Does Crop Insurance Affect Crop Yields?
Michael J. Roberts, Erik O'Donoghue, and Nigel Key *
Paper prepared for presentation at the Annual Meeting of the AAEA, Portland, Oregon, July 29-August 1, 2007.
*
Economic Research Service, U.S. Department of Agriculture. The views expressed are those of the authors and do not necessarily correspond to the views or policies of ERS, or the U.S. Department of Agriculture. Direct correspondence to: Michael J. Roberts,
[email protected], (202) 694-5567.
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Does Crop Insurance Affect Crop Yields?
Introduction Over the last ten to fifteen years, federally subsidized crop insurance has grown from a relatively modest agricultural program with little participation into one that encompasses the majority of productive cropland in the country. Participation has expanded mainly due to large and growing premium subsidies, particularly since the1994 Federal Crop Insurance and Reform Act. This Act, and most of the insurance Acts since 1994, have steadily increased subsidies and expanded the number of agricultural activities covered. In most years since 1995, over 200 million acres—nearly two thirds of U.S. cropland––have been enrolled. For those insured under the program, coverage levels have also grown. In nominal dollars, total liability insured under the program has steadily increased from almost $6 billion in 1981, to nearly $24 billion in 1995, to over $40 billion in 2003. How much has crop insurance influenced the way farmers manage their crops and their resulting yield outcomes? Many have theorized about possible production impacts resulting from insurance contracts that alter the distribution of income from crop production. A key concern is moral hazard. In the context of insurance arrangements, moral hazard occurs when incentives embodied by insurance contracts affect the insured’s hidden actions, such as managerial effort or input use. When bad outcomes are indemnified, the insured may have less incentive to prevent such outcomes from happening, which may make the hazards more likely to occur. Insurers, understanding agents’ incentives, may be less likely to provide insurance, so the private market for insurance can break down. Perhaps as a result of these information problems, and perhaps due to other problems (such as adverse selection), little crop insurance was available to the U.S. farmers until the government began providing it in the 1930s. In terms of economic efficiency, there are costs and benefits to subsidizing crop insurance. On 2
the one hand, government provision of insurance in the context of a market imperfection (asymmetric information) may facilitate insurance that private markets may not otherwise provide. Such insurance can raise farmers’ welfare by lowering the variability of their consumption and could also increase production efficiency. Efficiency gains could be captured from greater specialization (if farms would otherwise diversify to manage their idiosyncratic risk) and greater access to credit—if farmers face less risk, so do those that lend to them, which reduces farmers' borrowing costs. On the other hand, the asymmetric information problems of moral hazard and adverse selection may generate costs that outweigh the benefits. Social costs might arise if farmers alter their behavior in ways that lower expected profits net of insurance indemnities. As crop insurance becomes an ever more prevalent component of U.S. agricultural policy, it is increasingly important to assess the magnitude of these costs and benefits. In this paper, we focus on the effect of insurance on yield outcomes, an important piece of the cost-benefit calculation. Previous research shows strong evidence of adverse selection (Just, Calvin, and Quiggin; Makki and Somwaru). Early insurance adopters and those with higher coverage levels appear to benefit from the program, in part because their true risk is greater than the risk reflected by their premiums. In contrast, evidence of moral hazard is mixed. Horowitz and Lichtenberg (hereafter referred to as HL) found that crop insurance caused fertilizer and pesticide use to increase by 19% and 21%, respectively. They explained this counterintuitive result by arguing that these inputs increase yield risk. If correct, their findings imply potentially large negative environmental implications stemming from crop insurance subsidies. These findings, however, have been challenged in empirical work by Smith and Goodwin and Babcock and Hennessy, among others, who estimate modest declines in input use resulting from insurance adoption. One reason for the mixed findings may be due to the strong assumptions scholars have had to make to identify the incidence of moral hazard. Estimating the incidence of moral hazard involves 3
linking input decisions or yield outcomes to insurance decisions. This is typically accomplished by regressing input use or yields on an insurance indicator and other exogenous controls. The key challenge stems from the endogeneity of the insurance decision: Insurance adoption is not randomly assigned. Indeed, because of adverse selection, one could expect that insurance adopters will be quite unlike nonadopters. Thus, the assumption that there is no correlation between unobserved factors driving input levels and the decision to insure is immediately called into question. The existing literature deals with the endogeneity problem by modeling both the insurance and the input-use decisions. For example, HL use a Heckman selection model that allows the errors of the insurance equation to be correlated with the errors in the input-use equation. The key assumptions involve which control variables are included and excluded from the two equations, the exogeneity of the controls, and a joint normal distribution of the error (the unobserved factors). These crucial identifying assumptions are generally difficult to justify and their implications are not transparent. For example, in HL, the acreage shares in the input equation are arguably endogenous. Moreover, it is not clear why operator age and off-farm wages belong in the input-use equations but not in the insurance equation, or why the off-farm business indicator belongs in the insurance equation but not in the input-use equation. The strong correlation of the errors in the two equations means that the assumption of jointly normal errors may be important, in addition to the exclusionary restrictions mentioned above. These strong identifying assumptions have received considerable scrutiny in the literature (see Puhani for a review). Similar issues arise in Smith and Goodwin. This work extends a recent preliminary study by Roberts, Key, and O'Donoghue to report new empirical findings on the scope of moral hazard in the crop insurance program. The findings are based on confidential RMA data with millions of field-level observations obtained from all crop insurance participants from 1989 to 2002. Using panel data from 1991 to 2001, we directly estimate the incidence of moral hazard by examining how the whole distribution of farm-level yields changed as farmers 4
