Southwest Business and Economics Journal/2010
Eco-efficiency: Evidence from the Pharmaceutical Industry
Li Sun
Ball State University
Abstract
Eco-efficiency states that companies can achieve two things at the same time: (1)
improving economic performance and (2) improving environmental quality. Empirical
evidence supporting eco-efficiency is still scarce. The purpose of this study is to examine
the association between economic performance and environmental performance of
pharmaceutical companies (SIC=283X) for the period of 2004 -2007. I use relative
efficiency scores calculated by Data Envelopment Analysis (DEA) to measure a firm’s
economic performance. Environmental performance data, measured as the
environmental strength score, comes from Kinder, Lydenberg, and Domini, Inc.
Regression analysis reveals a significant and positive association between firm efficiency
and environmental performance in the pharmaceutical industry. The results support the
validity of eco-efficiency.
1. Introduction
Since 1990s, more and more firms have been voluntarily complying or even
overcomplying with the regulations by increasingly devoting resources to enhancing
environmental quality. However, little empirical research has examined the association
between environmental performance and economic performance. Burnett and Hansen
(2008) suggest that interest concerning the relationship between environmental
performance and economic performance is indeed increasing, due to an emerging socioeconomic theory known as eco-efficiency (Birkin and Woodward 1997; DeSimone and
Popoff 1997). Eco-efficiency means companies can improve environmental quality while
increasing their operational efficiency due to reduced environmental costs and waste.
Burnett and Hansen (2008) document a positive link between environmental performance
and economic performance supporting eco-efficiency in the electronic utility industry in
the United States. In addition, Burnett and Hansen (2008) call for more empirical
research on eco-efficiency. If eco-efficiency is true, then companies may need to
establish environmental management system (EMS) to keep track of environmental costs
and then control these costs.
The purpose of this study is to examine the association between environmental
performance and economic performance in the pharmaceutical industry (SIC=283X) for
the period of 2004 -2007. I use relative efficiency scores calculated by Data
Envelopment Analysis (DEA) to measure economic performance. Environmental
performance data, measured as the environmental strength score, comes from Kinder,
Lydenberg, and Domini, Inc. This study focuses on one industry, the pharmaceutical
industry (SIC=283X) for the following reasons: First, Beurden and Gossling (2008, p.
420) recently issued a call for industry-specific studies to advance the usefulness of
corporate social responsibility (CSR) 1 research by stating that “in order to continue to
have value for management practice and for the improvement of the business world,
future studies should focus on segments of groups of firms that practice (CSR). In this
1
One critical component of CSR is environmental performance.
1
Eco-efficiency: Evidence from the
Pharmaceutical Industry
respect, research in different industries may be helpful”. By focusing on the
pharmaceutical industry, this study answers that call. Second, the competition in
pharmaceutical industry is fierce. Profits are usually linked to the protection of patents.
Such protection is usually very limited on time. After the protection of patent is expired,
other pharmaceutical companies can manufacture the same drug (also known as the
generic drug). Thus, it is critical for pharmaceutical companies, especially the original
drug producers, to maintain a high level of efficiency, relative to their competitors. If
eco-efficiency is valid, then actively improving environmental performance can increase
a pharmaceutical firm’s efficiency.
Regression analysis reveals a significant and positive association between firm
efficiency and environmental performance of pharmaceutical firms. The results support
the validity of eco-efficiency. This paper delivers new evidence on the link between
economic performance and environmental performance. This contributes to the economic
literature and emerging accounting literature on pro-environmental management. The
results should interest managers who engage in behavior leading to or maintaining strong
environmental performance, financial analysts who conduct research on eco-efficiency,
and policy makers who design and implement guidelines on improving environmental
quality. Moreover, results in this study can increase individual investors’ confidence in
investing in pharmaceutical companies with stronger environmental performance.
The remainder of the paper is organized as follows. Section 2 reviews prior
research and develops the hypothesis. Section 3 describes the research design, including
measurement of primary variables and empirical specification. Section 4 describes
sample selection and descriptive statistics, while section 5 reports the results from
regression analysis. Section 6 summarizes the study.
2. Literature Review and Hypothesis Development
Much of the research in environmental accounting concerns the association
between the financial performance and the environmental performance of a firm. The
majority of studies looking at the relationship between the financial performance and the
environmental performance found a positive relationship (Christmann 2000; Delmas
2001; Hart and Ahuja 1996; Konar and Cohen 2002; McGuire et al. 1988; Melnyk et al.
