Environmental Impact of Crop Insurance Participation:

Evidence from EPA 305(b) Waters As Assessed

Katryn N Pasaribu

1 Introduction

To increase participation, US Congress has restructured the US Crop Insurance Program

since the authorization of the program in 1938 (Glauber, 2004; Goodwin, Vandeveer, and

Deal, 2004; Woodard, Schnitkey, Sherrick, Lozano-Gracia, Anselin, 2012). On August 13,

2015, the Congressional Research Service produced a report concerning the public cost of the

crop insurance program. One of the main reported concerns is that the subsidized insurance

premium exceeds farmers out of pocket cost to adequately protect themselves. If the claim is

true and farmers are rational agents, then, there must exist increasing insurance participation

after the amendment took effect. As participation increases resulting from crop insurance

policy changes, the insured farmers farming decision (e.g., agricultural chemical input use)

is likely to affect the condition of soil or body of fresh water within the area where crop

production is the economic activity.

Only a few studies have discussed the environmental impact of the crop insurance pro-

gram. Few studies conclude that insured farmer tends to reduce per-acre input application.

Hence, the environment may benefit from it (Ramaswami 1993; Quiggin, Karagiannis, and

Stanton, 1993; Smith and Goodwin, 1996). Two studies by Horowitz and Lichtenberg (1993)

and JunJie Wu (1999) show opposite conclusions.

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Ramaswami (1993) examines insurance induced supply response through changes in vari-

able inputs. To address the research question, Ramaswami develops two theoretical models

and a simulation assuming deterministic parameters. The theoretical models consider: (1)

a situation where a farmer only control one input production; (2) where the farmer control

more than one input. For each model, the author sets up two scenarios: (1) No insurance

available; and (2) insurance is available, and farmer participates. From the theoretical and

simulation analysis, the author finds that participating in crop insurance alters farmers risk

preference due to changes in income distribution. Further, in the case of moral hazard, since

an increase in output reduces insurance indemnities, participating in crop insurance leads

to a reduction in input use. Ramaswami (1993) conclusion infers that crop insurance might

promote water or soil protection from agricultural chemical use.

Quiggin, Karagiannis, and Stanton (1993) present a theoretical analysis and empirical

evidence on adverse selection and moral hazard issues within US crop insurance. From the

theoretical analysis, they conclude that farmers with a lower value of innate land quality

and farming ability are likely to use fewer inputs underinsured production compare to had

they been uninsured. From empirical evidence, using 1988 National Agricultural Statistical

Services survey data of 535 farmers, Quiggin et al., infer that insured farmers have lower

observed levels of variable inputs and lower total factor productivity than uninsured farmers.

An empirical study by Smith and Goodwin (1996) supports Ramaswami (1993) and

Quiggin’s et al., (1993) findings. Using survey data of 235 Kansas dryland wheat producers,

Smith and Goodwin (1996) examine the relationship between chemical input decisions and

crop insurance participation. The authors assume that there exists a recursive relationship

between producers decision purchasing insurance and choice on farming practices. They

use two-stage estimation procedure to find empirical evidence. Supporting Ramaswami

(1993) simulation results, the estimated outcome suggests that Kansas wheat producers, who

purchase crop insurance, use fewer agricultural chemical inputs. The results also indicate that

Kansas farmers who use more chemical are less likely to buy crop insurance. These results

2

imply that participating in crop insurance indirectly support soil and water protection in

the region.

Horowitz and Lichtenberg (1993) investigate how crop insurance coverage affects chem-

ical use decision for corn producers in the Midwest. In their analysis, authors ignore the

possibility that participating in crop insurance may alter cropping patterns. The study only

focuses on the corn planted current land. Similar to Smith and Goodwin (1996), Horowitz

and Lichtenberg also use two-stage estimation. The first stage examines corn farmers de-

cision purchasing insurance conditional on prices, yields, risk aversion, and other factors

affecting profitability. The second stage examines the input use decision conditional on in-

surance purchase decision and farms characteristics. From the results, authors conclude that:

(1) purchasing insurance affects the input use decision; (2) participating in crop insurance

promotes the agricultural chemical use as farmers who purchase insurance put more nitrogen

and spend more on pesticides. These results indicate that participating in crop insurance

tends to encourage reduction in the chemical pollution, rather than the reverse.

