METHODS DEVELOPMENT IN MEASURING BENEFITS

OF ENVIRONMENTAL IMPROVEMENTS

Volume I

EXECUTIVE SUMMARY

USEPA Assistance Agreement CR808-893-01

Project Officer

Dr. Alan Carlin

Office of Policy Analysis

Office of Policy, Planning and Evaluation

U.S. Environmental Protection Agency

Washington, D.C. 20460

OFFICE OF POLICY ANALYSIS

OFFICE OF POLICY, PLANNING AND EVALUATION

U.S. ENVIRONMENTAL PROTECTION AGENCY

WASHINGTON, D.C. 20460

DISCLAIMIER

This report has been reviewed by the Office of Policy Analysis, Office
of Policy, Planning and Evaluation, U.S. Environmental Protection
Agency, and approved for publication. Approval does not signify that the
contents necessarily reflect the views and policies of the U.S.
Environmental Protection Agency, nor does mention of trade names or
commercial products constitute endorsement or recommendation for use.

CONTENTS

[Page]{.underline}

Figures iv

Tables iv

Abstract 1

Volume II: Experimental Approaches for Valuing Environmental

Commodities (W. D. Schulze, R. G. Cummings, D. S. Brookshire,

M. A. Thayer, R. Whitworth, and M. Rahmatian) 2

Volume III: An Updating of Earlier Efforts to Estimate the National

Benefits of Controlling Acid Precipitation (T. D. Crocker,

R. G. Cummings, and B. A. Forster) 4

Volume IV: Valuing Ecosystem Functions: The Effects of Air

Pollution (T. D. Crocker, J. Tschirhart, R. M. Adams and

R. W. Katz) 8

A. Integrating Ecosystems and Economics 8

B. On the Value of the Condition of a Forest Stock 9

C. Accurate Yield Response Information and Economic

Assessments 10

Volume V: Air Pollution and Disease: An Evaluation of the NAS

Twins (R. C. d\'Arge, S. B. Kask, J. C. Case, S. Ben-David,

L. S. Eubanks, and C. L. Anderson) 15

Volume VI: An Economic Analysis of Air Pollution and Health: The

Case of St. Louis (S. D. Gerking, L. R. Stanley, and *W. N.*

Weirick) 17

Volume VII: Economic Benefits of Controlling the Effects of

Environmental Pollution on Children\'s Health (S. E. Atkinson,

T. D. Crocker, R. G. Murdock, and H. L. Needleman) 23

A. Economic Losses and Elevated Body Lead Burdens in

Children 23

B. Expectational Effects on Air Pollution Epidemiology.. 23

C. The Adequacy of Natural Science Information 24

Volume VIII: Measuring the Benefits of Clean Air and Water

(A. V. Kneese) 24

Bibliography 26

FIGURES

[Number]{.underline} [Page]{.underline}

1 Changes in Producer\'s Quasi-Rent and Consumer\'s Surplus 6

2 Distribution of Net Benefits, Corn 14

TABLES

[Number]{.underline} [Page]{.underline}

1 Overview of the Contribution of Study Experiments to

Study Purposes 3

2 Willingness to Pay (Weekday Sample, n = 36) 11

3 Willingness to Pay (Weekend Sample, n = 64) 11

4 Rank-Ordering of Environments 12

5 Total Cost Savings, by Symptom, for a 30 Percent Improvement

in U.S. Air Quality in 1981 Dollars 16

6 Variable Definitions 19

7 Estimates of the Health Production Function 820 Cases 20

8 Estimates of the Health Production Function 2197 Cases 21

9 Willingness to Pay for Reduction in Ozone Levels 22

EXECUTIVE SUMMARY

ABSTRACT

Any society directing its efforts towards continual economic growth must
learn to deal with the accompanying environmental Impacts. That is, if
society values the preservation of its natural environment, a portion of
the economic resources generated by growth trust be expended to create a
balance between the inevitable pollution of the biosphere and the
preservation of a pristine environment. Thus, policies to regulate
sources of environmental degradation will bring about economic costs as
well as environmental improvements. One method for assessing the merits
of particular environmental policies is to compare these economic costs
with the dollar benefits that they are expected to yield. However,
obtaining the needed dollar benefit estimates is complicated by the
\'public good aspects of environmental commodities. Since ccnmodities
such as air quality are not traded in easily defined markets, this value
can only be determined by indirect methods.

Volume II considers experimental or contingent valuation approaches to
valuing air and water quality improvements. Particular attention is
drawn to determining the benefits of improving visibility in the
national parks, improving national water quality, reducing the risks of
exposure to hazardous waste, and reducing ambient ozone concentrations
in the South Coast Air Basin. Volume III updates earlier efforts to
estimate the benefits of controlling acid deposition. Theoretical
advances are presented in addition to a discussion of the role of
nonconvexities in evaluating acid deposition impacts. Volume IV deals
with methods of valuing the economic impacts of air pollution on
ecosystems. A theoretical model is described for the imputation of
economic damages and an empirical illustration is presented in which the
contingent valuation method is used to value the condition of a forest
stock.

Volumes V, VI, and VII address the questions related to air pollution
impacts on human health. In Volume V, data on twins obtained from the
National Academy of Sciences is employed to examine the effects of
elevated levels of sulfur dioxide and total suspended particulates on
symptoms including chest pain, cough, and shortness of breath. Volume
VI, then, develops a new methodology for estimating the benefits of
reduced human morbidity stemming from improved air pollution control and
tests that methodology using data drawn from adult residents of St.
Louis, Missouri. Volume VII presents a collection of three papers that
assess the economic benefits of controlling pollutants, such as lead,
that affect the health of children. Finally, Volume VIII contains a
non-technical discussion of recent developments estimating benefits of
environmental improvements. The work summarized in this volume, which
was conducted by investigators at

Resources for the Future, the University of Wyoming, the University of
New Mexico, and the University of Chicago, describes research sponsored
by the USEPA through a sustained program over several years. The
contents of each of these volumes now will be summarized in greater
detail.

VOLUME II: EXPERIMENTAL APPROACHES FOR VALUING ENVIROMIENTAL COMMODITIES

Volume II discusses four separate experiments: (1) the National Parks
Visibility Experiment (Chapter 2); (2) the National Water Quality
Experiment (Chapter 3); (3) the Hazardous Waste Experiment (Chapter 4);
and, (4) the Ozone Experiment (Chapter 5). The overall objective was to
develop and assess the effectiveness of the contingent valuation method
CVM as a technique for the valuation of benefits from different
environmental. improvements. Four general issues were explored.

