Charity Hazard in Crop Insurance

Transcription

1 Charity Hazard in Crop Insurance Tatyana Deryugina and Barrett Kirwan PRELIMINARY AND INCOMPLETE. COMMENTS ARE WELCOME. April 14, 2014 Abstract The government acting as an insurer of last resort can cause moral hazard if agents respond by taking on more risk or reducing private insurance coverage, thinking they will be bailed out. This phenomenon dubbed charity hazard can increase the cost and impact of disasters. It is thought to be an important explanation for the relatively low rates of insurance take-up in some markets, but empirical studies are lacking. We estimate the extent of charity hazard in agriculture, where both private crop insurance and frequent federal disaster assistance are present. We use political variation within a county to instrument for disaster aid and identify the causal relationship between disaster assistance and insurance coverage. We show that bailout expectations are qualitatively and quantitatively important for the insurance decision. We also find evidence of moral hazard with respect to yields: higher expectations of disaster payments lead to significantly lower yields. University of Illinois at Urbana-Champaign. We are grateful to seminar participants at the American Economic Association Meetings, the Midwestern Economics Association Meetings, the Institute of Government and Public Affairs, and the University of Illinois. 1

2 1 Introduction The state often acts as the insurer of last resort during systemic shocks, such as natural disasters and economic crises. Although there may be benefits to doing so, the state potentially crowds out private insurance purchases and other preventative behavior by providing free assistance to those impacted by such shocks. This phenomenon dubbed charity hazard to reflect the moral hazard induced by the expectation of charity can increase the cost and impact of disasters. It is thought to be an important explanation for the relatively low rates of insurance take-up in some markets. Although there are numerous theoretical models of charity hazard, there are very few empirical studies and no consensus on how much charity hazard occurs in practice. 1 Effective policy design, however, requires an understanding of the size and extent of government-induced charity hazard. We estimate the extent of charity hazard in U.S. agricultural production, where crop insurance is widely available, yet there have been numerous ad hoc disaster payment bills passed allocating funds to uninsured producers. Insurance availability alongside ad hoc bailouts is typical of settings where charity hazard is a potential concern, including homeowners who face the risk of natural disasters, farmers who face the risk of crop loss, and banks who face the risk of defaults. The identification of charity hazard in these settings, however, is problematic due to the endogeneity of disaster payments. For example, if lawmakers desire to assist the uninsured, areas with lower insurance take up will receive more federal assistance, leading to a negative correlation between insurance take up and disaster payments. However, in this example, it is the low insurance take up that causes high federal assistance, not the other way around. Additionally, individuals in disaster-prone areas may purchase more insurance than those in less-risky areas, and, on average, receive more disaster payments. But this positive correlation masks the underlying charity hazard if individuals don t purchase as much insurance as they otherwise would if there were no possibility of ad hoc disaster payments. 1 For theoretical research on charity hazard see, for example, Lewis and Nickerson (1989); Kelly and Kleffner (2003); Kim and Schlesinger (2005); Raschky and Weckhannemann (2007). 2

3 We unravel the joint determination of insurance and disaster assistance by exploiting withincounty variation in political factors that affect the likelihood of disaster assistance without affecting the risk faced by the decision maker. Since politically-motivated disaster payments are orthogonal to the producers risk environment and farmers do not represent a significant fraction of the electorate in most counties, we can be confident that the behavioral response identified with this variation is due to the expectation of disaster payments and not other confounding effects. We find that the elasticity of insurance take up with respect to disaster payments is about 0.2. That is, a one-percent increase in disaster payments reduces the number of insurance policies by 0.2 percent. We confirm these estimates by using alternative measures of coverage, such as total liability and gross insurance premiums (including premium subsidies). The elasticity of farmers own expenditure on insurance, which does not include subsidies paid by the government, is between 0.3 and 0.5. We also find evidence of moral hazard with respect to yields: higher expectations of disaster payments lead to significantly lower yields. The yield elasticity with respect to disaster payments is also about 0.2. This suggests that charity hazard occurs both through reduced insurance purchases and greater risk-taking through reduced effort and/or input use. On a per-policy basis, we find that farmer premiums and liability are both lower when expectations of disaster payment are higher. This is consistent with farmers choosing less generous insurance plans. However, we cannot tell whether this result is driven by who drops insurance coverage or changes in which plans insured farmers choose. Finally, we estimate the insurance value of both disaster payments and market insurance payments. We find that the former do not provide substantial risk protection at the county level: the representative farmer in the average county is not willing to pay more than the expected value for disaster payments. The same is not true for market insurance. Because there are likely to be few idiosyncratic shocks to yields within a county, this suggests that disaster payments are not valuable as an insurance product. This is not inconsistent with our charity hazard estimates, which speak to the marginal dollar of disaster payments, rather than the average. However, it suggests that the 3

