Australian industries environmental performance: Depresses
accounting numbers but enhances market value
Noor Muhammad1, Frank Scrimgeour2, Krishna Reddy3, Sazali Abidin3
Abstract
In this study, we investigate the association between environmental performance and
financial performance of public-listed companies in Australian Stock Exchange which
submitted environmental report to the National Pollutant Inventory (NPI). After controlling
for unobserved firm effects, time variant affects and applying new empirical proxy for
environmental performance, this study finds that there is a strong association between
environmental performance and financial performance. From market based financial
performance perspective, overall results show that there is a positive significant effect of
environmental performance. However, from accounting based financial performance view,
overall results show negative association. Only one model finds positive association from
both accounting and market based financial performance. The results from this study suggest
that managers should have least or no concern for the depressed accounting performance as
firm environmental performance overall improves investors’ perception and enhances
corporate market value.
1
Doctoral Candidate, Department of Finance, University of Waikato Management School
Professor, University of Waikato Management School
3
Senior Lecturer, Department of Finance, University of Waikato Management School
2
1
Australian industries environmental performance: Depresses
accounting numbers but enhances market value
1
Introduction
The quest for financial stability and sustainable finance is an on-going process. Managers
scramble to bring financial stability to their companies. They must do so by having regard to
the common and precious commodity of environment. Managers are normally trying to
enhance company performance in terms of shareholders value. Therefore, it is argued that if
shareholders regard manager’s action in favour of environment, then it must be reflected in
company’s market value of equity. Therefore, in absence of regulatory compliance, corporate
environmental performance ultimately means shareholders response towards environment.
Extant literature over three decades has inconclusive results about this issue. According to the
Horváthová (2010) review of literature, 55% of studies find a positive, 30% find a negative
and 15% find no association between improved environmental practices and financial
performance. Therefore, the case for sustainable finance is inconclusive.
To identify whether or not shareholder regard environmental performance in terms of market
value of firm, we need to solve the puzzle on what actions of firm should be considered as
environmental performance. Unlike financial performance, environmental performance of
firm is not straight forward. It is more abstract phenomenon and it is hard to translate it in
quantitative measures. The risk of mis-measurement will clearly mis-represent environmental
performance and ultimately lead to inaccurate results and analysis. Historically, researchers
have used range of environmental performance proxies. It includes from qualitative measures
to quantitative measures like emission of pollutants. It is evident from literature that
researchers have either used some particular pollutants or gross amount of pollutants with
different level of toxicity to investigate environmental and financial performance nexus. This
2
lack of normalization in environmental performance measure is because of data availability
and limited knowledge about toxicity of different pollutants.
We adopted an innovative measure of environmental performance for the first time
presented by Muhammad and Scrimgeour (2012) to empirically investigate Australian listed
companies from 2005-2010. The motivation of this research is to investigate the
environmental performance and financial performance association in the Australian context.
Environmental consciousness among Australian firms is still an empirical mystery that needs
to be explored. It is pertinent to note that the literature is dominated by Anglo-American
empirical evidences (Horváthová, 2010). After adopting international accounting standards
for reporting purposes from 1 January 2005, Australian companies data is now more
comparable to corporation working in international accounting standards regimes (Australian
Accounting Standard Board, 2004). Environmental performance measure data is taken from
National Pollutant Inventory, Australia. A similar kind of database (European Pollutant
Release and Transfer) is also adopted by many European countries. Therefore, the results
from this study are comparable with other similar American and European studies.
In next section, we cover earlier work related to our topic followed by data and its
sources. We proposed econometric model in section 4. Results and discussion are presented
in section 5. Last section concludes the study.
2
Literature Review
The review of literature on nexus of environmental performance and financial
performance show inconclusive results (Horváthová, 2010). Waddock and Graves (1997)
argued that shareholders do not penalise manager for allocating some of resources towards
corporate social performance including environmental performance. Improved relations with
key stakeholder groups will indeed encounter fewer difficulties and attract new equity
3
investments to the firm and maintain benefits to firm’s value. Echoing this, Horváthová (2012)
concludes that investors value environmental performance. Although environmental
performance has negative impact in short run but it has positive impact in long run. Contrarily,
Cordeiro and Sarkis (1997) have showed that there is no proof that firms sacrifice profits for
social interest. Firms voluntarily adopt environmental protection measures and assume green
responsibilities only when it maximizes their market value, as a firm’s primary objective is
considered to maximize the stockholders wealth.
In general, corporate environmental performance literature can be divided in two
broader strands: 1) the first group of studies can be differentiated on the basis of
environmental performance measures and 2) the second group of studies can be identified on
the basis of econometric methodology. Each of the above group is further divided into three
subgroups.