2003; Russo and Fouts 1997; U.S. EPA 2000; Waddock and Graves 1997). For example,
Konar and Cohen (2002) find that bad environmental performance is negatively
associated with market value at a significant level. In particular, reducing chemical
emissions by 10 % can cause the firm’s market value increase by $34 million.
However, very few studies have examined the association between the
environmental performance and the economic performance of a firm. Burnett and Hansen
(2008, p.552) suggest that interest concerning the relationship between the environmental
performance and the economic performance is increasing, due to an emerging socioeconomic theory known as eco-efficiency (Birkin and Woodward 1997; DeSimone and
Popoff 1997). U.S. Environmental Protection Agency (EPA) describes eco-efficiency as
firms using a more proactive approach that includes conducting internal audits and
implementing environmental management system (EMS) to ensure regulatory
compliance while at the same time reducing inputs and productively using by-products
that were formerly wasted (U.S. EPA 2000). In other words, eco-efficiency states that
firms can improve environmental quality and at the same time increase productivity, due
to reduced environmental costs. Russo and Fouts (1997) state that a firm’s strategy to
2
Southwest Business and Economics Journal/2010
improve environmental quality includes the creation of environmentally friendly products
may require the firm to redesign the entire production process, which can lead to
uniquely efficient capabilities.
Although many case studies support a positive link between the economic
performance and the environmental performance, they still cannot replace empirical
testing (Porter and van der Linde 1995). Only a few empirical studies exist examining the
link between the environmental performance and the economic performance. For
instance, Murty and Kumar (2003) examine the effect of environmental regulation on the
productive efficiency of 92 Indian firms in the water polluting industries during the
period of 1996-1999. Murty and Kumar (2003) find that productive efficiency increases
with environmental performance. By using 84 electric utility plants, Burnett and Hansen
(2008) report that plants with lower emissions are more efficient than those with higher
emissions. The above empirical studies provide evidence supporting eco-efficiency,
which advocates a positive link between economic performance and environmental
performance. Burnett and Hansen (2008) call for more empirical evidence on ecoefficiency. If eco-efficiency is true, then companies may need to establish environmental
management system (EMS) to keep track of environmental costs and then control these
costs.
Consistent with eco-efficiency, this study posits that the environmental
performance is positively associated with the economic performance of a firm. The
hypothesis in both null (H0) and alternative (H1) format is stated as follows:
H0: Environmental performance is not positively associated with firm efficiency.
H1: Environmental performance is positively associated with firm efficiency.
3. Research Design
3.1 Measurement of the Primary Dependent Variable – Firm Efficiency
This study measures firm efficiency by using Data Envelopment Analysis
(DEA)—a nonparametric model. Charnes et al. (1978, p.429) describes DEA as “a
mathematical programming model applied to observational data that provides a new way
of obtaining empirical estimates of relations that are cornerstones of modern economics”.
DEA models produce measures of performance efficiency—the production of outputs
with quantities of inputs. Cooper et al. (2000) suggest that this DEA performance
efficiency measure is a better, more comprehensive performance measure than other
more traditional financial performance measures. First, DEA is a more general flexible
and adaptable measure of firm performance. DEA does not require a prescribed
functional form such as the Cobb-Douglas production function. DEA also does not
require users to assign weights to each input and output. Second, unlike the typical
parametric approach that compares each decision making unit (hereafter DMU)1 to an
average DMU, DEA compares each DMU to the ‘best’ DMU. In addition, Feroz et al.
(2008) argue that accounting measures like ROA and ROI may generate inconclusive
performance results since these measures are measure-specific and can be affected by
non-value-added factors. Instead, Feroz et al. (2008) suggest that incorporating traditional
accounting variables, such as sales and cost of goods sold, into a DEA model may
produce a more comprehensive measure of firm performance.
3
Eco-efficiency: Evidence from the
Pharmaceutical Industry
The term ‘best’ is used here to mean that the (outputs/inputs) ratio for each DMU
is maximized, relative to all other DMUs. For each DMU, DEA creates weights for
inputs (vi) and outputs (ui):
Input = v1 x10 + … + vm xm 0
Output = u1 y10 + … + u s y s 0
DEA determines the ‘best’ input and output weights that maximize the
(outputs/inputs) ratio for each individual DMU by using linear programming techniques.
Each DMU’s ‘best’ set of weights may differ from other DMUs.