4 value of continuing to provide disaster payments in their current form is low. This paper extends the literature on the crowd-out effect of publicly provided insurance. Much of the literature is concerned with crowding out of private health insurance by public health insurance. While many of the papers find significant crowd-out, some do not. 2 With respect to other crowd-out channels, Gross and Notowidigdo (2011) find that, following Medicaid expansions, consumer bankruptcy decreases, suggesting that bankruptcy acts as a form of insurance against high medical costs. Mahoney (2012) further shows that the existence of bankruptcy protection distorts the insurance decision. In the long-term care insurance market, Brown and Finkelstein (2008) find that government-funded Medicaid has the potential to significantly crowd out private coverage, even though the former is much less generous. Finally, Kousky et al. (2013) find that the expectation of disaster payments reduces the purchase of flood insurance. To our knowledge, we are the first to estimate crowd-out in the agricultural sector. There is scant evidence on the existence and size of charity hazard when it comes to disaster relief, likely due to the difficulty of credible identification. Browne and Hoyt (2000) find no evidence of charity hazard in flood insurance purchase, although the analysis is based on correlations. Kunreuther et al. (1978) find that homeowners in risky areas generally do not expect federal financial disaster relief. In a hypothetical choice experiment, however, Van Asseldonk et al. (2002) find that expectations of government relief are significant predictors of the decision to insure. Raschky and Schwindt (2009) find some evidence that foreign aid crowds out preventative measures for storms. With the exception of Kousky et al. (2013), the existing studies either use survey data or correlational analysis. Moreover, all the existing literature focuses on individuals or governments, rather than businesses or farmers. The rest of the paper is organized as follows. In Section 2, we provide background on crop insurance and disaster payments in the US and briefly explain the idea of charity hazard. In Section 3, we describe our data and empirical strategy. Section 4 presents the results. We provide backof-the-envelope calculations of the insurance value of disaster payments and formal insurance in 2 For a brief review of the existing evidence, see Gruber and Simon (2008). 4

5 Section 5 and conclude in Section 6. 2 Disaster Payments, Crop Insurance, and Charity Hazard Federal crop insurance and agricultural disaster payments have provided overlapping risk protection to farmers for over 40 years. The Agricultural Adjustment Act of 1938 established the Federal Crop Insurance Corporation to administer what was essentially, until 1980, an experimental crop insurance program. 3 While crop insurance was in an experimental phase, Congress established a standing Crop Disaster Payment (CDP) program in 1973 that was akin to free insurance coverage for a select group of crops. Low-yield payments were made to farmers who participated in income- and price-support programs when yields fell below two-thirds of normal. The Government Accountability Office (GAO) (1980) recognized the conflict between these programs and the potential for charity hazard when it reported that, where crop insurance was offered, [disaster] payments actually compete with crop insurance because they require no premiums. In 1980, Congress responded by ending the standing CDP program and expanding the Federal Crop Insurance (FCI) program. 4 In spite of the large expansion of federal crop insurance, Congress quickly established a pattern of having two parallel mechanisms for dealing with crop-loss risk by providing $1.4 billion in drought relief in 1981 and $6.9 billion in disaster payments along with $4.3 billion in crop insurance indemnities from (U.S. General Accounting Office, 1989). At the same time FCI participation stagnated at X million acres, less than 25% of insurable acres. In 1989 the GAO reported that, federal disaster assistance programs provide farmers with direct cash payments at no cost to the farmers, resulting in the perception [among farmers] that crop insurance is unnecessary. Despite GAO warnings, Congress continued to frequently authorize ad hoc CDP programs. Between Congress allocated $XX billion (2011 dollars) to CDP programs. 5 Figure 1 3 For a more detailed history of the early crop insurance program see Glauber and Collins (2002). 4 In 1981, the FCIC added 1,340 county programs (a county program is a particular crop-county combination), and in 1982, 8,278 county programs were added. 5 See the appendix for a list of public laws passed between 1989 and 2009 that authorize some disaster payments. 5

6 shows the pattern of indemnity payments, made by insurance companies, and crop disaster payments, made by the government, over the same time period. To control for the growth of insurance coverage, we show these quantities as percent of total liability. On average, disaster and indemnity payments are similar. There are several years in which disaster payments exceed indemnity payments, although in recent years disaster payments have been relatively low. There are no years in which no disaster payments are made, although in some years, the amount is nearly 0. Consistent with their ad hoc nature, disaster payments are much more volatile than indemnity payments. Not only were ad hoc disaster payments awarded frequently, but Congress consistently awarded payments to regions of the country that typically received high indemnity payments. The geographic distribution of crop disaster payments creates the potential for charity hazard. Figure 2 illustrates the geographic distribution of total indemnity payments from Indemnities appear to be concentrated in the Plains, along the Lower Mississippi, and the Southeast. Figure 3 shows that total crop disaster payments over the same time period have a very similar geographic distribution. In fact, the spatial correlation coefficient between these two payments is 0.X, which is consistent with the GAO s assessment that CDP programs and the Federal Crop Insurance program compete against each other. Of course, these patterns could also be driven by the spatial distribution of risk or crops. In addition to the frequent, geographically consistent disaster payments, the similarities between crop insurance and CDP programs may have made it easier for farmers to view ad hoc CDP programs as a supplement to, or even a substitute for, crop insurance. Equation (1) illustrates the basic structure of an indemnity or disaster payment. P ayment = } P {{ Ȳ } Protection max[0, Ȳ Y ] Ȳ } {{ } Trigger, (1) where P is the price guarantee, Ȳ is the covered yield, and Y is the actual yield. As equation (1) shows, the protection level for both crop insurance and disaster assistance equals the price guarantee multiplied by the covered yield. A triggering mechanism equal to the shortfall relative to 6