Delmas and Blass (2010) have divided environmental performance indicators into 3
main categories: (1) Environmental impact: emissions, usage of energy, toxicity/spills, plant
accidents and aftermaths of these accidents e.g. Bhopal Carbide factory incident in India or
more recently British Petroleum (BP) oil spills in the Gulf of Mexico (2) Regulatory
compliance: mandatory installation of treatment and recycling plant, lawsuits concerning
improper disposal of hazardous waste and fines for its clean up (Cordeiro & Sarkis, 1997;
Khanna & Damon, 1999; Klassen & McLaughlin, 1996). (3) Organization process:
improvement in environmental management systems, organisation processes and capital
expenditures in pollution control technology (Gilley, Worrell, Iii, & Jelly, 2000; Klassen &
McLaughlin, 1996; Montabon, Sroufe, & Narasimhan, 2007; Watson, Klingenberg, Polito, &
Geurts, 2004). Different stakeholders use a mix of the above categories to define
environmental performance.
Similarly, Ambec and Lanoie (2008) have categorized research methodologies into (1)
4
portfolio analysis, (2) event studies and (3) regression studies. In portfolio studies, different
equity portfolios’ financial performances are compared with environmental performance. For
example Cohen, Fenn, and Konar (1997) divided companies into high polluting firms
portfolio and low polluting firms portfolio. Event studies analysed the response of particular
events like release of emission data, awards or lawsuits cases of particular stock and its
financial performance. Whereas, in regression analysis, researchers compare the relationship
between firms’ characteristics which include environmental performance and financial
performance.
Bragdon and Marlin (1972) study is considered as the pioneer in this area of research.
They used pollution data from Council on Economic Priorities 4 (CEP) Environmental
Performance Rating5 in the United States for environmental performance. Simple correlation
coefficient showed positive impact of environmental performance on financial performance.
Later on, Spicer (1978) used firms in paper & pulp industry to measure association between
better environmental performance and financial performance. Although the sign for
correlation coefficients were aligned in the hypothesised direction but only 3 out of 6
financial measures were significant.
Studies in 1970’s faced criticism for using small sample size and measurement error.
For example Chen and Metcalf (1980) used the same data set as Spicer (1978) and showed
that the positive association disappear by introducing control variables. Despite of criticism,
these studies still sparked the idea that better environmental performance and financial
performance are complements. This idea is also acknowledged by Narver (1971) that
reducing externalities particularly reducing pollution will reduce variability in earnings and
hence increase firm’s value.
4
CEP is a non-profit organization formed in 1970 to promote socially responsible business policies and
practices including environmental performance of firm in four sectors: electric utilities, steel, oil and paper and
pulp (Abbott & Monsen, 1979)
5
Early US studies used this data exclusively for environmental performance measure (Al-Tuwaijri, Christensen,
& Hughes, 2004)
5
Despite findings of positive impact of environmental performance over financial
performance in early studies, researchers in 1980’s generally found negative relationship. For
example, Mahapatra (1984) claim that pollution control expenditure either legally or
voluntary is drain of resources and thus concluded that investors in general are not ethical
investors. Similarly, Rockness, Schlachter, and Rockness (1986) examined environmental
performance impact of chemical industry. They analysed hazardous waste disposal data from
a special site survey of US congress in 1979. Rockness et al. (1986) failed to report statistical
significant relation of environmental performance to financial performance.
After 1980’s, researchers adopted more advanced measures for both environmental
performance and financial performance. They were also enabled to use more advanced
statistical techniques. Studies in 1990’s generally employed cross sectional or pooled
regression to analyse environmental and financial performance while in next decade panel
data dominated the research (Horváthová, 2012).
Using cross sectional regression, Russo and Fouts (1997) extended knowledge from
the resource-based perspective examining a broader measure of environmental performance
and their effect on return on assets. For environmental performance, they used an independent
rating developed by Franklin Research and Development Corporation (FRDC). Although they
failed to account for some omitted variables and market based performance, they still found a
strong relationship between environmental and economic performance, particularly when
including the moderating role of industry growth.
Hart and Ahuja (1996) employed multiple regression analysis to analyse
environmental performance of companies from 1989 to 1992. Environmental performance
variable was drawn from Investor Responsibility Research Centre (IRRC) Corporate
Environmental Profile. Hart and Ahuja (1996) illustrated two ways to decrease pollution in
order to comply with environmental regulations. Their study assessed environmental
6
performance using accounting based financial performance measures. They suggested that
firms can improve environmental performance by (i) control: emissions and wastes are
trapped, stored, treated and disposed of using pollution control equipment; or (ii) prevention:
emissions and wastes are reduced, changed or prevented altogether through better
housekeeping, material substitution, recycling or process innovation.
They found that
pollution reduction initiatives are positively related to operating and financial performance
measures over a two-year horizon. Firms that made the largest improvements in
environmental performance experienced the most profound economic gains.
Garber and Hammitt (1998) examined the effects on costs of equity for 73 chemical
firms with identified Superfund6 liabilities. They found no relationship between the liabilities
and cost of equity for small firms, but they were able to find a robust positive relationship for
23 large firms. Nehrt (1996) investigated the timing and investment intensity in low pollution
producing paper & pulp manufacturing technology. He used chemical bleach (chlorine gas or
chlorine dioxide) which is a critical chemical in textile industry as environmental
performance variable. The research shows that firms having earlier investments in less
pollution emitting technologies experienced higher levels of growth in profit.
