Willingness to Pay for Water Quality Improvements:

The Case of Precision Application Technology

Diane Hite, Darren Hudson and Walaiporn Intarapapong∗

Corresponding Author:

Diane Hite

Department of Agricultural Economics and Rural Sociology

209-B Comer Hall

Auburn University

Auburn, AL 36849-5406

Email: dhite@acesag.auburn.edu

Phone: 334-844-4800

Fax: 334-844-5639

Third revision, JARE

August 8, 2002

∗ Hite is Assistant Professor, Department of Agricultural Economics and Rural Sociology, Auburn University. Hudson is Associate Professor, and Intarapapong is a Post Doctoral

Research Assistant, Department of Agricultural Economics, Mississipi State University. This

research was funded by a grant from the Advanced Spatial Technologies in Agriculture program. The authors thank Tasha Maupin and Somporn Meerangruang for research assistance

on the project. The authors are especially grateful to two anonymous referees for their constructive comments.
ABSTRACT

A contingent valuation survey conducted in Mississippi is used to assess public willingness to pay for reductions in agricultural nonpoint

pollution. We focus on implementation of a policy to provide farmers with precision application equipment to reduce nutrient runoff.

Findings suggest public support exists for such policies. This paper also finds that inclusion of debriefing questions can be used to

refine willingness-to-pay estimates in contingent valuation studies.

A nonparametric scope test suggests respondents are sensitive to

level of runoff reduction and associated water-quality benefits.

Keywords: Contingent Valuation; Econometrics; Site-Specific Management; Variable Rate Technology
Introduction

In recent years, public concern about potential environmental damages has spurred

development of environmentally friendly agricultural practices and technologies. New water

quality rules in the form of total maximum daily load (TMDL) standards for watersheds will

intensify regulatory attention on agricultural practices. Thus, new practices and technologies

aimed at reducing agricultural nonpoint pollution will be needed for producers to meet water

quality standards. One technology that holds promise for reducing runo¤ is variable rate

technology (VRT), which precisely matches nutrient and chemical application to local crop

needs.

A contingent valuation (CV) survey was conducted in Mississippi to measure public

willingness to subsidize the adoption of VRT to mitigate agricultural pollution. A survey of

the public in Mississippi is of particular interest because the state is critically located within

the lower Mississippi river basin, and has a high percentage of rivers and streams on the U.S.

Environmental Protection Agency’s (USEPA) 301(D) list of impaired waterways (USEPA,

1998). Additionally, in 1999 the National Oceanic and Atmospheric Administration issued a

set of reports on the hypoxic, or ‘dead,’ zone

1

in the Gulf of Mexico to the White House

Committee on Environment and Natural Resources (NOAA). The NOAA reports link the size

of the dead zone to in‡ux of nutrients, primarily originating from agricultural runo¤.

2 Based

on a cost-bene...t analysis, Doering et al. suggest that among the most economically e¢cient

means of controlling hypoxia would be to restore wetlands and riparian zones in a number of

geographic areas, with the Delta region of Mississippi being heavily targeted. Even though

the state’s net out‡ows of nitrogen into the basin are lower than those of corn belt states,

1
Mississippi may contribute more to hypoxia by dint of its proximity to the Gulf of Mexico.

A contingent valuation study in Mississippi is also of interest because Mississippi shares a

number of attributes with other states in the Midsouth. In particular, its economy is

dominated by agriculture, and it is among the poorest states in the United States. Further,

issues associated with TMDLs have received signi...cant publicity in recent years, and there is

public concern about the impact of environmental regulations on the state economy.

This contingent valuation study was designed to measure Mississippi taxpayers’

willingness to pay for a subsidy to producers that provides public bene...ts.

3 Citizens who

believe that adoption of variable-rate technology would reduce agricultural pollution of fresh

water, and who value environmental quality in the form of clean water, should be willing to pay

for a program to subsidize adoption of the technology. Public perceptions of agriculturally

related nonpoint pollution are assessed, and public willingness to pay for a program to adopt

precision application technology to improve water quality is estimated in this study.

Importance of Reducing Nonpoint Water Pollution

Since implementation of the 1972 Clean Water Act, point source pollution has been

signi...cantly reduced, but nonpoint pollution remains a problem, particularly runo¤ of

chemicals and nutrients into bodies of water. USEPA found that nationally, over one-third of

streams, lakes, rivers, and estuaries surveyed in 1996 did not fully support their designated

uses (USEPA, 1998), citing nonpoint pollution as the major cause.

4 Agriculture is alleged to

be the largest contributor to nonpoint pollution because farmers have intensi...ed their use of

agricultural chemicals over the past 50 years, leading to increased runo¤ of herbicide,

pesticide, sediment, and nutrients.

2
Water quality degradation attributable to agricultural pollution is costly in terms of

surface water damages (Lichtenberg and Lessley) and drinking water treatment (Forster,

Bardos and Southgate; Murray and Forster). Franco, Schad, and Cady cite nitrate removal

costs within one California district of approximately $375 per million gallons. Given that

USEPA (1999) reports annual average total treatment costs for the U.S. at $300 per million

gallons, the costs of nitrate removal alone can be signi...cant. Thus, developing ways to

decrease agricultural runo¤ to meet federal clean water standards is an important public policy

concern.

