the
Measuring
Nonuniform
Marginal
Cost
of
EnvironmentalRegulations
David L. Sunding
A method is presented for measuring the marginal welfare cost of environmental
regulations affecting agriculture. The method incorporates output market effects and
recognizes diversity in production conditions among crops, regions, and seasons. An
important advantage of the method is that only regional outputs and changes in
regional production costs are needed to calculate deadweight loss, thus simplifying
the measurement of welfare changes. This feature of the model is significant since the
complexity and substantial data requirements of most existing impact models cause
many environmental regulations to be enacted with inadequate analysis of their
economic impacts. The method also disaggregates welfare impacts by crop, place, and
time, thus encouraging the implementation of nonuniform interventions that achieve a
given level of environmental quality more efficiently than uniform policies.
Key words: environmental regulations, welfare analysis, microparameter models,
distributional impacts.
Regulations intended to improve environmental
quality often entail changes in agricultural production processes. Policies to improve water
quality, ensure worker safety, maintain soil
quality,enhance wetlands, and protectendangered
species frequentlyrequireadjustmentsin farming
practices. In this paper I develop a method for
calculating the marginal welfare costs of environmental regulations affecting agriculture.
To the extent that environmental regulations
imply changes in farming practices, there is a
natural tradeoff between environmental quality
and production costs. Requiring growers to reduce pesticide applications, for example, typically reduces yields and may increase per acre
production costs as growers adopt other pest
control methods such as integrated pest management. The result of reducing pesticide application, then, is to increase the marginal cost of
production.
Agriculture differs from many other industries in that farming is highly dependent on the
physical and biological environment. Because
David L. Sunding is senior economist with the Council of Economic Advisers, Executive Office of the President.
This research was supported by a grant from the Office of Pesticide Consultation and Analysis of the California Department of
Food and Agriculture. The author acknowledges helpful comments
from two anonymous referees, David Zilberman, Doug Parker,
Jerry Siebert, Adolfo Gallo, Steve Shaeffer, and participants in
seminars at UC Riverside and UC Santa Barbara.
environmental conditions vary widely among
regions, agricultural production processes also
differ among regions. Factors such as soil quality, drainage conditions, water availability, and
pest control problems partially determine the
methods and costs of production. Thus, the effects of environmental regulations on agriculture will most likely vary among regions, and,
as emphasized by Zilberman et al. and
Lichtenberg, Parker, and Zilberman, environmental regulations frequently have significant
distributional impacts.
Agriculture is also highly dependent on dynamic conditions such as temperature, humidity, rainfall, and pest population growth rates.
When calculating the cost of environmental
regulations of agriculture, it is also importantto
temporally disaggregate the impact of the regulations as opposed to performing an annual
analysis. There are several examples of timedependent environmental regulation in agriculture, which are discussed below, and many
more cases where varying regulations by season would reduce the welfare costs of improving environmental quality.
Disaggregating the marginal costs of environmental regulations encourages policy makers to
design and implement more flexible, nonuniform policies that tailor regulations to individual crops, regions, and seasons. This point is
emphasized in the Lichtenberg, Spear, and
Amer. J. Agr. Econ. 78 (November 1996): 1098-1107
Copyright 1996 American Agricultural Economics Association
Sunding
Marginal Cost of Environmental Regulations
1099
Zilberman analysis of re-entry intervals follow- Act, growers of a particular crop in a particular
ing pesticide applications that calculates first- region can request a Section 18 exemption from
best, region- and crop-specific regulations. The registration requirements in case of extreme
Lichtenberg, Zilberman, and Bogen study of need, usually defined as large profit losses redrinking water contamination also suggests that sulting from the absence of alternative controls.
different water quality standards should be de- Chemical bans and the taking of arable land for
veloped for urban and rural areas as a result of critical habitat protection for endangered spethe significant economies of scale in urban wa- cies are also done on a regional basis.
ter treatment.