Figure 1 shows a simple example of DEA. Assume one input and one output and
a variable-return-to-scale production function. Suppose there are only 5 DMUs, (A, B, C,
D, and E). DMUs (A, B, C, D) are on the production efficiency frontier, and thus their
values for the (outputs/inputs) ratio are one. The values of the (outputs/inputs) ratio for
DMUs which operate beneath the production efficiency frontier are between zero and
one. For instance, the efficiency of DMU (point) E is GF/GE 2.
Figure 1
An Example of DEA
Production Efficiency
Frontier
Output (Y)
D
C
B
G
F
E
Production Possibility Set
A
O
H
J
Input (X)
The first step in a DEA analysis is to select a specific DEA model. This study
applies the variable-return-to-scale DEA model, also known as the BCC model (Banker
et al., 1984). It is recommended by Cooper et al. (2000) to use the BCC model if there are
2
The output/input ratio of point F is FH/GF, while the output/input ratio of point E is EJ/GE. Thus, the
relative efficiency of point E is (EJ/GE)/(FH/GF) = GF/GE
4
Southwest Business and Economics Journal/2010
multiple inputs or outputs involved in DEA studies. The BCC model 3 estimates the
efficiency of DMUs by solving the following linear program:
Max
z = u ⋅ y0 − u0
Subject to
v ⋅ x0 = 1
− v ⋅ x + u ⋅ y − u0 e ≤ 0
v ≥ 0 , u ≥ 0 , u 0 free in sign
Where
x, y represent vectors of inputs and outputs respectively.
z and u 0 are scalars.
u 0 may be positive or negative.
e denotes a row vector in which all elements are equal to 1.
v and u denote weights associated with a particular DMU.
Selecting input and output variables to use in the DEA model is the next task.
Physical measures and monetary measures are common types of input / output variables.
This study uses monetary measures for three reasons. First, it is difficult to obtain
variable information in physical units. Second, Battese and Coelli (1995) suggest that it is
preferable to use monetary measures to measure efficiencies at the firm level since a firm
is often engaged in many different activities. Third, using monetary measures may
capture more information.
Selecting specific monetary input and output variables for this DEA model is the
next step. Similar to Bowlin (1999), this study includes three conventional input variables
(cost of goods sold, total assets, and selling, general and administrative expenses) and
two conventional output variables (sales and operating cash flows) in this DEA model.
Table 1 summarizes these variables.
Table 1
Variable Selection for Efficiency Model
Panel A: Input Variables
Variable Name
Cost of Goods Sold
(Compustat Item # 41)
Selling, General and
Administrative Expenses
(Compustat Item #189)
Total Assets
(Compustat Item #6)
Measurement
in dollars
in dollars
in dollars
Description
This item represents all
costs directly allocated to
production, such as direct
materials, direct labor and
overhead.
This item represents nonproduction expenses
incurred in the regular
course of business.
This item represents current
assets plus net long-term
assets.
3
More information on the BCC model can be found in Banker et al. (1984) and Cooper et al. (2000). The
software used to estimate DEA scores is DEA-SOLVER-PRO 6.0.
5
Eco-efficiency: Evidence from the
Pharmaceutical Industry
Panel B: Output Variables
Variable Name
Sales
(Compustat Item #12)
Operating Cash Flows
(Compustat Item #308)
Measurement
in dollars
in dollars
Description
This variable represents
sales after any discounts,
returned sales and
allowances for which credit
is given to customers.
This variable represents the
net change in cash in the
operating activities on a
cash flow statement.
3.2 Measurement of the Primary Independent Variable – Environmental Performance
Kinder, Lydenberg, and Domini, Inc (hereafter KLD) has been actively providing
rating data on corporate social responsibility since 1991. KLD provides rating data for
approximately eighty variables in seven qualitative areas for each selected firm. In
addition to these seven qualitative areas, KLD also evaluates six controversial issues that
include, for example, alcohol, gambling, and tobacco activities. This study selects
environmental strength rating scores to measure environmental performance.
Environmental strength means strengths in the following areas: beneficial products,
pollution prevention, recycling, alternative fuels, communications, property (plant,
equipment). A high environmental strength score indicates strong environmental
performance of a company.
3.3 Empirical Specification
This study runs DEA for each of the four years – 2004, 2005, 2006, and 2007,
since the efficiency frontier is different each year. This study then uses the firm
efficiency score (DEA) and environmental strength score (ENVSTR) in the following
regression model to test the association between a firm’s economic performance and
environmental performance.