Four policies have been used to combat agricultural pollution: voluntary education and

technical assistance programs; subsidy programs that promote adoption of conservation

practices; regulation of pesticides; and use of compliance mechanisms (Ervin). By the very

nature of nonpoint pollution, however, it is di¢cult to implement and monitor reduction

programs because of the large number of sources (Cabe and Herriges). Thus far, education

has been the primary method used to encourage reduction of agricultural nonpoint pollution

by promoting altruistic behavior and stewardship among agricultural producers (Ervin;

Ribaudo and Horan).

Because environmental stewardship is driven by personal attitudes, it is important to

understand the motivations of agricultural producers. Several studies suggest producers will

not fully participate in voluntary programs for nonpoint pollution reduction, underscoring the

relevance of the research presented here. In general it has been found that individual farmers

tend not to recognize their own contribution to nonpoint pollution, and would resist direct

limitations on nonpoint pollution, but would be willing to increase runo¤ controls under cost

sharing programs (Lichtenberg and Lessley; Pease and Bosch; Napier and Brown). Bosch,

3
Cook, and Fuglie found that regulation is more e¤ective than education to induce adoption of

site-speci...c management practices.

One possible strategy to reduce nonpoint pollution is the adoption of techniques such as

variable-rate technology (VRT) and site-speci...c management. These technologies have shown

some promise in reducing runo¤ of agricultural chemicals, thereby decreasing nonpoint

pollution (Oriade et al.; Schnitkey and Hopkins; Hite, Hudson, and Intarapapong). Although

VRT may provide environmental bene...ts, producers would be generally reluctant to adopt a

technology that has a certain, large ...xed cost for equipment but uncertain future pro...ts. The

results of studies comparing pro...tability of VRT and conventional application techniques have

been mixed (Blackmore et al.; Carr et al.; and Sawyer). Swinton and Lowenberg-DeBoer

found that revenues increased under VRT by about 7.2%, mostly from quality improvements.

A public program to help producers defray ...xed cost investments in equipment may be needed

to promote VRT adoption (Isik, Khanna and Winter-Nelson). These ...ndings provide impetus

for the examination of a cost-share program such as the one investigated in this study.

Survey Design and Instrument

The survey was devised as a single-price referendum CV instrument in which two

questions were examined: (i) public perceptions about agricultural impacts on the

environment; and (ii) how perceptions may in‡uence willingness to pay a one-time tax to

subsidize agricultural producers’ costs of purchasing VRT equipment. The survey instrument

was based on a format using follow-up debrie...ng questions. The sample frame was limited to

members of the voting-age public in Mississippi. A telephone survey was chosen as the means

of administration because previous mail surveys in Mississippi have resulted in extremely low

4
response rates. A pretest of the survey was conducted to test the survey instrument and

econometric methods.

The central goal of the survey instrument was to establish the perceived water quality

bene...ts of the proposed program. Because expected improvements in water quality depend on

weather conditions and soil erodibility, among many other factors, it is di¢cult to link runo¤

reduction with a speci...c measure of water quality improvement. In the survey instrument,

reductions in chemical, nutrient, and sediment runo¤ were assumed to improve water quality.

Measuring public awareness of agricultural nonpoint pollution in a survey is di¢cult.

Explaining the bene...ts of reducing nonpoint pollution in a way that could be understood by

respondents in a phone survey was equally challenging. In order to determine whether

bene...ts of the program were well understood, a scope test using two levels of runo¤ reduction

was employed.

Measures of the impacts of VRT adoption, based on Mississippi data, were included in the

survey. With no previous studies to use as guides, reduction in nonpoint pollution was

calculated by simulation using the Erosion/Productivity Impact Calculator, or EPIC

(Sharpley and Williams).

5 To obtain estimates of nonpoint pollution reduction from VRT, a

hypothetical farm representative of soil types, topography, and weather in the Mississippi

Delta (Intarapapong) was developed. Using EPIC and the representative farm data,

simulated runo¤ under single and variable-input application rates, based on continuous cotton,

corn and soybeans with conventional tillage, was calculated.

6 Nitrogen fertilizer, insecticides

and herbicides were the inputs of interest, and the combined runo¤ of sediment, nutrients,

pesticides and herbicides were the outputs of interest. Simulation results suggest that gross

runo¤ under VRT would be approximately 10% below that of the single-rate application.

5
Thus in the survey, the baseline environmental bene...t of precision application technology was

assumed to be a 10% reduction in nonpoint pollution.

The survey included questions ranking government spending programs (e.g. public

assistance, and crime ...ghting), about perceptions of agricultural nonpoint pollution (beliefs,

concerns, awareness, and knowledge), and about participation in recreational activities at or

near freshwater lakes, streams, or rivers. Household socioeconomic and demographic

information was also elicited.

To provide a framework for the valuation question, respondents were informed about

current water quality in Mississippi. Because the survey was conducted by telephone, water

quality information was imparted through a series of short questions regarding a respondent’s

knowledge of water quality facts. For these questions, information was used about the degree

to which surveyed bodies of water in Mississippi met designated uses (USEPA, 1998). A

typical question was

“Are you aware that 94% of tested rivers [in Mississippi] have fair and partially

supporting water quality? This means that the waters support a limited number of

...sh species and occasionally the water quality interferes with human activities in or

near the water?”

Eight versions of the survey instrument were developed, based on a factorial design of four

prices and two levels of nonpoint pollution reduction.

7 The baseline 10% reduction in

nonpoint pollution was obtained from the simulation model, while a 20% reduction was used

to formulate a statistical scope test of the robustness of the survey instrument design.