cycled into and out of the insurance program. Thus, we are able to capture effects that may stem from standard as well as dynamic moral hazards (Vercammen and van Kooten). This is possible because the insurance contracts include yield histories that often extend back to periods prior to enrollment in the insurance program, and we are able to link all contracts across years. Thus, for each farm, crop and practice, we are able to observe yield outcomes in years with and without insurance coverage. The wealth of data allow for extremely precise estimates and how insurance acts to influence the whole distribution of yield outcomes. The wealth of data also facilitate the use of relatively simple yet powerful ways to control for unobserved factors relating to land quality, technology, weather common to each county and year, and local commodity and input prices.
A simple approach to estimating the effect of insurance on yield distributions
For each farm, crop, and practice (irrigated or non-irrigated) we develop a history of yield outcomes derived from all insurance contracts in all years, including all yield histories. We also develop an indicator for whether a farmer had insurance coverage in each year. For each crop and practice, we first calculate the difference between a farmer’s yield, Yield(t), and the county average yield, County(t). This first difference accounts for time effects prevailing in each county in each year, including prices, technology and weather. We then calculate the change over time in the farm-level difference from the county average between consecutive years. This second difference removes factors relating to the specific land parcel—how each parcel generally differs from the county average parcel. What remains is the farm-specific anomaly: how it's deviation from the county average changed over time. We denote the anomaly in period t as A(t):
(1)
A(t) = [Yield(t) – County(t)] – [Yield(t-1) – County(t-1)]. 5
We then consider how these anomalies compare with insurance decisions. We categorize each farmer-level anomaly into one of four insurance decision groups: (1) Newly Insured--those insured in the current year and uninsured in the previous year; (2) Uninsured—those uninsured in both periods; (3) Insured—those insured in both periods; and (4) Newly Uninsured—those uninsured in the current year and insured in the previous year. Groups (2) and (3) serve as controls—their insurance decisions were the same in both periods, so we do not expect insurance to influence their anomaly. Anomalies in groups (1) and (4) include insurance effects of opposite tendencies. If insurance effects are negative (the usual moral hazard story), the Newly Insured (Group 1), would decline relative to the two control groups, as the anomaly would reflect the change in yields going from an environment without insurance to an environment where insurance acted to reduce yields. Similarly, the Newly Uninsured (Group 4) would increase relative to the control groups, as the anomaly would reflect the change in yields going to from an environment where insurance acted to reduce yields into one where insurance had no influence. Many farmers insured under the Federal program, particularly in the mid 1990s, held only 'catastrophic' coverage (CAT) for their crops. CAT coverage is fully subsidized by the government, requiring only a nominal administrative fee (typically $50 or $100, depending on the year) for each crop a farmer chooses to enroll in a given county, regardless of acreage total. Coverage provided by CAT, however, is very low, typically covering losses in excess of 50 percent of expected yield at 55 percent of the price. To focus on insurance more likely to influence behavior, we define the “insured” as those having coverage in excess of CAT coverage, generically referred to as “buy up” coverage in much of the literature. In separate analysis (not reported) we found no yield effects stemming from adoption of CAT insurance. Most farmers with buy up have coverage that insures at least 65 percent of the expected yield at 75 percent or more of expected price. Many also have revenue insurance, which 6
insures a price multiplied by an approved (expected) yield. Since we focus on a broad assessment of crop insurance, we do not differentiate between the alternative buy-up insurance products in this paper—all insurance coverage above the CAT level is considered “insured” and CAT or no insurance is considered “uninsured.” To evaluate insurance effects, we compare the average and standard deviation of anomalies for each group. We also consider the 10th percent quantile of each distribution of anomolies. It is interesting to examine specific quantiles, particularly those at the lower end of the distributions because lower quantiles are most relevant to the amount indemnity payments paid. Finally, we overlay plots of the empirical distributions of insurance groups to visually assess whether insurance coverage influences yield distributions. We replicate the analysis for many states and crops where federal insurance is most prevalent. This kind of replication is important because it is possible that yield effects are more prevalent for some crops and regions than it is for others, and thus may aid refinement of insurance contracts and monitoring.