First, the issue of validating CVM as a method of benefit estimation was
explored. The approaches taken have included.: (1) comparing CVM results
to those obtained from alternative benefit estimation techniques (i.e.,
the hedonic method), (2) analyzing individual CVM responses obtained
under iterative and payment card formats, and (3) utilizing alternative
consumption sets. The second issue considered dealt with aggregation
over: (1) attributes of a commodity, (2) different commodities, (3)
geography, and (4) individuals. Third, a set of issues related to how
individuals perceive the commodity to be valued was examined.
Specifically, two classes of environmental commodities were reviewed,
the first associated with very high risk and uncertainty (Chapter 4),
and the second for which risk and uncertainty are not major factors in
describing the commodity (Chapter 2). The fourth and final set of issues
involves the extent to which CVM measures are truly expenditures for
marginal changes in environmental condition. Additionally, the method of
soliciting CVM responses is analyzed. That is, a comparison of mail
(Chapter 5), door-to-door (Chapters 2, 3, 4), and pre-arranged
interviews (Chapter 5) is made. Table 1 illustrates a brief overview of
the contribution of each study experiment to these four issues.

In studying results from the different experiments, more questions were
raised than answered. Attempts to validate the CVM by comparing it to
the hedonic or property value technique revealed two confounding issues
(Chapter 5). The first dealt with the appropriateness of assuming
additive and separable utility functions over time with respect to an
individual\'s preferences towards ambient levels of ozone pollution. The
second related to a severe multicollinearity problem arising from high
correlation between the \"distance to beach\" and \"ozone\" explanatory
regression variables. In two of the experiments (the Parks Visibility
and Hazardous Waste experiments) iterative bidding processes yielded
contingent values significantly higher than initial starting bids.
However, with respect to bids given when individuals must concurrently
indicate which expenditure/savings item(s) would be reduced (to make the
bid), it was found no statistical difference existed from individual
bids made without budget constraints imposed. This result was obtained
in all of the experiments except the one for ozone. The effects of
altering the

  ---------------------------- ------------ ------------- ------------- --------------
  TABLE 1                                                               

                                                                        

  OVERVIEW OF THE CONTRIBUTION                                          
  OF STUDY EXPERIMENTS TO                                               
  STUDY PURPOSES                                                        

                                                                        

                                                                        

  Experiment                   Validation   Aggregation   Perceptions   Other
                               Issues       Issues        of CV         Experimental
                                                          Commodities   Issues

  The National Parks           x            x             x             
  Visibility Experiment                                                 

  National Water Quality       x            x                           
  Experiment                                                            

  The Hazardous Waste          x            x             x             x
  Experiment                                                            

  Ozone Experiment             x                          x             x
  ---------------------------- ------------ ------------- ------------- --------------

consumption set of a primary commodity by adding substitute
environmental and private market commodities had the effect of lowering
average bids for the primary commodity (Chapters 2, 4).

In regards to the aggregation problem, hypothesis tests were conducted
to determine the distinctiveness of the primary (and assumed
disaggregated) commodity in question. In the National Parks Visibility
Experiment, individual valuations of visibility at the Grand Canyon (the
specific commodity) was not affected by valuations for five other
regional parks or all other national parks. However, when summing the
visibility bids for the five other regional parks, this aggregate value
is greater than the corresponding bids for improving national air
quality. A similar result was discovered between bids for national water
quality relative to national water and air quality improvements. The
null hypothesis that air and water quality are disaggregated commodities
is rejected. In testing the geographical aggregation issue, the null
hypothesis of equal bids for the hazardous waste commodity in each of
these regions was not rejected.

In regard to the remaining issues, results from the Hazardous Waste
Experiment showed the CVM produced bids for marginal environmental
changes. Further, the null hypothesis of equal bids between different
solicitation modes (mail, door-to-door, and pre-arranged interviews) was
not rejected. Such a result is encouraging in light of the considerable
cost differences between modes.

Finally, the four experiments just summarized leave three questions
unanswered. First, additional research is warranted on the definition,
delineation and understanding of the aggregate commodity (or mental
account) issue relevant to a particular environmental improvement.
Secondly, additional experiments are required to test the means by which
values attributed to the aggregate commodity can be allocated to its
disaggregated attributes. A third important problem involves the role of
individual perceptions in formulating bids for environmental
improvements in a CVM framework.

VOLUME III: AN UPDATING OF EARLIER EFFORTS TO ESTIMATE THE NATIONAL

BENEFITS OF CONTROLLING ACID PRECIPITATION

Volume III contains two sections concerned with updating earlier efforts
to estimate the national benefits of controlling acid deposition. The
first section presents a formal theoretical structure appropriate for
assessing the economic benefits of reduced damages caused by acid
deposition. Emphasis is placed on the importance of capturing producer
and consumer adaptations, existence values, and price effects.
Additionally, a discussion of the information economists require from
natural scientists and engineers as well as prospects for empirically
implementing the theoretical structure is included. The second section
is motivated by Daniel Violette\'s (1984) critiques of a paper by
Crocker and Forster (19@1) which deals with nonconvexities in acid
deposition impacts.

More specifically, it discusses certain features of Violettes empirical
examples and his economic damage functions for biological/physical
impacts commonly associated with acid deposition and its precursors.
Also demonstrated is Violette\'s failure to show that nonconvexities in
acid deposition impacts are not a significant problem for economic
analysis. Moreover, in situations where nonconvexities are relevant to
consider, a greater understanding of the physical/biological system is
required to support benefit-cost analyses. The contents of each of these
two sections is described more fully below.

Research on materials damage caused by air pollution generally focuses
on isolated production inputs, such as physical structures. Hence, the
conventional procedure to materials damage estimates is to multiply the
reduction in that inputs\' stock by its prevailing market price. This
simple procedure, however, can produce misleading estimates of economic
losses since possible input substitutions and input and output price
changes are ignored. Consequently, a better theoretical procedure for
estimating economic losses from materials damage would be to examine the
changes in consumer surpluses and producer quasi-rents over varying
levels of pollution.

Figure 1 depicts one example of the changes an air pollution increase
can have upon consumer\'s surplus and producer\'s quasi-rent. The air
pollution increase reduces the desirable physical properties of the
output, diminishing the consumer\'s willingness to pay and causing his
demand function to shift from Do to D\'. Simultaneously, the air
pollution increase damages one or more materials in the production
process for the output, bringing about increased production costs and an
upward shift in the supply function from SO to S\'. Market price for the
output drops from PO to P', partly due to the greater relative magnitude
of the shift in the demand function, and partly to the lesser relative
slopes of the demand functions. Consumer surplus was the area aP 0 b; it
is now the area dP\'e. Producer quasi-rent was the area fP 0 b; it is
now the area gP\'e. Total social surplus from the production and use of
the output in question is thus reduced by the area fgbh plus the area
adeh.

Of course, differing relative shifts in demand and supply relations
reflecting different relative pollution induced impacts in the
production and consumption sectors, will yield results of a modified
character. For example, if the demand curve shifts to D\" rather than
D\', market price will rise to P\". Qualitative results are unchanged,
however, alterations in producer quasi-rents and consumer surplus are
obtained from the pollution-caused changes in the two sectors. These two
examples serve to illustrate the issues of concern here: consumers and
producers can bear very different economic gains or losses depending on
the absolute price elasticities of the demand and supply functions.
Moreover, the distribution of these economic consequences can differ
drastically with the price elasticity of the demand function relative to
the supply function.

