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sustainability
Article
The Deterrence Effect of a Penalty for
Environmental Violation
Yun Wang 1, Yanxi Li 1, Zhuang Ma 2,* and Jinbo Song 1
1School of Economics and Management, Dalian University of Technology, Dalian 116024, China
2School of Accounting, Hangzhou Dianzi University, Hangzhou 310018, China
*Correspondence: mazhuang@hdu.edu.cn
Received: 11 July 2019; Accepted: 2 August 2019; Published: 5 August 2019
Abstract:
The response to the penalty for an environmental violation on the firm level is a matter
of reactive corporate environmental practices, about which the existence of a penalty is critical for
environmental public policy. We propose that a penalty acts as a deterrence signal to enhance the
perceived threat of legal punishment and the peer effect serves as the path through which peer firms
learn from target firms. Based on the peer effect among firms and the deterrence effect in criminal
economics, we investigated whether and how the peer firm responds to the penalty for environmental
violation of target firms in the same industrial sector. Using samples of Chinese listed firms from 2008
to 2015, this paper finds that the penalty for the target firms can increase the peer firms’ environmental
investment, and compared to the sample with low-level environmental regulation, the increase in the
sample with high-level environmental regulation is more significant. These findings suggest that
a penalty for target firms has a deterrence effect on peer firms and the environmental regulation
strengthens the above deterrence effect. This is expected to help both theorists and practitioners
achieve a better understanding of the implementation of a penalty for an environmental violation.
Keywords:
penalty for environmental violation; deterrence effect; environmental investment;
peer effect
1. Introduction
While firms in developed countries are taking a proactive sustainability strategy by integrating
sustainability into business strategy in pursuit of sustainable growth [
1
], the firms in emerging countries
are taking reactive corporate environmental practices to deal with environmental regulation [
2
–
4
].
In most developing and transition countries, the government is an important driver of corporate
environmental practices, and the existence of the penalty is also critical for environmental public
policy [
5
]. It is thought to be an optimal penalty when the expected total penalty for any illegal
behavior equals the total social cost of such kind behavior [
6
], which seldom happens in most emerging
markets due to market imperfections and poor enforcement of environmental and social governance [
7
].
Therefore, many scholars still examine the relationship between the penalty for environmental violation
and reactive corporate environmental practices [
8
], in order to get a more comprehensive understanding
of whether and how the penalty works.
The penalty is believed to be effective in spurring specific and general deterrence. The basic theory
of deterrence relies on the notion that any profit-seeking firm is an “amoral calculator” [
9
]. According
to the extant literature, if the penalty for environmental violation is high enough to outweigh the cost
of compliance, the profit maximizing firms will intend to comply [
2
]. Earnhart (2004) [
10
] believes that
US federal fines on wastewater treatment plants affected their pollution reduction during the 1990s.
Shimshack and Ward (2008) [
11
] also proved that penalties were responsible for compliance with a
sample of pulp and paper plants. Shimshack (2014) [
12
] points out that the penalty can deter future
Sustainability 2019,11, 4226; doi:10.3390/su11154226 www.mdpi.com/journal/sustainability
Sustainability 2019,11, 4226 2 of 19
violations. However, with the limitations of capacity and information asymmetry, government officials
formally prosecute and obtain legal sanction against violators in only a small proportion [
13
], with the
detected violation at the bottom of the “pyramid of sanctions”, which makes a great challenge for the
regulatory officials. To deal with this, maximizing the deterrence not only on the violator or its future
violation but also on the potential violator could provide a better solution, which is worth investigating.
In this paper, peer effect is exploited to explain whether and how the penalty for environmental
violation deters potential violators and motivates them to reduce environmental harm. While the
peer effect is widely documented in settings like tax [
14
], audit [
15
], earning management [
16
], and
other financial policy [
16
,
17
], the study of peer effect in corporate environmental violation is novel.
The penalty for environmental violation sends a “threat message” through the community of related
firms, and thereby is likely to enable peer firms to learn about the consequences and the costs of
engaging in similar questionable environmental practices, leading the peer firms to increase their
investment in compliance. This phenomenon of perceived risk and cost of violation spreading from
the target firm to the peer firm is referred to as deterrence.