JunJie Wu (1999) examines the intensive and extensive margin effects of crop insurance on

farmers decision for cropping patterns and chemical use. Using USDA farm-level survey data

for Central Nebraska Basin, JunJie Wu (1999) uses two-stage estimation on a simultaneous

equation system with two equations explaining farmers decision on input used, such as

fertilizer application rate, in response to purchasing insurance (intensive margin) and on

cropland allocation, such as cropping pattern (extensive margin). From the analysis, the

author finds that there exists adverse selection (e.g., farmers who face soil erosion problem

are the ones who buy insurance) and moral hazard issue (e.g., farmers who purchase insurance

tend to convert hay and pasture land to corn cropland). Wu also finds that the extensive

margin effect of insurance participation surpasses the intensive margin indicating an increase

total chemical use in crop production. Thus, similar to Horowitz and Lichtenberg, JunJie

Wu analysis suggests that crop insurance may increase agricultural chemical pollution on

water or soil.

3

The external validity might be the source of contradicting conclusions amongst the stud-

ies as mentioned earlier. These pioneer studies use farm-level survey data from different

study areas and different crops (e.g., Smith and Goodwin (1996) use survey on wheat pro-

ducers whereas Horowitz and Lichtenberg (1993) and JunJie Wu (1999) use corn-producers

survey data). Another issue raised from the aforementioned studies is that none of these

studies link the crop insurance and environmental disadvantage parameter even though dis-

cussion of environmental impact is on the subject matter. Therefore, this paper provides

general empirical evidence of environmental disadvantage of US crop insurance participation

in freshwater quality in order to fill the gap of literature. Instead of using survey data, this

paper assembles US county-level data of crop insurance and US county-level- aggregated

spatial data of freshwater impairment to answer the research question.

Data of freshwater impairment status are collected from national geospatial dataset, the

305(b) Waters as assessed, by US Environmental Protection Agency (EPA). The Federal

Clean Water Act section 305(b) requires each state to monitor and assess the quality of its

waters. It also requires the state to report and submit the assessment biennially to EPA. The

state oversees the water at the segmented hydrological unit code-12 (huc-12) level and assigns

the water as good, impaired, or threatened given the water designated uses (e.g., agricultural

activity, industrial, etc.). Based on the EPA dataset, there is no exist biennially variation

of water impairment status for each segmented huc-12 in each state. Thus, the analysis is

done in cross-section form. The 2010 US county shapefile from US Census Bureau is also

used to identify the county location of each segmented huc-12 and to aggregate the water

impairment variables into a county level dataset.

This paper assumes pollution is an accumulation instead of a flow considering the cross-

section structure of water impairment data. Hence, for the empirical analysis, all crop

insurance variables are evaluated at average annual growth within the 5-year period prior to

the county's water assessment year. The crop insurance data are collected from the summary

of business reports provided by the Risk Management Agency (RMA), US Department of

4

Agriculture (USDA). These reports contain information on total insured acres, number of

insurance policies sold, total value of liability, total value of premium, total value of subsidy,

total indemnity amount, and loss-ratio percentage by county/state/agricultural district/crop

type/insurance policy/insurance coverage level. Additional data from United States Census

Bureau, such as county level population growth, and from National Agricultural Statistics

Service, USDA, such as county-level agricultural land and total acres, are used as well to

underpin the analysis. The number of observation for this paper is 2,451 counties across 49

states.

Goodwin, Vandeveer, and Deal (2004) use insurance liability and insured acreage as the

approximation of insurance participation to find empirical evidence of acreage effect resulting

from the crop insurance program. Their empirical model suggests a simultaneous relationship

between the two variables which is represented by a system of equations. Following Good-

win, Vandeveer, and Deal 's method, this paper develops an empirical model consisting of

three system equations representing county-level growth of insurance liability and growth of

insured acreage, and also total impaired water (km). This paper uses the a two-step system

Instrumental Variables - Generalized Method of Moments estimation to analyze the effect of

crop insurance participation on freshwater quality. The estimation results infer that county

with a higher agricultural land ratio has lower average growth of insured acres and the county

with lower insurance loss-ratio has larger growth of insured acreage. Crop insurance may

result in a farmer taking excess land into insured crop land (JunJie Wu, 1999; Goodwin,

Vanderveer, and Deal, 2004). Aside from insurance participation, this paper also assumes

that insurance liability is a loose approximation of expected returns/yield underinsured pro-

duction. The estimation results show that higher premium subsidy encourages higher growth

insurance liability. This result is similar to Young, Vandeveer, and Schnepf’s (2001) findings

that subsidy reduces per acre cost of insurance coverage, ergo, it raises farmer’s expected

returns on crop production.