Wahba (2008) analysed 153 Egyptian companies from 2003-2005. He used ISO
14001 awards as a proxy to measure environmental responsiveness. Using random effect
regression, Wahba (2008) concludes that firm having more environmental responsiveness
have higher market value. ISO awards are also used in event studies. For example, Francia
Ayerbe (2009) analysed investor response to ISO 14001 awards by conducting event study.
They find that company financial performance declined on the date of announcement of ISO
14001 awards. Klassen and McLaughlin (1996) tried to find a link between environmental
6
Superfund program was established in United States under the Comprehensive Environmental Response,
Compensation, and Liability Act of 1980 (CERCLA) and amended by the 1986 Act of Superfund Amendments
and Reauthorization Act (SARA). It is the program to clean up the US uncontrolled hazardous waste sites. US
environmental protection agency is committed to ensure that remaining National Priorities List hazardous waste
sites are cleaned up to protect the environment and the health of all US citizens (Garber & Hammitt, 1998).
7
performance awards winning companies and their financial performance. Market prices were
taken as a proxy for stakeholder response under the assumption of the efficient market
hypothesis 7 . It was found that firms receiving environmental performance awards and
increased positive publicity were experiencing higher market value, while negative publicity
had the opposite effect. Since the efficient market hypothesis is challenged (Brown, 2011),
Events studies are criticised for having a short time window. It also ignores the importance of
any other information on stock prices during the study period.
Apart from ISO awards, there are some other event studies in the corporate
environmental performance literature that investigated environmental effects on firm financial
performance. Among these, the well cited study of Hamilton (1995) investigated the effects
of the first Toxic Release Inventory (TRI) announcements of 1989 on firm financial
performance. This event study finds that firms suffered average stock market losses of $4.1
million on the day after negative Toxic Release Inventory (TRI) announcements. It is also
established that investors take TRI as news and takes it into account when making investing
decisions. Yamashita, Sen, and Roberts (1999) also conducted event study methodology and
find that companies with weak environmental consciousness score underperform as compared
to companies that have strong environmental consciousness in long term. Busch and
Hoffmann (2011) find results similar to Hamilton (1995), that firms who had been the target
of Environmental Protection Agency (EPA) investigations and those who had appealed
enforcement actions lost market value when these outcomes were announced.
Using portfolio analysis, Diltz (1995) analysed 28 common stock portfolios from
1989-1991. He concludes that ethical screens, good environmental performance and lack of
nuclear industry involvement enhances portfolio alpha. Gottsman and Kessler (1998)
conclude that portfolios having more environmentally responsible stocks out-perform
7
Majority of event studies assume that financial market has strong form of efficiency. They also ignore other
factor that may influence their analysis (Busch & Hoffmann, 2011).
8
S&P500 index and poor environmentally common stocks portfolio underperform the index.
Contrarily Cohen et al. (1997) argued that investors making investment in green portfolio is
not receiving any premium compared to those investors that invest in environmentally poor
portfolios. Similarly, Filbeck and Gorman (2004) analysed the environmental performance of
electric utilities companies from S&P500 markets. Their study used Investor Responsibility
Research Center (IRRC)’s 2000 Corporate Environmental Profiles Database (CEPD) for
environmental performance and investor holding period yield for financial performance. They
also failed to find any difference between less compliance companies and more compliance
companies in electric utility sector portfolios.
Considering heterogeneity in the extant literature, several researchers employed
different estimation models to analysed environmental performance and financial
performance association and evident different results. King and Lenox (2001) have assessed
652 US firms from manufacturing sector during 1987-1996. They measured environmental
performance as log of gross toxic chemical emission. Ordinary Least Square (OLS) regression
shows some association between market based financial performance and environmental
performance but the direction of this association is uncertain and contingent. They further
suggest that this association may be caused by firm unobserved specific effects and strategic
position. Later on, King and Lenox (2002) evident positive association between total emission
and future financial performance when they used fixed effect regression and controlled for
firm unobserved specific effect. Similarly, Telle (2006) finds a positive association between
environmental performance and financial performance using OLS regression. But he finds no
association after random effect regression. Telle (2006) concludes that such erroneous and
inconsistency in results arises because of omitted variable bias.
Salama (2005) compared OLS and robust median regression results for United
Kingdom based companies. As the median regression analysis is more robust to the presence
9
of firm unobserved heterogeneity and outliers therefore, the study shows stronger results
compared to OLS. Elsayed and Paton (2005) compared pooled, cross sectional, static and
dynamic panel data analysis using UK 227 firms from 1994-2000. Environmental
performance measure is taken from Management Today survey which is a community and
environmental responsibility score for the Britain’s most admired companies. A study by
Elsayed and Paton (2005) showed no association between environmental performance and
financial performance using dynamic and static models. Whereas, there is strong positive
relationship using cross sectional and pooled regressions.
3
Data and Methodology
In the following section, we describe the data used to analyse the effect of
environmental performance on financial performance. The yearly frequency data is used for
both dependant and independent variables.