Physical information is being used to develop
A method is presented for measuring the mar- localized environmental policies affecting agriginal costs of nonuniform environmental regu- culture. For example, the state of California is
lations that recognizes differences in produc- currently banning the use of pesticides likely to
tion conditions among crops, regions, and sea- leach into groundwater in certain Pesticide
sons. There have been several attempts to de- Management Zones (PMZ). Growers operating
velop methods for measuring marginal costs of within these areas are denied access to these
environmental regulations affecting agriculture, chemicals through the registration process,
most notably Lichtenberg, Parker, and wherein growers must file for permission to use
Zilberman. This paper extends the existing lit- certain agricultural chemicals at the time of
erature by explicitly considering temporal as purchase. The state has developed a Geographic
well as spatial diversity, thus facilitating the de- Information System (GIS) that enables the persign of environmental regulations that are sea- mit issuer to tell whether the grower's field is
son- and region-specific. This modification is within a PMZ and act accordingly.A similar proespecially important in markets for perishable gram is being developed for the Corn Belt by
the U.S. Environmental Protection Agency to
commodities that have widely fluctuating
control nutrient contamination of groundwater.
over
time.
shares
and
market
prices, quantities,
The formal analysis in the next section reFinally, there are a small number of current
sults in an equation characterizing marginal environmental regulations that are seasonal.
welfare impacts for each crop and season com- For example, some re-entry and preharvest inbination as a weighted average of the changes tervals after pesticide applications vary by seain regional marginal production costs. This son as foliar residue decay rates depend on
theoretical result is appealing on practical
temperature, humidity, and rainfall. Making
more environmental regulations season depengrounds because the method requires only
the
dent can have significant welfare benefits. In
to
assess
information
obtainable
readily
of
environmental
costs
fact, in the empirical example of a pesticide
regulations.
marginal
Further, the welfare impacts can be calculated cancellation presented below, seasonal differwith a spreadsheet, thus making the method ences in the marginal welfare impacts of canlow-cost and accessible to noneconomist policy cellation within a region are as large as the
makers. Econometric measures of demand and variation between regions in a given season.
The formal analysis, presented in the next
supply elasticities, which are difficult to estimate and interpret, are only needed to partition section, culminates in an expression for the
the total welfare losses into consumer and pro- marginal welfare costs of environmental reguladucer surplus changes; the aggregate welfare tion. The method is then used to calculate the
loss does not depend on these elasticities. The marginal costs of banning the pesticide,
welfare loss expression developed in the next mevinphos. State and federal agencies are insection also is shown to be a close approxima- vestigating whether this organophosphate intion to true welfare loss in an important class of secticide poses unreasonable risks to farm
workers and consumers, particularly infants
production models.
The practical value of the model developed in (State of California), and the state of California
this paper depends on the ability of regulatory has recently taken steps to ban its use in the
agencies to enforce crop, region, and time-spe- state entirely. Measures of the marginal welfare
cific environmental regulations. Nonuniform costs of banning mevinphos in California,
regulations are feasible, as the following para- based on information from four vegetable crops
graphs demonstrate. Nationally, under the Fed- grown in various parts of the state and elseeral Insecticide, Fungicide and Rodenticide
where, are presented.
1100
November 1996
Amer. J. Agr. Econ.
Marginal Welfare Costs of Environmental
Regulation
P
(5)
iel
Economic welfare is defined here as the
unweighted sum of producer and consumer surplus; the impacts described here are gross welfare changes from regulation since the analysis
does not quantify the benefits of regulation
such as increased levels of human and environmental health. Denote the level of production
of some crop in region i in period t by qit and
the market price in period t by the inverse demand function p,(q,), where q, is total production of I regions at time t. The cost of production in region i in period t is denoted by the
continuous and differentiable function ci,(qi,,
git), where git indexes environmental regulation
in region i and period t. Suppose that the production technology is characterized by
,
it)
acit (qit,
aqit
where Eitis the elasticity of supply in region i in
time t, and rt,is the elasticity of demand in time
t. The system of I + 1 equations can be solved
to obtain marginal changes in regional production and market price, dq,, and dp,. Note that
equations (4) and (5) can accommodate any
type of shift in regional supply curves and that
we have made no assumption about the functional form of regional marginal cost curves
other than eliminating increasing returns to size
and assuming continuity and differentiability.