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*ROAit + α5*MTBit +
α6*YEAR05 it + α7*YEAR06it + α8*YEAR07it + εit
[Equation 1]
Where
DEAit = Efficiency score of firm i in year t;
ENVSTRit = Environmental strength score of firm i in year t;
AGEit = Net property, plant and equipment (Compustat Item #8) / Gross property,
plant and equipment (Compustat Item #7) of firm i in year t;
LEVit = Leverage ratio [total liabilities (Compustat Item #9 + #34) / total assets
(Compustat Item #6) of firm i in year t;
ROAit =Return on assets [income before extraordinary items – available for common
equity (Compustat Item #237)] / total assets (Compustat Item #6) of firm i in year t;
MTBit = Market to book ratio {[common shares outstanding (Compustat Item
#25) × stock price – fiscal year-end (Compustat Item #199] / total common
equity (Compustat Item #60)} of firm i in year t;
YEAR05=1, if t=2005, otherwise 0;
YEAR06 = 1, if t = 2006, otherwise 0;
YEAR07 = 1, if t = 2007, otherwise 0.
6
Southwest Business and Economics Journal/2010
The firm performance variable of interest, the efficiency score (DEA), is the
regression model’s dependent variable consistent with McGuire et al. (1988) and
Waddock and Graves (1997). The environmental strength score is the independent
variable of interest. Seven control variables are included to control for age of long-term
assets, leverage ratio, return on assets, market to book ratio, and time (2005-2007). This
study excludes the control variable for size in this regression model, since DEA model
indirectly captures the size effect from the total assets of a firm.
4. Sample Selection and Descriptive Statistics
KLD contains approximately 3,000 firm observations each year because the KLD
database contains firms on the Russell 3,000 Index. After confining the sample to
pharmaceutical firms (SIC = 283X) and matching KLD observations with Compustat
financial data, the final sample consists of 96 pharmaceutical firms for 2004, 91
pharmaceutical firms for 2005, 94 pharmaceutical firms for 2006, and 98 pharmaceutical
firms for 2007. The total number of sample observations is 379.
Table 2 summarizes the sample firms’ descriptive statistics for each of the four
years. Information including mean and median of selected variables is provided. For
instance, the mean value of DEA is 0.77, 0.75, 0.84 and 0.81 while the mean value of the
ENVSTR score is 0.07, 0.16, 0.21 and 0.21 in 2004, 2005, 2006, and 2007, respectively.
Table 2
Descriptive Statistics for the sample firms (SIC = 283X)
Panel A: 2004 (n=96)
Variable
Mean
Std. Dev. 25th Percentile
DEA
0.77
0.23
0.63
ENVSTR 4
0.07
0.30
0.00
SALES
2785.30 8215.03
51.72
OCF
657.34 2293.67
-12.17
COGS
664.97 1721.33
12.01
XSGA
1243.23 3717.43
42.09
TA
5014.65 15451.61
104.97
TD
920.27 2614.58
0.19
LEV
0.16
0.20
0.00
AGE
0.55
0.17
0.45
MTB
6.63
10.93
3.13
4
Median 75% Percentile
0.81
0.99
0.00
0.00
195.80
8272.00
25.63
175.34
59.97
446.49
121.34
447.80
411.16
2145.82
17.60
406.13
0.08
0.26
0.57
0.68
4.13
6.12
Environmental strength scores range from 0 to 3 in KLD database. Rather, the score is a relative measure.
A score of 0 does not necessary mean the firm has no environmental activities.