6
Respondents were asked to vote for a bid-runo¤ pair consisting of a one-time tax payment and

a runo¤ reduction percentage (e.g., $50 tax and 10% runo¤ reduction, $50 tax and 20% runo¤

reduction, etc.). Respondents were told the program would be nationally implemented, and

the tax would be added to their federal tax return the following year.

8

To obtain a realistic tax payment ...gure, the cost of precision agricultural application

equipment was calculated based on 3 prices–$10,000, $15,000 and $20,000 per equipment

package–for all farms in the United States.

9 The total number of VRT packages required to

implement the program in the U.S. was calculated to arrive at the total cost of the program

under each of the three prices.

10 The resulting cost was then divided by the number of

individual tax returns ...led in the United States in 1998. A one-time tax price for the

program was estimated to range from approximately $27 to $76 per taxpayer, providing a

basis for the referendum prices used in the survey ($25, $50, $100, and $150).

11

Referendum CV as used here has been the preferred format to elicit WTP for public

programs since publication of the NOAA Blue Ribbon Panel report on contingent valuation

(Arrow et al.). In addition, Carson, Flores and Meade, and Carson, Groves and Machina

demonstrate that single-price CV minimizes incentive incompatibility, thereby reducing

strategic behavior. To improve estimates, the Blue Ribbon Panel proposed the inclusion of a

“Don’t Know” response to provide more information about WTP, but studies following such a

design have had mixed results. Carson et al. found that most survey participants who would

respond to the Don’t Know category would most likely vote against the program and should

be coded as “No” vote respondents. A split-sample study by Champ, Alberini and Correas

...nds that a model including a Don’t Know category, as in Wang, is not statistically superior

to the standard discrete choice model. In this study, the convention of coding Don’t Know as

7
No votes was followed.

Although explicitly modeling Don’t Know increases the number of censoring intervals,

which should improve estimates of willingness to pay, WTP remains fully censored. As an

alternative to a Don’t Know category, a follow-up question was included in the survey used in

this study. In the follow-up question, all No and Don’t Know vote respondents were asked if

they would pay any positive amount for the program. This follow-up question therefore

elicits non-censored responses from those not willing to pay anything, which should improve

precision of econometric estimates.

The survey was administered by trained enumerators using random-digit dialing of

Mississippi residents at the Telephone Survey Unit, Social Sciences Research Center,

Mississippi State University, during the ...rst two weeks of July 1999. A pretest of

approximately 10 randomly dialed respondents was used to evaluate respondents’

understanding of questions. To ensure a random sample of adults 18 years of age or older in a

household, respondents were limited to the adult who had most recently observed a

birthday–i.e. day and month–at the time the survey was administered. Of 1,048 total eligible

respondents, 828 completed the interview, representing a 79.0% completion rate.

Survey Results

The sample is comprised of 65.8% white and 29.8% black respondents, whereas the U.S.

Census estimates for Mississippi’s voting age population in 1999 was 65.7% white, and 33.3%

black. The average age of survey respondents is 45.4 years, while Census estimates the

average voting populace age as 45.3 years. Thus, the sample represents population age and

race quite well, with blacks slightly underrepresented. Classifying by respondents’ place of

8
residence, the sample consists of 8.6% farm or ranch respondents, 41.9% non-farm rural

respondents, and 46.5% urban respondents.

In general, respondents ranked government spending on environmental programs fairly

low, behind such programs as highway improvement, public education, crime prevention and

health care. However, about 45% of respondents felt too little was spent on preventing air

and water pollution, suggesting some concern for environmental issues.

Statistics regarding beliefs and attitudes about pollution are presented in Table 1.

Respondents exhibited little awareness of agriculture’s contribution to nonpoint pollution.

When asked to name which one or two sources were believed to contribute most to nonpoint

pollution, agricultural runo¤ from livestock was the second least mentioned (9.4%), and

agricultural runo¤ from crops was the third least mentioned (14.6%). However, most

respondents believed that agricultural pollution reduces biodiversity (69.2%), and a clear

majority felt that a national goal of protecting nature and preventing pollution would be at

least a somewhat important national goal. Finally, most respondents felt that technology

could be used in ways that are bene...cial to the environment while maintaining or increasing

standards of living.

Table 1 goes here

Of the 828 total respondents, 62.4% voted for the program to promote precision

application technology, and 24.3% voted against the program. 1.3.% of respondents voted

Don’t Know, and were subsequently coded as No votes for the econometric analysis. Referring

to Table 2, the highest-ranked reason for a Yes vote is ‘protecting the environment for human

health’ (81.4%), and the second highest-ranked reason is to help farmers (7.74%), while the

9
most commonly cited reason for a No vote is ‘we already pay too much in taxes’ (45.8%).

Table 2 goes here

Scope Test and Econometric Analysis

Before proceeding with econometric analysis, a nonparametric scope test was employed to

detect so-called part-whole bias that results from a variety of sources, particularly improper

speci...cation of program bene...ts. Part-whole bias arises when respondents value a larger or

smaller entity than intended by the researcher (Mitchell and Carson, page 237) . In the

context of the current study, part-whole bias would occur if program bene...ts were misspeci...ed

and respondents then voted for the reduction of nonpoint pollution regardless of the amount of

reduction o¤ered by the program.

Because the survey was administered via telephone, scope was of particular concern.