Since a farmer input decision is unobserved without survey data, expected returns from

5

crop production represented by insurance liability might capture the decision on inputs un-

conditional to the existence of moral hazard and risk preference of farmers within a county.

Adopting JunJie Wu's terminology on crop insurance effects, this paper concludes that

the extensive effect of crop insurance from the expansion of agricultural land could lead

to broader surface water impairment as the estimate of insured acreage growth is positively

correlated with impaired water (km). However, growth insurance liability is negatively corre-

lated with impaired water (km) indicating with the possibility of moral hazard, and different

farmers’ risk preference within a county, the intensive effect of crop insurance through per-

acre input decision could benefit surface water condition holding the insured acres constant.

Since the coefficient estimate of the growth of insured acreage is larger than the growth of

insurance liability, the environmental disadvantage of crop insurance seemingly outweighs

the environmental benefit from the policy changes in the crop insurance program. This

result implicitly answers the question posed by Glauber (2004) on whether the positive ef-

fect of crop insurance on acreage could compensate the negative effect of input use on yield

resulting from moral hazard.

Lambert, Boyer, and He (2016) point out that data aggregation within spatial unit could

create issues resulting from spatial dependence because of measurement error. Authors

also mention that the measurement errors compromise the statistical inference of estimation

results. Therefore, to address this issue, Lambert, Boyer, and He’s (2016) approach on

Spatial Heteroskedasticity and Autocorrelation Covariance (SHAC) estimator in a system

of equations. Not knowing other studies that demonstrate SHAC estimator in a GMM

system with endogenous covariates, this chapter use the approach as an optimal weighting

matrix in the estimation. From this SHAC-GMM estimation results, there are no substantial

differences in inference and conclusion with GMM estimation under robust optimal weighting

matrix.

6

2 Conceptual Framework

This study assumes that farmer is a price taker, p, in output market, and farmer faces a

total cost of production, C in which farmer is also price taker in input market. Farmer aims

for break-even or positive profit from total crop production, Y . Underinsured production,

there exits M which is the total insurance premium that farmer has to pay. S is the total

amount of premium subsidy that farmer accepts. Therefore, underinsured production with

subsidy, farmer aims for,

p.Y − C −M + S ≥ 0 (1)

(2)

or in per-acreage terms

p.y − c−m+ s ≥ 0 (3)

where,

· y denotes for per acreage yield (Y/acreage)

· c denotes for input cost per acreage (C/acreage)

· m denotes for for insurance premium per acreage (M/acreage)

· s denotes for for insurance premium per acreage (S/acreage)

Rearranging the equation, to have positive profit or break-even underinsured production

with a subsidy, thus, it has to be that,

y ≥ c+m− sp

(4)

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Graph 1 narrates the hypothetical condition of crop production under insurance or not,

and with or without subsidy premium. The vertical line represents the per acreage yield,

whereas the horizontal line represents crop production acreage. E0 denotes for marginal

productivity of acreage under optimized per acreage input decision. The negative slope of

E0 indicates that farmer begins production from highest productive land and continues ex-

panding to lower productive land until the point where positive profit is no longer attained.

Without insurance, acreage production, A0, is at y = cp. Underinsured production, but

without subsidy, acreage production A1, is at y = c+mp

. This condition might explain the

historical situation when US government first introduced the crop insurance program pro-

viding ”all risk” insurance, and yet, the participation was considered inadequate (Kramer,

Randall, A., 1983). As mentioned in the literature, to increase the participation, the govern-

8

ment provides a subsidy that consequently adds more land into crop production. Therefore,

there must exists s that is substantial enough to push A1 to A2, where y = c+m−sp

. It shows

that there exists extensive effect of crop insurance through more land allocation for crop pro-

duction resulting from premium subsidy (Young, Vandeveer, and Schnepf, 2001; Goodwin,

Vandeveer, and Deal, 2004). Under this circumstances, A2, total chemical input production

is higher than at A0. Therefore, expectedly, there exists a larger level of environmental disad-

vantage (e.g., agricultural water pollution) as additional acreage incorporated into cropland

(Lubowski et al., 2006).