3.1
Data and Measurement:
3.1.1
Financial Performance
Previous empirical works have used several measures of financial performance. These
measures include return on assets, return on equity, return on sales and Tobin’s Q. We are
using two measures, returns on assets and Tobin’s Q for our study. The return on assets ratio
is expressed in the form of a percentage. This accounting measure of profitability has
significant importance because it shows the effective and efficient use of firm total assets to
generate earnings (Cohen et al., 1997; Horváthová, 2012). In other words, return on assets
shows the amount of profit a firm generates for each unit of investment in assets (Palepu et al.,
2010). There is slightly variation in formulation of this measure in the literature. We adopt
Palepu et al. (2010) measure of ROA:
10
𝑅𝑂𝐴 =
𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝐵𝑒𝑓𝑜𝑟𝑒 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑎𝑛𝑑 𝑇𝑎𝑥(𝐸𝐵𝐼𝑇)
𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Similarly, Tobin’s Q is measure of firm financial performance when analysing more abstract
phenomenon. It measures the market value of a firm relative to the replacement cost of its
assets. In other words, it is the intangible assets market worth in the form of tangible assets
(Chung & Pruitt, 1994). Following Guenster, Bauer, Derwall, and Koedijk (2011) and Perfect
and Wiles (1994), we compute Q as the total market value of firm’s assets divided by the
book value of firm assets.
𝐴𝑝𝑝𝑜𝑥𝑖𝑚𝑎𝑡𝑒 𝑄 =
𝑀𝑉𝐴 + 𝑃𝑆 + 𝐷𝑒𝑏𝑡
𝑇𝑜𝑡𝑎𝑙 𝐴𝑠𝑠𝑒𝑡𝑠
Where MVA is the product of a firm’s share price and the number of common stock
outstanding, PS is the product of firm’s preferred stock price and number of preferred stock
outstanding.
The simplified procedure involved in the calculation of Q shows a compromise between
analytical precision and calculation efforts. The true measure of any such simplification
technique is its degree of accuracy when compared with values obtained from following the
theoretical more correct model (Chung & Pruitt, 1994). However, Perfect and Wiles (1994)
found 0.9856 observed correlation between simplified Q with Lindenberg and Ross (1981) Q
through empirical investigation of 62 firms. Thus, simple estimation of Q is continued to be a
useful measure of financial performance (Chung & Pruitt, 1994). All financial data are
obtained from commercial firm database DATASTREAM. The database maintain firms’
majority of information from statement of income and balance sheet items.
3.1.2
Environmental performance (EnvPer)
Environmental performance measures vary in previous studies. Quantitative
measurement of Environmental Performance (EP) by companies is a difficult task. The most
11
challenging part is the development of a reliable proxy that is widely accepted. This
challenge has been well documented in the literature by Ilinitch, Soderstrom, and Thomas
(1998). To date, there is no uniform environmental performance definition accepted by a
range of stakeholders. In recent years, significant progress has been made to define
environmental performance construct both theoretically and empirically. Some of extant
literature used different ratings to measures environmental characteristics. These rating
include Kinder, Lydenberg & Domini (KLD), Corporate Environmental Data Clearinghouse
(CEDC) and Ethical Investment Research Services (EiRiS). KLD is a private consulting
organisation that keeps records for 9 areas of environmental and social performance of firms
(KLD Research & Analytics, 2003). Corporate Environmental Data Clearinghouse (CEDC)
collects data for over 700 companies including the S&P500 firms. Firms’ social and
environmental performance is measured using 11 objective criteria and published in a report
‘SCREEN’ (Gerde & Logsdon, 2001). Ethical Investment Research Services (EiRiS)
provides its independent services to different investors on corporate environmental, social
and governance (ESG) related issues. It keeps record of ethical performance indicators for
3,000 companies globally (EIRIS Foundation, 2012).
Apart from these ratings, other qualitative and quantitative proxies are also used to
measure corporate environmental performance. For example, environmental certificates like
ISO-14001 (Ann, Zailani, & Wahid, 2006; Paulraj & Jong, 2011; Wahba, 2008), perceptual
measures like environmental strategy (Sharma & Vredenburg, 1998), environmental
competitive advantages (Karagozoglu & Lindell, 2000), environmental management practices
(Carmona-moreno & Cééspedes-lorente, 2004; González-benito & González-benito, 2005)
and integration of environmental performance issues into strategic planning process (Judge &
Douglas, 1998). These performance measures are not common across all countries and are
influenced by the overall business, social and legal environment of respective countries. The
12
desire to have a similar environmental database is fulfilled after the United Nation
Conference on Environment and Development (UNCED) at Rio de Janeiro in 1992, where
countries agreed to maintain industrial chemical emission data on specific substances that
have potential risk to the environment and public health (Fenerol, 1997). Later on, the
Organization for Economic Co-operation and Development (OECD), in cooperation with the
United Nations Economic Commission for Europe (UNECE) and the United Nations
Environment Program developed and maintained the first Pollutant Release and Transfer
Register database (PRTRs, 2012).
This database maintains records of chemicals released to the environment. Different
countries use different nomenclatures for PRTRs. For example, the National Pollutant
Inventory (NPI) in Australia, the Toxic Release Inventory (TRI) in the United States, the
Pollutant Emission Register (PER) in the Netherlands, and the National Pollutant Release
Inventory (NPRI) in Canada. According to the OECD Council Recommendation
C(96)41/FINAL, as amended by C(2003)87, the core objectives of a PRTR system are to
group substances that have a harmful impact on humans and the environment, report its
source on periodic basis preferably annually and make it available to different stakeholders
including community and workers (PRTRs, 2012). In Australia, the PRTR is maintained
under the Ministry for Environment and Heritage with the name of National Pollutant
Inventory. It collects over 90 chemical from majority of factories in Australia.