7 coverage determines the magnitude of payments from both sources. 6 The indemnity payment trigger can be determined by (a) individual yield, (b) individual revenue, (c) mean county yield or (d) mean county revenue. Farmers cannot take out multiple insurance plans for the same plot. Within these plan types, farmers can choose from several coverage levels ranging from 50% to 90%. 7 The coverage level implicitly defines the amount by which yield or revenue has to fall (relative to a baseline) before any payment is made. If a farmer chooses a 65% coverage level, for example, he does not receive payments until his yield, revenue, the county yield, or the county revenue (depending on the type of plan chosen) falls to more than 35% below the established baseline. The plan type and coverage level largely describe the insurance plans available to farmers. 8 In contrast, Congress legislates the trigger for free-to-the-farmer disaster payments. Yield shortfall forms the basis for the disaster-payment trigger. Historically, Congress has set the yield guarantee to 65% of a farm s historical-average yield. To encourage future uptake of crop insurance, the legislation provides a higher price guarantee for farms with crop insurance vis-à-vis farms who could have purchased crop insurance but did not. 9 Besides payment formula similarities, CDP programs are administered in such a way that disaster payments are a de facto supplement to indemnity payments. In an effort to be equitable and not discourage crop insurance purchase, disaster payments are structured to ignore crop insurance payments in determining eligibility for assistance (Budget of the United States Government, Fiscal Year 2006, 2005). Since crop insurance coverage does not affect CDP eligibility, after crop losses exceed the CDP trigger, an insured farmers income increases as crop losses grow. In other words, disaster payments top up indemnities. Figure 4 illustrates the change in farm revenue as crop losses grow for a farm with 65% crop insurance coverage that is eligible for disaster pay- 6 For a more comprehensive overview of the US crop insurance market, see Babcock (2012). 7 Not all coverage levels are available for all plan types and in all counties. 8 Farmers also have some choices within a plan-coverage-level combination, such as how to combine different plots and how much to get paid in the case of a shortfall. 9 During the period of our analysis, , only the U.S. Troop Readiness, Veterans Care, Katrina Recovery, and Iraq Accountability Appropriations Act of 2007 (2007) excluded farmers who could have purchased crop insurance but did not from receiving disaster payments. 7

8 ments. Under this scenario, a farm receives more revenue from a total crop loss than from a 10% crop loss, which might encourage moral hazard. 10 Because disaster payments are paid in addition to crop insurance, this might reduce the amount of charity hazard in this market. Although Congress has regularly responded to agricultural disasters with CDP programs, it has not been without reluctance. Over the period of our analysis, Congress attempted to move away from CDP programs by strengthening the Federal Crop Insurance program and weakening its own ability to pass disaster-assistance legislation by tightening budgetary constraints. From disaster payments came from emergency supplemental appropriations that were exempt from discretionary spending caps. The Federal Crop Insurance Reform Act of 1994 made future agricultural crop disaster payments subject to discretionary spending caps by restricting them from being classified as emergency payments. The 1994 Act also greatly expanded the crop insurance program by mandating catastrophic-level (CAT), i.e., 50%, coverage for farms receiving subsidies. Together, these requirements sent a signal that future disaster payments were unlikely (see Jose and Valluru, 1997). Congress, however, rescinded the catastrophic-coverage mandate after just one year, and implemented a multi-year CDP program in 1998 (Agriculture, Rural Development, Food and Drug Administration, and Related Agencies Appropriations Act, 1999, 1998) something it said it would not do four years earlier. Since Congress ended the standing CDP in 1980, it has subsidized crop insurance premiums to encourage farmers to purchase more coverage and thereby reduce the need for ad hoc disaster payments. Despite premium subsidies of 30% for 55 65% coverage and 17% for 75% coverage, voluntary participation in the FCI program remained low through the 1980s and early 90s. The 1994 FCIR Act attempted to increases participation by increasing the subsidy rate on crop insurance premiums. Figure 5 illustrates the evolution of the crop-insurance premium subsidy rates from The subsidy rate varies by coverage level; for clarity, we have grouped premiums into four coverage groups: 50%; 55, 60, and 65%; 70 and 75%; and 80, 85, and 90% coverage. 10 Typically, a CDP program stipulates the sum of the value of the crop not lost, if any; the disaster payment received under this part; and any crop insurance payment... for losses to the same crop, cannot exceed 95 percent of what the crop s value would have been if there had been no loss ( Crop Disaster Program, 2008) 8

9 The figure shows the dramatic rise in the subsidy for 50% coverage due to the 1994 legislation and a modest increase for the other groups of about 10 percentage points each. Farmers response to the increased premium subsidies in 1994 was slight. Figure 6 illustrates the share of total acres insured by FCI by coverage group from Nearly all of the increase in participation in 1995 came from the mandated CAT (50%) coverage. Once the mandate was removed, participation levels fell until premium subsidies increased even more. The Agricultural Risk Protection Act of 2000 (ARPA) increased premium subsidies by 50% for the 65% coverage level, more than doubled the 75% coverage level subsidy, and nearly tripled the 85% coverage level subsidy. Even with these dramatically increased subsidy rates, participation didn t return to the 1994-mandated level until Meanwhile, Congress continued to provide ad hoc crop disaster payments. Overall, the structure of market insurance and disaster payments in this setting makes it difficult to determine ex ante whether we should expect significant charity hazard. First, the fact that insurance is not free whereas disaster payments are should discourage insurance takeup when disaster payments are present. However, due to the heavy premium subsidies, crop insurance is cheap, which should reduce the amount of crowd out. Second, disaster payments are given fairly regularly, which lowers the risk of being uninsured or underinsured and not getting bailed out. However, disaster payments are still more uncertain than insurance payments. Third, the fact that indemnity payments are largely ignored when calculating disaster payments should result in lower crowd out than when insured producers cannot receive disaster payments. However, insurance payments are not ignored completely: once the sum of indemnity and disaster payments reaches 95% of the farmer s baseline, the farmer is not eligible for more disaster payments. Finally, if the conditions that trigger crop insurance and disaster payments are very similar, then the latter might be a good substitute for the former. However, as we show in later sections, disaster payments are heavily influenced by politics and do not appear to function as an insurance product in the average county. Thus, the extent of charity hazard is ultimately an empirical question in this and other markets. 9