Expressions for the change in producer and
consumer surplus follow naturally from the calculated marginal changes in quantities and
prices. Consumer surplus in period t is given as
a,
a2Cit(qit, git )
>0; >0. _
dqit - dpt = 0 Vt
tqit
(6)
aqit2
CS, = Jq(p,)dp,
Pt
Thus, technology in each region exhibits constant or decreasing returns to size.
Consider a perfectly competitive output market. The period t profit of region i is given as
(1)
where p, is a variable of integration and a, is the
vertical intercept of the demand curve. The
marginal change in consumer welfare is then
dCS, = -qdp,.
Producer surplus in region i and period t is
given as
it = P,qit - cit(qit, Jit).
The first-order condition for profit maximization is
qa7
(7)
PS,, = p,q, - JMCi,(Oi,,
i)di,
0
(2)
acit (qi, [tit )
-
,
aqi
a,qit
t = O Vi,t
where 0, is a variable of integration. The marginal change is found using Leibniz's Rule as
which simply requires that price equal regional
marginal cost in each time period and region.
Market equilibrium is characterized by
q'aMCi(Oit.git)
aOit
o
(3)
p,(qt) - pt = 0 V t
-
where qt is total production in period t.
Totally differentiating equations (2) and (3),
and using the fact that competition implies marginal cost is equal to price, we have the system
4
(4)
P
- dP
,itqit
dq,
+
aMCi,,(q,,,
d)di
t
_
0Vit
it)
t
- dpt = 0 Vi, t
i
MCi, (i,
[it)dqi,
which simplifies to
(8)
aMC,, (qit,
dPSit = qi,dp, -
it )dqit
agit
In regions that are not directly affected by environmental regulation, this expression reduces to
q,dp,. These growers should gain from the
Marginal Cost of Environmental Regulations
Sunding
regulation to the extent that it raises marginal
costs in competing regions and increases output
price as a result. Expression (8) can also be interpreted in terms of changes in quasi-rent: the
first term is simply the change in farm revenue
resulting from the regulation, while the second
term is the change in production costs.
The gross marginal welfare cost of a change
in environmental regulations is calculated by
summing the expressions for the change in consumer and regional producer surplus to obtain
(9)
(9)
(qit, tit)dqit q
q
i,.
_jI aMCit
ddW,
= = -,"-,
iEl
agit
The marginal change in social welfare in each
time period is thus equal to a weighted average
of changes in regional marginal costs of production, with weights given by preregulation
output levels. Expression (9) is highly intuitive:
the marginal cost of environmental regulation is
equal to the increase in the value of inputs to
agricultural production. This expression also
shows that marginal welfare loss is separable in
the change in regional marginal production
costs and separable over time, a feature that
greatly simplifies the computation of impacts.
Expression (9) is significant for policy analysis since it implies that decision makers can assess the total marginal welfare costs (i.e., the
marginal effects on consumer and producer surplus) of environmental regulation with information that is simple to obtain. Lichtenberg,
Parker,and Zilberman show that when marginal
production costs for each region are equal to
average costs, as in case of the step-function
supply curve, the change in regional marginal
cost at time t is equal to
( MC,it(qit,git)
ait
Ptit
+ (dCit/it)
1i,t
where Vritis the percent change in regional
yield, Yitis regional yield, and dCitis the change
in per acre production costs, all at time t. While
there may be a large number of regions and
time periods considered [in fact this is preferred as it increases the accuracy of equation
(9), as discussed later], each individual datum
in equation (10) is readily available. Prices, regional outputs, and yields are published by
state and federal sources. Changes in per acre
yield and production cost can be obtained from
noneconomists via survey methods, which we
discuss in more detail below. Thus, demand and
1101
supply elasticities are not required to assess the
marginal welfare impacts of environmental
regulation; elasticities are needed only to decompose aggregate deadweight loss into producer and consumer surplus components. The
simplicity of the method developed here is an
important practical advantage over theoretically
more exact, but also more complicated, procedures. Currently, many environmental regulations affecting growers are enacted with no
economic analysis of their impacts. This lack of
analysis is due in part to the complexity of
many existing agricultural impact models, and
to the fact that the basic data used in these
models, especially supply and demand elasticities, are difficult to obtain.
