Download References - Marriott School

THE IMPACT OF COMPANY AIR POLLUTION EMISSIONS
AND RELATED HUMAN HEALTH RISKS
ON THE CROSS-SECTION OF STOCK RETURNS
Dinah A. Koehler
Bernell K. Stone
Abstract
This study investigates how company air pollution emissions and the associated health impact
(cancer risk) are reflected in the cross section of stock returns when other return impacting
variables are isolated from pollution and health effects. Rather than the usual multivariate
regression models used to explain the cross-section of stock returns, we use an alternative
approach (response subsurface methodology) that generates a cross-section of stock portfolios
having a wide range of pollution values while being matched on all the usual cross-sectional
return variables plus additional control variables that are especially pertinent to testing for crosssectional pollution effects. The response subsurface methodology uses a mathematical
assignment program to form a cross-section of portfolios that are matched on all of the usual
cross-sectional return variables (risk, size, book-to-market ratio, earnings-price ratio, dividend
yield, financial structure, profitability, etc.) while simultaneously having a wide range of values
of pollution emissions and/or pollution-related health effects. Thus, this methodology isolates
pollution/health effects from all of the other return-impacting variables by creating a crosssection of matched portfolios that represent a univariate subsurface of the multivariate crosssectional return dependency. By creating a univariate subsurface, this methodology avoids many
of the issues arising from multicolinearity, model misspecification, and other impact
confounding problems.
We use pollution emissions data and pollution health risk measures for a large cross-section of
public companies for 1997 and assess subsequent impact on the cross-section of stock returns for
holing periods of one year (1998) to five years (1998 through the end of 2002). Work in process
finds weak support for a cross-sectional pollution impact when pollution is measured simply by
the volume of emissions but strong support for a health risk impact when emissions of different
types are converted into measures of cancer health risk and lung health risk.
The empirical results are interesting and different from much of the already published literature
on the financial market impact of air pollution. In terms of financial market impact, our results
establish at high levels of statistical significance that how air pollution is measured, emission
volume versus health risk, is an important difference in terms of having a cross-sectional impact
on stock values. Beyond the empirical results per se, the major contribution of this study is
illustrating the response subsurface methodology, contrasting it with the conventional

EPA and Brigham Young University, respectively. Correspondence: Bernell K. Stone, Marriott School of
Management, 626 TNRB, Brigham Young University, Provo, UT, 84602; [email protected]; 801-422-2295
1
multivariate regression methodology, and showing that the response subsurface methodology
provides superior resolution of conditional functional dependencies.
2
THE IMPACT OF COMPANY AIR POLLUTION EMISSIONS
AND RELATED HUMAN HEALTH RISKS
ON THE CROSS-SECTION OF STOCK RETURNS
Dinah A. Koehler
Bernell K. Stone
INTRODUCTION AND OVERVIEW
This research addresses the question, “Do air pollution emissions impact company
value?” In the context of modern portfolio-based valuation theory, empirically assessing
whether pollution impacts stock market value means assessing whether pollution has a
statistically significant impact on the cross-section of stock returns.
Hypothesis Statements
Null Hypothesis: No Impact. Air pollution emissions have no statistically significant
impact on the cross section of common stock returns.
There are two complementary alternative hypotheses.
Alternative Hypothesis One: Positive Return Impact. When all other factors that
influence the cross-section of stock returns are isolated from pollution effects, an increase in air
pollution is associated with a statistically significant increase in stock returns.
Alternative Hypothesis Two: Negative Return Impact. When all other factors that
influence the cross-section of stock returns are isolated from pollution effects, an increase in air
pollution is associated with a statistically significant decrease in stock returns.
The Grouping of Observations in Cross-Sectional Return Tests
In modern cross-sectional return research, the prototypical design for answering the
question of a cross-sectional return dependency is to rank companies on a hypothesized
dependency variable, in this case a measure of air pollution. One then groups these ranked

EPA and Brigham Young University, respectively. Correspondence: Bernell K. Stone, Marriott School of
Management, 626 TNRB, Brigham Young University, Provo, UT, 84602; [email protected]; 801-422-2295
3
companies (stocks) into fractile portfolios. Next, statistical tests are performed to see whether
these fractile portfolios exhibit a significant cross-sectional return dependency after correcting
for all other known return explaining variables.
The most common fractile size has been deciles with quartiles even being common in
recent research. With either quartiles or deciles, the number of observations is somewhat small
for many parametric tests.
One of the questions that we address by forming cross-sections having different numbers
of fractile portfolios is the question of which number of portfolios gives the best resolution of
any cross-sectional dependency of returns on our pollution measures.1
The logic for grouping stocks into fractile portfolios is three-fold. First, any companyspecific performance that is not related to the rank ordering variable (pollution in this study)
should diversify away. Second, some of the included explanatory variables (e.g., beta and
financial structure) that are measured with error will also average out.2 Third, it is hoped that
other value-relevant variables that are not included in the assumed explanatory model for the
cross section of stock returns will also tend to average out and not significantly distort inference
about pollution effects (i.e. omitted variables bias). In the context of assessing cross-sectional
pollution dependencies, some company attribute variables that are hard to measure and
incorporate in a cross-sectional return study include attributes such as company image,
management quality, corporate governance, and socially responsibility other than pollution.
Cross-Sectional Return Tests: Synthesis
To synthesize, cross-sectional return studies use the grouping of rank ordered stocks into
fractile portfolios:
1. to reduce dramatically unexplained return variation in individual stocks (possibly greater
efficiency);
1
Stone [2003] shows that low correlation in the sample of stocks can be magnified by rank-based grouping,
especially when the sample is reduced to fewer than 15 portfolios. The implication of this research is that the cross
section should contain at least 20-plus portfolios whenever sample size permits. The common use of deciles and
especially quartiles seems to have overemphasized efficiency (reduction in unexplained variance) at significant cost
in statistical power. See Stone [2003] for more details.