10 3 Empirical Strategy 3.1 Identification strategy The central empirical question examined in this paper is whether farmers purchase less crop insurance (Y ct ) when they expect ad hoc disaster payments (Disaster ct ) conditional on (1) county fixed effects (a c ) that account for the underlying soil type, climate, and other characteristics that determine the inherent riskiness of producing in each area and (2) year fixed effects (a t ) to account for macroeconomic shocks such as annual price variation and broad changes in the crop insurance program over time. In other words, conditional on a county s inherent riskiness, do farmers exhibit charity hazard by purchasing too little crop insurance when they expect to receive more disaster payments? If so, we would expect estimates of γ in equation (2) to be negative. ln (Y c,t ) = a c + a t + γdisaster c,t + X c,t 1φ + ε c,t. (2) The main econometric problem in interpreting an estimate of γ as evidence of Charity Hazard is that unobservable factors could influence both farmers crop insurance purchases and disaster payments. For instance, farmers in areas prone to disasters (and hence disaster payments) may be more likely to insure than those in safer areas, leading to spurious positive correlation. Even within a county, higher disaster payments may be positively correlated with risk perception and, thus, with crop insurance takeup. Finally, a desire to help uninsured or underinsured producers may result in a negative correlation between insurance and disaster payments even in the absence of charity hazard. We address this problem with county-level voting patterns that (a) affect farmers future disaster assistance and (b) should not affect the benefit from holding insurance through channels other than disaster aid, such as risk. The key identification assumption we make is that voting behavior changes are unrelated to unobservables that also affect insurance preferences. The literature on the role of politics in funding allocation is extensive. Some papers find support for the core voter theory, where legislators distribute funds to their supporters (e.g., Levitt and Snyder, 1995). Others find that funds are targeted toward areas where the votes are split close 10

11 to evenly between the major parties (e.g., Dahlberg and Johansson, 2002). There is also evidence for heterogeneity of preferences for different kinds of spending by party. For example, Albouy (2013) finds that states with Republican representatives receive more defense and transportation money, while states with Democratic representatives receive larger education and urban development grants. More narrowly, the non-agricultural disaster aid process has also been shown to be significantly affected by politics (e.g., Downton and Pielke, 2001; Garrett and Sobel, 2003; Kousky et al., 2013). In particular, Garrett and Sobel (2003) estimate that as much as half of all disaster aid is politically motivated. In agriculture, Garrett et al. (2006) find that the presence of a state s congressmen on committees that have the power to approve agricultural disaster aid is correlated with higher payments. Thus, if there is charity hazard, we should also expect farmers in these areas to be less likely to insure. 11 Other political factors may also have an effect on the propensity of an area to receive disaster payments. The factors that operate at the state level may be different from those that operate at the county level. For example, presidential candidates have an incentive to target states that are close to a split between Republicans and Democrats because of the winner-take-all design of presidential elections. At the county level, however, the absolute number of votes for one s party is likely to be more important. The number of votes can be increased by a combination of increasing turnout of loyal voters, decreasing turnout of non-loyal voters, or changing how people vote (which would involve targeting undecided voters). Consistent with the first two mechanisms, Chen (2013) finds that, in 2004, federal disaster aid in Florida increased turnout among incumbent voters (Republican) and decreased turnout among opposition party voters (Democrat). To capture these channels, we use two measures of political changes in the county: the extent to which a county is heavily Democratic or Republican ( polarization ) and the percentage of voters voting for a third-party candidate. Both measures are constructed using data from Presidential elections./footnotethe targeting of aid might also depend on the combination of which party is 11 We do not use the variables used by Garrett et al. (2006) because they are not strong enough instruments once state fixed effects are included. 11

12 more prevalent in a particular county and which party controls Congress. Unfortunately, during much of our sample period, the two chambers of Congress were controlled by different parties. Previous research has shown that the majority of Americans favor agricultural subsidies (Ellison et al., 2010). Thus, it may be rational for congressmen to influence agricultural disaster spending even in areas where farming is not a large fraction of the economy. Figure 7 shows the spatial distribution of our instruments for the counties in our preferred regression sample. To replicate the variation used in regression analysis, we first demean each political measure as well as account for year fixed effects. We then take the absolute value of these residuals and average them by county. The resulting map demonstrates where the largest sources of political variation are for each of the variables. Darker areas correspond to larger fluctuations in politics over our time period. Overall, there is little geographic concentration in these fluctuations, suggesting that our results will not be driven by a particular area of the country. Because political changes in an area should not directly affect an individual farmer s incentives to insure, the exogeneity requirement is likely to hold. However, we might be concerned that both insurance and political opinions are driven by some other factor, such as income. Fortunately, in most of the counties in our sample, farming is a small fraction of the economy, so farm income is not likely to have a significant impact on political opinions. Thus, political variation is likely to meet the exogeneity requirement. We estimate the relationship between politics and disaster payments using the following regression specification: ln (Disaster c,t ) = a c + a t + βp ctind c,t 1 + γp olarization c,t 1 + θx c,t 1 + ν c,t (3) where c indexes the county and t the year. The variable ln (Disaster c,t ) is the log of total disaster payments made to county c in year t. To avoid missing values, we add 1 to Disaster ct prior to taking the log. The variable P ctind c,t 1 measures what percent of the electorate voted for a 12