After receiving the deterrence message, corporate environmental investment is studied as the
reactive corporate environmental practices of peer firms. In today’s business climate, corporate
environmental investment is an important aspect of corporate social responsibility (CSR), which
is monitored and judged by many stakeholders [
18
]. The investment in environmental protection
activities is disclosed in the Sustainable Development Report and Environmental Report, which is part
of the CSR Report. Because the disclosure is not compulsory, the sample selection is biased due to
unobservable data. So the Heckman two-stage model was employed to correct the selection bias in
this paper.
Based on the peer effect, we attempt to investigate the influence of penalties of target firms on
peer firms’ environmental investment. The data of penalties for environmental violation comes from
the websites of the Institute of Public and Environmental Affairs (IPE), the local government and
local environmental protection bureau, and is manually collected. The empirical results show that the
penalties of target firms will give incentive to peer firms to perform compliance by increasing corporate
environmental investment, proving that the penalty for environmental violation has a deterrent effect
on potential violators. Furthermore, as firms in different provinces of China face varied pressure
from environmental regulation, with a high level of environmental regulation the deterrence effect
is stronger.
The main contributions of this paper focus on two aspects. First, our study contributes to the
sustainability literature by integrating the deterrence theory in criminal economics into environmental
governance. Extant work shows that penalized firms have the incentives to invest more in environmental
protection to meet the regulatory requirement [
19
,
20
]. Deterrence from the penalty focuses on
discouraging the violator itself and its future violation, for the potential violation whether the
deterrence works is largely unexplored and related empirical evidence is scarce [
21
]. We are the first
to empirically prove the deterrence effect of the penalty for environmental violation on the potential
violator with firm-level data and explain how deterrence of the penalty, an important mechanism of
environmental governance, works on potential violators. Second, this paper highlights the importance
of peer effect to explain the mechanism or channel through which deterrence of the penalty plays a role.
Hirshleifer and Teoh (2009) [
22
] document that market participants can learn from each other, and
Kedia, et al. (2015) [
23
] argue that peer members learn from each other via communication, observation
of others’ actions, or what happens after these actions. We provide evidence that the decision to
invest in environmental activities for a manager is not made in isolation and is affected by other firms’
actions and the consequence of the violation. Further evidence shows that firms facing a high level
of environmental regulation react more strongly to the penalties of target firms, consistent with the
documented effect being greater in those firms.
The remainder of the paper is organized as follows. First, we analyze the theory and develop
the hypotheses. Second, the empirical methodology is designed to provide evidence associated with
Sustainability 2019,11, 4226 3 of 19
the above hypotheses. Next, we discuss the empirical results. Afterward, robust tests are described.
In conclusion, we elaborate on the theoretical and practical implications of our findings.
2. Theoretical Analysis and Hypothesis
By drawing on the relational view of peer effect on corporate behavior and the deterrence effect
in criminal economics when observing the penalty of a target firm for an environmental violation,
whether and how the peer firms should react is investigated in this paper. We propose that the penalty
for environmental violation of target firms may have a deterrence effect to encourage peer firms to
invest more in environmental protection while environmental regulation may strengthen the deterrence
effect. The theoretical framework is shown in Figure 1.
Sustainability 2019, 11, x FOR PEER REVIEW 3 of 20
above hypotheses. Next, we discuss the empirical results. Afterward, robust tests are described. In
conclusion, we elaborate on the theoretical and practical implications of our findings.
2. Theoretical Analysis and Hypothesis
By drawing on the relational view of peer effect on corporate behavior and the deterrence effect
in criminal economics when observing the penalty of a target firm for an environmental violation,
whether and how the peer firms should react is investigated in this paper. We propose that the
penalty for environmental violation of target firms may have a deterrence effect to encourage peer
firms to invest more in environmental protection while environmental regulation may strengthen the
deterrence effect. The theoretical framework is shown in Figure 1.
Figure 1. Theoretical framework.
2.1. The Deterrence Effect of Target Firms’ Penalties for Environmental Violation on Peer Firms’
Environmental Investment
The classical criminal theorists Beccaria and Marchese Di Beccaria (2009) [24] believe that the
probability of a penalty would achieve a preventive effect and a penalty serves to deter others from
committing crimes and to prevent the criminal from repeating his crime. Bentham (1887) [25] argues
that punishment had a deterrent function to prevent all sins in all possible and worthwhile ways.