Researchers have found a number of reasons for scope failure, and telephone surveys that do

not provide visual inputs to the survey process have been suspected in some cases (see, for

example, McFadden). Other researchers, such as Whitehead, Haab and Huang have

concluded that scope e¤ects may be measured in telephone surveys with a properly designed

survey instrument. The following test results provide further evidence that scope can be

detected in telephone surveys.

The abatement level–or nonpoint pollution reduction–variable (ABATE_LVL) provides a

basis for an external scope test.

12

Initial tests are based on di¤erences in nonparametric

estimates of mean WTP calculated from the Kaplan-Meier estimator (Mead) to validate

respondent understanding of potential water quality bene...ts. The tests are based on

univariate analysis of the response to the WTP question: Yes votes were coded as 1, while No

10
and Don’t Know votes were coded as 0. The responses from di¤erent survey versions were

then pooled to determine the proportion of respondents in each with WTP greater than the

tax prices.

13

If respondents are consistent in demonstrating higher WTP for 20% as opposed

to 10% abatement levels from the nonparametric means, then the di¤erence between estimated

WTP should be signi...cantly di¤erent.

Table 3 presents the results of univariate nonparametric estimates based on the response

to the WTP question. Respondents at all price levels were randomly assigned 10% and 20%

abatement levels and the data were then segmented into subsamples based on the two levels.

Following Hosmer and Lemeshow (pp 53-60), nonparametric estimates of means and variances

of WTP for each subsample were used to calculate a log-rank test of the di¤erence in

distribution of means. In the test of response to the WTP question, the mean estimate is

$46.97 for the 10% abatement level and $49.94 for the 20% abatement level. The null

hypothesis is that there is no di¤erence in distribution of mean WTP for di¤erent abatement

levels versus the alternative that WTP for 20% abatement is signi...cantly greater than WTP

for 10% abatement. The value of the log-rank statistic suggests the di¤erence in distribution

of means (i.e. di¤erence in mean at each price level) is signi...cant at the 99% level (Chi-Sq1=

48.5).

Table 3 goes here

It is important to note that the ABATE_LVL variable is derived from background

information prefacing the WTP question:

“Next, we want your opinion on a new program that reduces agriculturally related

nonpoint source pollution. This program uses a new technology, which would require

11
individual farmers to make a signi...cant investment in new farm equipment. The

equipment would reduce chemicals such as fertilizers and pesticides applied on crops. As

a result, nonpoint source pollution going into streams, lakes and rivers should decrease

over time.

By using this new technology, scientists predict a XX% (where XX% is randomly

assigned to be 10% or 20%) decrease in total chemical runo¤. Because these predictions

are uncertain, in the near future the program may realistically only prevent water bodies

from becoming more polluted.”

There are two ways in which the above could be interpreted by respondents. First,

respondents might believe that the runo¤ reductions will actually improve water quality by

some unknown amount. Or they may believe the program will provide no improvement in

water quality. If a subset of respondents interprets the statement to mean that there will be

no program bene...t, then there should be no sensitivity to scope among that group. The fact

that WTP estimated from the nonparametric mean passes the scope test implies that the

proportion of residents who believed that the program would provide bene...ts was greater than

the proportion that did not. This suggests that the survey was successful in explaining the

program bene...ts of incremental water quality improvement to most respondents.

The econometric analysis employs a ...rst-price probit model that censors WTP at

threshold values, but allows for uncensored values for individuals who state a zero WTP on the

follow-up question. Because the survey used a hypothetical tax referendum format, we have

only the information that WTPi  ̧ ¿ j for individuals who vote Yes, where ¿ j (¿ j = $25; $50;

$100 or $150) represents the tax price posed to the i

th respondent. Thus, we can only model

the probability of a Yes vote as Pr(WTPi  ̧ ¿ j ) or 1 ¡ Pr(WTPi < ¿ j ). In the standard ...rst

price model in which votes on only the tax price are obtained, a sample consists of two

censored groups. The ...rst of these groups, for which Pr(WTPi  ̧ ¿ j ), has WTP values that

are censored over the interval [¿ j;1); while the second group, for which Pr(WT Pi < ¿ j ), is

12
censored over the interval (¡1; ¿ j ). The information from the follow-up question is used to

limit the WT P probability interval for No voters to Pr(0 < WT Pi < ¿ j ); and therefore our

data are censored over (0; ¿ j ) rather than (¡1; ¿ j ), with additional information on an

uncensored subgroup of respondents (i.e. those who vote such that WTP = 0).

The additional information obtained through the follow-up question can be used to re...ne

the estimated distribution of willingness to pay. Voting patterns of respondents can be

broken out accordingly: (i) respondents who vote No on the tax price and state zero WTP in

the follow up question fall into group I1; (ii) respondents who vote No on the tax price but

would be willing to pay some positive amount fall into group I2; and (iii) those who vote Yes

on the tax price fall into group I3. The resulting log-likelihood function is given by

(1) ln(L) =

P

i2I1

ln

1

3⁄4

Á

¡ ¡xi ̄

3⁄4

¢

+

P

i2I2

ln ©

3

¿ j¡xi ̄

3⁄4

 ́

+

P

i2I3

ln

n

1 ¡ ©

3

¿j ¡xi ̄

3⁄4

 ́o

;

where Á and © represent the p.d.f. and c.d.f. of the normal distribution, respectively. ¿ j is

once again the tax price posed to an individual, xi represents a row vector of explanatory

variables for the i

th

respondent,  ̄ is a column vector of parameters to be estimated, and 3⁄4

represents the standard error. The model represented by (1) is called the partially censored

model (PCM), because one point of the distribution corresponding to individuals with zero

WTP (I1) is not censored. The results of this model are later compared those of the censored

probit, which is referred to as the fully censored model (FCM). The log-likelihood function

for the FCM is identical to Equation 1, except that the ...rst summation term for I1 is dropped.