Under existence of a moral hazard and risk-averse assumption, a farmer is expected to

reduce input application rate to earn higher indemnity due to declining in yield production

(Ramaswami, 1999; Glauber, 2004). This intensive effect of crop insurance causes E0 shifting

down to E1. This condition pulls back A2 to A3. From this hypothetical condition, reduction

in the application rate of inputs could result in an environmental advantage, ergo benefiting

the surface water condition within the crop production area. The total environmental impact

of the crop insurance with presumably large enough subsidy depends on the whether the

extensive effect outweighs intensive effect or vice versa.

3 Data

This study assembles a set of data from different publicly-accessible sources to investigate

the environmental impact of crop insurance to water impairment status at a county level.

There are three groups of data consisting of crop insurance variables, agricultural produc-

tion, and waters data. Crop insurance data originates from Risk Management Agency, USDA

database. Agricultural statistics are collected from United States Census Bureau and Na-

tional Agricultural Statistics Service, United States Department of Agriculture. And water

impairment data comes from Environmental Protection Agency database.

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Freshwater Impairment Data

Federal Clean Water Act section 305(b) requires each state to monitor and assess the qual-

ity of its waters at the segmented huc-12 level. It also requires the state to report the

assessment biennially (good, impaired, or threatened) given the waters designated uses (e.g.,

agricultural activity, industrial, aquatic live harvesting, recreation, etc.) established in ac-

cordance to each states water quality standards. The impairment status is based on the total

maximum daily load decided by EPA. Once the state government assigns a water body as

impaired/threatened water, it stays as impaired/threatened in the water list until it is fully

restored. There are six cycle years recorded on the 305(b) datasets ranging from 2002-2012

in two year interval. Figure 1 shows the listed waters that are reported in 305(b) geospatial

datasets based on cycle year from 2002-2012. From the figure, it shows that for each cycle

year, EPA compiled water reports from selected states. For instance, for 2002 cycle year,

EPA compiled the water assessment reports from mainly Maryland and Hawaii. For 2006,

EPA compiled water reports from Oregon. For 2012, EPA compiled reports from Alabama,

Arkansas, Arizona, Connecticut, District Columbia, Florid, Georgia, Iowa, Idaho, Illinois,

Kansas, Kentucky, Louisiana, Minnesota, Missouri, Mississippi, Montana, North Carolina,

North Dakota, and so forth. Given the nature of the 305(b) geospatial datasets, this study as-

sumes that there exists variation of water impairment status across the body of water within

a state or even counties, yet, there is no variation of water impairment status between cycle

years. Thus, the analysis is done with a cross-section dataset.

2010 US county shapefile from US Census Bureau is used to identify the county location

of each segmented huc-12. Using Arc-GIS software package, the water impairment shapefile

is intersected with 2010 US county shapefile. From the intersection process, there are 717,161

segmented huc-12s bodies of water that are well identified by county location. For the next

step, two dummy variables are created which are impaired (impaired/threatened = 1; good

= 0) and use (agricultural use = 1; non-agricultural use = 0). From these two dummy

variables, length of impaired water (km) and length of water for agricultural purpose (km)

10

Figure 1: EPA 305(b) Waters As Assessed by Cycle Year

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are determined. The impaired water length and agricultural purposed water length are then

summed over the county resulting total length of impaired water (km) and total length

of water purposed for agricultural activity (km). From this aggregation process, a water

impairment county level dataset is constructed. This impairment dataset consists of 3,080

counties and contains information of the three variables along with the year of assessment,

named cycle year.

Crop Insurance Data

Data on crop insurance variables are taken from the summary of business reports of county

level federal crop insurance business reports. The summary of business reports contains

information of total insured acres, number of policies sold, the total value of liability in-

surance, insurance premium, subsidy, indemnity amount, and loss-ratio percentage for each

county/state/agricultural district/crop type/insurance policy/coverage level. Insurance lia-

bility, insured acres, loss-ratio, and insurance subsidy are the primary variables of interest in

this study. The crop insurance liability is defined as the maximum amount that the insur-

ance company would pay to the insured farmer when there exist zero yields (RMA-USDA,

2017). Loss-ratio (%) is calculated from total insurance indemnity value divided by total

insurance premium. This data is publicly accessible from USDA-RMA official website.