2
Readers are reminded that the standard econometric reason for grouping observations is not diversification of true
random error but rather reduction in measurement error and possibly mitigation of omitted variable and/or
specification error to the extent that the omitted variables have low correlation with the rank-ordering variable.
4
2. possibly to mitigate specification, measurement, and/or omitted variable distortion;
3. to ensure a wide range of values of the ranked explanatory variable (in our case,
pollution).
This rank-based grouping into fractile portfolios can be viewed as a heuristic (judgmental ruleof-thumb) statistical design framework that seeks to trade off the two prototypical objectives of
any systematic statistical design, namely greater efficiency (suppressed measurement error and
also suppressed return variation from individual stock performance) while not losing very much
power (range and distribution of the observations of the explanatory variable). In addition, one
may also mitigate other specification problems, especially averaging away distortion from
omitted variables to the extent that any omitted variables are not highly correlated with the rankordering variable.3
Building on the Conventional Design Framework
The point here is not to critique the conventional rank-into-fractile approach that is
widely employed in contemporary cross-sectional return studies. Rather, the intent here is twofold. First, it is to explain the underlying statistical logic for grouping a large number of stocks
(observations) into a small number of fractile portfolios. Second, it is to recognize that this
design can be improved upon. Rather than stopping with the ranked cross section of fractile
portfolios, we recognize that we can view the set of non-pollution explanatory variables as
“controls.” Rather than dealing with the cross-sectional variation in the many non-pollution
explanatory variables that impact the cross-section of stock returns by estimating a difficult-tospecify multivariate regression model, we recognize that we can input the fractile portfolios into
an optimization program that can find the best reassignment of stocks to ensure that all
explanatory variables other than the pollution variable of interest are identical in each fractile
portfolio in the cross section while preserving a wide range of well-distributed pollution values.
In effect, we can isolate the impact of pollution on the cross section of stock returns by
3
Stone [2003a] questions this common argument, especially when the number of portfolios is fewer than 20. The
problem is that small sample correlation can be magnified by rank-based grouping so that, contrary to the current
conventional wisdom, omitted variable distortion can be worse for the ranked portfolios than it would be in the
underlying sample.
5
forming an optimal cross section of portfolios that have no variation in a pertinent set of
explanatory variables other than pollution.4
The major benefit of a cross section of matched portfolios is reducing the question of
whether pollution has any impact on value from a difficult-to specify-and-estimate multivariate
statistical test to a simple univariate test. From the viewpoint of discussing the results of a cross
sectional return dependency study, we can show policy makers and others a “picture” that is a
plot of return versus pollution knowing that the impact of all other explanatory variables has
been removed from this cross section. The return-pollution plots presented in Exhibit 3 of our
results section illustrate these pictures.
POLLUTION MEASURES
To operationalize a test of the impact of pollution on the cross section of stock returns,
we must define a pertinent measure of pollution. Our focus is air pollution. One obvious
measure is volume of emissions. To fit a cross sectional return study, any volume measure of air
pollution emissions should be size scaled, e.g., annual tons per dollar of assets, annual tons per
dollar of “production assets” (property, plant, and equipment), annual tons per dollar of annual
sales, or possibly annual tons per dollar of stock market value.
Air pollutants are not all equal! In terms of both economic effects and return valuation
effects (including especially legal liability), air pollution health risk is more pertinent than sizescaled emission tonnage as this more appropriately reflects the variability in impact on human
health. We focus here primarily on the aggregate U.S. population heath impacts of emissions of
toxic chemicals and of particulate matter less than 2.5 micrometers in diameter (PM 2.5).
Portfolios are formed based on rank-ordering by five different continuous measures of pollution:
(i) 1998 mass air emissions of toxic chemicals (TRI), the current approach of social science
researchers and the SRI community; (ii) 1998 mass TRI air emissions per $1 million value-added
4
Assume that return is a function of K explanatory variables. Empirically estimating how R depends on these K
variable is an K-variable response surface estimation problem, a very difficult estimation task when there is
significant multicolinearity and K is five or more and when at least some of the functional dependencies are
nonlinear and interactive (e.g., financial structure and tax effects) rather than linear and separable. In using a
mathematical assignment program to eliminate any cross-sectional variation in the fractile portfolios, we are
generating observations that belong to a univariate subsurface XXX of the K-variable multivariate. For more on
response surface-subsurface methods, readers are referred to book by Khony and Cornell [1996] and especially the
article by Stone, Adolphson, and Miller [1993].
6
(VA); (iii) the U.S. population cancer risk per $1 million VA associated with these 1998 TRI air
emissions; (iv) premature mortality associated with 1998 emissions of particulate matter less
than 2.5 micrometers in diameter (PM 2.5); and (v) combined TRI and PM2.5 risk per VA. Prior
research on public health impacts of U.S. industry supply chains find that direct industry impacts
associated with emissions from a sector are not representative or indicative of that sector’s total
supply chain impacts (direct + upstream) (Koehler, Bennett et al. 2005; Nishioka, Levy et al.
2003). Therefore, we prefer to use total supply chain public health impacts to more
comprehensively represent the social impact of an industry’s economic activity.