7
Eco-efficiency: Evidence from the
Pharmaceutical Industry
Panel B: 2005 (n=91)
Variable Mean
DEA
0.75
ENVSTR
0.16
SALES
3089.51
OCF
689.75
COGS
732.84
XSGA
1410.92
TA
5352.35
TD
975.02
LEV
0.18
AGE
0.57
MTB
8.19
Std. Dev. 25th Percentile
0.25
0.61
0.64
0.00
8613.33
83.10
2233.29
-13.75
1891.07
20.59
3976.49
55.47
15557.39
124.68
2600.74
0.05
0.21
0.00
0.16
0.46
17.14
2.88
Median 75% Percentile
0.81
1.00
0.00
0.00
279.36
1646.20
42.96
280.50
66.44
330.83
135.89
449.28
510.24
2527.63
131.46
506.20
0.11
0.31
0.58
0.69
4.24
6.14
Panel C: 2006 (n=94)
Variable Mean
DEA
0.84
ENVSTR
0.21
SALES
3135.50
OCF
791.07
COGS
723.10
XSGA
1470.29
TA
5491.20
TD
965.79
LEV
0.18
AGE
0.57
MTB
7.21
Std. Dev. 25th Percentile
0.18
0.73
0.73
0.00
8568.06
68.16
2556.12
-15.21
1862.12
21.34
4017.36
50.27
15805.69
149.21
2304.97
0.12
0.20
0.00
0.18
0.46
10.48
2.86
Median 75% Percentile
0.89
1.00
0.00
0.00
239.54
1611.82
38.72
318.90
58.54
426.62
142.14
557.27
448.39
2735.79
62.61
515.40
0.12
0.28
0.58
0.68
4.76
6.99
Panel D: 2007 (n=98)
Variable Mean
DEA
0.81
ENVSTR
0.21
SALES
3443.19
OCF
850.72
COGS
789.30
XSGA
1599.20
TA
6147.18
TD
1218.14
LEV
0.18
AGE
0.57
MTB
36.42
Std. Dev. 25th Percentile
0.19
0.67
0.72
0.00
9236.44
64.03
2399.56
-15.51
2082.44
12.50
4303.86
53.72
16620.91
145.28
2864.44
0.18
0.20
0.00
0.19
0.45
211.30
2.72
Median 75% Percentile
0.82
1.00
0.00
0.00
259.37
1609.94
51.73
348.94
57.65
462.33
166.30
553.59
449.08
2884.97
54.39
677.92
0.12
0.30
0.58
0.69
4.92
8.19
8
Southwest Business and Economics Journal/2010
Variable Definitions:
DEAit = Efficiency score of firm i in year t;
ENVSTRit = Environmental strength score of firm i in year t;
SALESit = Net sales (Compustat Item #12) of firm i in year t;
OCFit = Net cash flows from operating activities (Compustat Item #308) of firm i
in year t;
COGSit = Cost of goods sold (Compustat Item #41) of firm i in year t;
XSGAit = Selling, general and administrative expenses (Compustat Item #189) of
firm i in year t;
TAit = Total assets (Compustat Item #6) of firm i in year t;
TDit = Total liabilities (Compustat Item #9 + #34) of firm i in year t;
LEVit = Leverage ratio [total liabilities (Compustat Item #9 + #34) / total assets
(Compustat Item #6) of firm i in year t;
AGEit = Net property, plant and equipment (Compustat Item #8) / Gross property,
plant and equipment (Compustat Item #7) of firm i in year t;
MTBit = Market to book ratio {[common shares outstanding (Compustat Item
#25) × stock price – fiscal year-end (Compustat Item #199] / total common
equity (Compustat Item #60)} of firm i in year t.
Table 3 reports the Pearson correlation matrix for selected variables in each of the
four years. For each pair of variables, the Pearson correlation coefficient and related pvalue are provided. In general, the results indicate that DEA is positively correlated with
ENVSTR and ROA in each of the four years. Of particular interest to this study, DEA is
significantly (p < 0.10) positively correlated with ENVSTR in each of the four years of
our sample. The significant correlation between DEA and ENVSTR for each of the four
years (2004 – 2007) suggests that firm efficiency is positively associated with
environmental performance and provides initial evidence supporting the hypothesis.
Table 3
Pearson Correlation among Selected Variables
Panel A: 2004 (n=96)
ENVSTR
(p-value, two-tailed)
AGE
(p-value, two-tailed)
LEV
(p-value, two-tailed)
ROA
(p-value, two-tailed)
ROE
(p-value, two-tailed)
MTB
(p-value, two-tailed)
DEA
ENVSTR AGE
LEV
ROA
ROE
0.2216
0.0300
0.1022
0.0701
0.3243
0.4997
-0.0611
0.9464
0.1752
0.5540
0.3590
0.0895
0.6394
0.1266
0.1871 0.0423
<0.0001 0.2191
0.0694 0.6825
0.5469
0.1142
0.1279 -0.3311 0.6508
<0.0001 0.2681
0.2167 0.0010 <0.0001
0.0736
-0.0181 -0.1276 0.3416 -0.0348 -0.1622
0.4762
0.8610
0.2177 0.0007 0.7365 0.1144
9
Eco-efficiency: Evidence from the
Pharmaceutical Industry
Panel B: 2005 (n=91)
ENVSTR
(p-value, two-tailed)
AGE
(p-value, two-tailed)
LEV
(p-value, two-tailed)