Assembling Water Impairment Data and Crop Insurance Data

The previous paragraphs show that the water impairment data and crop insurance data are

in different form data structure. The water impairment data is in cross-sectional structure,

whereas the crop insurance data is in longitudinal structure. To address the issue in assem-

bling the two data sets, crop insurance variables are evaluated at average growth (%) within

the last five years prior to the county’s water assessment year unconditional to the type of

insurance, coverage level, and crops. For example, segmented huc-12s of Autauga County,

Alabama were assessed in 2012 based on EPA 305(b) geospatial data. Thus, Autauga’s

12

County 2007-2011 crop insurance data are evaluated and used for the county’s empirical

analysis. Another example, segmented huc-12s of Dunn County, Wisconsin were assessed in

2004 according to 305(b) EPA geospatial data. Thus, for the county’s analysis, 1999-2002

county’s crop insurance data are used in the analysis. Merging the two datasets results in

2,451 counties that have no missing value. Figure 2 shows the map displaying the distri-

bution of average growth of county’s insurance liability across the US. Figure 3 shows the

map displaying the distribution of average growth of county’s insured acres across the US.

Other data such as county’s total agricultural land, population growth, and the descriptive

statistics are explained in the following sections.

Agricultural Land and Population Data

Goodwin, Vandeveer, Deal (2004) add county’s agricultural land as one of the covariates

explaining the variation of insured acreage within a county. For this study, agricultural land

ratio to total available land is used as one of the covariates explaining the growth of insured

acres. County’s agricultural land data is collected from 1997-2012 US Census of Agriculture,

USDA National Agricultural Statistics Service, whereas the total available land is collected

from US Census Bureau. For this study, the average ratio of agricultural land across the

data year is used as the covariate.

Population growth data is collected from US Census Bureau. US Census Bureau provides

10 year growth, G, from 1990 to 2000, or from 2000 to 2010. Thus, for the purpose of this

study, it is assumed that annual growth, g, is relatively constant. Therefore, annual average

growth rate is solved from logical relation between annual average population growth and

10-year population growth,

Pop0(1 + g)10 = Pop0(1 +G) (5)

g = (1 +G)110 − 1 (6)

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where Pop0 denotes for population at year 1990 or at year 2000, g denotes for annual average

population growth, and G denotes for 10-year population growth. The average of calculated

annual growths in 1990-2000 and 2000-2010 is used as the approximation of annual average

growth for each county.

Summary Statistics

As mentioned in the previous paragraphs, the total number of observation is 2,451 counties

from 49 states. Table 1 displays the summary statistics of the covariates and variables of

interest for the empirical analysis.

Several details are worth to discuss. First, the growth of subsidy across counties on

average, 19.07%, outweighs the growth of number policies sold, 1.03%, the growth of insured

acres, 4.20%, and insurance liability, 15.49%. Further, the growth of insurance liability is

larger than the growth of insured acres. It may indicate that as the cropland expansion is

conditional to available land, the expected yield of insured of crop production is seemingly

contrived to increase. Second, aside from subsidy, the growths of insured acres, insurance

liability, number of policies sold, and loss-ratio are spread out over a large range of values.

Figure 2 and 3 indicate the distribution of insurance liability growth and insured acres growth

across 2,451 US counties in 49 states.

Figure 2 maps the ratio of impaired water (km) to insurance liability growth across the

2,451 counties. Whereas, figure 3 maps the ratio of impaired water (km) to the growth of

insured acres. The maps are equally categorized into thirty-two classes. Seemingly, the maps

display the Goodwin, Vandeveer, and Deal’s (2004) claim on the simultaneous relationship

between the insurance liability and the insured acres. The spectrum of the maps is seemingly

identical across the United States. However, a closer examination on particular region or

state, such as California, shows there is variation. Figure 4 and 5 show California maps.

The maps infer that several counties have high growth of insured acres, yet, a moderate

or low growth of insurance liability, or vice versa. Siskiyou County has approximately low

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Figure 2: Ratio of Impaired Water (km) to Insurance Liability Growth (%)

growth of insurance liability but has roughly high growth of insured acres. In contrast, Santa

Clara County seemingly has high growth of liability, yet, it appears to have moderately low

growth of insured acres. It indicates that opposite correlation between impaired water with

insurance liability growth and impaired water with insured acres growth.