The model described above is estimated to examine factors contributing to WTP.

Included in the model are variables to control for attitudes, such as beliefs about the impact of

agriculture on environmental quality, as well as socioeconomic and demographic variables and

13
a variable used to provide the basis for an econometric scope test. Table 4 lists the variables,

their de...nitions, and provides descriptive statistics.

Table 4 goes here

Discussion

The results of the econometric estimation discussed here are presented in Table 5, under

the heading ‘Partially Censored Model.’ The e¤ective sample size used in the estimation was

reduced from 828 to 762 because of item nonresponse across explanatory variables. Overall,

the parameter estimates of the partially censored model are statistically signi...cant, and the

estimate for ABATE_LVL is positive and signi...cant. The positive sign of ABATE_LVL

suggests that WTP increases with program bene...ts. This result can be taken as a validation

of the survey design in that information was conveyed in such a way that respondents were

sensitive to levels of bene...ts provided.

Table 5 goes here

As pointed out by Cameron and James, parameter estimates from censored logit and

probit models can be loosely interpreted as representing the marginal contribution of each

factor to WTP. An analogous interpretation applies to the model in its partially censored

form. The overall marginal contribution of the 20% abatement level includes the impact of

the interaction terms of ABATE_LVL with BLV_TECH and FARM (RB and RF in Table 4).

To estimate the impact, WTP was predicted twice using the econometric model, once with the

variable HI_ABATE set at 0 for each respondent, and once with HI_ABATE set at 1. The

14
di¤erence between the two WTP estimates was taken for each individual and averaged over

the sample, yielding a marginal contribution of $5.10 for the HI_ABATE variable. The

sample standard deviation for the combined variables was estimated at 23.01, suggesting that

abatement level is insigni...cant in the model. However, a likelihood ratio test can be used to

judge whether the coe¢cients for ABATE_LVL, RB and RF are jointly equal to zero. The

hypothesis is rejected at the 95% level (Â

2

(3) = 8.24), implying scope is found jointly for these

variables. Although the results on scope are mixed, they tend to solidify the results of the

nonparametric scope test, and accord with the ...ndings of Whitehead, Haab and Huang.

Indicator variables for those respondents who believe that agricultural practices cause

pollution (AG_POL), for those who believe that agricultural practices reduce biodiversity

(AG_BIO), for those who believe that a national policy goal of improving the environment is

important (ENV_GOAL) and for those who had made a contribution to an environmental

cause (CONTR) in the previous 12 months contribute positively to WTP. These results

imply environmentally sensitive respondents have higher levels of support for the program

than others. The variable that contributes most to WTP (as measured by the marginal WTP

increase of $57.48) is the attitudinal variable BLV_TECH. This outcome is not surprising,

because voting for such a program should be consistent with the beliefs of those who claim

that technology can be used to improve the environment. It is interesting, however, that RB

has a negative e¤ect on WTP, which may mean that those who believe in technology will vote

for the program regardless of the level of nonpoint pollution abatement. The combined

marginal e¤ect for BLV_TECH and RB, estimated using econometric predictions is $41.51

(standard error=15.69), so impact of the belief in technology is still the largest of any variable

in the model even when the negative sign of RB is accounted for.

15
The parameter estimate of FARM is not signi...cant, but that of RF indicates farm

residents have a signi...cantly lower marginal WTP for the higher abatement level. This

suggests that farmers may perceive that the marginal cost to them of achieving the higher

(20%) abatement level would exceed the marginal bene...t. In addition, producers may feel

that a 20% level of abatement would either be too expensive to achieve, or an unrealistic

target. However, there may be another explanation. Bosch, Cook and Fuglie found that

although farmers are concerned with water quality, they tend not to believe that their own

farm contributes to the problem, and Lichtenberg and Lesley found that producers perceived

water quality problems at the state level to be worse than for their own land. Thus, farmers

may not understand how small amounts of nonpoint pollution at local levels accumulate into

large scale water quality problems.

Of the other variables, male respondents are willing to pay less than female respondents.

There is no expectation for males, and this result may not be comparable to surveys in other

geographic regions. Those who are employed either full or part time (WORK) have higher

WTP, which is consistent with ability to pay. However, those with the highest education

level, HI_ED, have WTP $19.04 less than average, which is possibly inconsistent with

expectations about ability to pay.

14 However, a similar result has been found in studies of

pesticide-free and organic foods.

In Table 5, the results of the partially censored probit model (PCM) represented by

Equation 2 are compared to the results of the fully censored probit model (FCM). Model

selection cannot be based on the log-likelihood functions, because the two models involve a

data transformation in terms of censoring intervals. In both models, the likelihood ratio test

for joint signi...cance of the parameter estimates is signi...cant at the 99+% level. However,

16
parameter estimates in the PCM are more precise than those of the FCM, as indicated by Â

2

statistics.