As suggested by Freeman (1984), one way to determine social goods is to determine the
costs of social damage relative to the benefits of industry production. Koehler et al (Koehler,
Bennett et al. 2005) use value-added (VA) as a measure of economic benefit to normalize public
health damage. The aggregate of all VA in the U.S. is gross domestic product (GDP). Other
researchers have used revenue or market capitalization to control for differences in firm
economic scale (Banz 1981; Fama and French 1992; Fama and French 1993; Konar and Cohen
1997c). To compare between firms and industries, value-added is clearly more pertinent than
traditional financial measures based on market capitalization, the balance sheet and income
statement, which can be distorted by use of debt financing (i.e. financial structure) and are
subject to market sentiment.
Human exposure to TRI emissions and to PM2.5 can be estimated with the intake
fraction, defined as the total potential human dose as a function of total exposure relative to the
source term (Bennett, McKone et al. 2001). The intake fraction is multiplied by linear dose
response and concentration-response functions of human health hazard for TRI and PM2.5
respectively to estimate excess cancer cases and premature mortality (Koehler, Bennett et al.
2005; Nishioka, Levy et al. 2003). The concentration-response function underlying PM2.5
premature mortality predicts annual adverse outcomes due to respiratory ailments and lung
cancer that are specific to an emissions year. Thus, 1998 emissions are associated with 1998
premature deaths. To estimate cancer risk associated with TRI emissions, on the other hand,
human exposure to pollutants is averaged over a 70-year biological lifetime to which a doseresponse function is applied. It is impossible to say with certainty (i) when that cancer case will
occur over the 70-year exposure duration, and (ii) whether the cancer type is fatal or not. The
simplest approach would be to assume a uniform distribution of excess cancer cases for the US
7
population over 70 years and 100% mortality. Assuming a uniform distribution Koehler et al
(Koehler, Bennett et al. 2005) estimate that 1998 emissions of a subset of carcinogenic TRI
compounds yield 260 excess cancer cases.
Assuming 100% mortality for excess cancer cases is one possible manner to calculate an
aggregate public health index that combines cancer risk of TRI air emissions and premature
mortality associated with PM2.5 emissions. In so doing we combine outcomes (death), rather
than incidence of disease. This approach is plausible if premature mortality from PM2.5
emissions per unit of economic benefit is greater than TRI cancer risk per unit of economic
benefit. In fact, the average 1998 risk of premature mortality is 6 x 10-2 per $1 million 1998
value added, which is greater than the average annual cancer risk, 1.4 x 10-4 per $1 million 1998
value added, as consistent with earlier analyses (Abt-Associates, ICF-Consulting et al. 2000;
Woodruff, Caldwell et al. 2000). Should estimated TRI cancer risk be greater than estimates of
PM2.5 premature mortality in a particular year in future analyses, additional refinement to reflect
the fact that many cancers are treatable and thus nonfatal is possible using 5-yr survival rates for
cancer (Rowe, Lang et al. 1995; Koehler, Bennett et al. 2003). This question of comparability
between cancer risk and PM2.5 mortality arises in only one of our cross-sectional dependency
assessments, namely where we use an aggregate measure of human health impact.
CROSS SECTIONAL RETURN VARIABLES
Exhibit 1 lists and defines possible explanatory variables.
The CAPM (Capital Asset Pricing Model) asserts that returns should be a fair return for
time plus a return for non-diversifiable market risk as measured by beta. Other researchers have
shown that there are other return impacting variables. Basu [1972] showed that the cross section
of stock returns generally had a significant dependency on the price-earnings ratio of the stocks.
Keim [1981] and Banz [1981] established that firm size as measured by stock market
capitalization (the market value of a common stock) was another cross-sectional return variable.
Fama and French [1992] established that both the book-to-market ratio and firm size were
pertinent cross-sectional return variables along with beta.
Gerard, Gultiken, and Stone [1997] show that, in addition to book-to-market ratio and
earnings-price ratio (the two standard measures of value attractiveness), both cash-to-price and
sales to price are not only explanatory variables for the cross-section of stock returns but also
8
have sufficient persistence to give an appropriately weighted combination of past values of these
four value variables significant portfolio-level predictive power.
Going Beyond the Conventional Fair Risk-Return Trade-Off Models
We view the capital asset pricing model and the now widely used Fama-French threefactor model and other established return valuation variables such as cash-to-price, sales-to-price
as a starting point in fully explaining the cross section of stock returns. There are other wellestablished cross-sectional valuation effects.
1. Taxes. The differential taxation of capital gains and dividends says that the form of
return (gains versus dividends) is pertinent to the extent that the marginal investor has a
positive tax rate.5
2. Financial Structure. Theories of financial structure say that the mix of debt and equity
is pertinent to company value and therefore pertinent to explaining the cross section of
stock returns.
3. Financial Performance: Growth and Profitability. Clearly, both firm growth and
profitability are relevant to value and therefore should be pertinent as well to explaining
the cross section of stock returns. We use five year sales’ growth as the best predictor of
future growth and cash flow. We use return on investment and return on equity as
accounting measures of profitability that are both possibly pertinent to the cross section
of stock returns.
The Issue of the Interaction of Pollution With Growth and Profitability
In the list of additional valuation variables that should be included in any explanation of
the cross-section of stock returns, the reasons for both tax effects and financial structure are well
established in the finance literature and are subject to very little debate as to relevance. Any
controversy pertains to how to properly model tax and financial structure effects, especially since
tax and financial structure effects interact and are generally nonlinear. Of course, we side-step
the question of how to properly model tax and financial structure effects by ensuring that both
tax and financial structure effects are the same in all portfolios. Therefore, we ensure at the
portfolio level (when we constrain our portfolios to have the same average value of earnings
5
For instance, see Blume (1980), Brennen (1970), and Lease (2000).
9
yield, dividend yield, and percentage of debt financing) that neither tax nor financial structure
effects can distort the return-pollution cross-section.
