data Debt-to-Asset Ratio, by Commodity and Insurance, (n=25,954) Debt-to-Asset Ratio Percent Not Commodity n Insured All Insured Insured States Corn 2, NE Corn/Soy 11, IA, IL, IN, MN Cotton 3, AR, GA, MS, TX Soybeans 1, OH Wheat 7, KS, MT, ND, OK, WA Belasco and Bekkerman October 9, / 25

17 Data Relationship between DAR and Insurance Participation, Belasco and Bekkerman October 9, / 25

18 Data Relationship between Government Payments and Insurance Premiums, Belasco and Bekkerman October 9, / 25

19 Data Time Trend for DAR, Based on Crop Insurance Purchase Decision, Belasco and Bekkerman October 9, / 25

20 Data Weighted Mean Statistics for Instrumental Variables, (n=25,954) Historic Precip. (in.) Percent Percent Farmers YP CY PY Commodity Insured Irrigated Loss Ratio Rates Feb-May Mar-Oct Corn Corn/Soy Cotton Soybeans Wheat includes NE 2 includes IA, IL, IN, MN 3 includes AR, GA, MS, TX 4 includes OH 5 includes KS, MT, ND, OK, WA Belasco and Bekkerman October 9, / 25

21 Results First-stage Regression Results Dependent Variable Insurance Participation Acres Operated Instruments Percent Insured (d) CY Precip (d) Percent Irrigation (d) PY Precip (d) HFLR (c) HFLR (c) Insurance Rates (c) Adjusted R-squared F-test (joint significance) p-value < Wu-Hausman Statistic p-value <0.001 <0.001 Belasco and Bekkerman October 9, / 25

22 Results Second-stage Regression Results Dependent Variable: Debt-to-Asset Ratio Variables Estimate Std Error t-stat Crop Insurance Participation Govt Payments per Acre Acres Operated (in 1,000s) Revenue per Acre (in $1,000s) Fixed Expenses per Acre (in $1,000s) Variable Expenses per Acre (in $1,000s) Operator Age (in years) < Education: less than High School Education: Some College Education: 4 Yr. College Degree County Clustered Std. Errors Yes State Fixed Effects Yes Commodity Fixed Effects Yes,, and indicates significance at.01,.05, and.10 levels, respectively. Belasco and Bekkerman October 9, / 25

23 Results Implications Crop insurance participation associated with higher DAR by 0.09 (64% of mean) Insurance may provide more access to credit Insurance may be a substitute for self-saving Government payments associated with lower DAR An additional $5 in government payments per acre (21% of mean) lowers DAR by 0.06 (43% of mean) Belasco and Bekkerman October 9, / 25

24 Concluding Remarks Conclusions Subsidized crop insurance is shown to have incentive distorting outcomes for savings These results suggest that expansion of crop insurance programs may have similar impacts on financial solvency Insurance participation is likely to encourage more loans 1 to be demanded by producers 2 to be provided by banks Farm policy needs to consider any additional costs o substitution of personal savings for insurance safety net Belasco and Bekkerman October 9, / 25

25 Concluding Remarks Thank you for your time. Questions? Eric J. Belasco Department of Agricultural Economics and Economics Montana State University (406) Belasco and Bekkerman October 9, / 25

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