The information gained by inclusion of a follow-up question asking if a respondent who

votes No is willing to pay anything appears to be signi...cant. In the PCM, the mean WTP

generated using noncensored values when WTP = 0 is $114.98, whereas the mean WTP

estimate using the FCM as in Cameron and James is $216.25. The result is that the PCM

predicts mean WTP that is 137% higher than that of the nonparametric estimate of $48.46,

while the FCM estimate is 346% higher than the nonparametric estimate.

Figure 1 plots estimated WTP from the partially and fully censored model for each

individual in the survey. It can be seen that WTP estimates from the partially censored

model demonstrate considerably less variability in the range of estimates, and perhaps more

importantly, relatively few individual estimates fall outside the range of the bid vector

($0 ¡ $150) as compared to the fully censored model. Speci...cally, 11.8% of WTP estimates

in the FCM model fall below $0, and 67.6% fall above $150. In comparison, only 0.5% of the

PCM estimates fall below and 18.5% fall above the $0-$150 bounds. This result suggests that

not only are the parameter estimates more precise in the PCM, but that the WTP estimates

are as well.

In addition, di¤erences in the magnitude of the parameter estimates and WTP estimates

between the PCM and FCM suggest the PCM may be superior. For example, if one

interprets the coe¢cient estimates as marginal e¤ects, the FCM places about 4.5 times the

weight on certain factors (e.g. ABATE_LVL) than does the PCM, while placing nearly 9

times the weight on other factors (e.g. RF). The di¤erence in relative contribution between

the two models can lead to qualitatively di¤erent inferences. From a policy perspective,

17
misplaced emphasis of such factors may be misleading. In a bene...t-cost analysis, WTP from

the FCM has the potential to seriously overestimate program bene...ts in relation to WTP from

the PCM. Haab and McConnell provide a thorough study of di¤erent survey design and

econometric speci...cations to estimate referendum data, distinguishing between the stage in

which parameters are estimated and that in which WTP is calculated; it appears that our

model performs well both in precision of estimated parameters and WTP.

One intuitive explanation of why WTP estimated by the PCM is lower than that of the

fully censored model is that the FCM may overestimate the probability that a No vote is

greater than zero; such an e¤ect may be exacerbated when the percentage of Yes votes in a

survey is high. It is worth noting that in our sample, of those voting No on the WTP

question, only 41.6% would be willing to pay any positive amount. Because the sample

probability of Yes votes based on univariate analysis ranged from 0.61 to 0.71 depending on

tax price, it is possible that the FCM overestimates the probability of a Yes vote for censored

WTP values.

A second possibility why WTP estimated by the PCM is lower than from the FCM is

related to potential bias introduced by follow-up questions or by the inclusion of a quasi-open

ended question allowing for zero stated WTP. With respect to double-bounded follow-up

questions, Carson, Flores and Meade and Alberini, Kanninen and Carson point out that the

existence of second prices may cause preferences to shift. Cameron and Quiggin ...nd that

WTP estimates using follow-up questions are lower than those based on ...rst-price referenda

only. Cameron and Quiggin suggest the reason for these results is that the observed

probability of a Yes on the follow-up is lower than what would be expected based on the

probabilities generated by the ...rst vote. Furthermore, downward bias can be introduced by

18
the follow up question because the possibility of zero WTP allows for ‘protest’ votes (Green et

al.).

To address these questions, we investigated the zero WTP votes, and compared them to

the entire sample. The ...ndings revealed that among the No votes, males had a signi...cantly

higher percentage of zero WTP than would be expected by the probabilities in the ...rst price

(64.5% vs 42.4%). Individuals whose follow-up responses revealed an unwillingness to pay

more taxes were also overrepresented. However, ...nancial factors may have also played an

important role, in that the percentage of those unemployed in the zero WTP group was much

higher than those voting No in the overall sample (54% vs. 33%), suggesting that some of the

zero WTP responses may have been due to inability to pay. Nonetheless, there does appear

to be evidence suggesting existence of a substantial portion of protest votes.

To ...nd out the potential e¤ect of protest votes on the model outcome, 59 respondents

from the zero WTP group were randomly assigned to the category 0 < WT P < Tax P rice so

that the percentage of zero WTP among the No votes was just slightly higher than the

...rst-price distribution of No votes. The partially censored model was rerun, producing the

result that mean WTP increases by only about $4 to $118.24. Once again, the estimated

WTP stayed primarily within the bounds of the bid vector, demonstrating that even when

protest votes are taken into account, the partially censored model may be superior to the fully

censored model.

Conclusions

The ...ndings of this study have important implications for programs to promote

environmentally friendly agricultural practices. In particular, even though Mississippi is poor

19
and rural, our study demonstrates signi...cant public support exists for a program to reduce

nonpoint pollution from agriculture. Although a national program would not necessarily

require each state’s tax revenues to match its program expenditures, it is useful to demonstrate

the viability of a VRT adoption program based only on the Mississippi data used in our survey.

Mississippi had 5,947,311 crop acres under cultivation in 1998. If it is assumed that one VRT

unit can e¤ectively service 1,000 acres per year, the total cost to implement this program in

Mississippi would range between $59 million and $119 million depending on the assumed price

of the technology. There were 1,066,156 individual tax returns ...led in Mississippi in 1998

(Mississippi State Tax Commission), resulting in estimated tax revenues for the VRT adoption

program ranging from $52 million, based on the nonparametric mean WTP, and $122 million

based on mean WTP from the econometric model. Thus, potential tax revenues should be

su¢cient to cover a substantial portion of the program’s cost. Because a subsidy would not

be expected to cover 100% of adoption costs, our analysis suggests that public WTP could

provide a sizeable incentive for producers to adopt precision application technology.