Equation (9) is a first-order approximation to
the change in deadweight loss and thus is accurate only for small changes in marginal production costs. For large changes, it may be necessary to solve a system of supply and demand
equations directly and compute new equilibrium
prices following imposition of the environmental
regulation.While more elaborate methods are required to assess the impacts of quantumchanges
in environmental regulations, such as massive
pesticide cancellations (e.g., Chambers and
Lichtenberg), such regulations are the exception rather than the rule. Indeed, environmental
regulations affecting agriculture are increasingly promulgated at the state or local level.
There is an interesting connection between
the impact framework in equation (9) and the
"microparameter"or "putty-clay" class of production models of Hochman and Zilberman;
Berck and Helfand; Paris; and Moffit,
Zilberman, and Just. These authors have shown
that local von Leibig production functions are
consistent with Cobb-Douglas or other continuous aggregate production functions if there is
continuous variation among individual atomistic productionunits. In the microparameterframework with a finite number of individual production units, each with positive measure, aggregate
marginalcost is a step function, with the flat portion of each step given by constant marginalcost
in a particularproductionregion. Each region also
faces outputcapacity constraints due to a limited
natural resource base or the type of production
technology employed. In the finite microparameter case, the marginal change in welfare resulting from environmental regulation is approximated by equation (9) since variation in production conditions is completely captured by
inter-regional differences in marginal cost (provided, of course, that the underlying yield and
cost change estimates are accurately measured).
1102
Amer. J. Agr. Econ.
November 1996
$
p+dp
p
MCI +dMC1
qI
MC1
ql
q
Figure 1. Welfare loss in the microparameter case
Figure 1 further develops the welfare loss
calculations for the microparameter case. The
level of each step of the base supply curve is
constant marginal cost in a particular growing
region, and the width of the step is given by the
regional output capacity constraint. Changes in
marginal cost resulting from environmental
regulations alter the height of each step differently depending on the nonuniformity of the
regulation and regional production and environmental conditions; indeed, the regulation may
not change marginal cost at all in some regions.
The shaded area in figure 1 is the gross marginal welfare loss as given by equation (9),
which is the sum of the changes in regional
marginal production costs. Note that this analysis can incorporate any type of supply shift
(i.e., parallel, proportional, or more complicated types) as determined by the regional data.
There is some ex ante uncertainty about the
yield impacts of adopting alternative production technologies. Zilberman et al. suggest that
this uncertainty should be explicitly incorporated into impact analyses of environmental
regulations. Formally, they suggest treating the
percentage yield change in a particular region
and season, jit, as drawn from some density.
Given this density, it is straightforward to calculate the mean impacts of the regulation,
E(dW,), as well as the associated distribution of
marginal impacts. It is convenient to describe
the distributionof impacts in terms of confidence
levels, where a denotes the probability that the
impacts exceed some level. Formally, define
dWtaas the solution to prob(dWt> dW,a) = a.
In the empirical example below, dWt is calculated for several different confidence levels.
There are several possible sources for the
density of yield impacts of the regulation in a
particular crop, region, and season. The error
on statistical assessments of field trials is one
possible source, but this error may not represent actual farm conditions. Instead, it is preferable to conduct interviews with industry experts, including growers, pesticide dealers, university researchers, extension agents, and
chemical company representatives. The survey
responses are then used to calculate the distribution of impacts by a Monte Carlo method.
Yield change estimates for each crop, region,
and season are selected randomly from the set
of survey responses, and marginal welfare impacts are then calculated according to equation
(9). This procedure is repeated many times to
create a set of marginal welfare impacts. Finally, the set of impact estimates is used to obtain dW,a for various confidence levels.
Application to Mevinphos Regulation
In this section the method developed above is
applied to a specific problem:measuringthe marginal welfare costs of banning the mevinphos
Sunding
pesticide for vegetable production in California. Mevinphos (2-carbomethyoxy-l-methylvinyl dimethyl phosphate) is an insecticide-acaricide with contact and systemic activity that is
most commonly used by California growers to
control aphids on broccoli, cauliflower, head lettuce, and leaf lettuce. Mevinphos is used to eradicate aphids just prior to harvest so that growers
can meet the stringent U.S. Department of Agriculture quality standards.