Few studies have used these databases in their studies. For example, Al-Tuwaijri et al.
(2004) used toxic waste recycled to total toxic waste generated for their study using TRI.
The problem with such ratio is that it ignores the dangerousness of each chemical emitted by
the company. In most recent study Horváthová (2012) has tried to overcome this problem. He
calculated environmental performance by dividing each toxicant to its threshold using PRTR
for Czech Republic. He argues that as each chemical threshold is set up according of its
13
dangerousness therefore, it represent the harmfulness of each toxicant. He used chemicals
with above threshold emission whereas in practice company may have emission for a
chemical that is under threshold level.
This study goes one step further and adopts environmental performance index
discussed by Muhammad and Scrimgeour (2012). This robust formulation for making index
is not used by existing studies. The most important aspect of such formulation is that it caters
for the harmfulness from three different aspects (environment, human health and exposure).
The cumulative impact of these three dimensions is called environmental risk score. This risk
score is then multiplied with each of the emitted chemical level of emission called Weighted
Average Risk (WAR) for respective chemical. This process is repeated for all emitted
chemicals for a given facility and finally added up to form facility level WAR (Muhammad
& Scrimgeour, 2012).
93
WAR𝑓 = ∑ 𝑅𝐹𝑖 ∗ 𝐸𝑖
𝑖=1
Where:
WARf
Weighted Average Risk for a facility
RF
= (𝐸𝑛𝑣𝑖𝑟𝑜𝑛𝑚𝑒𝑛𝑡 𝐸𝑓𝑓𝑒𝑐𝑡 + 𝐻𝑢𝑚𝑎𝑛 𝐻𝑒𝑎𝑙𝑡ℎ 𝐸𝑓𝑓𝑒𝑐𝑡)𝑋 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒
E
Emission in kg of a given substance to environment in a year
If a company has more than one facility, WAR is added up to form company level WAR. To
normalise the weighted average risk of company, we divided WAR by total assets of the
company (EnvPer).
14
3.1.3
Environmental Concern
We include environmental concern in our model as predetermined phenomenon. Ullmann
(1985) emphasised on the inclusion of management strategy in models analysing the
company social responsibility. Telle (2006) in his study concluded that the firm positive
environmental performance is probably because of omitted variable bias. This is also
consistent with Al-Tuwaijri et al. (2004) who included variables for environmental concern in
their study. We use three variables for environmental concern. The first variable is firm Crisis
Management System (CMS). If a company report on crisis management systems or
reputation disaster recovery plans to reduce or minimize the effects of reputation disasters,
we code CMS 1 otherwise it is coded as 0. The second environmental concern characteristic
is Environmental Supply Chain Management (ESCM). If the company use environmental
criteria (ISO 14000, energy consumption, etc.) in the selection process of its suppliers or
sourcing partners, then ESCM is coded as 1 otherwise it is coded 0. The last measure,
Corporate Social Responsibility and Sustainability Committee (CSRSC), is a coded 1 if the
company have a CSR committee or team otherwise it is coded 0. As companies have the
discretion to involve in these activities therefore, this study considers it as top management
concern for environment.
3.1.4
Environmental Image
Al-Tuwaijri et al. (2004) claim that firms having greater public visibility are exposed to high
political cost for poor environmental performance. Therefore, high visible firms are having
high standards for environmental performance. Firms having better environmental
management systems, products or services normally used it in its promotional campaign that
they are more environmental responsible or green concern. Hence, we control for the
environmental image propagation by company. We use two variables to measure this
phenomenon. The first environmental image variable is ISO-14000 certification. Companies
claim to have ISO certification during their promotional campaign. ISO-14000 certification is
15
used as environmental responsible/green symbol. If company is ISO certified it is coded 1
otherwise it is coded 0. The second variable is Environmental Management Team (EMTeam).
If the company reports an environmental management team, then it is coded 1 otherwise 0.
These all variables are taken from DATASTREAM database and counterchecked with
company annual reports. We selected all company that report data to NPI and are listed on
Australian Stock Exchange (ASX) from 2005-2006. Our final sample includes 76 companies
from different sectors. The summary of statistics is presented in Table 1.
3.2
Econometric Model
There are several studies that have analysed firm financial and environmental relationship by
simple correlation coefficients (Jaggi & Freedman, 1992; Orlitzky, 2001). The objection on
these studies are on the positive sign of the correlation as it may be due to the confounding
variable (Orlitzky, 2001; Telle, 2006). A number of studies have overcome this problem and
analysed firm financial performance and environmental performance by regression analysis
where they accounted for firm heterogeneity. Following literature, we also estimated
following generic model for both dependant variables (ROA and Tobin’s Q) Model_1
𝐹𝑃𝑖𝑡 = 𝛼 + 𝑏𝐸𝑁𝑉𝑖𝑡 + 𝑑𝑋𝑖𝑡 + ɛ𝑖𝑡
(1)
Where FP is the firm financial performance, ENV is environmental performance, X is vector
of control variables, ɛit is error term. Here α is constant, b and d are vector that capture the
marginal effect of environmental performance and control variables respectively. i=1,2,…,N
cross sectional unit (companies) for periods t=1,2,…..,T.