Mississippi is a rural state and residents may be more inclined to support programs

perceived to bene...t farmers. However, ‘helping farmers’ did not appear to be the primary

motivation for voting for the program as measured by the survey instrument. Nevertheless,

these results should be viewed with caution, as they may not generalize to other regions of the

country. For a complete policy analysis, it would be necessary to replicate the current study

at national level.

One caveat with respect to any subsidy program for adopting technology is that, although

environmental bene...ts may be gained at the intensive margin, increased production at the

extensive margin may actually increase overall pollution (National Research Council) so that

20
subsidies alone might be counterproductive. However, Khanna and Zilberman found that

site-speci...c management practices may not be pro...table if combined with a tax. We would

thus expect that a subsidy program would be most successful as part of a package of policies

that include standards such as TMDLs.

From a methodological perspective, the value of adding a follow-up question for zero

WTP in the survey instrument and of econometrically modeling zero WTP as noncensored

responses is illustrated. The mean WTP estimates derived from this method are nearly half

those obtained from the censored probit model, and are closer in magnitude to WTP measures

derived from nonparametric methods. Furthermore, there are e¢ciency gains in the

parameter estimates of the econometric model and, more importantly, in the predicted WTP

estimates. Another important ...nding is that the type of follow-up question used to generate

the model does not cause signi...cant bias from shifting of preferences or from protest votes,

implying that the PCM method can improve WTP estimates while maintaining the desirable

incentive compatibility characteristics of single-price referendum CV.

21
Footnotes

1The Gulf of Mexico hypoxic zone is an area about the size of Massachusetts where oxygen levels are

below those necessary to sustain most animal life. The quality of sports and commercial ...sheries is

adversely a¤ected by hypoxia.

2Model results from NOAA suggest that 20-30% reductions in nutrient loads would result in 3-15%

reductions in surface chlorophyll concentrations and 15-50% increases in bottom-water dissolved

oxygen concentrations in the hypoxic zone.

3However, because of the scope of the problem, the hypothetical program proposed in this study was

framed from a national perspective. That is, the survey participants were told that the program

would bene...t all U.S. farmers and be paid for by all U.S. citizens.

4Nonpoint pollution also has a substantial impact on groundwater degradation in parts of the US.

However, groundwater contamination is not a major issue in Mississippi because of its geological

characteristics.

5EPIC simulates biophysical systems processes over long periods of time, based on soil, climate, and

cropping practices. Outputs of the model include crop yields and edge-of-...eld environmental

parameters associated with various agricultural practices.

6The single rates used in this study were obtained from producers’ planning budgets of major crops

in the Delta, Mississippi’s primary agricultural production region. Prescription variable application

rates for di¤erent soil types and conditions were obtained through consultation with an expert on soil

chemistry from the Plant and Soil Science Department, Mississippi State University (personal

communication, James L. Oldham; details available upon request).

7The survey instrument is available from the authors on request.

8Because the program was assumed to be implemented nationally, federal tax returns were used as

the payment vehicle. It is a shortcoming of the instrument design that we did not clarify to

individuals receiving federal tax refunds that the program would reduce the amount refunded.

9These price levels were obtained from GPS, Inc., Inverness, MS, for three di¤erent packages of

equipment. Price di¤erences were a result of di¤erent levels of sophistication of GPS receivers,

computer hardware, and VRT controllers.

10

It was assumed that one sprayer could e¤ectively service 1,000 crop acres, and would be used to

cover those 1,000 acres three to ...ve times a year. Total cropland acreage in the U.S. was then

divided by 1,000 to arrive at the necessary number of VRT packages, which was then multiplied by

the assumed price per package. The program was assumed to be a one-time subsidy to speed the

rate of adoption of technology, and as such, equipment depreciation was not taken into account in the

calculation of the tax payment.

22
11

It is a fairly common practice in CV to obtain bid vectors from focus groups or pretesting. In this

case, we had the advantage of knowing a target tax price–not unlike a school levy referendum, for

example–that could be used to obtain a bid vector.

12An external scope test, as de...ned by Carson, Flores and Meade (2000) is based on split samples

with a di¤erent bene...t level posed to each sample.

13The observations from the di¤erent questionnaire versions can be pooled because the samples were

randomly selected. It is then assumed that any respondent who received a questionnaire with a tax

price of $50 and who exhibited WTP>$50 would also have WTP>$25.

14Education was used as a proxy for income, due to high item nonresponse for income (13.3%).

Furthermore, a likelihood ratio test based on the subset of the sample responding to the income

question showed that a model including only education was superior to a model using only income

(99% signi...cance level).

23
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28
Table 1: Survey Responses Concerning Nonpoint Pollution

a

Believe primary cause of nonpoint pollution is...

...discharge of factory waste 51.3%

...sewage from cities and towns 41.9%

....leaking garbage dumps 39.4%

...agricultural runo¤ from crops 14.6%

...agricultural runo¤ from livestock 9.4%

...runo¤ from roads and highways 8.8%

Is a national goal of protecting nature and

preventing pollution

Very Important?

Somewhat Important?

Not at all Important?

58.8%

36.8%

3.5%

Can technology be used to achieve

a cleaner environment while promoting

an increasingly good standard of living?