The four crops considered here are produced
in four California regions: Imperial Valley,
Monterey, South Coast, and San Joaquin Valley.
Output from other domestic areas, especially
Arizona, Texas, and Florida, are aggregated
into an "other domestic" category. Monthly
crop prices and regional outputs are taken from
the Federal-State Market News Service; 1990 is
taken as the base year to avoid complications
caused by the silverleaf whitefly infestation
that began in 1991. Yields are taken from various University of California crop budgets from
the counties comprising the four California production regions, and mevinphos use percentages for the California regions are found in the
1991 State of California Pesticide Use Report.
The basis of the marginal welfare analysis,
equation (9), indicates that it is necessary to
compute the effects of the cancellation on marginal production costs separately for each crop,
region, and season. Equation(10) relates changes
in marginal cost to changes in per acre production costs and yields when growers adopt the
next-highest profit alternative to mevinphos.
Several alternative controls were considered:
dimethoate, diazinon, thiodan, imidacloprid
(available only to Imperial Valley growers under a Section 18 exemption), and pyrellin.
Telephone interviews were conducted with
fifty-six growers, pesticide dealers and applicators, extension advisers, commodity group representatives, produce packers and distributors,
and university researchers to assess the yield
effects from switching to each of the alternative
aphid controls. Interview subjects were asked
to give the countywide yield change resulting
from replacing mevinphos with alternative
aphid controls and specifically were asked to
assess actual changes rather than report the results of experiments on highly managed plots.
It is important to remember that the alternative
chemical controls listed above have existed for
many years, and survey respondents were generally familiar with the actual field performance of the alternatives. These survey re-
Marginal Cost of Environmental Regulations
1103
sponses are the basis for the Monte Carlo
analysis of welfare impacts described in the
previous section. Changes in per acre production costs are determined by calculating per
acre chemical expenditures for each of the alternatives (at standard application rates and
market prices) and subtracting this number
from the per acre cost of mevinphos application. Generally, changes in per acre cost are
empirically insignificant for the set of crops
considered here, since chemical cost is only a
small fraction of the crop budget.
Table 1 shows the highest-profit alternatives
to mevinphos for each of the crop, region, and
season combinations, and also reports the associated yield and per acre cost changes. The
highest-profit alternatives to mevinphos are
imidacloprid (all crops in Imperial), diazinon
(broccoli, cauliflower, and leaf lettuce in the remaining regions) and dimethoate (head lettuce
in regions other than Imperial). Expected yield
losses obtained from the industry survey vary
by season and by region; generally, expected
yield losses are higher in the Monterey and
South Coast areas and higher in the summer
months due to weather conditions favoring
aphid growth. Table 1 also gives sample standard deviations for the assessed yield changes.
Table 2 presents the marginal welfare costs of
banning mevinphos use in California, calculated using equations (9) and (10) and the data
in table 1. The expected marginalwelfare costs of
banning mevinphos are $53.3 million annually,as
compared to total annual revenues of $924 million for these four crops. Mean monthly deadweight losses, where the expectation is taken
over the sample distribution, vary widely over
the year according to market share among
growing regions and yield and cost changes,
thus underscoring the importance of disaggregating impacts over time as well as region.
The marginal welfare costs of a mevinphos
ban are highest for head lettuce since this is the
largest market considered and a significant
share of the nation's output comes from California. Mean losses are largest during MayJune and November-December, during which
time most head lettuce is produced in
California's Monterey and San Joaquin Valley
regions. It is also interesting to note that mean
losses are virtually zero during December-February, during which time nearly all output is
produced in the Imperial Valley and other domestic regions that do not rely on mevinphos.
Since disaggregating environmental regula-
1104
November 1996
Amer. J. Agr. Econ.