In Model_1, we control for several apparent relevant factors. These factors include
environmental concern, environmental image and previous year weighted average emission
of the company. Despite of these control variables, omitted unobserved variables could still
16
be the primary reason for the estimated coefficients effects. Therefore, Model_1 may produce
bias and inconsistent estimates.
This problem may be overcome by including omitted variable to estimated model. According
to Telle (2006) good quantitative data is sometimes unavailable to include omitted variable
e.g quality of management. He further suggests that instrumental variable may be applied to
overcome omitted variable bias. A valid instrumental variable would be the one that is
correlated with environmental performance variable and uncorrelated with the error term or
omitted variables which itself is very difficult task (Telle, 2006). To address this issue, we
control for unobserved firm characteristics. This could be done either random or fixed effect
regression estimates. In cross-sectional random and fixed effect approach the error term is
divided into unit-specific error (λi), which is not changing over time (firm specific) and an
idiosyncratic error (µi), which is observation specific (varies over unit and time). If we add (λi)
to the intercept (α), this model is called cross-sectional fixed effect model (Model_2). We
assume that each unit (firm) has a constant individual specific effect that shifts the dependant
variable by fixed magnitude. Generic estimation of Model_2 is as follows.
𝐹𝑃𝑖𝑡 = (𝛼 + 𝜆𝑖 ) + 𝑏𝐸𝑁𝑉𝑖𝑡 + 𝑑𝑋𝑖𝑡 +µ𝑖𝑡
(2)
In this way we allow each unit (firm) to have different intercept. Whereas, the coefficients
for the regression i.e. slopes are still the same.
Similarly, for cross-sectional random effect we assume that unit specific error (λi) is random
phenomenon and distributed independently of explanatory variables. As a result, λi is treated
as part of error term and the Model_3 is estimated as follows.
𝐹𝑃𝑖𝑡 = 𝛼 + 𝑏𝐸𝑁𝑉𝑖𝑡 + 𝑑𝑋𝑖𝑡 + (µ𝑖𝑡 + 𝜆𝑖 )
17
(3)
Equation 2 and equation 3 may not rule out all possibilities of omitted variable bias. After
controlling for firm specific effects (cross-sectional effects), we also estimated models to
control for time specific effects. In cross-sectional time specific approach the error term (ɛ) is
divided into three parts. The first part is individual specific effects which are time invariant
(λi), the second part is time specific effects that affect all firm with same magnitude (€t) and
the third part is observation specific idiosyncratic (µit).
If we add firm specific effects (λi) and time specific effect (€t) to the intercept (α), this model
is called cross-sectional time invariant fixed effect model (Model_4). We assume that each
unit effect (firm) and time specific (across years) have constant effect that shifts the
dependant variable by fixed magnitude. Generic estimation of Model_4 is as follows.
𝐹𝑃𝑖𝑡 = (𝛼 + 𝜆𝑖 + €𝑡 ) + 𝑏𝐸𝑁𝑉𝑖𝑡 + 𝑑𝑋𝑖𝑡 +µ𝑖𝑡
(4)
Similarly, for cross-sectional time invariant random effect we assume that unit specific error
(λi) and time specific (€t) are random phenomenon and are uncorrelated explanatory variables.
As a result, λi and €t are treated as part of error term. Model_5 is estimated as follows.
𝐹𝑃𝑖𝑡 = 𝛼 + 𝑏𝐸𝑁𝑉𝑖𝑡 + 𝑑𝑋𝑖𝑡 + (µ𝑖𝑡 + 𝜆𝑖 + €𝑡 )
4
(5)
Results and Discussion
This section presents the results from our estimated models on each of the dependant variable
of financial performance. Table 1 presents the basic descriptive statistics for study dependant,
independent and control variables. The mean (standard deviation) of first dependant variable
which is market based financial performance (Tobin’s Q) is 3.59 (5.84). The mean (standard
deviation) of second dependant variable (accounting based financial performance) ROA is 0.16 (31.4) percentage point. The mean (standard deviation) of variable of interest
(independent or environmental performance) variable is 153.9 (553.8). This suggests that on
18
average 153.9 units of toxic chemicals are released for every one thousand units of total
assets.
Table 1 Summary of statistics – Cross sectional data for 76 ASX listed companies
Variable Name
Mean
SD
Min
Max
Tobin's Q (TQ)
3.59
5.84
-1.57
39.7
Return on Assets (ROA)
-0.16
31.4
-280
63.8
Environmental Performance (EnvPer)
153.9
553.8
0.01
4668.7
ISO-14000 (ISO)
0.37
0.48
0
1
Crisis Management System (CMS)
0.23
0.42
0
1
Environmental Supply Chain Management (ESCM)
0.32
0.47
0
1
CSR-Sustainability Committee (CSRSC)
0.57
0.50
0
1
Environmental Management Team (EMTeam)
0.41
0.49
0
1
Table 2 present the pairwise correlation results. Independent variable (environmental
performance) is negatively correlated to both dependant variables ROA and Tobin’s Q, but
the pairwise linear relationship is weak as the coefficients are less than 0.40 level. This result
is consistent with prior studies that find mix result (Al-Tuwaijri et al., 2004). Other pair-wise
correlations in Table 2 are weak too. None of the estimated correlation represents obvious
anomalies to theoretical explanation.