Results Roberts, Key, and O'Donoghue examined three major crops (corn, soybeans, and wheat) in three geographically diverse states (Iowa, North Dakota, and Texas) that each produced a substantial amount of the three crops. In this study, we expanded coverage of states and crops to see if farmers producing different crops in different regions exhibited different behavior. Specifically, we expanded the crops to include cotton and rice and selected states from different parts of the country that were major producers of at least one of the five commodities. Our goal was to select states such that we had geographical diversity while at the same time choose enough states to cover at least 50 percent of total production of each commodity. We selected nine states for corn, soybeans and wheat: Kansas, Illinois, Indiana, 7
Iowa, Ohio, Nebraska, North Dakota, Montana, and Texas; and we selected three states each for cotton and rice: Arkansas, California, and Texas. In 2000 these states constituted almost 66% of total corn production, more than 60% of total soybean production, over 55% of total wheat production, over 51% of total cotton production, and nearly 73% of total rice production in the United States. Table 1 reports means and standard deviations of yield anomalies separately for each of the four insurance adoption categories in each state and each crop. Units for corn, soybeans, and wheat are bushels per acre; units for cotton and rice are pounds per acre. We use bold-faced font to highlight the lowest mean anomaly across the four categories in each state and we use italicized font to highlight those with the highest standard deviation. If insurance causes yields to generally decline and their variance to increase (the standard moral hazard story), we would expect mean yields of the Newly Insured (column 1) to be lowest and mean yields of the Newly Uninsured (column 4) to be highest. Differences between the columns are generally small and a slightly disproportionate share of the lowest means lie in the first column of the table. Standard errors of the means are also small, although these may be meaningless as we consider the population of insurance contracts. Across all crops except rice, absolute differences in the means across categories are generally on the order of 1-2 percent of the average aggregate yield. Differences in standard deviations are also small, but a slightly larger proportion of the highest of these lie in the first column, particularly in the case of wheat, providing a hint of moral hazard. In table 2 we report the 10th percent quantile for each insurance category, state, and crop. Here we see a slightly stronger pattern, with the Newly Insured generally having lower values than the other insurance categories. This suggests that the low end of the yield distribution gets slightly thicker as farmers enroll in the insurance program. This suggests that as farmers enroll in the insurance program, they are slightly more likely to experience a large drop in yields. But again, the absolute differences are quite small. 8
In figures 1 through 5 we pool all states in all years for each crop and show the empirical distribution of anomalies of the Newly Insured together with the distribution including all anomalies. All distributions display a pattern that is extremely close to the normal distribution. It is important to note, however, that this does not imply that yields themselves are distributed normal, given the way we have defined yield anomalies. Except for rice, differences between the distributions of the Newly Insured and all anomalies are very similar—indeed, almost indiscernible to the naked eye. Below each distribution plot we also display a quantile-quantile or “Q-Q” plot that compares the anomaly distribution of the Newly Insured to all anomalies. The Q-Q plot is a graphical technique for determining if two data sets come from populations with a common distribution. A q-q plot is a plot of the quantiles of the first data set against the quantiles of the second data set. Many distributional aspects can be simultaneously tested using a Q-Q plot, including shifts in location, shifts in scale, changes in symmetry, and the presence of outliers. What appears to be a solid line in each plot is actually a scatterplot with a very large number of observations, each corresponding to the quantiles of each distribution plotted against the other. If the distributions were exactly the same, all points would lie on a line with a slope of one and passing through the intercept. We show this using a dashed line to represent “Theoretical No Yield Effect.” For each crop, the QQ plot displays a pattern that is slightly steeper than dashed line. This indicates that when pooling across all states, the tails are slightly thicker for the Newly Insured anomalies. While very slight in magnitude, this may indicate a small degree of moral hazard. Rice is the only crop that shows a clearly discernable difference between the Newly Insured anomalies and all anomalies. The Newly Insured distribution is shifted well to left of the total distribution. Two issues are notable here. First, rice is a very high value crop, so moral hazard incentives may be larger than for other crops. Second, there are far fewer rice farmers than corn, soybean, wheat, and even cotton farmers, so the empirical distributions of anomalies may be less 9
representative of the true distributions. Nevertheless, our data set of rice farmers includes 3,801 observations in the Newly Insured category and 82,536 overall. While this distribution has, by far, the fewest number of observations, they should be sufficient to draw reasonably accurate estimates of the true distributions. Looking again at Table 2, it appears the most likely state where insurance contracts may be affecting yield outcomes is for rice in Arkansas. The 10 percent quantile for Newly Insured in Arkansas is -1686 lbs. compared to an average of -1255.6 lbs for the other categories. The difference of more than 400 lbs. equals about 7 percent of the average yield in Arkansas.