The Nasty Question of Industry Effects Versus Pollution Per Se
Pollution levels and type are often attributes of industries due to a common technology.
Thus, there is danger that any measure of pollution is an instrument of industry effects.
Controlling for industry effects is always pertinent in a cross-sectional return study and is
especially critical for having a meaningful assessment of the impact of pollution on the crosssectional stock returns.
One way to deal with industry effects would be to use standard industry classification
codes as dummy variables and to reassign companies in the ranked cross section of fractile
portfolios to obtain a reasonable balance of industries in all of the portfolios in a cross section.
There are numerous problems with this idea. One is the loss of power if there is
extensive reassignment to obtain an industry balance. More severe is simply having a good
industry classification measure, especially when we recognize the fact that many companies
produce goods in several different industries. General Electric is an extreme example of a multiindustry company.
An alternative to using industry codes is to use the financial attributes of companies to
represent economic reasons that industry membership can impact performance. In additional to
profitability, growth, financial structure, dividend yield, and earnings yield (which are already
included explanatory variables), we note some additional measures:
 Capital intensity ( Sales per dollar of total investment)
 Asset mix (fraction of total assets that are 1) short-term, 2)long-term property, plant,
and equipment, or 3) other).
 Profit margins (earnings per dollar of sales)
We believe that reassigning stocks to ensure an exact match on industry characterizing
variables should avoid an undue concentration of industries in one or two portfolios of the cross
section. Of course, this belief can be checked empirically by assessing both the initial industry
distribution in the starting cross section of fractile portfolios and the final industry distribution in
the cross section of matched portfolios. We return to this assessment in our presentation and
discussion of results.
10
SAMPLE
Our pollution data and conversion to measures of health risk is developed in Koehler et al
[2005] and Nishioka et al [2003] and are normalized by value-added. In this analysis valueadded (VA) refers to the non-industrial (i.e. non-commodity) inputs to production, such as labor
and capital, that comprise the U.S. GDP (Lawson 1997). VA is broken into three component
parts: (i) compensation of employees, (ii) indirect business tax and nontax liability and (iii) other
value added. The first two are estimated based on data from the National Institute of Pension
Administrators (NIPA), the Bureau of Labor Statistics, Bureau of Census, Office of Management
& Budget and the U.S. Dept. of Treasury. “Other value added” relates to additional non-material
inputs to production, such as corporate profits and consumption of fixed capital.
Financial data for 1998-2002 are from CRSP for financial returns and Compustat for
financial statement databases for a sample of 1748 firms for which financial data were available
over the entire five-year study period.6 By using firm level financial data to evaluate portfolios
based upon industry level health impacts, we assume that publicly traded firms are representative
of each industry group in terms of average pollution and average financial characteristics.
Analysis of data used by King and Lenox (King and Lenox 2001) shows that 50% of TRI
reporting facilities are owned by a publicly traded parent, and in 1997 these accounted for 67%
of all releases. The important assumption is that any differences between public and private firms
are not systematically related to pollution, and that they can sufficiently be diversified across
portfolios. In other words, by excluding privately owned firms from this analysis we do not
induce an effect of pollution on returns. 7
LOGIC OVERVIEW
Exhibit 2 is an overview of the study logic and is self explanatory.
MATHEMATICAL ASSIGNMENT PROGRAM: THE OPTIMIZER
6
This sample results after eliminating all records that had missing observations for any of the control variables and
financial companies sic codes 6000-6999 and those with incorrect shares. 623 were lost due to missing returns
between 1998-2002.
7
We note that private firms may be less likely to submit to public pressures or concerns with image than publicly
traded firms. This is possible in the aluminum industry, where one privately-owned firm, the Ormet Corporation, has
until recently continued to emit high levels of a very toxic air pollutant despite regulator and community pressure.
11
Ranking on pollution and grouping into fractile portfolios produces a set of pollutionordered portfolios that will almost certainly have a wide range of pollution values but will almost
never have the same values in every portfolio for all of the control variables listed in Exhibit 1.
To produce a cross-sectional match on any of the control variables, we must reassign
stocks. For instance, if we were trying to make each portfolio have the same beta value, we
could move a stock with an above-average beta value into a portfolio whose average beta value
is below the population average. At the same time, we could shift a stock with a below-average
beta value into the above average portfolio from the below-average portfolio. Just to produce a
match on a single explanatory control variable such as beta clearly entails an immense number of
possible reassignments of stocks across portfolios. Moreover, the objective is not just to match
each portfolio in the cross section on the portfolio average value of beta but to find that particular
match that preserves as much as possible both range and the cross-sectional distribution of beta.
Fortunately, we do not have to use trial-and-error switching of stocks between portfolios
to find the best reassignment that produces a cross-sectional match on beta or any other
explanatory variable. This reassignment problem can be formulated as a mathematical
assignment program. The objective is to maximize the range of pollution values in the cross
section. The operational constraints include:
1.
All fractile portfolios should have explanatory controls equal to their population
average value.
2.
The cross-sectional distribution of pollution values should be preserved as much as
possible.
So far we have illustrated the problem in the context of producing a cross-sectional match on one
explanatory variable: beta. To move to the Fama-French three-factor model, we could add to
the mathematical assignment program constraints that all portfolios in the cross section have the
same population average value of the book-to-market ratio and also the same population average
value of size.