13 third party candidate, based on the most recent (but not the current) presidential election. The variable P olarization c,t 1, defined as LastF racrep c,t 1, where LastF racrep c,t 1 is the fraction of voters voting for a Republican candidate, measures the degree of polarization in the county. 12 Finally, X c,t 1 represents the time-varying control variables that could potentially affect both the crop insurance decision and political attitudes: population, the number of farm proprietors, per capita income, total farm income, total employment, and the share of total employment in the agricultural sector. These are all lagged because the insurance decision deadline is in March of a given year for most farmers. Standard errors are clustered by county. In the second stage, we test whether expectations of disaster payments affect insurance decisions: ln (P olicies c,t ) = a c + a t + γ ln (Disasterc,t ) + φx c,t 1 + ε c,t. (4) where ln (P olicies c,t ) is the log of total insurance policies in county c in year t and ln (Disasterc,t ) represents the predicted value of the log of disaster payments from the first stage. As above, X c,t 1 represents the time-varying control variables that could affect the crop insurance decision. In this specification, γ < 0 indicates charity hazard. While the total number of policies is an intuitive way to quantify charity hazard, it is not the only possible measure of insurance coverage. We also consider total liability, which measures the value of insurance purchased by the farmers, the premiums paid by the farmers, which corresponds to total insurance expenditure, and the unsubsidized premiums received by the producers. Finally, we also test whether there is any moral hazard with respect to yields, that is, whether yields are lower when farmers are expecting larger disaster payments. 12 We lag the voting data by at least one year because crop-insurance decisions happen earlier in the year (the spring) than presidential elections, which occur in November. 13

14 3.2 Data Insurance data used in the estimation come from the Risk Management Agency (RMA). The data contain information about the total number of policies, acres insured, premiums paid, liability, subsidies, and indemnity payments for each county where crop insurance was available between 1990 and Disaster payment data were obtained by a Freedom of Information Act request from the Farm Services Agency (FSA). Since livestock insurance was unavailable during the period of our analysis, we focus on disaster payments made for crop losses. After 1994, disaster payments for crops for which insurance was not available are classified under the Non-insurable Crop Disaster Assistance Program (NAP). We do not include NAP payments in our measure of disaster payments. Rather, we focus on disaster payments to producers of crops for which insurance is available because these are the payments to which farmers should respond when making their insurance decisions. 13 Because livestock and NAP payments are not necessarily indicative of potential disaster payments to producers of insurable crops, they may not affect insurance decisions. 14 Yield data are from the National Agricultural Statistical Service (NASS). We use data on yields for seven major crops barley, corn, oats, rice, sorghum, soybeans, and wheat and combine them into a single yield measure on the county-year level. 15 Specifically, we transform the yields into a uniform bushels per acre measure and then take a weighted average for each county-year combination, where the weights are the number of acres harvested for that crop. We use Regional Economic Information Systems (REIS) and County Business Patterns (CBP) data to control for county-level changes that might affect crop insurance decisions (X c,t 1 ). From REIS, we use data on population, the number of farm proprietors, total farm income, and per capita income. CBP provides us with information about the fraction of total employment in forestry and agriculture sectors. 13 Examples of program names are given in Appendix Table A2. A full list of program names is available upon request. 14 Given the highly specialized nature of U.S. agriculture, it is unlikely that farms are diversified enough to rely on cross-subsidization provided by livestock and NAP disaster payments. 15 These crops are also referred to as program crops. 14

15 Finally, our political instruments are constructed from data on county-level voting results in presidential elections. The 2004 and 2008 data come from Dave Leip s Atlas of U.S. Presidential Elections, while earlier data were generously shared by James Snyder. 4 Results 4.1 Summary statistics Table 1 shows economic and political summary statistics for our main regression sample, where we exclude counties that appear fewer than 18 times during our sample and observations that are missing control characteristics. All monetary amounts are reported in 2011 dollars. The average county has over 86, 000 residents, out of whom less than 1% or 710 people are farm proprietors. About one percent of total employment is in the forestry/agriculture sectors. Farm income represents about 3% of total personal income in the average county. Table 1 also shows summary statistics for voting in Presidential elections between 1988 and In the average county, about 34, 000 votes were cast, with a little less than 7% of those votes being cast for a third-party candidate. Our polarization measure ranges from 0 (representing a county where exactly half the voters voted Republican) to 0.85, representing a county that leans heavily toward one of the two major parties. In the regression analysis, we convert these political measures into standard deviations to make interpretation more intuitive. Table 2 shows insurance statistics for our main regression sample. On average, there are about 420 crop insurance policies issued per county in each year, covering over 70, 000 acres. Farmers in the average county spend about $640, 000 (in 2011 dollars) on insurance, with the government contributing an additional $840, 000 in premium subsidies. The mean total liability in a county is about $17 million. Over our sample period, insurers paid $1, 000, 000 in indemnity payments each year, on average, while the government contributed an additional $424, 000 in disaster payments. Thus, disaster payments are close to half the size of indemnity payments, while subsidies are over two thirds of the size. Taken together, subsidies and disaster payments exceed indemnity payments, 15