Conclusion In this paper we combine a very rich administrative data set with simple techniques to show how yields are affected by crop insurance. We identify the yield effects by comparing how crop yields change for those cycling into insurance relative to those in the same county who were insured in both periods, uninsured in both periods, or who cycled out of insurance. Though simple, this identification strategy controls for a tremendous amount of land heterogeneity and for time effects that might otherwise confound insurance effects. The large size of the data set (all insurance contracts from 1992 to 2002), allows us to examine how the whole distribution of yield outcomes is influenced by insurance decisions. We find that insurance generally has little effect on yield distributions. More work will need to be done to further explore the very modest increases in yield variability for corn, soybeans, and wheat that we found. The one exception to our general finding of small yield effects is rice in Arkansas, where we find negative effects of insurance on yields at the 10th percent quantile of the yield distribution. Our current estimates suggest that at this quantile, yields declined by about 7 percent due to insurance adoption.
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References Babcock, B.A., and D. Hennessy. “Input Demand under Yield and Revenue Insurance.” Amer. J. Agr. Econ. 78, no. 2(1996): 416–27. Horowitz, J., and E. Lichtenberg. “Insurance, Moral Hazard, and Chemical Use in Agriculture.” Amer. J. Agr. Econ. 4, no. 75(1993):926–35. Just, R., L. Calvin, and J. Quiggin. “Adverse Selection in Crop Insurance: Actuarial and Asymmetric Information Incentives.” Amer. J. Agr. Econ. 81(November 1999):834–49. Makki, S.S., and A. Somwaru. “Evidence of Adverse Selection in Crop Insurance Markets.” J. Risk Ins. 68, no. 4(2001):685–708. Puhani, Patrick A. “The Heckman Correction for Sample Selection and its Critique.” J. Econ. Surveys 14, no. 1(2000):53–68. Roberts, M. J., N. Key, and E. O'Donoghue. “Estimating the Extent of Moral Hazard in Crop Insurance Using Administrative Data.” Rev. Ag. Econ., 28 no. 3 (2006): 381-390. Smith, V.H., and B.K. Goodwin. “Crop Insurance, Moral Hazard, and Agricultural Chemical Use.” Amer. J. Agr. Econ. 78, no. 2(1996): 428–38. Vercammen, James A., and G. Cornelis van Kooten, 1994. “Moral hazard cycles in individualcoverage crop insurance.” Amer. J. Agr. Econ. 76(2): 250-61.
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Table 1. Yield Anomalies—Means and Standard Deviations Newly Insured
CORN Illinois Indiana Iowa Kansas Montana Nebraska North Dakota Ohio Texas SOYBEANS Illinois Indiana Iowa Kansas Nebraska North Dakota Ohio Texas WHEAT Illinois Indiana Iowa Kansas Montana Nebraska North Dakota Ohio Texas COTTON Arkansas California Texas RICE Arkansas California Texas
Uninsured
Insured
Mean
Std Dev
Mean
Std Dev
Mean
Std Dev
Newly Uninsured Mean Std Dev
-0.41 -4.04 -1.73 -1.29 1.13 0.97 0.51 -3.08 -2.05
26.8 28.4 28.9 35.4 26.0 31.1 31.7 28.6 32.7
-0.91 -0.62 -2.12 -2.63 -0.11 0.48 4.79 -2.04 -1.19
27.6 27.8 28.7 36.1 18.8 30.9 29.9 27.6 33.1
-0.76 -0.59 -1.38 -0.41 0.55 0.31 3.70 -1.13 -2.13