We have intentionally illustrated going from pollution-ranked fractile portfolios with no
control constraints, then to a pollution-ranked fractile portfolio with just a beta constraint
(CAPM). And then to a third cross section with the Fama-French three-factor variables (beta,
book-to-market, size) as the control constraints. This step-wise imposition of control constraints
enables us to see how producing a match on each control or combination of control variables
12
impacts of the cross section of stock returns. Without further elaboration, we simply state the
primary order in which we look at matched cross sections:
1. no constraints
2. beta only
3. Fama-French, three-factor
4. Additional financial-tax controls: %debt, earnings yield, dividend yield
5. Additional growth and profitability variables: 5-year sales growth, 5-year sustainable
growth, return on investment and return on equity
6. industry: sales intensity, asset intensity, asset composition
SYNTHESIS OF REASSIGNMENT OPTIMIZER
Appendix A (not included with this draft) gives a detailed development of the
mathematical assignment program with elaboration on the step-wise imposition of the control
constraints. However, the substance of the reassignment process is well understood by knowing
input and output. The input is a cross section formed by ranking stocks into fractile portfolios,
which is the focus of most cross-sectional return analysis in past work on cross-sectional return
dependencies. The output is a cross section of fractile portfolios that are matched on a specified
set of explanatory controls. Optimization arises in finding the particular reassignment that
maximizes a trade-off between the range of pollution values and minimizing distortion in the
original distribution of pollution values across the cross section of pollution-ranked fractile
portfolios.
The original input cross section is transformed by the optimal reassignment of stocks to
produce a new cross section that is matched on values of a set of explanatory controls while
optimizing power to resolve any dependency of returns on the measure of pollution. Given our
sample data, a decision about the number of portfolios to be formed, and an initial rank-ordering
in fractile portfolios, we are maximizing the statistical power to test for pollution dependencies
by generating portfolio observations that belong to a well-defined univariate subsection of the
overall cross sectional return dependency.
SOME RESULTS AND ILLUSTRATIVE RETURN POLLUTION CROSS SECTIONS
FOR THE 1998-2002 TIME PERIOD
13
Our pollution data is for 1998. We used return and financial statement data from 1998
and before in forming portfolios and in producing in a stepwise fashion a series of more
completely matched cross-sections. For each of these steps, we generated return cross-sections
for 1, 2, 3, 4, and 5 year holding periods beginning in 1998.
We provide here illustrative cross-sectional return plots for just the five-year holding
period from the start of 1998 through the end of 2002. We note that the five-year period from the
start of 1998 through the end 0f 2002 is one in which:

the economy moves up and then down including a period of recession

interest-rates move up and then down

market indices first move up and then down.
The point to be made here is that this five-year period very nicely covers ups and downs
in the real economy, in interest-rates, and in the stock market itself. Thus, critical issues such as
the type of economy/market and directional bias tend to be averaged out for this time period. To
the extent that we can assume that 1998 pollution levels persist over the next five years, the 1998
period is the most interesting of the five holding periods because it provides the largest sample of
return observations and averages out both economic and market cycles.
Exhibit 3 provides cross-sectional return-pollution plots for four alternative measures of
pollution.
1. Emission volume measured by the log of kilograms of emissions.
2. Emission volume measured by the log of kilograms of emissions per dollar of
economic value added (emissions scaled by size of economic production).
3. Cancer health risk (See Koehler et al [2005]) as measured by the log of cancer risk
per unit of economic value added.
4. Lung health risk (See Nishioka et al [2003]]) as instrumented by the log of PM 2.5
per unit of economic value added.
The four figures in Exhibit 3 are for the case of all of our set of explanatory variables being
matched. The figures correspond to Model 5 in the table of regression results summarized in
14
Exhibit 4. Visual inspection of the four figures in Exhibit 3 suggests no strong cross-sectional
return dependency in the first two figures and a very strong negative dependency in the last two
figures.
Exhibit 4 summarizes a linear regression estimate of the extent to which one of the
pollution measures explains the cross-sectional variation of stock returns. In addition to the four
pollution measures already discussed, we add a composite of cancer and lung health risk. Recall
that the five models tabulated in Exhibit 4 correspond to our step-wise addition of progressively
more explanatory variables as matched controls, namely:
1. Model 1: no controls
2. Model 2: controlling only for beta (CAPM)
3. Model 3: controlling only for beta, book-to-market, and size (Fama-French 3-factor)
4. Model 4: adding financial structure, earnings yield, and dividend yield (adding to the
Fame-French 3-factor models controls to suppress any valuation effects from
financial structure and/or taxes beyond the Fama-French model)
5. Model 5: adding to Model 4 variables to control for differences in growth,
profitability, and hopefully industry differences.
Models 4 and 5 go well beyond the conventional return-risk trade-off controls and are clearly the
most complete and relevant for assessing cross-sectional return dependencies on pertinent
pollution measures. Exhibit 4 shows that each of the three measures of air pollution health risk
have a significant negative cross-sectional return dependency for both models 4 and 5, generally
beyond the .001 significance level.
Looking for patterns/trends in coefficient significance, F-value, and R-square values as
we move down each column in Exhibit 4, we see that for each of the three health risk cross
sections, going from Model 1(no controls) to Models 4 and 5 generally produces an
“improvement” (greater explanatory power). We believe that this increase in significance and
explanatory power shows that just using the conventional return-risk trade-off frameworks is not
adequate for assessments of pollution effects in cross-sectional return cross-sections. Given the
difficulty of correctly modeling the valuation effects associated with Models 4 and 5 in a
15
multivariate regression framework, we believe the increase in explanatory power makes a strong
case for the value of the response subsurface framework illustrated here.