In order to better appreciate the subtleties lying behind these
observations which describe the social costs and benefits from

Figure 1

Changes in Producer\'s Quasi-Rent and Consumer\'s Surplus

Price![](media/image1.emf){width="6.330555555555556in"
height="4.3597222222222225in"}

Quantity

pollution-induced materials damages, formal analyses are made by
considering pollution-related costs on the production, household, and
public goods sectors. Specifically, a producer's constrained profit
maximization problem is analyzed to examine the least cost method of
altering the operation, maintenance, and replacement processes in the
overall production activity as the level of pollution induced materials
damage changes.

Additionally, standard utility theory is employed to generate the
household\'s constrained utility maximization problem. The analysis
assumes certain goods provide a flow of services through time and are
subject to depreciation from normal wear, as well as from
pollution-related materials damages. Demand functions, as in Figure 1,
are analytically derived to address the issue of impacts from pollution
changes in the household sector.

These implied demand, or average revenue, relationships are implicitly
used in the analysis mentioned above. That is, they are used to derive
the marginal revenue expressions, which when combined with the relevant
marginal cost functions, the loss in benefits\--social
costs\--attributable to pollution-caused materials damage from higher
pollution levels can be determined.

Finally, a simplified analytical model to illustrate damages to the
class of public goods is developed. The modeled representative citizen
obtains utility from three classes of goods: (1) purchased goods, (2) a
stock of historical structures which are valued even if the sites are
not visited (existence value), and (3) a stock of goods accruing value
from use, including historical structures. Again, demand relationships
are defined for the stock historical structures. However, these
relationships lack interpretive neatness because of the absence of
market prices for the structures. Consequently, even though a conceptual
measure for pollution damages to public goods is defined, the issue of
how to obtain the measure remains open.

Regarding empirical implementation, discussion focuses on estimating
social costs of pollution-related materials damages to public goods as
well as in the private household and production sectors. The most
tractable method for estimating social costs of pollution damage in the
production sector is the direct costs method (i.e., costs of
pollution-related incidence, of premature maintenance and/or replacement
costs). A similar method is prescribed for the social cost of pollution
damages on the household sector. Finally, survey methods are recommended
as a means of estimating both the consumption and existence values
relative to the social costs of pollution in the public sector.

Recommendations from this section focus mainly on aggregate approaches
to estimating materials damages. In short, the offered conclusion is
that the acquisition of information on the covariation of pollution and
maintenance/replacement policies, defined in terms of their economic
categorization rather than physical manifestations, is necessary

before performing economically meaningful studies on Pollution-induced
materials.

As noted previously, the second section of Volume III is concerned with
updating theoretical discussions related to nonconvexities in acid
deposition control policies. Contrary to previous work by Violette
(1984), it is argued that nonconvexities at the aggregate level are not
always a disappearing phenomenon. Moreover, the fact they could be
important even in the aggregate establishes their importance,
particularly with respect to the acid deposition problem.

Therefore, circumstances in which the nonconvexities of the function
relating biological damages to the acidity of aquatic ecosystems (caused
by the shape and nature of the biological/physical dose-response
function) could be policy relevant, and greater understanding of the
biological/physical system and its economic counterparts will be
required to improve the accuracy of benefit-cost analyses.

VOLUME IV: VALUING ECOSYSTEM FUNCTIONS: THE EFFECTS OF AIR POLLUTION

Volume IV presents three papers dealing primarily. with the theoretical
implications of employing economic assessment techniques to value
impacts of air pollution on existing ecosystems. First, a general
equilibrium model is described for the imputation of economic damages to
ecospecies. Secondly, a specific example applying the contingent
valuation method to value the condition of a forest stock is related.
Finally, a methodology with illustrations is covered in the concluding
section that deals with the value of yield response information
functions used by economists to compute pollution impact damages on
managed ecosystems.

A. [Integrating Ecosystems and Economics]{.underline}

Economic development, as usually envisioned, is often accompanied by
dwindling natural environments. In the United States, and worldwide,
lost habitat and species extinction are familiar by-products of this
development. Yet, the cost of these losses are never adequately weighed
against the benefits of development. What is labeled \"economic
development\" may be, in some cases, economic abatement. In order to
measure the costs of development, the ability to quantify the value of
the species that comprise ecosystems is necessary.

This need is addressed by modelling an ecosystem using techniques
familiar to economists. Each ecospecies encountered is assumed to behave
as if to maximize stored energy (which is the difference between energy
inputs and outputs). Species are then linked together in food chains,
with the entire ecosystem operating in a fashion similar to a perfectly
competitive economy. Concepts of equilibrium stability have clear
interpretations. The ecosystem is self-sustaining in that human
intervention is unnecessary for its existence.

Next, the ecosystem is placed within a general equilibrium framework.
Humans intervene in the ecosystem through farming, hunting, fishing,
polluting, and so on. Each human intervention represents an exogenous
shock to the ecosystem; and in turn, the ecosystem seeks a new
equilibrium with a greater or lesser abundance of various species. The
changes in abundance may or may not be predictable, depending on the
information the intervenors possess concerning the interaction of
species. Situations that result in unexpected ecosystem changes are
termed \"ecosystem externalities.\" Finally, a demonstration of value
derivation is made. The model allows any species, including those that
are neither culled nor an argument in the social welfare function, to be
valued. The value derives from the linkages among all species.

B. [On The Value of the Condition of a Forest Stock]{.underline}

A specific example integrating economics and ecosystems is illustrated
in section two of Volume IV, in which economic analysis is applied to
the valuation of the condition of a forest stock. Basic tenets of
economic theory are examined in relation to individual preferences -For
the aesthetic features of the forest, valued as an environmental asset.
Specific to the test is the notion of transitive preferences and the
symmetry of the Slutsky matrix.

Several techniques have been employed in the past to assess preferences
for a large set of nonmarket environmental commodities. One basic
premise from which these different methods (e.g., the travel cost,
hedonic, and contingent valuation) proceed is that individual
preferences are transitive. Appropriateness of the transitivity axiom,
which these techniques invoke without exception, has never been
scrutinized; in fact, the axiom is necessary for the uniqueness of
preferences. If preferences are transitive (i.e., Slutsky matrix is
symmetric), observations on usage of the good across price and income
settings can be utilized to generate demand functions from which unique
inferences concerning underlying preferences can be drawn.

In this section of Volume IV, findings in experimental economics and
psychology are applied to construct a contingent valuation
questionnaire. The survey is employed to gather economic data on
environmental damages due to air pollution in a national forest;
however, with result which cast doubt on the validity of the axiom.
Specifically, the survey conducted in southern California where the
problems resulting from air pollution are well recognized by local
residents, elicited as truthful responses as possible.