Table 1. Changes in Yield and Per Acre Cost
Region
Highest Profit
Alternative
Mean Percent
Change in Yielda
Per Acre
Cost Change ($)
Broccoli and Cauliflower
Imperial
Imidacloprid
Monterey
Diazinon
San Joaquin
Diazinon
South Coast
Diazinon
0
(1.73)
-10
(4.08)
-4
(1.41)
-8
(2.89)
50
-10
-10
-10
Head Lettuce
Imperial
Imidacloprid
0
(0.00)
50
Monterey
Winter
Dimethoate
-11
(4.91)
-20
(7.12)
-5
(5.00)
-10
Summer
Dimethoate
San Joaquin
Dimethoate
South Coast
Winter
Dimethoate
Summer
-9
(4.04)
-12
Dimethoate
-10
-10
-10
-10
(5.13)
Leaf Lettuce
Imperial
Imidacloprid
0
(0.00)
50
Monterey
Winter
Diazinon
-17
(7.53)
-28
(9.83)
-8
-10
Summer
San Joaquin
Diazinon
Diazinon
-10
-10
(3.54)
South Coast
Winter
Summer
a
Diazinon
Diazinon
-14
(4.78)
-18
(2.89)
-10
-10
Standard deviations are in parentheses.
tions along the lines suggested in this paper has
some cost implications for the administrative
agency designing and enforcing the rule, it is
importantto assess the social welfare value of a
disaggregated as opposed to a uniform regulation. The Lorenz Curve, which is often used to
represent inequality in the distribution of income, is a useful way of summarizing the ben-
efit from implementing nonuniform regulations.
Figure 2 is constructed by first calculating the
number of pounds of mevinphos applied and
the marginal welfare impact per pound applied
in each crop, region, and month. Next, the per
pound marginal welfare impacts are ranked in
ascending order, cumulated and plotted against
cumulative pounds applied.
Marginal Cost of Environmental Regulations
Sunding
1105
Table 2. Expected Marginal Welfare Impact of Banning Mevinphos ($ Thousands)
Broccoli
Cauliflower
Jan
253
100
31
144
528
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
257
420
791
785
549
566
641
737
836
901
727
155
384
653
637
621
329
378
405
500
567
351
2
194
2,952
4,552
3,910
628
5,327
7,178
4,325
5,522
225
94
125
328
622
408
580
766
1,052
629
1,065
72
508
1,123
4,724
6,596
5,488
2,103
7,112
9,372
6,290
8,055
1,375
7,463
5,080
34,846
5,885
53,274
Month
Total
100
HeadLettuce
LorenzCurve
--
Leaf Lettuce
Total
- - - 45
DegreeLie
80
_ _
%Expected
Deadweight
Loss
60
-O..,
'
.0 ,-- "
40
'
_
20
0
,_0.
~.,-
..0
'__
80
100
%Mevinphos
Use
Figure 2. Lorenz curve for expected marginal welfare loss
The heterogeneity in the gross marginal welfare impacts of banning mevinphos is depicted
by the size of the area below the 45? line and
above the Lorenz Curve in figure 2. Of course,
if all uses of the chemical generated the same
level of producer and consumer surplus, then
the Lorenz Curve would lie on the 45? line. The
size of the area above the Lorenz Curve and below the 45? line shows the welfare improvement from using nonuniform regulations as
compared to proportional use reductions that
treat each crop, region, and season similarly.
For example, suppose that regulators wish to
reduce mevinphos use by half. Nonuniform
regulations that target specific crop, region, and
season combinations can reduce welfare losses
by over 50% as compared to a uniform proportional reduction that ignores differences in marginal productivity.
This type of Lorenz Curve analysis can be
used to assess the benefits of many types of
nonuniform environmental regulations. For example, many economists have observed that
permit trading to reduce point and nonpoint
source emissions can lower the welfare costs of
improving air and water quality, as opposed to
programs that proportionally reduce emissions.
The method described here can measure the
benefits of decentralized, market-based regulations, such as permit trading, that efficiently allocate the burden of emissions reductions vis-avis proportional reduction. Other potential applications of this technique include measuring
the benefits of agricultural water trading and
conservation reserve programs.