While this is still ongoing research, we here suggest a few interpretations of our initial
findings. The negative market reaction to this information may be due to (i) investor’s
interpreting high hazardous air emissions to imply high future control costs due to regulation, (ii)
a low value-added relative to other industries, or (iii) a combination of both. We discuss the
environmental regulation scenario first. While we cannot expect investors to be aware of the
public health impacts associated with emissions of specific pollutants, it may still be possible
that investors are aware of regulation of industries that emit these specific pollutants. In fact, in
the late 1990’s the most hazardous TRI compounds included in this analysis, dioxins and
polycyclic aromatic hydrocarbons, have been the focus of industry specific regulation that
implies control costs. Industries, such as aluminum and cement, that emit these pollutants have
been regulated in 1997 and 1999 respectively, and need to comply with stringent Maximum
Achievable Control Technology (MACT) standards that limit emissions from both new and
existing facilities.
Similarly, ambient air PM2.5 concentrations are regulated under the National Ambient
Air Quality Standards (NAAQS) promulgated in 1997. PM2.5 regulation will have the greatest
impact on the east and west coasts of the U.S. which are plagued by a contiguous haze that has
significantly increased from 1960 to 1990. The impact of PM2.5 regulation is likely to be
greater, because secondary particles known to be hazardous to human health can be formed with
ammonium sulfate and ammonium nitrate. Reducing PM2.5 concentrations will thus also involve
regulation of sources of sulfur dioxide, nitrogen oxides and ammonia (USEPA 1998). These
regulations in combination are likely to have an effect on future firm cash flows related to
anticipation of pollutant related control costs. Such an expectations effect, measured over five
years, could be an event if it were clear when costs will occur, or an ongoing revision of
expectations with respect to pollution news should the cost impact be uncertain. The latter is
possible with implementation of the PM2.5 ambient air concentration standard, which while
promulgated in 1997, has been challenged in court and thus delayed its implementation.
Another plausible explanation is that investors are not attracted to industries with low
value-added and generally low expected cash flows, which may be more likely for firms using
old polluting technologies. Our measure of environmental impact may in fact be a proxy for age,
16
or “vintage.” It is possible that for this firm sample effective tax (ET), a cash flow measure of tax
impact, may be a control for “vintage.”8 ET may nevertheless be an insufficient control for a
vintage effect, and thus our pollution measure may be picking up some of this effect.
Alternatively, investors may be reacting to information embodied in the measure of value-added
used here to reflect differences in economic size. However, ROI will tend to control for crosssectional variation in value-added. Thus value-added should not be a primary source of crosssectional dependence on a measure of public health impact after Model 5 controls are imposed,
though this question merits further research.
ON-GOING RESEARCH
We have in process work that is looking at alternatives to value added for size scaling, doing
extensive sensitivity analysis including especially the issue of industry effects, and using more
portfolios in our return cross-section. In our forthcoming workshop, we plan to report on this
research in addition to discussing and getting feedback on the results highlighted here.
8
A relatively higher ET during this time period would arise due to a relatively lower depreciation expense, which is
related to the average age of facilities in that industry. With the use of accelerated depreciation a firm can write-off
more costs in the early years of a facility, versus a firm with older facilities, which cannot write-off as much and
hence pays higher taxes.
17
REFERENCES
Barnett, V., and T. Lewis (1978). Outliers in Statistical Data, New York: John Wiley & Sons,
Inc.
Banz, R. W. (1981). “The Relationship between Return and Market Value of Common Stocks”
Journal of Financial Economics 9(1): 3-18.
Bauer, R., K. Koedijk, et al. (2002). International Evidence on Ethical Mutual Fund
Performance and Investment Style (Maastricht: Limburg Institute of Financial Economics,
Maastricht University).
Bennett, D. H., T. E. McKone, et al. (2001). “Defining Intake Fraction” Environmental Science
and Technology 36: 206A-211A.
Blume, M. (1980). “Stock Return and Dividend Yield: Some More Evidence,” Review of
Economics and Statistics, 62, 567-577.
Blume, M.E., M.N. Gultekin, & N.B. Gultekin (1990). “Validating Return-Generating Models.”
Working Paper.
Brennen, M. (1970). “Taxes, Market Valuation and Financial Policy,” National Tax Journal, 23,
417-429.
Box, G.E.P. (1954). “The Exploration and Exploitation of Response Surfaces,” Biometrica, 10,
16-60.
Box, G.P.E., and N.R. Draper (1959). “A Basis for the Selection of a Response Surface Design,”
Journal of the American Statistical Association, 54, 622-654.
Campbell, J. (1991). “A Variance Decomposition of Stock Returns” The Economic Journal
101(March): 157-179.
Campbell, J., A. Lo, et al. (1997) The Econometrics of Financial Markets. (Princeton: Princeton
University Press).
Campbell, J. Y. (2000b). “Asset Pricing at the Millennium” Journal of Finance 55(4): 15151567.
Cerin, P. and P. Dobers (2001). “What does the Performance of the Dow Jones Sustainability
Group Index tell us?” Eco-Management & Accounting 8: 123-133.
Clough, R. R. (1997). “Impact of an Environmental Screen on Portfolio Performance: A
Comparative Analysis of S&P 500 Stock Returns.”
Fama, E. (1998). “Market Efficiency, long-term returns and behavioral finance” Journal of
Finance Economics 49: 283-306.
18
Fama, E. and K. French (1992). “The Cross-Section of Expected Stock Returns” Journal of
Finance 47: 427-465.
Fama, E. and K. French (1993). “Common Risk Factors in the Returns on Stocks and Bonds”
Journal of Financial Economics 33: 3-56.
Fama, Eugene F., & Kenneth R. French (1995). “Size and Book-to-Market Factors in Earnings
and Returns.” Journal of Finance, 50, 131-155.