Each respondent was told that air pollution was the source of perceived
differences in the health status of described forest environments in the
region. Nearly all respondents were engaged in outdoor recreation in the
forest at the time of the interview. Each was asked to state his maximum
WTP in terms of an additional fee to a daily \$6.00-\$7.00 access fee
already paid on the interview day. Some of the features of the data
obtained from interviewing 36 respondents during a June, 1983 weekday
and 64 on a July, 1983 weekend are reviewed in the

tables below. Interviews were conducted at the same forest location.

Tables 2 and 3 report the mean and median bid (WTP), representing the
additional access fees, for three depicted environments. Environment
[A]{.underline} represents a \"very slight injury\" to the forest from
acid deposition. Environment [B]{.underline} represents a \"very severe
injury\" to the forest\'s condition, Environment [C]{.underline} a
\"moderate injury\" to the condition of the forest stock. Note that the
willingness to pay for the [A]{.underline} environment in each sample
was more than twice the willingness to pay for either the
[C]{.underline} or [B]{.underline} environments. Table 4 reports the
frequency count of respondent\'s rank-orderings across the three
environments.

Environment [A]{.underline}, which has the \"very slight injury,\"
obviously rules the preference structure. Yet, the nearly equivalent
frequencies of [A]{.underline} \> [B]{.underline} \> [C]{.underline} and
[A]{.underline} \> [C]{.underline} \> [B]{.underline}, along with ten
respondents who expressed [A]{.underline} \> ([BC]{.underline}), imply
the individuals were unable to express a clear preference when asked to
choose between [B]{.underline} and [C]{.underline}. This same preference
pattern emerges in the previous Tables 2 and 3, when individuals elicit
their cardinal evaluations. No respondent exhibited differences between
the rank assigned to environment [A]{.underline} in the rank-ordering
and the cardinal ordering of their bids. However, twelve respondents who
indicated [B]{.underline} \> [A]{.underline}, or [C]{.underline} \>
[B]{.underline}, stated identical positive bids for [C]{.underline} and
[B]{.underline}.

To explain the source of the patterns displayed in the WTP bids of
Tables 2 and 3, the underlying preference structure was analyzed for the
three environments. Reduction in oxidant-induced damages was found to
have not decreasing, but increasing marginal benefits. Underlying
physical and biological relations or adjustments in some endogenous
variables (e.g., frequency of visits) could be the source of the
nonconvexities. Due to the linearity of the biological damage index,
this result is expected from a nonconvex preference ordering, arising
either from the inappropriateness of the transitivity or the
multidimensional character of the depicted forest environment as sources
of the observed increasing marginal benefit function.

In particular, a multiplicative interdependence may be present in
utility terms between healthy pine trees and other elements (bushes,
terrain, etc.) present in the forest environment. The minimum value of a
combination of healthy pine trees and these other elements may be much
greater than the sum of their values when treated separately; that is,
the health of the pine trees must necessarily be some minimal scale in
order for the other elements to be valued. If so, the values obtained
here, and perhaps in other contingent valuation studies as well, are
relevant only to the entire environmental situation. One could not then
repackage particular elements in order to extrapolate their values to
other settings.

C. [Accurate Yield Response Information and Economic
Assessments]{.underline}

Ambient levels of ozone pollution create a pervasive negative effect on
not only economically valued environmental commodities, but also on
other commodities such as agricultural yields. Hence, national control

  ------------------------------ ----------------- ----------------- -----------------
  TABLE 2                                                            

  WILLINGNESS-TO-PAY (Weekday                                        
  Sample, n=36)                                                      

                                                                     

                                 [A]{.underline}   [B]{.underline}   [C]{.underline}

                                                                     

  Injury score midpoint          4.5               18                32

                                                                     

  Arithmetic mean bid            \$2.54            \$1.07            \$0.69

  (Standard Error)               (.604)            (.337)            (.202)

                                                                     

  Median bid                     \$1.00            \--               \--
  ------------------------------ ----------------- ----------------- -----------------

  ------------------------------ ----------------- ----------------- -----------------
  TABLE 3                                                            

  WILLINGNESS-TO-PAY (Weekend                                        
  Sample, n=64)                                                      

                                                                     

                                 [A]{.underline}   [B]{.underline}   [C]{.underline}

                                                                     

  Injury score midpoint          4.5               18                32

                                                                     

  Arithmetic mean bid            \$1.84            \$0.55            \$0.65

  (Standard Error)               (.257)            (.102)            (.102)

                                                                     

  Median bid                     \$1.00            \--               \--
  ------------------------------ ----------------- ----------------- -----------------

  ------------------------------------ ----------------------- ------------------------- -----------------------
  TABLE 4                                                                                

                                                                                         

  RANK-ORDERING OF ENVIRONMENTS                                                          

                                                                                         

                                                                                         

  [Rank-Ordering]{.underline}                                  [Incidence]{.underline}   

                                                                                         

                                       [Weekday]{.underline}                             [Weekend]{.underline}

                                                                                         

  [A]{.underline} \> [B]{.underline}   12                                                20
  \> [C]{.underline}                                                                     

                                                                                         

  [A]{.underline} \> [C]{.underline}   12                                                22
  \> [B]{.underline}                                                                     

                                                                                         

  [A]{.underline} \>                   4                                                 6
  ([BC]{.underline})                                                                     

                                                                                         

  ([ABC]{.underline})                  5                                                 13

                                                                                         

  [B]{.underline} \> [C]{.underline}   2                                                 1
  \> [A]{.underline}                                                                     

                                                                                         

  [C]{.underline} \> [A]{.underline}   1                                                 \--
  \> [B]{.underline}                                                                     

                                                                                         

  [C]{.underline} \> [B]{.underline}   \--                                               1
  \> [A]{.underline}                                                                     

                                                                                         

  [B]{.underline} \>                   \--                                               1
  ([AC]{.underline})                                                                     

                                                                                         

                                                                                         

  NOTE:                                                                                  

                                                                                         

  ([BC]{.underline}) is indifference                                                     
  between [B]{.underline} and                                                            
  [C]{.underline}.                                                                       

  ([ABC]{.underline}) is indifference                                                    
  among [A]{.underline},                                                                 
  [B]{.underline} and [C]{.underline}.                                                   

  ([AC]{.underline}) is indifference                                                     
  betwee [A]{.underline} and                                                             
  [C]{.underline}.                                                                       
  ------------------------------------ ----------------------- ------------------------- -----------------------

Policies altering the allowable ambient pollution level will most likely
translate into improvements in agricultural yields. However, the
benefits realized from control-induced improvements are unclear due to a
relatively high level of uncertainty surrounding yields (dose-response
effects) responding to different levels of control.

This report attempts to provide a methodological approach for predicting
the extent to which improved yield response information will affect net
benefit estimation, thereby influencing policy choices. A Bayesian
approach is taken to address statistical uncertainty in the precision of
biological estimates of yield responses. The basic question asked is
then what are the economic consequences of varying control policies on
benefits given varying degrees of statistical imprecision of yield
response estimates.