Agree

Neutral

Disagree

80.2%

10.0%

4.6%

Do you believe that agricultural pollution

causes reduced biodiversity?

Yes

No

69.2%

18.7%

a Sample size is 828

29
Table 2: Respondents’ Reasons for Yes and No Votes to WTP Question

a

Yes %

To protect the environment for human health 81.4

To help farmers 7.7

The cost of the program is low compared to the bene...ts 3.9

To protect the environment for biodiversity 2.7

To protect the environment for uses like hunting and ...shing 2.1

To protect the environment for uses like swimming and boating 0.2

None of these, some other reason 1.2

Don’t know 0.8

No %

We already pay too much in taxes 45.8

I don’t want government involvement 15.9

I don’t believe the program will help the environment 14.9

The program costs too much 9.4

None of these–some other reason 13.4

Don’t know 0.5

a Sample size is 828

30
Table 3: Kaplan-Meier Nonparametric Estimates of Mean WTPa;b

Pooled Sample

Price # WTP ̧Price

c Pr(WTP ̧Price)

$0 828 1.00

$25 517 0.62

$50 380 0.46

$100 241 0.29

$150 113 0.14

Mean WTP $48.46

Variance WTP 3.37

10% Subsample 20% Subsample

Price # WTP ̧Price Pr(WTP ̧Price) # WTP ̧Price Pr(WTP ̧Price)

$0 412 1.00 416 1.00

$25 253 0.61 264 0.63

$50 183 0.44 197 0.47

$100 115 0.28 126 0.30

$150 54 0.13 59 0.14

Mean WTP $46.97 $49.94

Variance WTP 6.67 6.83

Log-Rank Test Â

2

(1) = 48:46

a Sample size is 828

b Number of respondents to each questionnaire version are: $25, 10%=104; $25, 20%=105; $50,

10%=104; $50, 20%=104; $100, 10%=101; $100, 20%=104; $150, 10%=103; and $150, 20%=105.

c # WTP ̧Price represents the total number of Yes votes over all questionnaire price levels (c.f.

footnote 14).

31
Table 4: Descriptive Statistics and De...nition of Variables used in Analysis

a

Variable Name Description Mean Std. Error

ABATE_LVL Abatement level, (0=10%,1=20%) 0.51 0.50

AG_POL Believe ag. practices result in

nonpoint pollution (0=No, 1=Yes) 0.68 0.47

AG_BIO Believe ag. practices reduce biodiversity (0=No, 1=Yes) 0.69 0.46

BLV_TECH Technology can be used to achieve a

cleaner environment (0=No, 1=Yes) 0.79 0.40

FARM Respondent is farm resident(0=No, 1=Yes) 0.09 0.29

ENV_GOAL Protection of environment/prevention of

pollution very important (0=No, 1=Yes) 0.59 0.49

WORK Respondent is employed (0=No, 1=Yes) 0.61 0.49

HI_ED Respondent education level

Associate degree or higher (0=No, 1=Yes) 0.17 0.38

CONTR Respondent contributed to

environmental cause in last year (0=No, 1=Yes) 0.24 0.43

MALE Male Respondent (0=No, 1=Yes) 0.35 0.48

RB Interaction term ABATE_LVL*BLV_TECH 0.40 0.49

RF Interaction term ABATE_LVL*Farm resident 0.05 0.22

COST Tax Price ($25, 50, 100, 150) 81.33 48.14

HOWVOTE Response to referendum (0=No vote, 1=Yes Vote) 0.63 0.48

PAYANYTX Respondent not WTP proposed tax,

but WTP some positive amount (0=No, 1=Yes) 0.04 0.20

a Sample size is 762, re‡ecting item nonresponse for variables used in the estimation.

32
Table 5: Estimation Results–Dependent Variable is Willingness to Pay

Partially Censored Model

Variable Estimate Std. Err. Â

2

Intercept 8.74 14.87 0.34

ABATE_LVL 36.23 17.04 4.95 **

AG_POL 29.41 9.37 12.94 ***

AG_BIO 22.93 9.33 6.19 **

BLV_TECH 57.48 13.91 16.27 ***

FARM 15.27 24.49 0.53

ENV_GOAL 29.82 8.66 13.88 ***

WORK 16.12 8.70 5.46 *

HI_ED -19.04 11.52 2.46 *

CONTR 17.24 10.49 3.89 *

MALE -21.70 8.82 7.45 **

RB -31.37 19.27 2.73 *

RF -68.34 30.16 5.39 **

3⁄4 94.09 4.47

Sample size=762 Ln(L) ¡1946:89 LR T est Â

2

13

=110.45

¤¤¤

Fully Censored Model

Estimate Std. Err. Â

2

-253.91 138.39 3.37 *

167.31 97.83 2.92 *

133.56 62.21 4.61 **

88.40 49.44 3.20 *

249.07 110.57 5.07 **

84.99 107.06 0.63

136.25 60.498 5.07 **

69.15 44.93 2.46

-60.95 46.15 1.33

85.95 53.33 2.60

-93.92 50.59 3.45 *

-140.90 100.16 1.98

-334.43 174.30 3.68 *

360.85 131.47

Ln(L) ¡449:68 LR T est Â

2

13

=97.01

¤¤¤

¤

Signi... cant at 0.90;

¤¤

Signi...c ant at 95% ;

¤¤¤

S ign i...c an t at 99%

33
Figure 1: Plot of Predicted WTP from Fully Censored and Partially Censored Probit Models

34