Fama, Eugene F., & Kenneth R. French (1996). “Multifactor Explanations of Asset Pricing
Anomalies.” Journal of Finance, 51, 55-84.
Fama, E. F. and J. D. MacBeth (1973). “Risk, Return and Equilibrium: Empirical Tests” Journal
of Political Economy 81: 607-636.
Garber, S. and J. K. Hammitt (1998). “Risk Premiums for Environmental Liability: Does
Superfund Increase the Cost of Capital?” J. Environ. Econom. Management 36: 267-294.
Guerard, J.B., Jr. (1997). “Is There a Cost to Being Socially Responsible in Investing?” Journal
of Forecasting, 16, 475-490.
Guerard, J.B., Jr., M. Gultekin, & B.K. Stone (1997). “The Role of Fundamental Data and
Analysts” Earnings Breadth, Forecasts, and Revisions in the Creation of Efficient Portfolios,” in
A. Chen (Ed). Research in Finance, Greenwich, CT. JAI Press, 69-91.
Hamilton, J. T. (1995) “Pollution as News: Media and Stock Market Reactions to the Toxics
Release Inventory Data,” J. Environ. Econom. Management 28: 98-113.
Heinkel, R., A. Kraus, et al. (2001) “The Effect of Green Investment on Corporate Behavior”
Journal of Finance and Quantitative Analysis 36(4): 431-449.
Ilinitch, A. Y., N. S. Soderstrom, et al. (1998) “Measuring Corporate Environmental
Performance” Journal of Accounting & Public Policy 17(4,5 (Winter)): 383-408.
Lease, Ronald C., Kose Joh, Avner Kalay, Uri Loewenstein; and Oded H. Sarig (2000).
Dividend Policy: Its Impact on Firm Value, Boston: Harvard Business School Press.
Karpoff, J. M., J. R. Lott, et al. (1998). Environmental Violations, Legal Penalties, and
Reputation Costs : John M. Olin Law & Economics Working Paper No. 71).
Khanna, M., W. R. H. Quimio, et al. (1998). “Toxics Release Information: A Policy Tool
for Environmental Protection” J. Environ. Econom. Management 36: 243-266.
King, A. and M. Lenox (2001). “Does it Really Pay to be Green? Accounting for Strategy
Selection in the Relationship between Environmental and Financial Performance” Journal of
Industrial Ecology 4(4 Fall): 105-116.
19
Khuri, A.I., and J.A. Cornell (1996). Response Surfaces: Designs and Analysis, New York:
Marcel Dekker, Inc.
Konar, S. and M. A. Cohen (1997a). “Information as Regulation: The Effect of Community
Right to Know Laws on Toxic Emissions” J. Environ. Econom. Management 32: 109-124.
Konar, S. and M. A. Cohen (1997b) Does the Market Value Environmental Performance?
Lawson, A. M. (1997) “Benchmark Input-Output Accounts for the U.S. Economy, 1992; Make,
Use and Supplementary Tables” Survey of Current Business November 1997: 36-82.
Lease, Ronald C., Kose Joh, Avner Kalay, Uri Loewenstein; and Oded H. Sarig (2000).
Dividend Policy: Its Impact on Firm Value, Boston: Harvard Business School Press.
Lintner, J. (1965) “The Valuation of Risky Assets and the Selection of Risky Investments in
Stock Portfolios and Capital Budgets” Review of Economics and Statistics 47: 13-37.
Mitchell, R. K., B. R. Agle, et al. (1997) “Toward a Theory of Stakeholder Identification and
Salience: Defining the Principle of Who and What Really Counts” Academy of Management
22(4): 853-886.
Myers, R.H. (1976). Response Surface Methodology, Boston: Allyn & Bacon.
Myers, R.H., A.I. Khuri, W.H. Corter, Jr. (1989). “Response Surface Methodology: 1966-1988,
Technometrics, 31, 137-157.
Peterson, P., D. Peterson, and J. Ang (1985). “Direct Evidence on the Marginal Rate of Taxation
on Dividend Income, “ Journal of Financial Economics, 14, 267-282.
Reinganaum, Mark R. (1981). “Misspecification of Capital Asset Pricing: Empirical Anomalies
Based on Earnings” Yields and Market Value,” Journal of Financial Economics, 9, 19-46.
Rowe, R. D., C. M. Lang, et al. (1995). The New York Electricity Externality Study Volume 1:
Introduction and Methods (new York: Empire State Energy Research Corporation).
Sharpe, W. (1964). “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of
Risk” Journal of Finance 19: 425-442.
Sharpe, W. F. (1991). “The Arithmatic of Active Management” The Financial Analysts”
Journal 47(1): 7-9.
Sharpe, W. F. (1992). “Asset Allocation: Management Style and Performance Measurement”
Journal of Portfolio Management Winter: 7-19.
20
Stone, B. K. (2003a). Rank-Based Security Grouping, Multicolinearity, and the Assessment of
Cross-Sectional Return Dependencies, Working Paper. (Provo, Utah: Marriott School, Brigham
Young University).
Stone, B. K. (2003b). Fundamental Determinants of the Impact of the Book to Market Variable
on the Cross-Section of Stock Returns, Working Paper (Provo, Utah: Marriott School, Brigham
Young University).
Stone, B. K. and G. L. Adams (2003d). The Impact of Cross-Sectional Variation in the Effective
Tax Payment Rate on the Cross-Section of Stock Returns, Working Paper (Provo Utah: Marriott
School, Brigham Young University).
Stone, B. K., D. L. Adolphson, and Tom W. Miller (1993). “Optimal Data Selection and
Grouping: An Extension of Traditional Response Surface Mythology to Observational Studies
Involving Noncontrolled Empirical Data Generation” Advances in Mathematical Programming
and Financial Planning 3: 39-68.