Actual measures are derived to demonstrate the value of more precise
dose-response information for benefit cost analysis. The worth of this
information is described in terms of the different estimates of benefits
that result; given varying levels of precision in the information
available. Biological dose response information was generated from the
United States\' Environmental Protection Agency\'s National Crop Loss
Assessment Network (NCLAN). An empirical example is provided which
considers estimates of the economic value of controlling ozone impacts
in the U.S. for four specific crops: corn, cotton, soybeans, wheat.

As shown in this empirical example, when a particular ozone standard is
to be enforced, it is desirable to know the entire probability
distribution of the economic surplus. Thus, rather than assume the
estimated yield response parameters are the \"true\" ones, any
statistical uncertainty can be explicitly included in the estimated
model to obtain insight as to what may be gained by additional
observations. That is, additional observations on yield responses of the
selected crops to ozone levels will provide reductions in the variance
of the yield response parameter, ultimately causing it to converge to
some stable yet unknown magnitude.

Figure 2 illustrates the uncertainties in economic surpluses caused by
the estimated parameters of the yield responses for the case of corn.
(The report also considers three additional crops; cotton, soybeans, and
wheat.) This figure depicts the density function of the difference in
economic surplus between the current ambient standard of 0.12 ppm and
alternative standards (reading from left to right in each figure) of
0.14 ppm, 0.10 ppm, and 0.08 ppm. The scales on the vertical axes are
probabilities per unit of output. The mass of the surplus estimates in
the immediate area of the mean is very great for each crop-ambient ozone
combination. Only the distributions for the 0.08 ppm standard found on
the far right of each figure exhibits much variability. Secondly, there
is no overlap between distributions, with the exception of the 0.10 and
0.08 ppm surplus distributions for corn and wheat. This lack of overlap
implies that the tradeoff between Type I and II statistical error need
not be considered in discriminating between these alternative ambient
ozone standards. Similar results are obtained for the three other crops

Figure 2: Distribution of Net Benefits, Corn

![](media/image2.emf){width="6.095833333333333in"
height="5.386805555555555in"}

considered. In summary, the value of more precise yield response
information is ultimately dependent upon the extent to which the
variability of economic surplus estimates affect the probabilities of
either overestimating ultimately realized economic surplus and thereby
failing to plan for a more lax ambient standard, or underestimating the
realized surplus and thereby failing to plan for a stricter ambient
standard.

VOLUME V: AIR POLLUTION AND DISEASE: AN EVALUATION OF THE NAS TWINS

Human disease is caused by a multitude of events including aging,
genetic background, and the environment in which the individual resides.
Air pollution is but one of the many factors potentially influencing
morbidity and mortality rates of the population. In Volume V, the
central questions which arises in this volume is whether or not the net
effect of air pollution can be assessed and measured such that a
scientifically defensible estimate can be made of the change in health
resulting from a change in ambient outdoor concentration of air
pollutants.

In recent years, a number of studies have been undertaken to estimate
this net effect. Lave and Seskin (1977) conclude that air pollution,
when other factors are taken into account, contributes substantially to
increased mortality across cities in the U.S. More recently, Graves and
Krumm (1982) have demonstrated a nonlinear association between hospital
admission rates and concentrations of carbon monoxide and sulfur oxides.
Also, Ostro (1982) has demonstrated a relationship, between work loss
days and particulate concentrations.

In this study, an attempt is made to evaluate the impact of higher
ambient concentrations of air pollutants on certain symptoms and
reported diseases of a sample of approximately 14,000 twins who served
in the Armed Forces during World War II. More specifically, this study
attempts to determine the extent to which exposure to higher
concentrations of air pollutants leads to a higher level of reported
symptoms and incidence of certain diseases. However, problems arise from
several sources while using this approach. For example, a symptom such
as cough or shortness of breath can be related to the presence of many
conditions including asthma, emphysema, chronic bronchitis, or ischemic
heart disease. Secondly, the presence of a disease may not be detected
because of a lack of one or more symptoms, or not seeking treatment. In
addition, symptoms may be related to the presence of more than one type
of disease. Therefore, identifying the role of air pollution in
producing symptoms is not a trivial undertaking.

Results from examining data on twins obtained from the National Academy
of Sciences indicated that sulphur dioxide contributes to the incidence
of chest pain and levels of total suspended particulates contribute to
severe chest pain, coronary heart attack, cough, and shortness of
breath. Statistical measures used in this analysis were probit analysis
and ordinary least squares. Total estimates of the savings in health
care costs due to a 30 percent reduction in ambient

  ----------------------------- -------------------------- ---------------------------
  TABLE 5                                                  

                                                           

  TOTAL COST SAVINGS, BY                                   
  SYMPTOM, FOR A 30 PERCENT                                
  IMPROVEMENT IN                                           

  U.S. AIR QUALITY IN 1981                                 
  DOLLARSa                                                 

                                                           

                                                           

  Symptom                       Total for males between    Total U.S.~b~ Population
                                55-64 years of age         

  Cough                         \$1,546,000                \$34,416,000

  Shortness of Breath           1,218,000                  27,110,000

  Chest Pain^c^                 1,050,000                  23,357,000

  Severe Chest Pain             210,000                    4,665,000

  Cardiac Failure               [  344,000 ]{.underline}   [  7,658,000]{.underline}

  TOTAL                         \$4,368,000                \$97,206,000

                                                           

  ^a^mean values for SO~2~ and                             
  TSP were used as initial                                 
  values.                                                  

                                                           

  ^b^1980 census of population                             
  data.                                                    

                                                           

  ^c^SO~2~ is the air pollution                            
  variable used here and TSP is                            
  used for all other                                       

  symptoms                                                 
  ----------------------------- -------------------------- ---------------------------

concentrations of TSP and SO2 are derived and found to have substantial
magnitudes. The actual figures are shown in Table 5. Total cost savings
are presented, by symptom, for males between the ages of 55 and 64 and
the total population in the United States. Male members of the U.S.
population between 55 and 64 years of age most closely represent the
twins sample as characterized by 1980 census data. Summation over the
five symptoms yields an overall measure of the health benefits of air
quality improvement. Note that for the age group nearest to the twins
sample, total cost savings from disease is over \$4 million. If these
results are extrapolated to the entire U.S. population, a savings of
nearly \$100 million is obtained.

VOLUME VI: AN ECONOMIC ANALYSIS OF AIR POLLUTION AND HEALTH: THE CASE OF

ST. LOUIS

Volume VI develops a new methodology for estimating the economic
benefits of reduced human morbidity stemming from improved air pollution
control. The main goal is to formulate and empirically test a
theoretical health model which builds upon the earlier efforts of
Grossman (1972), Cropper (1981), Rosenzwieg and Schultz (1982a, 1982b),
and Harrington and Portney (1983). The model is empirically tested using
survey data on adult workers drawn from households in St. Louis, MO and
air pollution data obtained from the Regional Air Pollution Study
(RAPS). The empirical results support the hypothesis that reduced
ambient ozone levels will reduce human morbidity. On this basis,
marginal compensating variation type benefit estimates for various
percentage reductions in ozone concentrations are computed based on
these empirical results.