Becker (1968) [6] rationally opts for the crime by weighing the cost and benefit of illegality and
documents that people commit crimes when they rationally see that the benefit of their crime
outweighs the cost. Therefore, deterrence works when people are discouraged from future criminal
acts by instilling in them the consequences of the penalty.
The deterrence to intimidate potential violators so as to refrain from crime is regarded as the
general deterrence theory [26], which can also be applied in environmental violation. Additionally,
the peer effect can be exploited to explain how deterrence works, in which case the violators are target
firms and the potential violators are peer firms. The peer effect explains how and why the imitation
of behaviors and social learning occurs from the perspectives of social norm and psychology [27–29].
When the environmental violation of target firms becomes the invisible norm in a social group [17],
similar behavior might spread from target firms to peer firms. However, when environmental
violation is considered as undesirable behavior, or the penalty is imposed on the target firm, which
sends a meaningful “threat message”, then the peer firms learn about the risk of engaging in
environmental violation and the cost rising from the penalty, inducing them to increase their
compliance-related efforts, such as investing more in corporate compliance with environmental
standards.
Figure 1. Theoretical framework.
2.1. The Deterrence Effect of Target Firms’ Penalties for Environmental Violation on Peer Firms’
Environmental Investment
The classical criminal theorists Beccaria and Marchese Di Beccaria (2009) [
24
] believe that the
probability of a penalty would achieve a preventive effect and a penalty serves to deter others from
committing crimes and to prevent the criminal from repeating his crime. Bentham (1887) [
25
] argues
that punishment had a deterrent function to prevent all sins in all possible and worthwhile ways. Becker
(1968) [
6
] rationally opts for the crime by weighing the cost and benefit of illegality and documents
that people commit crimes when they rationally see that the benefit of their crime outweighs the cost.
Therefore, deterrence works when people are discouraged from future criminal acts by instilling in
them the consequences of the penalty.
The deterrence to intimidate potential violators so as to refrain from crime is regarded as the
general deterrence theory [
26
], which can also be applied in environmental violation. Additionally, the
peer effect can be exploited to explain how deterrence works, in which case the violators are target
firms and the potential violators are peer firms. The peer effect explains how and why the imitation
of behaviors and social learning occurs from the perspectives of social norm and psychology [
27
–
29
].
When the environmental violation of target firms becomes the invisible norm in a social group [
17
],
similar behavior might spread from target firms to peer firms. However, when environmental violation
is considered as undesirable behavior, or the penalty is imposed on the target firm, which sends a
meaningful “threat message”, then the peer firms learn about the risk of engaging in environmental
violation and the cost rising from the penalty, inducing them to increase their compliance-related
efforts, such as investing more in corporate compliance with environmental standards.
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According to the above theories, it is believed that target firms’ penalties for environmental violation
would have a deterrence effect on peer firms and motivate them to invest more in environmental
protection. Some research points out that even if the manager promises to be compliant with
environmental standards, the employees or the subordinates will choose not to comply because of
performance pressure [
30
]. When that is this case, the deterrence message from the target penalized firm
may not motivate the peer firms to comply, but serves as a reminder of the managers’ commitments,
leading them to check whether anti-pollution measurements are taken, or if emission-control equipment
should be upgraded. The head of the U.S. OPA (Office of Price Administration) Chester Bowles during
World War II said that 25% of the public would comply with any legislation, and 70% would choose
to comply or not depending on whether the remaining 5% is caught or penalized. So the regulatory
department penalizing the “bad apples” can keep the “contingently good apples” good [
31
]. When
penalties for environmental violations occur, and the target firms suffer the consequence of losing their
competitive position or reputation, the peer firms learn that compliance is both prudent and right, and
in this way, an eternal deterrence message has a “reassurance function”.
Firms within the same industry face the same economic situation, competitive pressure and
common risk [
32
], and are benchmarked to each other by analysts and investors, therefore they are the
natural peers [
23
]. In this paper, peer firms are in the same industry as the target firms. Based on the
above theoretical analysis, thus we posit:
Hypothesis 1.
The target firms’ penalties for environmental violation can increase the environmentalinvestment
of peer firms in the same industry sector.