Stone, B. K., J. B. Guerard, et al. (2001). “Socially Responsible Investment Screening: Strong
Empirical Evidence of No Significant Cost for Actively Managed Value-Focused Portfolios”
Working Paper (Provo, Utah: Marriott School, Brigham Young University).
Stone, B. K. and T. W. Miller (2002). The Impact of Accounting Profitability Measures on the
Cross-Section of Stock Return, Working Paper. (Provo, Utah: Marriott School, Brigham Young
University).
USEPA (1998) Stationary Source Control Techniques Document for Fine Particulate Matte
(Research Triangle Park, NC: US EPA Air Quality Strategies and Standards Division).
21
Exhibit 1
Summary of Return Impacting Variables
SYMBOL VARIABLE DEFINITION
VARIABLE NAME
Beta

 = Cov(Rs-Ro, RM – Ro) / Var(RM – Ro) measured over 3
years of past monthly returns, where Ro is the riskless rate
and RM is the market index.
Book-to-Market ratio
B2M
Ratio of BV/MV where BV is accounting book value (total
common equity) and MV is market value of common stock
Market Cap (Market
MV
The market value of common stock at a point in time
EY
The ratio of Net Income to market value, the reciprocal of
Value)
Earnings Yield
the price-earnings ratio
Dividend Yield
DY
The ratio of Annual Dividends to Market Value
Financial Structure
FS
The fraction of Total Investment provided by debt and
preferred stock
Effective Tax Rate
ET
The ratio of Tax Payments to Net Income
Return on Investment
ROI
The ratio of Operating Income (before extraordinary
income and expenses) to Total Investment
Return on Equity
ROE
The ratio of Net Income to Book Value
Sales Intensity
SI
The ratio of Sales to Total Investment
Sales Growth
SAG
Five-year average sales growth
Average Margin
AM
The Ratio of Operating Income to Sales
Sustainable Growth
SUG
The growth of common equity from retained earnings
22
Exhibit 2-A: Study Logic 1
STUDY LOGIC
DATA
1. Rank Order into Industry Portfolios
2. Step-Wise use of MAP
Model 1: No control restrictions
Model 2: CAPM beta
Model 3: Fama-French 3-Factor
Model 4: 7-Factors: EY, DY, FS, ET
Model 5: Add Growth and Profitability
23
STUDY LOGIC
DATA
1. Rank Order into Industry Portfolios
2. Step-Wise use of MAP
Scatter Plots
Regression Fits
Hypothesis Tests
24
Exhibit 3
The Cross Section of Returns for Different Pollution Measures
y = -0.0244x + 0.2034
R2 = 0.1621
Figure 1
1998-2002 Portfolio Return
0.4
0.3
0.2
0.1
0
-0.1
-0.2
-3.5
-1.5
0.5
2.5
log kg
4.5
Figure 2
y = 0.0049x + 0.1497
R2 = 0.0135
0.3
1998-2002 Portfolio Return
6.5
0.2
0.1
0
-0.1
-6
-4
-2
0
2
log kg/va
25
4
Figure 3
y = -0.3052x - 1.0514
2
R = 0.5615
1998-2002 Portfolio Return
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-5.5
-5
-4.5
-4
-3.5
log total TRI risk/VA
26
-3
-2.5
Exhibit 3. The Cross Section of Returns for Different Pollution Measures (continued)
Figure 4
y = -0.3059x - 0.4848
R2 = 0.5567
1998-2002 Portfolio Return
1.2
1
0.8
0.6
0.4
0.2
0
-0.2
-3.5
-3
-2.5
-2
log total pm2.5 risk/VA
Figure 5
-1.5
-1
y = -0.314x - 0.5059
R2 = 0.591
1.2
1998-2002 Portfolio Return
1
0.8
0.6
0.4
0.2
0
-0.2
-3.5
-3
-2.5
-2
log total TRI + pm2.5 risk/VA
-1.5
27
-1
Exhibit 4. Univariate Regression Results 1998-20021
Model
Coefficient
T-value
F-value
R2
log direct mass emissions (kg)
1
-0.02
-1.28
1.63
0.14
2
-0.03
-1.63
2.65
0.21
3
-0.03
-1.74
3.03
0.23
4
-0.01
-0.32
0.10
0.01
5
-0.02
-1.39
1.93
0.16
log direct kg/VA
1
-0.0272
-1.8042
3.2552
0.2656
2
-0.0226
-1.2258
1.5026
0.1431
3
-0.0179
-1.0864
1.1803
0.1159
4
0.0070
0.4533
0.2055
0.0223
5
0.0049
0.3508
0.1231
0.0135
log total TRI risk/VA
1
-0.23
-1.98
3.92
0.23
2
-0.15
-1.48
2.20
0.14
3
-0.17
-1.67
2.80
0.18
4
-0.30
-5.01**
25.10**
0.66
5
-0.31
-4.08*
16.65*
0.56
log total PM2.5 risk/VA
1
-0.21
-2.42
5.84
0.29
2
-0.16
-1.84
3.39
0.19
3
-0.15
-1.88
3.55
0.20
4
-0.27
-3.21*
10.32*
0.42
5
-0.31
-4.19**
17.58**
0.56
log total TRI + PM2.5 risk/VA
1
-0.23
-2.54
6.44
0.32
2
-0.18
-2.12
4.50
0.24
3
-0.14
-1.78
3.18
0.19
4
-0.25
-3.07*
9.40*
0.40
5
-0.31
-4.50**
20.23**
0.59
1
Estimating equation: R = 0 + 1x1 + 
* p < .01
** p < .001
28