ROA
(p-value, two-tailed)
ROE
(p-value, two-tailed)
MTB
(p-value, two-tailed)
DEA
ENVSTR AGE
LEV
ROA
ROE
0.2124
0.0432
0.1348
0.0275
0.2051
0.7972
-0.0112
0.0283
0.1045
0.9164
0.7899
0.3272
0.7294
0.1528
0.1632 -0.0574
<0.0001 0.0782
0.1243 0.5889
0.2811
0.0581
0.2240 -0.3919 0.3151
0.0069
0.5841
0.0338 0.0001 0.0023
-0.0958 -0.0530 -0.1271 0.5044 -0.1493 -0.8421
0.3666
0.6178
0.2326 <0.0001 0.1578 <0.0001
Panel C: 2006 (n=94)
ENVSTR
(p-value, two-tailed)
AGE
(p-value, two-tailed)
LEV
(p-value, two-tailed)
ROA
(p-value, two-tailed)
ROE
(p-value, two-tailed)
MTB
(p-value, two-tailed)
DEA ENVSTR AGE
LEV
ROA
ROE
0.1779
0.0863
0.0824 0.0195
0.4299 0.8521
0.1086 0.0060
0.0727
0.2973 0.9540
0.4860
0.3313 0.1587
0.1184 0.0023
0.0011 0.1267
0.2557 0.9827
0.1279 0.0669
0.0916 -0.1784 0.6138
0.2195 0.5219
0.3797 0.0854 <0.0001
0.0501 -0.0698 -0.1072 0.4345 -0.2841 -0.5612
0.6317 0.5038
0.3040 <0.0001 0.0055 <0.0001
Panel D: 2007 (n=98)
ENVSTR
(p-value, two-tailed)
AGE
(p-value, two-tailed)
LEV
(p-value, two-tailed)
ROA
(p-value, two-tailed)
ROE
(p-value, two-tailed)
MTB
(p-value, two-tailed)
10
DEA
ENVSTR AGE
LEV
ROA
ROE
0.2340
0.0204
0.1387
-0.0105
0.1733
0.9184
-0.0936
0.0171
0.0814
0.3594
0.8675
0.4257
0.4168
0.1610
0.1296 0.1231
<0.0001 0.1132
0.2034 0.2274
0.1352
0.0426
0.2184 -0.2732 0.1863
0.1845
0.6769
0.0307 0.0065 0.0663
-0.1074 -0.0452 -0.1960 0.3247 -0.1299 -0.8804
0.2926
0.6587
0.0531 0.0011 0.2024 <0.0001
Southwest Business and Economics Journal/2010
Variable Definitions:
ROAit =Return on assets [income before extraordinary items – available for common
equity (Compustat Item #237)] / total assets (Compustat Item #6) of firm i in year t;
ROEit = Return on equity ratio [ income before extraordinary items – available for
common equity (Compustat Item #237) / common shareholders’ interest in the
company (Compustat Item #60)] of firm i in year t,
For definitions of other variables, please see Table 2.
5. Results
5.1 Primary Regression Test
I run the regression model in Equation 1 to additionally test the hypothesis of a
positive relationship between efficiency and environmental performance. Panel A of
Table 4 presents the results.
Table 4
Regression Analysis
Panel A: Primary Regression Test
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*ROAit + α5*MTBit +
α6*YEAR05 it + α7*YEAR06it + α8*YEAR07it + εit
[Equation 1]
N=379; Adjusted R2 = 0.3152
Variable
Parameter Estimate Standard Error t Value
p > |t|
Intercept
0.75983
0.03501
21.71
<0.0001*
ENVSTR
0.04174
0.01488
2.81
0.0053*
AGE
0.05671
0.05334
1.06
0.2884
LEV
-0.08822
0.04686
-1.88
0.0605***
ROA
0.00381
0.00032
11.6
<0.0001*
MTB
3.71E-04
8.76E-05
0.42
0.6727
YEAR05
-0.01582
0.02612
-0.61
0.5451
YEAR06
0.08063
0.02589
3.11
0.002*
YEAR07
0.05316
0.02578
2.06
0.0399**
Panel B: Additional Regression Test
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*YEAR05 it +
[Equation 2]
α5*YEAR06it + α6*YEAR07it + εit
2
N=379; Adjusted R = 0.0694
Variable
Parameter Estimate Standard Error t Value
p > ItI
Intercept
0.69232
0.04010
17.27
<0.0001*
ENVSTR
0.06727
0.01714
3.92
0.0001*
AGE
0.14165
0.06100
2.32
0.0208**
LEV
-0.07945
0.05326
-1.49
0.1366
YEAR05
-0.02203
0.30450
-0.72
0.4697
YEAR06
0.06757
0.03015
2.24
0.0256**
YEAR07
0.03590
0.02985
1.20
0.2299
11
Eco-efficiency: Evidence from the
Pharmaceutical Industry
Panel C: Additional Regression Test
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*ROAit + α5*MTBit +
α6*RDINTit + α7*LATit + α8*YEAR05 it + α9*YEAR06it +
α10*YEAR07it + εit
[Equation 3]
N=379; Adjusted R2 = 0.3335
Variable
Parameter Estimate Standard Error t Value
p > ItI
Intercept
0.79223
0.04141
19.13
<0.0001*
ENVSTR
0.04027
0.01402
2.87
0.0044*
AGE
0.05131
0.06409
0.80
0.4240
LEV
-0.11413
0.04948
-2.31
0.0218**
ROA
0.00354
0.00041
8.65
<0.0001*
MTB
0.00004
0.00008
0.51
0.6131
RDINT
-0.00222
0.00115
-19.20 0.0555***
LAT
0.01909
0.01909
2.79
0.0056*
YEAR05
-0.01609
0.02836
-0.57
0.5711
YEAR06
0.06172
0.02765
2.23
0.0264**
YEAR07
0.01830
0.02774
0.66