The Monte Carlo simulation of gross welfare
impacts using the expert survey also shows the
importance of explicitly accounting for yield
1106
November 1996
change uncertainty when measuring marginal
welfare impacts. Table 3 gives the marginal
welfare impacts of canceling mevinphos at
various confidence levels, represented as different choices of a. Ex ante uncertainty
about the yield impacts of switching to alternative pest controls clearly affects the calculation of marginal costs. Mean marginal welfare impacts of banning mevinphos in California
are $53.3 million annually;due to ex ante uncertainty about yield changes, impacts exceed
$64.8 million annually with a 25% level of confidence and exceed $85.9 million with a 5%
level of confidence.
The magnitude of the confidence intervals on
welfare impacts is significant. If producer
and consumer surplus impacts are large in relation to total industry revenues, environmental regulations may result in bankruptcy
and extreme consumer effects. Thus, firms
and consumers may oppose environmental
regulations based on worst-case impacts even
if mean impacts are small in relation to the size
of the industry.
Implications for Policy Design
Standard arguments in the economic theory of
environmental policy suggest that regulations
be set at their first- or second-best levels. In the
notation of this paper, the first-best regulation
satisfies max{B(git) - C(Qit)} Vi, t, where
B((i,t) is the benefit from the regulation derived
from higher environmental quality or public
health, and C(it,) is the cost of the regulation in
terms of lost producer and consumer surplus.
Alternatively, the regulation may be set to
min{C(Qi,)} s.t. B(iit) > , Vi, t, where P is
some predetermined level of benefit. It is
also possible to find efficient regulations incorporating the uncertainty inherent in the
impact analysis by using dW,, in conjunction
with "safety-fixed" rules (Kataoka) for environmental quality in the manner suggested
by Lichtenberg and Zilberman. The large degree of variation in the confidence interval
estimates in table 3 implies that there is
value in obtaining better scientific information about the yield impacts of environmental regulations. A risk-averse regulator will
change regulations significantly in response to
uncertainty about marginal welfare impacts,
and, thus, reducing ex ante uncertainty about
yield changes will result in regulations that
more accurately balance marginal costs and
benefits.
Amer. J. Agr. Econ.
Table 3. Distribution of Marginal Welfare Impact
a
MarginalWelfareImpact
($ Thousands)
0.05
0.10
85,872
74,275
0.25
0.50
0.75
0.90
64,838
53,274
41,870
32,336
0.95
21,214
Regardless of whether environmental regulations affecting agriculture are set at their firstor second-best levels, it is necessary for policy
makers to assess the marginal cost of the intervention. The method developed in this paper is
a simple algorithm for performing such an
analysis. Expression (9) is an improvement
over previous impact assessment methods in
that it gives a theoretically appealing and easily
computable formula for measuring region- and
time-specific welfare loss.' The method developed here is valid for marginal changes in environmental regulations, including regulations
promulgated at the state or local level. Global
methods are needed to evaluate quantum
changes in regulations.
Finally, the framework developed here can be
used to integrate economic information with
existing earth science data in a single regulatory approach. Geographic Information Systems are rapidly gaining acceptance among environmental professionals. These data bases
contain highly detailed information on spatial
characteristics, such as land use patterns, and
environmental conditions, such as groundwater
depth and quality, soil characteristics, and microclimate. These data bases may also contain
dynamic information for particular locations,
such as lateral groundwater flow. GIS data is
increasingly used to identify environmentally
sensitive areas, for example, agricultural areas
where pesticides have a high probabilityof infil-
It is interesting to compare the regional distribution of marginal costs and benefits. Since most citizens live in urban areas,
the benefits from improving environmental quality will often be
concentrated in these regions, particularly if environmental regulation results in increases in wildlife populations. The benefits from
environmental regulations of agriculture can be local as well. For
example, regulations to ensure farm worker safety and improve
drinking water quality primarily will benefit rural residents. Producer surplus losses that are the bulk of the welfare costs of environmental regulations primarily are felt in rural areas.
Sunding
trating groundwater,or regions with high densities of an endangered species. The model developed in this paper can be used to measure the
marginal costs of regulating agriculturalproduction at a disaggregated level, and it thus can be
paired with detailed GIS data to give regulators
a full picture of the marginal costs and benefits
of localized environmental regulation.
[Received July 1994;
final revision received September 1996.]
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