In the model applied, individuals derive satisfaction (utility) from the
consumption of two classes of goods: their own stock of health capital,
H; and X, goods which yield direct utility but do not affect health. The
health stock, H, is treated as an endogenous variable determined by a
production relationship in which M, medical care, and a, a set of
exogenous variables (e.g., air quality) affect the efficiency of the
individual to produce H. Utility is then maximized subject to this
household production function, as well as income and time constraints.

The comparative static properties of the model shed light on the
appropriate method for estimating the WTP for better air quality. The
entire equation set describing the model is utilized to generate the
expression:

[dB]{.underline} = [H~a~q~M~]{.underline}

da H~[M]{.underline}~ \> 0

where: B = the WTP, or bid for improved air quality.

H~[a]{.underline}~ = the change in the health stock given changes in air

quality; H~a~ \> 0.

H~M~ = the change in the health stock given changes in medical

care; H~M~ \> 0.

and q~M~ = the \"full\" price, including pecuniary prices of the

medical care commodity and time required to consume one

unit of the commodity times the wage.

This expression suggests the individual would be willing to pay more for
air quality improvements the greater the associated improvement in
health (Ha). Also, that bid would be higher, the lower the productivity
of medical services and the higher their cost. As a consequence, if
medical services are an expensive and ineffective means of producing
good health, then quite naturally the individual would be willing to pay
more for air quality improvements. In that situation, air quality
improvements simply became a more attractive mechanism through which to
augment the health stock. This WTP expression does not include any
utility terms indicating that empirical estimation is relatively
straight forward. Such estimates are reported in the final chapter of
the report.

The empirical results can be summarized by noting that two approaches
are available to estimate the marginal WTP. In the first approach,
separate estimates of H a and H M are generated from the health
production function, whereas in the second, an estimate of the ratio of
these two terms, H a /H M, is obtained. The second approach is adopted
in this study. A simultaneous equation logit model was developed to
estimate the entire model due to the discrete nature of the dependent
variable (medical care) and because H was also included as a covariate
in the objective function.

Table 6 enumerates the variables, with definitions, used to estimate the
willingness to pay for improved air quality. The table also provides the
arithmetic mean of each variable in each of the two samples. The
marginal effects at the mean of each independent variable are reported
in Tables 7 and 8. In parenthesis are the X2 statistics (distributed
with one degree of freedom) for each variable. Table 7 provides
estimates of the health production function based on the 820 cases for
which wage data was available. Note the OZONEM air pollution variable is
significant at either the 5 or 10 percent level in all six equations.

Table 8 parallels these results for the 2197 observation sample in which
the missing observations on the wage were replaced with the mean of that
variable. The air pollution variable OZONEM performs better than in the
820 observation sample; i.e., it always is significant at the 1 percent
level. Since the ozone variables, including interaction measures between
ozone and other pollutants, were the only air quality measures to
perform significantly, the willingness to pay measures reported in Table
9 pertain only to that pollutant. As shown there, these WTP estimates,
interpreted as annual per employed person benefit to St. Louis
residents, were between \$2.42 and \$2.69 for a 10 percent reduction in
mean ozone levels, and between \$7.27 and \$8.08 for a 30 percent
reduction in mean ozone levels.

+-----------+-----------------------------------+----------+---------+---+
| TABLE 6   |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| VARIABLE  |                                   |          |         |   |
| DE        |                                   |          |         |   |
| FINITIONS |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   | SAMPLE   | SAMPLE  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   | SIZE     | SIZE    |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   | 820      | 2197    |   |
+-----------+-----------------------------------+----------+---------+---+
| [VAR      | [DEFINITION]{.underline}          | [M       | [MEA    |   |
| IABLE]{.u |                                   | EAN]{.un | N]{.und |   |
| nderline} |                                   | derline} | erline} |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| MED       | 1: Denotes respondent sees a      |          |         |   |
|           | doctor                            |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           | at least once annually            | > .747   | > .755  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| PMED      | Price of medical care             | > 40.744 | > 45.38 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| HWAGE     | Hourly wage                       | > 5.078  | > 5.078 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| RACE      | 1: Indicates person is black      | > .277   | > .241  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| AGE       | Age in years                      | > 39.28  | > 40.33 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| EDUC      | Years of schooling completed      | 12.65    | > 12.79 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| SEX       | 1: Indicates respondents is male  | > .539   | > .583  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| OZONEM    | Mean ozone level (ppm)            | > .019   | > .019  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| SULDIOM   | Mean sulfur dioxide level (ppm)   | > .024   | > .024  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| TSPSMLM   | Total suspended particulates,     |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           | small size, mean levels,          |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           | (micrograms/m^3^)                 | 22.81    | > 23.77 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| LEADSMLM  | Lead, small size, mean            | 706.084  | >       |   |
|           |                                   |          |  705.42 |   |
+-----------+-----------------------------------+----------+---------+---+
|           | (micrograms/m^3^)                 |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| OZSULM    | OZONEM x SULDIOM                  | > \<.000 | >       |   |
|           |                                   |          |  \<.000 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| OZNITM    | OZONEM x OXNITM                   | > \<.000 | >       |   |
|           |                                   |          |  \<.001 |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| CHRO      | 1: Denotes respondent reports     |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           | presence of a chronic illness     | .105     | > .108  |   |
+-----------+-----------------------------------+----------+---------+---+
|           |                                   |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
| LENGTH    | Number of years respondent has    |          |         |   |
|           | had                               |          |         |   |
+-----------+-----------------------------------+----------+---------+---+
|           | chronic illness                   | 1.374    | > 1.327 |   |
+-----------+-----------------------------------+----------+---------+---+

  -------------------------------------- --------------- ----- --------------- ---- --------------- ------ --------------- ------ --------------- -------- --------------- -------- --
  TABLE 7                                                                                                                                                                           

                                                                                                                                                                                    

  ESTIMATES OF THE HEALTH PRODUCTION                                                                                                                                                
  FUNCTION                                                                                                                                                                          

  (DERIVATIVES EVALUATED AT THE MEAN)                                                                                                                                               

  820 CASES                                                                                                                                                                         

                                                                                                                                                                                    

                                                         1                     2                    3                      4                      5                        6        

                                                                                                                                                                                    

                                                                                                                                                                                    

  CONSTANT                               -.064                 -.307                -.040                  -.010                  -.290                    .226                     

                                         (.046)                (.269)               (.018)                 (.001)                 (.231)                   (.392)                   

                                                                                                                                                                                    