0.5101
Notes: significance level: *p≤0.01, ** p ≤0.05, *** p ≤0.1
Variable Definitions:
RDINTit = R&D Intensity (research and development expenditures (Compustat
Item #46) / Sales (Compustat Item #12) of firm i in year t;
LATit = natural log of total assets (Compustat Item #6) of firm i in year t;
YEAR05=1, if t=2005, otherwise 0;
YEAR06 = 1, if t = 2006, otherwise 0;
YEAR07 = 1, if t = 2007, otherwise 0;
For definitions of other variables, please see Table 2 and 3.
As expected, ENVSTR’s coefficient, α1t, is positive (0.04174) and significant (p =
0.0053). This finding suggests that there is a positive and significant association between
DEA score and environmental strength score. Additional evidence reveals that DEA is
significantly positively associated with ROA, and negatively associate with LEV. This
study also checks the variance inflation factors (VIFs), multicollinearity is not an issue in
the regression model. The results support the hypothesis and conclusion of a positive
relationship between economic performance and environmental performance in the
pharmaceutical firms.
5.2 Additional Regression Tests
Although ROA and MTB are commonly used control variables, it is may be
difficult to understand why these two variables will affect DEA scores. I remove ROA
and MTB from Equation 1 and run the following modified regression model.
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*YEAR05 it +
α5*YEAR06it + α6*YEAR07it + εit [Equation 2]
12
Southwest Business and Economics Journal/2010
Results are reported in Panel B of Table 4. ENVSTR’s coefficient, α1t, is positive
(0.06727) and significant (p < 0.0001). This finding still supports the hypothesis.
Research and development activities and firm size may be critical and may affect
DEA scores in the pharmaceutical industry. To investigate any possible impact of these
two variables on DEA scores, I add these two variables (R&D Intensity and Total Assets)
to Equation 1 and run the following modified model.
Model: DEAit = α0 + α1*ENVSTRit + α2*AGEit+ α3*LEVit + α4*ROAit + α5*MTBit +
α6*RDINTit + α7*LATit + α8*YEAR05 it + α9*YEAR06it +
α10*YEAR07it + εit
[Equation 3]
Where
RDINTit = R&D Intensity (research and development expenditures (Compustat
Item #46) / Sales (Compustat Item #12) of firm i in year t;
LATit = natural log of total assets (Compustat Item #6) of firm i in year t;
Results are reported in Panel C of Table 4. ENVSTR’s coefficient, α1t, is positive
(0.04027) and significant (p = 0.0044). This finding still supports the hypothesis.
Additional evidence reveals that R&D Intensity is negatively associated with DEA at a
relatively significant level (p = 0.0555). This suggests pharmaceutical companies heavily
engaging in research and redevelop activities do not operate very efficiently. Firm size
(LAT) is positively related to DEA scores at a significant level (p = 0.0056). This
suggests larger pharmaceutical companies perform more efficiently than smaller firms.
6. Conclusion
The purpose of this study is to examine the association between environmental
performance and economic performance in the pharmaceutical industry (SIC=283X) for
the period of 2004 -2007. I use relative efficiency score calculated by Data Envelopment
Analysis (DEA) to measure economic performance. Environmental performance data,
which is measured as environmental strength score, comes from KLD. Regression
analysis reveals a significant and positive association between firm efficiency and
environmental performance. The results support the validity of eco-efficiency. The results
should interest managers who engage in behavior leading to or maintaining strong
environmental performance, financial analysts who conduct research on eco-efficiency,
and policy makers who design and implement guidelines on improving environmental
quality. Moreover, results in this study can increase individual investors’ confidence in
investing in pharmaceutical companies with stronger environmental performance.