4 Estimation and Results

Estimation Approach

A system of equations is used to represent the endogenous attributes of the crop insurance

participation that subsequently contributes to surface water impairment. Goodwin, Vande-

veer, and Deal (2004) consider insurance liability and insured acreage as the approximations

of insurance participation that have a simultaneous relationship. Combining the work by

Goodwin, Vandeveer, Deal (2004) and the idea introduced in the conceptual framework, this

study considers that growth of insured acres is not only the approximation of crop insur-

ance participation but also the extensive effect of policy changes in crop insurance program

(JunJie Wu, 1999). Whereas, the growth of insurance liability is considered as the inten-

15

Figure 3: Ratio of Impaired Water (km) to Insured Acres Growth (%)

Figure 4: Ratio of Impaired Water (km) to Insurance Liability Growth (%) - California

16

Figure 5: Ratio of Impaired Water (km) to Insured Acres Growth (%) - California

sive effect and the approximation of farmers’ unobserved decision on inputs application rate

underinsured production. The latter consideration is based on the condition that without

survey data, input decision is unobservable. As insurance liability is determined by the ex-

pected yield per-acre, total acreage of land and insurance price or valuation on per-acre yield,

therefore, it is expected that the average growth of insurance liability overtime captures the

changes in farmer’s input decision.

The regression model consists of three equations that are specified as followed,

%∆acresi = f(%∆liabilityi, Ag.Land− ratioi,%∆loss− ratioi) (7)

%∆liabilityi = f(%∆acresi,%∆policiesi,%∆subsidyi) (8)

impairedi = f(%∆acresi,%∆liabilityi,%∆popi, usei) (9)

Where, %∆acresi and %∆liabilityi denote for percentage growth of insured acres and growth

of insurance liability at county i, respectively; Ag.Land− ratioi and %∆loss− ratioi denote

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for exogenous covariates explaining the growth of insured acres, which are defined as ratio

of agricultural land to total available land and percentage growth of insurance loss-ratio

at county i ; %∆policiesi and %∆subsidyi denote for exogenous covariates explaining the

growth of insurance liability, which are defined as percentage growth of number of insurance

policies sold and growth of total insurance subsidy at county i ; impairedi denotes for total

length (km) of impaired water at county i, whereas %∆popi and usei denote for population

growth and total length (km) of water for agricultural purpose at county i, respectively.

Two step linear Instrumental Variables Generalized Method of Moments is used given

the structure of regression model. Under the IV-GMM estimation, the system of equations

is characterized in a form (Wooldrige, 2010):

y = Xβ + u, u ∼ (0,Ω) (10)

with X(N × k) denotes for set of covariates explaining the dependent variable, y. Define

Z(N × l) that denotes for a set appropriate instruments, where l ≥ k. Thus, the set of l

moments condition :

gi(β) = Zi′(yi − xiβ), i = 1, ...N (11)

where gi is an l-vector. The GMM approach considers each of the l moment as the sample

moment. This approach estimates the parameter set βGMM that solves g(βGMM) = 0. Under

the case of over-identification, l > k. The GMM estimator minimizes the criterion:

J(βGMM) = N g(βGMM)′Wg(βGMM) (12)

where W is an l × l symmetric weight matrix that makes sure the g(βGMM) ∼ 0. To

address the issue of unknown form of heteroskedasticiy and possible arbitrary intra-cluster

correlation, robust and cluster optimal weighting matrix are used in the estimation.

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Results and Discussion

Table 2 summarizes the GMM parameter estimates for the system of equations with different

forms of weight matrix. Column two, ”Model 1”, presents estimation results given the second

step optimal weighting matrix under an unknown form of heteroskedasticity (robust weight

matrix). Whereas column three and four present estimation results conditional to clustered

weighting matrix by state, ”Model 2”, and agricultural district group ”Model 3”, respectively.

Estimation results for the growth of insured acres (equation 1.1) show that the estimates

are statistically significant at 5% alpha and different forms of optimal weight matrix lead to

a slightly different magnitude of the estimates. The results also infer that average annual

growth of insured acres across counties in the United States is negative. Also shown in the

results, higher growth insurance liability may lead to higher insured acres. Holding constant

cropland acres and per-acre yields, the variation of insurance liability is determined by the

insurance price or valuation on yields which is different from actual crop’s price. Under this

case, higher insurance price on yield may induce farmers to participate, ergo, putting more

land into crop production. The estimates of agricultural land ratio and growth of insurance

loss-ratio show negative relationship with the growth of insured acres. It indicates that

county with the predominantly sizeable agricultural land has less growth of insured acres

as the amount of available productive land might be saturated in that county. Higher loss

ratio may result in a lower growth of insured acres. It infers that if the existing insured land

is experiencing production loss meaning higher indemnity value, thus, it is irrational to put

additional land into production.