  OZONEM                                 30.52\*\*             33.14\*\*            31.25\*\*              29.74\*\*              33.06\*\*                25.72\*                  

                                         (5.04)                (5.15)               (5.40)                 (4.86)                 (4.86)                   (3.31)                   

                                                                                                                                                                                    

  SULDIOM                                                      -.416                .390                                                                                            

                                                               (.055)               (.042)                                                                                          

                                                                                                                                                                                    

  TSPSMLM                                                      -.009                                                              .008                                              

                                                               (.322)                                                             (.266)                                            

                                                                                                                                                                                    

  LEADSMLM                                                                                                                                                 -.0003                   

                                                                                                                                                           (2.07)                   

                                                                                                                                                                                    

  OZSULM                                                                                                                          -8.82                    95.94                    

                                                                                                                                  (.009)                   (.785)                   

                                                                                                                                                                                    

  OZNITM                                                                            91.90                  78.12                                                                    

                                                                                    (.424)                 (.370)                                                                   

                                                                                                                                                                                    

  AGE                                    -.003                 -.003                -.004                  -.003                  -.003                    -.003                    

                                         (1.34)                (1.66)               (1.96)                 (1.56)                 (1.61)                   (1.16)                   

                                                                                                                                                                                    

  EDUC                                   -.012                 -.011                -.009                  -.010                  -.012                    -.013                    

                                         (1.29)                (1.37)               (.949)                 (.975)                 (1.50)                   (1.84)                   

                                                                                                                                                                                    

  SEX                                    -.198\*\*\*           -.200\*\*\*          -.202\*\*\*            -.I99\*\*\*            -.1999\*\*\*             -.196\*\*\*              

                                         (29.62)               (30.46)              (30.73)                (29.84)                (30.29)                  (29.14)                  

                                                                                                                                                                                    

  RACE                                   .064                  .065                 .079\*                 .077\*                 .065                     .069                     

                                         (2.12)                (2.19)               (3.01)                 (3.00)                 (2.60)                   (2.44)                   

                                                                                                                                                                                    

  CHRO                                   -.100                 -.036                .203                   .044                   -.078                    -.248                    

                                         (.008)                (.001)               (.036)                 (.002)                 (.006)                   (.055)                   

                                                                                                                                                                                    

  LENGTH                                 .049                  .050                 .041                   .042                   .052                     .057                     

                                         (1.11)                (1.20)               (.823)                 (.808)                 (1.32)                   (1.68)                   

                                                                                                                                                                                    

  NC                                     820                   820                  820                    820                    820                      820                      

                                                                                                                                                                                    

  X^2^(df)                               51.1(7)\*\*\*         51.7(9)\*\*\*        51.7(9)\*\*\*          51.3(8)\*\*\*          51.7(8)\*\*\*            53.3(9)\*\*\*            

                                                                                                                                                                                    

  FCP                                    618                   618                  618                    618                    618                      618                      

  \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_                                                                                                                                              

  \*\*\*denotes significance at 1%                                                                                                                                                  
  level.                                                                                                                                                                            

  \*\*denotes significance at 5% level.                                                                                                                                             

  \*denotes significance at 10% level.                                                                                                                                              
  -------------------------------------- --------------- ----- --------------- ---- --------------- ------ --------------- ------ --------------- -------- --------------- -------- --

  -------------------------------------- ------------- ----- ------------- ----- ------------- ----- -------------- ------ ------------- ----- ------------- ----- ---------- --
  TABLE 8                                                                                                                                                                     

                                                                                                                                                                              

  ESTIMATES OF THE HEALTH PRODUCTION                                                                                                                                          
  FUNCTION                                                                                                                                                                    

  (DERIVATIVES EVALUATED AT THE MEAN)                                                                                                                                         

  2197 CASES                                                                                                                                                                  

                                                                                                                                                                              

                                                       1                   2                   3                    4                    5                   6                

                                                                                                                                                                              

                                                                                                                                                                              

  CONSTANT                               -.793\*\*\*         -1.38\*\*\*         -.936\*\*\*         -.949\*\*\*           -1.32\*\*\*         -1.08\*\*\*                    

                                         (26.58)             (23.73)             (20.92)             (24.18)               (22.40)             (23.24)                        

                                                                                                                                                                              

  OZONEM                                 53.85\*\*\*         56.64\*\*\*         54.28\*\*\*         55.86\*\*\*           53.64\*\*\*         55.13\*\*\*                    

                                         (52.53)             (52.86)             (44.66)             (49.02)               (52.46)             (50.58)                        

                                                                                                                                                                              

  SULDIOM                                                    2.97\*\*            2.73\*\*                                                                                     

                                                             (6.04)              (5.03)                                                                                       

                                                                                                                                                                              

  TSPSMLM                                                    .020\*\*                                                      .020\*\*                                           

                                                             (6.60)                                                        (6.59)                                             

                                                                                                                                                                              

  LEADSMLM                                                                                                                                     .0003\*\*                      

                                                                                                                                               (5.48)                         

                                                                                                                                                                              

  OZSULM                                                                                                                   151.71\*\*          91.04                          

                                                                                                                           (6.04)              (1.79)                         

                                                                                                                                                                              

  OZNITM                                                                         121.65              197.94\*\*\*                                                             

                                                                                 (2.10)              (7.14)                                                                   

                                                                                                                                                                              

  AGE                                    -.012\*\*\*         -.011\*\*\*         -.011\*\*\*         -.011\*\*\*           -.011\*\*\*         -.011\*\*\*                    

                                         (41.51)             (39.85)             (35.41)             (39.76)               (40.4)              (39.84)                        

                                                                                                                                                                              

  EDUC                                   .005                .005                .004                .004                  .005                .004                           

                                         (1.26)              (1.44)              (.957)              (.975)                (1.44)              (.899)                         

                                                                                                                                                                              

  SEX                                    -.274\*\*\*         -.307\*\*\*         -.300\*\*\*         -.313\*\*\*           -.311\*\*\*         -.306\*\*\*                    

                                         (119.94)            (119.40)            (113.29)            (117.2)               (119.6)             (118.9)                        

                                                                                                                                                                              

  RACE                                   .101\*\*\*          .105\*\*\*          .094\*\*\*          .088\*\*\*            .106\*\*\*          .104\*\*\*                     

                                         (15.51)             (16.16)             (12.89)             (11.82)               (16.52)             (15.87)                        

                                                                                                                                                                              

  CHRO                                   5.71\*\*\*          5.19\*\*\*          4.85\*\*\*          4.88\*\*\*            5.20\*\*\*          5.12\*\*\*                     

                                         (41.08)             (42.46)             (36.84)             (40.69)               (42.96)             (41.98)                        

                                                                                                                                                                              

  LENGTH                                 -.035               -.033               -.027               -.030                 -.033               -.028                          

                                         (1.52)              (1.49)              (.979)              (1.16)                (1.51)              (1.16)                         

                                                                                                                                                                              

  NC                                     2197                2197                2197                2197                  2197                2197                2197       