Table 2 Correlation coefficients matrix for all variables included in the model.
TQ
ROA
EnvPer
ISO
CMS
ESCM
CSRSC
TQ
1
ROA
0.233***
1
EnvPer
-0.115**
-0.176***
1
ISO
0.255***
0.163**
0.0238
1
CMS
0.289***
0.0960
-0.0379
0.148**
1
ESCM
0.379***
0.165**
-0.146*
0.500***
0.360***
1
CSRSC
0.343***
0.256***
0.0475
0.236***
0.233***
0.357***
1
EMTeam
0.122*
0.00607
-0.169**
0.235***
0.343***
0.346***
0.303***
EMTeam
1
Notes: (1) * denotes significance at 10% (p<0.10), ** denotes significance at 5% (p<0.05), *** denotes
significance at 1% (p<0.01)
19
The regression results are presented in Table 3 (Tobin’s Q) and Table 4 (ROA). Our results in
Table 3 show that hazardous chemical emission has an effect on financial performance.
Simple OLS (Model_1), cross-sectional random effect model (Model_3) and cross-sectional
Table 3 Environmental performance and its impact on market based financial performance (Tobin’s Q)
EnvPer
ESCM
CSRSC
CMS
ISO
EMTeam
WAR t-1
Model_1
-0.018***
(-3.09)
4.5831***
(2.74)
0.5694
(0.62)
3.0675**
(2.25)
1.4889
(0.97)
-0.0872
(-0.08)
0.0010***
(6.71)
Model_2
0.0055
(1.28)
3.8769
(1.09)
-0.7625
(-0.76)
1.9283
(0.68)
-2.4296*
(-1.8)
3.3284
(1.44)
0.00021***
(3.32)
Model_3
-0.0098**
(-2.03)
4.3855*
(1.7)
-0.063
(-0.08)
2.1066
(1.26)
0.6737
(0.35)
1.5791
(0.89)
0.00010***
(6.38)
2.8553***
(4.31)
153
0.3519
No
No
Robust
3.9573***
(4.37)
153
0.2375
Yes
No
Cluster
2.3887***
(4.34)
153
2006.Year
2007.Year
2008.Year
2009.Year
2010.Year
Constant
N
R2
Firm effect
Year effect
vce
Yes
No
Robust
Model_4
0.0151**
(2.14)
2.842
(0.83)
-1.6253
(-1.4)
1.2497
(0.47)
-3.4840**
(-2.31)
2.5532
(1.11)
0.0001**
(2.11)
1.2284
(1.08)
0.87
(0.84)
3.4959**
(2.15)
1.9754
(1.1)
3.9084*
(1.82)
3.1992***
(3.05)
153
0.2955
Yes
Yes
Cluster
Model_5
-0.0095*
(-1.96)
4.5042*
(1.85)
0.2023
(0.24)
2.1657
(1.28)
0.7626
(0.4)
1.6399
(0.94)
0.00015***
(6.01)
-0.2698
(-0.23)
-1.6393*
(-1.68)
0
.
-2.5401***
(-2.59)
-0.8853
(-1.14)
3.4306***
(4.14)
153
Yes
Yes
Robust
Notes: (1) * denotes significance at 10% (p<0.10), ** denotes significance at 5% (p<0.05), *** denotes
significance at 1% (p<0.01); (2) Number in parenthesis below each coefficient show t-statistics
time invariant random effect model (Model_5) have positive association between
environmental performance and financial performance. Whereas, cross-sectional time
invariant fixed effect model (Model_4) has negative effect. These results are similar to King
20
and Lenox (2002) and Elsayed and Paton (2005). Only cross-sectional fixed effect model
(Model_2) has showed no association between environmental performance and financial
performance.
Two of environmental concern variables (Environmental Supply Chain
Management (ESCM) and Crisis Management System (CMS)) have positive impact on
financial performance in OLS estimation. Environmental Supply Chain Management (ESCM)
is weakly significant (p<0.10) in Model_3 and Model_5 as well. Corporate Social
Responsibility and Sustainable Committee (CSRSC) is the only environmental concern
variable that has no impact in any model. Overall, environmental concern has weak or no
association with financial performance except in OLS estimation.
Environmental image measured by ISO award are significant in cross-sectional fixed effect
(Model_2) and cross-sectional time invariant model (Model_4). Whereas, Environmental
Management Team (EMTeam) has shown no association with financial performance. Three
out of four significant models suggest that overall environmental performance has positive
association with market based financial performance.
Table 4 presents the results of environmental performance impact on return on asset. Simple
OLS in Model_1 shows positive impact of improved environmental performance on ROA.