Estimation results of insurance liability growth also show significance at 5% alpha, ex-

cluding estimates from ”Model 2”. Higher growth of insured acres could lead to a lower

growth of insurance liability. Holding the per-acre yields constant, therefore, higher demand

on insurance for resulting from higher growth of acres cropland could lead to lower insurance

price or valuation on per-acre yields. The results also indicate that county higher growth

of policies sold has higher growth of insurance liability. As mentioned in the literature,

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the results also show that higher subsidy leads to higher insurance liability meaning higher

participation.

Estimation of the third equation captures the subsequent environmental impact of the

policy changes in crop insurance program. Aside from population growth, the estimates are

significant at 5% alpha. A county with larger waters purposed for agricultural activity has

larger impaired waters. The estimation results infer that county with higher growth of in-

sured acres has a higher number of total impaired water (km). In contrast to insured acres,

coefficient estimate of insurance liability growth shows negative. These results support the

idea introduced in the conceptual framework. There must exist a substantial premium sub-

sidy that reduces individual premium cost causing more land allocation into crop production

(extensive effect of policy changes in crop insurance program). Accordingly, larger impaired

water is expected. As explained in the estimation section, this study considers that the

growth of insurance liability is the approximation of farmers’ unobserved decision on inputs

application rate underinsured production (the intensive effect of policy changes in crop in-

surance program). Allowing the possibility of individual unobserved moral hazard and risk

preference, higher insurance price on per-acre yield (i.e., higher insurance liability value)

may allure the risk-averse farmers reducing input application rate to earn higher indemnity

due to declining in yield production. Thus, subsequently, it provides benefits to the surface

water condition. As the coefficient of estimates of insured-acres growth is greater than the

liability growth, therefore, policy changes in crop insurance program that alters insurance

participation could result in an environmental disadvantage.

5 Robustness Check

Lambert, Boyer, and He (2016) mention that data aggregation within the spatial unit may

create issue resulting from spatial dependence between spatial unit because of measurement

error. Authors also state that the measurement error weakens the statistical inference of

20

estimation results. As county water impairment data is aggregated from segmented huc-12s,

thus, to address possible measurement error issue, this study adopts the works by Lambert,

Boyer, and He (2016) on Spatial Heteroskedasticity and Autocorrelation Covariance (SHAC)

estimator in a system of equations. Having no knowledge of other studies that demonstrate

SHAC estimator in a GMM system with endogenous covariates, this chapter use Lambert,

Boyer, He’s SHAC estimator as an optimal weighting matrix, W in the estimation. The

optimal weight matrix is characterized as followed,

W = Zi′uiK(di,j)

′uj′Zi, i, j = 1, ...N (13)

where Zi denotes for the instruments of the regression model; ui are the errors from the

1st step estimation; and, K(di, j) is a N ×N kernel function spatial weighting matrix with

county j is defined as county i’s neighbor. Assuming N1/4 number of neighbors that are

spatially dependent (Kalejian and Prucha, 2007), K(di, j) is defined as,

K(di, j) =

k(z) if j ∈ i’s neighbor

0 otherwise

(14)

k(z) = (1− z2)2 (15)

z =di, jdL

(16)

where di, j denotes for distance between county i and j, and dL denotes for maximum distance

amongst the N1/4 neighbors.

Column 5 in table 2, ”Model 4”, shows estimation results using the SHAC’s optimal

weight matrix. Based on results, there is no substantial differences in inference and conclusion

with GMM estimation under robust optimal weight matrix.

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6 Conclusion

This study provides a general conceptual framework and empirical evidence of environmental

disadvantage of US crop insurance participation in freshwater quality to answer contradictory

conclusions in the literature. From the conceptual framework, a considerable amount of

insurance subsidy may induce farmers to put not productively optimal land into cropland

that subsequently contributes to environmental disadvantage. Higher subsidy reduces the

per-acre premium cost with the same amount of indemnity value. In the case of moral

hazard and risk-averse farmers, the substantial amount of subsidy value may consequently

allure farmers to reduce input application rate that causes the decline in yields. Thus,

it benefits the environment. The conceptual framework introduces two possible opposite

impacts to the environment from the policy changes in crop insurance program.

The estimation results support the idea introduced in the conceptual framework. The

extensive effect of policy changes in crop insurance program which is represented by the

growth of insured acres provides an environmental disadvantage to surface water condition

within a county. Whereas, the intensive effect of policy changes in crop insurance program

which is represented by the growth of insurance liability provides environmental benefit

assuming the existence of a moral hazard and risk-averse farmers. As the magnitude of

estimates of the insured acres growth is larger than insurance liability growth, the overall

effect of policy changes in crop insurance program provides an environmental disadvantage

to surface water within the area where agriculture is the primary economic activity.

