(Over)reacting to bad luck: low yields increase crop insurance participation. Howard Chong Jennifer Ifft

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1 (Over)reacting to bad luck: low yields increase crop insurance participation Howard Chong Jennifer Ifft

2 Motivation Over the last 3 decades, U.S. farms have steadily increased participation in the Federal crop insurance (FCI) program Past research has considered various drivers of FCI participation: risk management, premium subsidies, risk balancing, moral hazard, adverse selection, etc. We consider the role of recent experience in driving insurance participation Relates to literature on dynamic learning models in behavior economics Additional perspective on crop insurance adoption

3 Research Question To what degree do recent yields influence crop insurance enrollment? Economic theory (expected utility with Bayesian updating) says the influence should be small Results can inform FCI design and other farm policies that focus on risk management Implications for behavioral economics research on decision making under risk

4 Theory Expected utility with Bayesian updating implies that recent yield outcomes should only have a small influence on insurance participation Many studies show recent information can have more weight under certain conditions, but Farmers on average have 25 years of experience (average age is 58) and access to a wealth of yield and weather data Low yields occur often (5-15% of the time) Farmers are almost-ideal group to test decision making theories Recent experiences should have a very small effect, with good and bad information processed similarly

5 Literature Managers overreact to salient risks (Dessaint & Matray 2014) After hurricanes cash holdings increase (but eventually decline) Various salience theories of choice support this finding (Tversky and Kahneman, 1973, 1974; Bordalo, Gennaioli and Shleifer, 2012a, 2012b, 2013) Flood insurance increases after major floods (Gallagher 2014) Then insurance steadily declines to baseline Floods are infrequent Results consistent with Bayesian learning with forgetting or incomplete information, availability bias

6 Empirical Strategy Data: NASS and RMA county level data Reduced form panel model of share of planted acres insured County FE, year FE Measures of yield shocks in recent years, including direction of shock State*year FE Rainfall and temperature shocks as an exogenous proxy for previous yields Rainfall deviation used to regularly be used as an IV, now the reduced form (proxy) is considered more appropriate

7 Data and Key Variables Corn, soybeans, wheat, sorghum, oats, barley All results presented will be corn only Share acres insured (buyup) RMA net acres/nass acres planted DDInsured= Change in share insured Relative Yield (NASS) (yield-e(yield))/e(yield) Expected yield with linear trend yrel minus yrel plus

8 Corn yields by state and year

9 Relative corn yields by state and year

10 Relative corn yields in 1995, Nebraska

11 Shares corn acres insured in 2002, Illinois

12 Data and Key Variables Rainfall Aggregate and monthly Similar formulation as yield rrel minus rrel plus Principal operator age Census of Agriculture Farm size Census of Agriculture

13 Model DInsured ist = ββ 1 yyyyyyll mmmmmmmmssiiiiii 1 + ββ 2 yyyyyyll ppppppssiiiiii 1 + f i + F s t + εε iiiiii i=county, s=state, t=year Alternative specifications Different lags of yyyyyyll Analysis by state, analysis by size /age Restrict to large counties, states with several counties, counties with stable acreage, allow CAT coverage, frequency good/bad yield

14 Relative yield and change in share acres insured

15 Primary Results Change in Share Insured yrel minus *** (0.018) yrel plus 0.071*** (0.011) Change in Share Insured rrel minus *** (-0.001) rrel plus (0.001) Observations 22,678 22,678 R-squared

16 Results by State Illinois Iowa Nebraska Minnesota yrel minus *** *** *** *** (0.020) (0.031) (0.024) (0.036) yrel plus *** ** (0.027) (0.035) (0.033) (0.050) Observations R-squared

17 Other Results Same effect for older vs younger farmers. Same effect large farm vs small farms Results hold for different decades, pre and post ARPA Results largely hold for other crops Effect is larger for counties with lower yield variation

18 Results New information (yield shocks) have a large impact on insurance purchase There is an asymmetric response to bad versus good information Behavior seems to violate Bayes-SEU model and cumulative prospect theory Consistent with reinforcement learning or the hot-hand fallacy (Rabin & Vayanos, 2010)

19 Jennifer Ifft

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