Cross-sectional fixed effect (Model_2) and cross-sectional time invariant fixed effect model
(Model_4) have negative effect on improved environmental performance and financial
performance this result is align with the finding of King and Lenox (2001). Whereas, crosssectional random effect (Model_3) and cross-sectional time invariant random effect
(Model_5) has shown no association between improved environmental performance and
accounting based financial performance. Telle (2006) also finds similar results and consider
that such erroneous and inconsistency in results is probably because of omitted variable bias.
21
Table 4 Environmental performance and its impact on accounting based financial performance (ROA)
EnvPer
ESCM
CSRSC
CMS
ISO
EMTeam
WAR t-1
Model_1
-0.046***
(-4.03)
0.8789
(0.48)
2.1749
(1.21)
0.8943
(0.42)
1.7237
(0.89)
-1.9703
(-1.2)
Model_2
0.0345***
(3.49)
2.5819
(1.26)
0.0419
(0.03)
-3.7172
(-1.47)
-8.3353***
(-3.52)
-1.6782
(-0.74)
Model_3
-0.0219
(-0.96)
2.1467
(1.28)
1.3081
(0.78)
-2.0258
(-0.83)
-0.5333
(-0.17)
-1.9236
(-0.85)
Model_4
0.0304**
(2.39)
3.2009
(1.25)
0.4519
(0.26)
-3.5901
(-1.4)
-7.6713**
(-2.62)
-1.3145
(-0.53)
Model_5
-0.0227
(-1.3)
3.6196*
(1.74)
3.1114*
(1.74)
-0.5766
(-0.21)
-0.3679
(-0.14)
-0.3977
(-0.17)
0.000031***
(3.34)
0.000015
(0.48)
0.000011
(1.58)
7.8063***
(4.58)
152
0.1334
No
No
Robust
11.4950***
(10.63)
152
0.1133
Yes
No
Cluster
8.0378***
(3.51)
152
0.00001
(0.57)
(-0.94)
-1.6465
(-0.94)
-1.0975
(-0.55)
-1.7084
(-0.64)
-2.8419
(-0.87)
-1.4966
(-0.48)
12.4035***
(9.45)
152
0.1234
Yes
Yes
Cluster
0.0000012
(1.6)
(-1.32)
-2.4463
(-1.32)
-3.4773*
(-1.71)
-5.6142**
(-1.98)
-7.8010**
(-2.47)
-7.4626***
(-2.66)
11.7695***
(5.91)
152
2006.Year
2007.Year
2008.Year
2009.Year
2010.Year
Constant
N
R2
Firm effect
Year effect
vce
Yes
No
Robust
Yes
Yes
Robust
Notes: (1) * denotes significance at 10% (p<0.10), ** denotes significance at 5% (p<0.05), *** denotes
significance at 1% (p<0.01); (2) Number in parenthesis below each coefficient show t-statistics
Environmental concern measured by Environmental Supply Chain Management (ESCM) and
Corporate Social Responsibility & Sustainability Committee (CSRSC) have association with
financial performance at 10% level in Model_5. Environmental image measure by ISO
awards has negative impact in Model_2 and Model_4. Environmental Management Team has
22
no association in any of the observed models. Apart from OLS results, fixed effect models
suggest that environmental performance depresses accounting based financial performance.
Since panel data analysis is used therefore, it is possible that observations contain intra-firm
correlations. To avoid the effect of these correlations, the models used in this study provide
results using robust standard errors cluster by company. We performed Variance Inflation
Factor (VIF) test to check whether two or more variables are having linear combination with
one another. It is important because if observed model estimates and coefficients are unstable
then standard error of coefficients is also inflated many folded. All variables’ VIF values are
less than 10 meaning that there is no relationship among independent variables. This level of
VIF is considered to be the conventional rule of thumb (O’Brien, 2007).
5
Conclusion
This study investigates the association between environmental performance and financial
performance. After considering unobserved firm effects and time variant effects (which is
also in direction with Al-Tuwaijri et al. (2004), Telle (2006) and (Ullmann, 1985) argument
that the execution of corporate environmental performance is determined by corporation
overall strategy (unobserved effects)) and applying new empirical proxy for environmental
performance, this study finds that there is a strong association between environmental
performance and financial performance. Only two models results are consistent in both
accounting based and market based financial performance. The first model is OLS that
suggest positive association (p<0.01) and second model is cross sectional time invariant fixed
effect model that suggest negative association (p<0.05).
From market based financial performance perspective, overall random effect model find a
positive significant effect of environmental performance. Whereas, in accounting based
financial performance, fixes effect model find negative significant association. These results
23
are important for investors and managers. Manager should have least concern for the
depressed accounting performance as it overall suggests that environmental performance
depresses accounting number but at the same time, investors interpret it as a source of
information and value it as factor that improve corporate market value.
Future research should focus on inclusion of more relevant unobserved factors. If more than
one variable are used for a single category of unobserved variable, then factor analysis can be
used to combine more than one variable to form a single category for control variable. While
analysing environmental performance and financial performance nexus, future research can
utilize econometric models to seek the general concern of causality. Providing different
theoretical basis to corporate environmental performance will also add knowledge to existing
literature.
24
6
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