16 reinforcing the idea that agriculture is heavily subsidized. 16 The average loss ratio from the farmer s point of view (defined as indemnity payments divided by subsidized premiums) is 2.3, which implies that for the average farmer who buys crop insurance, a dollar spent on insurance will result in 2.3 dollars of indemnity payments. Thus, crop insurance has historically been very profitable for those taking it up. Below, we also show the loss ratio from the insurance provider s point of view, defined as the ratio of indemnity payments and unsubsidized premiums. In this case, the average is 0.82, implying that for every dollar received in premiums, insurance companies paid out 82 cents in indemnity payments. Overall, the summary statistics demonstrate that farmers represent a small fraction of the electorate and the economy in most counties, and are thus unlikely to be driving the political trends. Furthermore, while there is substantial government intervention in the form of disaster payments, crop insurance is also heavily subsidized, making it profitable to hold. This makes it unclear ex ante as to whether we are likely to find substantial crowd out of insurance by disaster payments. 4.2 OLS We proceed by looking at the raw relationship between disaster payments and insurance takeup. Table 3 shows the results of a naive regression of the number of policies in a county on same-year and previous-year disaster payments. All specifications include county and year fixed effects, while Columns 4-6 also control for lagged county-level characteristics. There is a positive and significant relationship between contemporaneous disaster payments and insurance takeup throughout. There is also a positive relationship between lagged disaster payments and takeup. Both estimates remain significant and relatively constant across the specifications. Specifically, a 1% increase in disaster payments is associated with a % increase in takeup in the following year and a % increase in takeup in the current year. This relationship can exist for a number of reasons. First, in some cases farmers who receive disaster payments are required to purchase crop 16 The government also reinsures private insurance providers; thus, this calculation of government expenditure represents a lower bound. 16

17 insurance in the next one or two years. Second, an adverse event can trigger disaster payments and change farmers beliefs about risk to their crops, resulting in more insurance in the following year on net. Thus, without a valid instrument that holds risk constant, we cannot say much about the existence of charity hazard. Table 3 also reveals other predictors of takeup. 17 As expected, the number of farm proprietors are strong predictors of the insurance decision. However, total employment and the share of employment in agriculture are not. Increases in the per capita personal income are associated with a decrease in insurance takeup. Finally, a higher population is associated with higher insurance takeup in some specifications. 4.3 First stage Because of potential confounders and reverse causality, the OLS analysis cannot illuminate the causal relationship between disaster payments and insurance takeup. We thus proceed with our instrumental variables strategy. Table 4 shows the estimated relationship between total disaster payments and political changes, as measured by the percent of the voters who voted for a thirdparty presidential candidate in the most recent election and by the degree to which the county is polarized toward one of the two major parties. Both measures are in standard deviations. Due to the presence of many zeros, we add 1 to the amount of disaster payments before taking the log. The results show a strong relationship between both measures of political changes in the county and disaster payments. Specifically, a one standard deviation increase in the percent of people who voted for a third party candidate in the last presidential election increases disaster payments in that county by 8.6%. Polarization is also associated with higher disaster payments: for every standard deviation increase, disaster payments increase by 16.9%. All else equal, counties with fewer farm proprietors, lower population, and lower per-capita income receive more disaster payments. The F-statistic in the specification that includes characteristic controls, as well as year and county fixed effects (Columns 4) is slightly above the conventional threshold of Farm income deciles are included in the regression but have been omitted from the table for readability. 17

18 4.4 Second stage Having established that political changes in the county are predictive of disaster payments, but should not affect the crop insurance decision in other ways, we proceed to use these variables as instruments in the second stage. Table 5 shows the estimated effect of disaster payments on crop insurance takeup in a county. Our preferred specification, which includes characteristics controls, is shown in Column 4. A one percent increase in expected disaster payments causes the number of crop insurance policies to drop by 0.2 percent. This estimate is highly significant. 18 The estimates above capture only the extensive margin of the insurance decision (choosing whether or not to have crop insurance). Due to the large number of plan choices faced by farmers, the intensive margin (choosing how much crop insurance to purchase) could be important as well. Because there could be differential selection out of insurance, we cannot estimate the intensive margin separately. However, we can look at other measures of insurance that include both the intensive and extensive margins. In Table 6, we estimate how disaster payment expectations change total liability and gross premiums (which include subsidy payments) in a county. We again find evidence of charity hazard: a one percent increase in expected disaster payments lowers total liability and gross premiums by about 0.34%. In dollar terms, this corresponds to about $8, 800 and $790, respectively. 19 In Table 7, we look at net premiums (which exclude subsidy payments), subsidy expenditure, and yields. In some counties in our sample, farmers pay no premiums out of pocket because of high subsidy levels. Thus, we consider net premiums both with and without adding 1 to them. If we consider only observations where farmers pay some premiums out of pocket (Column 1), we find significant charity hazard, with an estimated elasticity with respect to disaster payments of 0.3. If we also include observations where no premiums were paid (Column 2), our estimate of charity hazard becomes substantially larger, about 0.5. Regardless of the specification, these results 18 Because we do not have high frequency variation in voting, we cannot determine whether farmers are reacting to past disaster payments or expected disaster payments. If we instrument for lagged disaster payments, we get very similar estimates of the farmers response. 19 All dollar figures are calculated by exponentiating the mean of the variable, as shown in the second stage tables, and multiplying by the corresponding percentage. 18