This study has several limitations. First, like prior studies (Burnett and Hansen
2008; Murty and Kumar 2003), this study includes using only one industry in the
analysis. Whether the results from the pharmaceutical industry can be generalized to
other industries still remains unknown. This study would recommend that different
industries be tested to investigate the association between economic performance and
environmental performance. Secondly, this study lacks some justification on the selection
of control variables. Third, possible feedback effects may exist. For instance, DEA scores
may affect other variables, such as ROA and ENVSTR.
13
Eco-efficiency: Evidence from the
Pharmaceutical Industry
References
Banker, R.D., Charnes, A., & Cooper, W.W. (1984). Some models for estimating
technical and scale inefficiencies in data envelopment analysis. Management
Science 30: 1078-1092.
Battese, G.E., & Coelli, T.J. (1995). A model for technical inefficiency effects in a
stochastic frontier production function for panel data. Empirical Economics 20:
325-332.
Birkin, F., & Woodward, D. (1997). Management accounting for sustainable
development, part 2: from economic to ecological efficiency. Management
Accounting (U.K) 75:42-45.
Beurden, P.V., & Gossling, T. (2008). The worth of values - a literature review on the
relation between corporate social and financial performance. Journal of Business
Ethics 82:407-424.
Bowlin, W.F. (1999). An analysis of the financial performance of defense business
segments using data envelopment analysis. Journal of Accounting and Public
Policy 18: 287 – 310.
Burnett, R., & Hansen, D. (2008). Ecoefficiency: defining a role for environmental cost
management. Accounting, Organization and Society 33:551-581.
Charnes, A., Cooper, W.W., & Rhodes, E. (1978). Measuring the Efficiency of Decision
Making Units. European Journal of Operational Research, 2, 429-444.
Christmann, P. (2000). Effects of ‘best practices’ of environmental management on cost
advantage: the role of complementary assets. Academy of Management Journal
43: 663-680.
Cooper, W.W., Seiford, L.M., & Tone, K. (2000). Data Envelopment Analysis: A
Comprehensive Text with Models, Applications, References and DEA-Solver
Software. Kluwer Academic Publishers, Boston.
Delmas, M. (2001). Stakeholders and competitive advantage: the case of ISO 14001.
Production and Operations Management 10: 343-358.
DeSimone, L.D., & Popoff, F. (1997). Eco-efficiency: the business link to sustainable
development. MIT press. Boston, MA.
Feroz, E. H., Goel, S., & Raab, R. L., (2008). Performance measure for accountability in
corporate governance: a data envelopment analysis approach. Review of
Accounting and Finance 7: 121-130.
14
Southwest Business and Economics Journal/2010
Hart, S. L., & Ahuja, G. (1996). Does is pay to be green? An empirical examination of
the relationship between emissions reduction and firm performance. Business
Strategy and the Environment 5:30-37.
Konar, S., & Cohen, M., (2002). Does the market value environmental performance. The
Review of Economics and Statistics 83:281-289.
McGuire, J., Sundgren, A., & Schneeweis, T. (1988). Corporate social responsibility and
firm financial performance. Academy of Management Journal 31(4): 854-872.
Melnyk, S. A., Sroufe, R. P., & Calantone, R. (2003). Accessing the impact of
environmental management systems on corporate and environmental
performance. Journal of Operations Management 21:329-351.
Murty, M. N., & Kumar, S. (2003). Win-win opportunities and environmental regulation:
testing of the Porter Hypothesis for Indian manufacturing industries. Journal of
Environmental Management 67:139-144.
Porter, M. E., & van der Linde, C. (1995). Towards a new conception of the
environment-competitiveness relationship. Journal of Economic Perspectives
9(4): 97-118.
Russo, M. V., & Fouts, P. A. (1997). A resource-based perspective on corporate
environmental performance and profitability. Academy of Management Journal
40: 534-559.
U.S. EPA. (2000). Green dividends? The relationship between firms’ environmental
performance and financial performance. Report by the Environmental Capital
Markets Committee of the U.S. Environmental Protection Agency.
Waddock, S., & Graves, S. (1997). The corporate social performance – financial
performance link. Strategic Management Journal 18(4):303-319.
15
Eco-efficiency: Evidence from the
Pharmaceutical Industry
16