26.0 27.8 26.5 33.6 23.2 29.3 31.9 28.9 30.8
0.67 1.85 -1.02 1.07 -6.4 1.64 8.66 1.06 2.37
26.9 26.7 29.0 35.2 24.7 29.3 29.6 29.3 35.2
-0.30 -0.91 -0.26 -0.40 -0.20 -0.20 -0.52 -1.38
8.6 9.3 8.8 10.5 10.2 9.2 9.3 11.4
0.001 0.09 -0.35 -1.41 -0.09 1.03 -0.26 -1.59
7.9 9.1 8.8 10.6 10.0 9.2 8.7 11.2
-0.01 0.12 -0.21 -0.65 -0.18 0.41 0.16 -0.78
7.8 8.6 8.1 9.8 9.1 8.2 9.2 11.7
0.29 0.64 -0.22 -0.11 0.45 2.35 0.51 -0.11
7.7 8.8 9.4 10.6 9.8 10.7 8.6 10.0
-1.22 0.79 -1.70 0.90 -0.55 1.61 -1.24 -0.73 -1.40
19.1 19.9 18.4 13.8 12.5 16.1 11.4 16.6 13.3
-0.57 0.70 -7.46 -0.30 -1.05 0.60 -2.33 0.35 -0.70
16.8 16.4 19.2 12.8 11.6 14.0 10.7 14.3 12.2
0.87 1.15 0.82 0.43 -0.70 0.87 -1.84 0.49 -0.20
20.5 19.0 16.2 12.9 11.1 13.9 11.2 15.3 12.7
0.25 2.47 2.45 -0.96 -1.28 1.49 -1.82 1.12 -0.22
21.9 17.3 14.7 12.2 10.9 13.1 10.3 15.4 11.7
-87.4 -16.8 4.3
255.5 241.5 204.7
-18.1 -24.9 -17.1
186.7 272.2 181.0
6.7 5.6 -2.8
303.2 217.1 183.6
27.4 24.1 2.0
247.8 227.3 151.9
-217.9 98.2 -29.1
1310.5 1452.3 1343.0
63.5 -83.8 85.9
1222.8 1345.1 1281.6
76.1 19.7 49.6
1368.0 1577.3 1327.6
34.2 198.4 -39.0
1328.3 1616.8 1315.2
Notes: Boldface fond indicates the lowest group mean for the state. Italicized and underlined font indicates the highest group standard deviation for the state.
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Table 2. Yield Anomalies—10th percentile of distribution Newly Insured
Uninsured
Insured
Newly Uninsured
CORN Illinois Indiana Iowa Kansas Montana Nebraska North Dakota Ohio Texas
-30.1 -36.7 -33.5 -41.3 -47.1 -31.4 -34.2 -37.7 -39.4
-31.5 -31.5 -34.7 -43.1 -15.9 -33.5 -27.3 -35.0 -38.7
-29.5 -30.9 -30.8 -38.0 -26.3 -30.3 -30.3 -34.3 -37.2
-28.8 -28.5 -31.2 -39.7 -47.7 -30.3 -26.4 -36.5 -35.7
SOYBEANS Illinois Indiana Iowa Kansas Nebraska North Dakota Ohio Texas
-9.1 -11.2 -10.0 -12.0 -11.4 -11.4 -11.4 -15
-8.6 -9.6 -10.0 -13.8 -11.3 -9.9 -10.4 -15.2
-8.5 -9.5 -9.0 -12.0 -10.3 -9.9 -10.3 -15.8
-8.1 -8.4 -10.5 -11.7 -10.4 -10.2 -9.2 -13.1
WHEAT Illinois Indiana Iowa Kansas Montana Nebraska North Dakota Ohio Texas
-24.0 -22.9 -22.4 -15.0 -15.1 -17.0 -14.5 -19.8 -17.6
-20.4 -18.2 -31.1 -15.5 -14.6 -15.5 -15.7 -16.4 -15.3
-22.2 -20.5 -16.8 -14.3 -13.7 -14.9 -15.1 -16.5 -15.3
-24.2 -17.1 -19.1 -13.3 -13.9 -13.9 -14.4 -15.7 -13.7
COTTON Arkansas California Texas
-416.8 -326.5 -216.4
-229.0 -311.3 -229.5
-343.6 -268.4 -213.4
-243.6 -215.4 -167.5
RICE Arkansas California Texas
-1686.0 -1163.5 -1473.8
-1135.2 -1472.5 -1410.8
-1381.6 -1448.8 -1497.9
-1250.1 -1818.3 -1406.2
Notes: Boldface font indicates the lowest value for each state.
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Figure 1. Distribution of Corn Anomalies for Newly Insured and All Other Farms
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Figure 2. Distribution of Soybean Anomalies for Newly Insured and All Other Farms
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Figure 3. Distribution of Wheat Anomalies for Newly Insured and All Other Farms
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Figure 2. Distribution of Soybean Anomalies for Newly Insured and All Other Farms
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