19 suggest that farmers do reduce their expenditure on insurance in response to disaster payments. In Columns 3 and 4 of Table 7, we look at total subsidies and yields. The former represent an estimate of the government crowding out its own spending. The latter is an estimate of direct moral hazard with respect to disaster payments. We find that the elasticity of subsidies with respect to disaster payments is also about That is, for every percent increase in disaster payments, subsidy payments decrease by 0.34%. Finally, we find evidence for direct moral hazard: a one percent increase in disaster payments leads to a statistically significant decline in yields of 0.18%. Finally, in Table 8, we look at changes in insurance coverage conditional on buying a policy. In this case, the estimates will include both selection (i.e., who chooses to forego insurance) and changes in insurance decisions among those who continue to insure. We find that insurance coverage, as measured by liability per policy, falls by about 0.28% in response to a one percent increase in disaster coverage. Both gross and net premiums per policy fall by about 0.30%, while the subsidy per policy does not change significantly. Overall, our estimates imply that the elimination of disaster payments would significantly raise insurance coverage. 5 The Insurance Value of Disaster Payments As discussed earlier, disaster payments are more variable than indemnity payments. Furthermore, we find that they are significantly influenced by politics. Thus, their value as insurance against income shocks may be quite low. In this section, we estimate the certainty equivalent of the current disaster program for a representative farmer in each county. We compare this to the actual disaster payments and to the certainty equivalents of the formal crop insurance program in order to gauge the insurance value of disaster payments. We assume that there exists a representative farmer in each county, whose income, insurance choice, and disaster payments correspond to those of the average farmer. The farmer has a Constant Relative Risk Aversion (CRRA) utility function, U(w) = w1 ρ. Specifically, the farmer s utility is 1 ρ given by: 19

20 U(w, I, d, π) = (w + I + d π)1 ρ 1 ρ where w is income, net of crop losses, I is indemnity payments from the formal insurance program, d is disaster payments, π is insurance premiums, and ρ > 0 is the coefficient of risk aversion. Chetty (2006) estimates that ρ is likely to be close to 1, in which case the utility function is simply the log of the sum of its components. We vary risk aversion around this value. In our baseline scenario, we use the empirical counterparts of w, I, d, and π to calculate the expected utility in each county. To calculate the certainty equivalents of the disaster and insurance programs, we then simulate two counterfactual scenarios. In the first scenario, farmers no longer receive disaster payments but instead get a fixed transfer payment such that their utility is equal to that of the baseline scenario. These transfer payments are the certainty equivalents. We assume that insurance decisions are unchanged, which means that the calculated certainty equivalents are upper bounds. In the second scenario, we assume that farmers no longer receive indemnity payments or pay insurance premiums, while leaving the disaster payments unchanged. For each county, we calculate the fixed transfer payment that would leave farmers indifferent between this and the baseline scenario. We allow this transfer payment to vary by county. Estimating the empirical joint distribution of the utility components correctly is important. Luckily, we observe actual joint realizations of the number of farmers, farm incomes, insurance premiums, indemnities, and disaster payments in each county, in some cases for 20 years. The number of farm proprietors and their net income is reported by REIS, while the disaster and insurance variables are obtained from the RMA and FSA, as described in Section 3.2. Net farm income excludes production expenses and should capture income losses from a poor harvest. One complication is that reported farm income also includes disaster and indemnity payments, and it is important for us to be able to separate them from income itself. Our measures of dis- 20

21 aster and indemnity payments includes payments made to corporate farms, however, so simply subtracting them from income would be inappropriate. We proceed by assuming that disaster and indemnity payments are allocated to farm proprietors based on their average income share relative to corporate farms in that county and subtract them from net farm income. Another complication is that it is not uncommon for farm income to be negative in any given year. We address this issue in two different ways ways. The first way is to assume that farmers can borrow from future incomes for up to 5-10 years at a real interest rate of 3%. To incorporate this into our model, we calculate the mean of per farmer farm income, indemnities, disaster payments and premiums for each county, as well as their residuals. We then assume that the farmer s utility is a function of the sum of the discounted mean income, mean indemnities, and disaster payments minus premiums over either 5 or 10 years plus the residual fluctuations in these variables in any particular year. The second way is to take into account the fact that many farmers may have nonfarm income. Specifically, we assume that the farmer s non-farm income is equal to the per capita income in his county (but we do not allow for borrowing). We restrict the sample to counties for which we have at least 18 years of data. In order to avoid missing utility values, we also drop any county where the average annual income (taking into account insurance and disaster payments as well as premiums) is lower than $10, 000. This leaves us with a sample of 1,350 counties. Table 9 summarizes the average disaster payments and their certainty equivalents. Column 3 shows the estimates for the central risk aversion coefficient of 1, while the other columns show the results for lower and higher levels of risk aversion. The current disaster payments are simply taken from the data; thus, they do not vary by risk aversion. Overall, the certainty equivalents are very similar to the actual disaster payments. The average certainty equivalent is strictly lower than the average disaster payment in all scenarios, implying that the average farmer would not be willing to pay for the current disaster program. The certainty equivalents also do not vary much by risk aversion. In fact, there is a small but significant negative correlation between certainty equivalents and risk aversion in the underlying data, suggesting that 21