                                                                                                                                                                              

  X^2^(df)                               149.5(7)            152.8(9)            146.4(9)            149.59(8)             153.1(9)            151.1(9)            114.5(10   

                                                                                                                                                                              

  FCP                                    1669                1676                1668                1667                  1678                1668                           

  \_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_\_                                                                                                                                        

  \*\*\*denotes significance at 1%                                                                                                                                            
  level.                                                                                                                                                                      

  \*\*denotes significance at 5% level.                                                                                                                                       

  \*denotes significance at 10% level.                                                                                                                                        
  -------------------------------------- ------------- ----- ------------- ----- ------------- ----- -------------- ------ ------------- ----- ------------- ----- ---------- --

  -------------------- -------------- -------------- -------------- --------
  TABLE 9                                                           

                                                                    

  WILLINGNESS TO PAY                                                
  FOR REDUCTIONS IN                                                 
  OZONE LEVELS                                                      

                                                                    

                                                                    

  PERCENT REDUCTION IN                                              
  MEAN OZONE LEVELS                                                 

  EQUATION             10%            15%            20%            30%

  3(Table 3)           \$2.42         \$3.63         \$4.84         \$7.27

  3(Table 4)           \$2.69         \$4.04         \$5.39         \$8.08
  -------------------- -------------- -------------- -------------- --------

VOLUME VII: ECONOMIC BENEFITS OF CONTROLLING THE EFFECTS OF
ENVIRONWMNTAL

POLLUTION ON CHILDREN\'S HEALTH

In Volume VII three additional reports are presented to assess the
economic benefits related to the control of pollutants that may affect
the health of children. The first section describes the economic
consequences of elevated body lead burdens in children. The second
section reports on a unique statistical method which scrutinizes the
hypothesis that prior expectations regarding aggregate air pollution
epidemiology may dictate or bias the statistical estimates of
dose-response functions. Finally, the third section reports on the
adequacy of natural science information to reliably evaluate
environmental regulations pertaining to lead exposures.

A. [Economic Losses and Elevated Body Lead Burdens in
Children]{.underline}

Efforts by economists to value environmental health effects have focused
almost entirely upon adult population losses in productivity and their
willingness to pay to avoid health risks, given their current
occupations. The economic value of health impacts upon children have
been thus far neglected in such studies. Of all people, children and the
elderly are generally considered to be the most susceptible to health
damages from environmental pollutants. Children are thought to be
particularly susceptible to neurological, neuromotor, and behavioral
impacts from ambient lead concentrations.

Thus, in this report, an outline of an analytical framework suitable for
estimating the economic losses that parent/guardians suffer from
declines in their children\'s health is proposed. Additionally, given
the effects of lead-induced changes in the health status of children on
the length of schooling and schooling-success, it is shown how such
health status changes can influence subsequent occupational choices and
life-cycle incomes.

B. [Expectational Effects of Air Pollution Epidemiology]{.underline}

In this section, the role of priors in aggregate air pollution
epidemiology is analyzed. The possibility of a significant relationship
between urban air pollution and human morbidity is not disputed, yet,
the purpose of the analysis is to demonstrate the crucial role that
priors play in attempts to infer this relationship from aggregate
epidemiological data. The reasoning here rests on the idea that the lack
of strong priors leads to difficulty in choosing among the candidate
explanatory regression variables (see Chappie and Lave, 1982). A
conclusion of this section is that previous study results are most
likely dominated by their choice of variables to consider and particular
regressions to report.

This specification uncertainty causes their (Chappie and Lave) estimates
to be fragile. Only if their sample information is very precise relative
to the prior information, can a significant positive association between
air pollution and mortality be supported. As the precision of the prior
information increases relative to that of the sample information, the

precision of the air pollution-mortality association declines and even
includes negative values.

In spite of the results, the painstaking and original work of Lave and
his colleagues which focused a great deal of academic and regulatory
interest on the existence and the size of an air pollution-human
mortality relationship must be recognized. Nevertheless, there is a
pressing need for additional research into reducing the specification
uncertainty associated with this relationship. To accomplish this goal,
the suggestion that further air pollution epidemiology research employ
data on individuals is made. The effect will be to allow the use of a
limited set of stronger Bayesian priors.

C. [The Adequacy of Natural Science Information]{.underline}

In the third and concluding section of Volume VII, an alternative
Bayesian framework is presented with the purpose of assessing the
adequacy of natural science information to effectively evaluate
environmental regulations. In effect, the framework explicitly captures
the statistical and the model uncertainty associated with yield response
estimates. Moreover, by penalizing alternative ambient standards which
-are possibly too conservative or too optimistic, the framework allows
for uncertainty to be incorporated directly into the decision process.

The framework is applied to four major field crops having dissimilar
growing requirements and diverse market levels differing by as little as
10 percent and by no more than 25 percent from levels associated with
the current Federal ambient standard of 12 ppm. Results indicate that
for all four crops, individually and in the aggregate, 40 or fewer
dose-response observations from typical plant science experiments appear
adequate to discriminate among the differences in economic surplus that
the aforementioned ambient ozone levels generate.

VOLUME VIII: MEASURING THE BENEFITS OF CLEAN AIR AND WATER

Volume VIII of this report contains a nontechnical discussion of the
work of a number of investigators at Resources for the Future, the
University of Wyoming, the University of New Mexico, and the University
of Chicago. The focus of these efforts was to develop improved methods
for the economic evaluation of environmental improvements. The work,
sponsored by the U.S. Environmental Protection Agency in a sustained
program in this area of research, is centered on analyses of actual
behavior with respect to environmental goods, including travel to
recreational opportunities of varying quality, prices paid for houses in
environments of different quality, decisions about farm crops depending
upon how they are affected by air pollution.

While a certain confidence adheres to methods based on actual decisions
due to their nonhypothetical nature, these methods are not applicable to
all environmental benefits. For example, they are not

suitable for evaluating visibility effects of air pollution in large
landscapes or to a category of benefits termed nonuser or intrinsic. The
latter are benefits of people who have a preference for environmental
quality in situations in which they do not actually participate. In
these situations contingent valuation methods come into play. These
methods rely on questioning respondents about their willingness to pay
for various hypothetical changes in environmental quality. While doubts
about the accuracy of these methods necessarily arise because of their
hypothetical nature, the research reported in this volume that the
identified sources of possible bias can be controlled for by careful
questionnaire design. There remain, however, some questions for future
research. In particular, the matter of how to get respondents to
evaluate their replies in terms of their overall budgetary situations
invites further inquiry. While the central focus of the research
reported in this volume was methods development, some broad insights
concerning the quantitative benefits from environmental maintenance or
improvement also emerged.

BIBLIOGRAPHY

Chappie, M. and L. B. Lave. 1982. \"The Health Effects of Air Pollution:
A Reanalysis,\" [Journal of Urban Economics]{.underline} 12 (November):
346-376.

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