The findings have two possible implications. First, the amount of insurance subsidy ought

to be at the level where total insured cropland equals to total cropland without insurance. It

leads to the second implication. High growth of insured acres yet not accompanied by a steep

increase of insurance liability could be used as a signal for RMA to decide the appropriate

level of subsidy or other policy instruments in crop insurance program.

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References

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Table 1: Summary Statistics

County level variables Mean Std. Min Max1Crop Insurance variables:

2policies sold count (% growth) 1.03 7.84 -69.31 81.092net reported acres (% growth) 4.20 17.18 -325.42 207.112liability amount (% growth) 15.49 17.53 -529.16 160.332subsidy amount (% growth) 19.07 15.87 -447.87 92.7423loss-ratio (% growth) 4.7 41.57 -268.17 337.87

Demographic & agricultural statistics variables:4population growth 1999-2000 (%) 10.04 13.95 -26.30 106.004population growth 2000-2010 (%) 4.71 12.58 -38.20 110.405population annual avg. growth (%) 0.13 0.20 -0.36 0.576county area (km2) 2,309.75 2,724.38 211.89 51,947.246county ag. Land area 1997 (km2) 330.81 756.69 0.12 9,945.476county ag. Land area 2002 (km2) 319.25 693.09 0.02 7,130.036county ag. Land area 2007 (km2) 327.80 681.19 0.05 6,866.736county ag. Land area 2012 (km2) 317.05 656.85 0.02 7,204.737annual avg. County ag. Land ratio (%) 10.59 16.47 0.00 96.41

8Water impairment variables:water length (km) 441.43 660.90 0.002 12,858.87impaired water (km) 268.78 406.50 0.00 7,767.31agricultural purpose water (km) 47.28 114.28 0.00 2,187.74

Number of observation: 2,451 counties1Data comes from Crop Insurance Business Reports by Risk Management Agency, USDA.2The variables are a five-year unconditional average.3Total insurance indemnity over total premium4Data from Census Bureau of United States5Mean of annualized average from the 1999-2000 and 2000-20106Data from National Agricultural Statistics Service, USDA7Mean of agricultural land ratio from census 1997, 2002, 2007, and 20128Data from WATERS as Assessed, EPA

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Table 2: Estimation Results

Model 1 Model 2 Model 3 Model 4Weight matrix (robust) (state) (Ag.District) (SHAC)Equation 2.1: %∆acres

intercept -6.49*** -6.37*** -6.22*** -5.92***(0.91) (1.06) (0.67) (0.85)

%∆liability 0.74*** 0.69*** 0.74*** 0.71***(0.06) (0.06) (0.04) (0.05)

Ag.Land− ratio % -0.07*** -0.07** -0.07*** 0.08***(0.01) (0.03) (0.01) (0.01)

%∆loss− ratio 4.7 41.57 -268.17 337.87(0.007) (0.008) (0.005) (0.007)

Equation 2.2: %∆liabilityintercept -9.65** -10.49* -10.12* -10.43***

(3.28) (4.99) (4.53) (3.11)%∆acres -0.83** -0.79 -0.85* -0.74*

(0.29) (0.59) (0.42) (0.35)%∆policies 0.38** 0.27 0.41* 0.21

(0.12) (0.22) (0.18) (0.19)%∆subsidy 1.48*** 1.49*** 1.53*** 1.51***

(0.23) (0.39) (0.34) (0.23)Equation 2.3: impaired (km)

intercept 397.50*** 327.00*** 399.22*** 499.00***(54.90) (92.35) (88.21) (125.00)

ag. Purposed water (km) 0.89*** 0.83* 0.96*** 0.90***(0.18) (0.37) (0.20) (0.19)

%∆population -53.57 -2.52 -63.17 490.10(56.55) (90.05) (105.00) (404.60)

%∆liability -15.72*** -9.85 -16.52* -20.89**(4.48) (6.62) (6.76) (7.85)

%∆acres 19.16*** 12.63 21.12* 27.35*(5.04) (8.46) (8.46) (10.13)

N (county) 2,451 2,451 2,451 2,451Hansen’s J test (p-value) 0.2579 0.7073 0.2919 1.0000s.e. in parentheses *p<0.05 **p<0.01 ***p<0.001

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