22 disaster payments are made in times of relatively high incomes, on average. Our assumptions about the time horizon do not seem to affect the estimates much. Accounting for the fact that many farmers have non-farm income substantially lowers the certainty equivalent. Overall, this table shows that disaster payments do not appear to be a valuable insurance product, at least at the county-year level. Because our simulation does not allow for a change in crop insurance decisions, these certainty equivalents are an upper bound. As we showed in the previous section, it s likely that farmers would compensate for some of the lost disaster payments by increasing their insurance coverage. These estimates also cannot speak about the value of the marginal dollar of disaster payments and are thus not inconsistent with our earlier findings of crowd out. Alternatively, it may be that even though disaster payments are not a good insurance product, they nonetheless create crowdout (Brown and Finkelstein, 2008). Table 10 shows the corresponding certainty equivalents of the formal insurance program. In contrast to the previous results, the average certainty equivalent always exceeds the average net insurance payment (indemnity minus premiums). The average farmer with a risk aversion coefficient of 1 is willing to pay between $131 and $206 more for the formal crop insurance program. Moreover, the certainty equivalents are significantly increasing in risk aversion, as would be expected in the case of an insurance program. We confirm that disaster payments do not appear to serve an insurance role at the county level by looking at the aggregate relationship between farm incomes, disaster payments, and indemnity payments. The results are shown in Table 11. A decrease of per farmer income by $1,000 is associated with a $44 rise in per farmer indemnity payments, but is completely unrelated to the amount of per farmer disaster payments in levels. In log terms (Columns 3-4), there is a small and only marginally significant association between per farmer disaster payments and per farmer income. By contrast, Column 3 indicates that a one percent decrease in per farmer income is strongly associated with a 0.25 percent rise in per farmer indemnity payments. A few discussion points are in order. First, the absence of a significant correlation between county-level farm income and disaster payments does not rule out that it exists at an individual 22

23 level. In that sense, our estimates of the certainty equivalent may be lower bounds. Unfortunately, farm-level insurance and income data are not available to us. However, many events that adversely affect farm incomes, such as droughts, floods, and pest outbreaks operate at a level above the individual farm, implying that farm incomes within a particular county should be highly correlated. Moreover, we find a substantial willingness to pay for the formal insurance program at the county level. Second, our analysis assumes that the distribution of disaster payments, indemnity payments, and incomes is not changing throughout this time period. As discussed in Section 2, the crop insurance program has undergone a number of changes and its penetration has increased substantially. The development and subsequent rise in popularity of revenue (as opposed to yield) insurance should have made the insurance program more valuable over time. Third, because we only have 20 years of data, our estimates may miss truly disastrous (and rare) realizations of farm incomes. However, it is unclear that this omission would impact the certainty equivalents of disaster payment more than that of the formal crop insurance program. Finally, counties with very low farm incomes are omitted from the estimation to accomodate the functional form of CRRA utility. If utility truly exhibits constant absolute risk aversion, then this omission is not likely to affect our estimates, as shocks to farm income are likely proportional to income levels. However, if shocks are relatively larger when incomes are smaller or if farmers do not have utility functions that are close to CRRA, then we are likely underestimating the value of both insurance and disaster payments. 6 Conclusion The existence of systemic risk may sometimes make it optimal for the government to act as an insurer of last resort. However, the possibility of a government bailout in such cases may also encourage risk-taking and crowd out private insurance takeup, leading to what is known as charity hazard. This type of moral hazard may exist in many areas of the economy, from banks taking 23

24 on excessive risk to homeowners foregoing flood insurance. Thus, understanding it and measuring its magnitude is extremely important. However, the extent to which charity hazard is a problem is debated, and few empirical papers exist that confirm or disprove its existence. We test for the existence of charity hazard in US agriculture, an area in which it has long been posited to be a problem. From the establishment of modern crop insurance in 1980, Congress has passed ad hoc bills granting disaster aid to farmers who did not have insurance or whose insurance did not cover all their crop losses. We instrument for disaster payments using political variation at the county level. We then estimate how expected disaster payments affect total crop insurance liability in the county. We find that charity hazard exists and is significant. The estimated elasticity of insurance takeup with respect to disaster payments is 0.2. Total insurance expenditure is also sensitive to disaster payments, decreasing by 0.3 percent for every percent increase in expected disaster payments. We also find that the government crowds out its own premium subsidies at about a 30% percent rate. Finally, we find evidence of moral hazard with respect to yields: expectations of higher disaster payments lead to modest but significant reductions in the county s yield. Our results imply that a nontrivial fraction of disaster payments is being transfered to farmers who would have had insurance otherwise. Moreover, it appears that disaster payments do not have a high insurance value, on average. Of course, eliminating disaster payments would require the government to be able to commit to not grant them ex post, something that it has not been able to do thus far in the realm of agricultural disaster payments. 24

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28 Figures Figure 1: Indemnity and disaster payments over time All amounts are in 2011 dollars. Disaster payment series represents payments to producers of crops for which insurance is available. 28

29 Figure 2: The Geographic Distribution of Total Indemnity Payments from Figure 3: The Geographic Distribution of Total Crop Disaster Payments from

30 Figure 4: Farm Revenue as Crop Losses Increase 30

31 Figure 5: The Crop Insurance Premium Subsidy Rates from

32 Figure 6: Acres Covered by Crop Insurance from

33 Figure 7: Absolute mean changes in political behavior Panel A: Polarization Panel B: percent voting for third party candidate. Illustrates mean absolute changes between 1988 and National trends and county fixed effects have been accounted for. Darker areas indicate larger changes. Shown only for counties included in the regression sample. 33