2.2. The Influence of Environmental Regulation on the Deterrence Effect
Academics and regulators argue that environmental regulation is the dominant determinant in
the dramatic improvement of environmental quality in developing countries [
33
]. With gradually
strengthening of environmental regulation, under high-profile environmental regulation the violation
cost increases largely. Facing potential threat, and combined with the normative obligation and
reputation concern, most firms should be committed to comply with environmental monitoring
and enforcement, which obviously is not consistent with the reality of a great many environmental
violations. A proportion of 80% of China’s environmental pollution stems from business activities,
and their low extent of environmental compliance seems to point out that both the monitoring and
enforcement of environmental regulation are becoming controversial [2].
The enforcement of environmental regulation varying in different regions of China may have
several reasons. First, the efficiency of administrative management of the government of each province
is not the same, and some may be highly renowned for regulatory incompetence and administrative
inaction. Second, without enough environmental awareness and the incentive from political promotion
(in some provinces the performance of a government officer is evaluated based on the growth rate of
GDP), some local governments outweigh economic development and ignore environmental protection.
Besides, corruption attributes to the variation in environmental regulation and could weaken the
strength of environmental regulation in the official economy, resulting in the ineffective enforcement of
environmental regulation [34,35].
Firms in different provinces of China face varied pressure from environmental regulation, which
makes them have different expectations about the risk and cost of the potential penalties. For peer
firms, even observing the penalty for the target firm, they may think that it was an accident or other
factors but violation, such as no political connection with the government, that led to the penalty.
In that case, peer firms would not increase environmental investment.
Hypothesis 2.
In regions with high-level environmental regulations, the target firms’ penalties for environmental
violation can increase more the environmental investment of peer firms in the same industry sector.
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3. Materials and Methods
3.1. Sample and Variables
Sample: Our sample contains data of non-financial Chinese listed firms for the period between
2008 and 2015. We selected all observations with penalties for environmental violation from the
websites of IPE, the local government and local environmental protection bureau, the environmental
investment from CSR reports and financial data from the China Stock Market and Accounting Research
(CSMAR) database.
Dependent variables: The environmental investment data (ENI) is disclosed in the Sustainable
Development Report and Environmental Report of the CSR Report, and includes the investment on
technical innovation in environmental protection, expenditure on pollution abatement, installation of
emission-control equipment, pollution discharge fees, etc. The number of 852 firm-year observations
with environmental investment were selected manually by the author and processed with a logarithm
to limit the extent of changes in the range of the data. The penalized firm may respond to the penalties
by increasing its environmental investment, and in order to study the deterrence effect of the penalized
target firms on the peer firms, the peer firms were distinguished from the target firms, the observations,
which have environmental investment and at the same time get penalized, were excluded and then 550
firm-year observations were obtained. Finally, 506 firm- year observations were used in the empirical
tests due to the missing data of other variables and the lagging process. ENI01 is a dummy variable,
which equals 1 when a firm has environmental investment and discloses it.
Independent variables: Penalties for environmental violation come from penalty notices
announced in the websites of the IPE, the local government and local environmental protection
bureau, and 2679 pieces of penalty notice were collected. According to the study of Ma (2017) [
36
]
and Kedia, et al. (2015) [
23
], two variables are constructed as the proxies of penalty for target firms.
One is Number of target firms, which is the log-transformed number of firms penalized in a particular
industry and year plus one, the other is Ratio of target firms, which is the ratio of the firms penalized
in a particular industry and year to the total number of firms in the same industry and year.
The proxy of environmental regulation is measured by the ratio of pollutant discharge fees to a
total industrial output value in each region (EER), which comes from China’s Environmental Yearbook
and China Statistic Yearbook. The categorical variable (EERid) equals 1 when EER is higher than the
mean of EER and 0 otherwise. In the robust test, another proxy, measured by the number of local
laws and regulations promulgated (GOV) in various regions from the China Environmental Yearbook,
is adopted. The categorical variable (GOVid) equals 1 when GOV is higher than the mean of GOV and
0 otherwise.
Control variables: In addition, financial situations are key factors to determine whether the firm
invests in environmental protection activities. As it is already documented in the literature [
36
], the
financial indicators, such as the asset (SIZE), asset-liability ratio (LEV), return on total assets (ROA),
total sales scaled by total assets (SALES), Tobin’s Q (the usual proxy for investment opportunities)
(TQ), can impact the environmental investment decision. In addition, corporate governance indicators
also play roles, such as agency cost measured by the management fees scaled by the sales (MRF), and
independent directors (INDDIR), whose reputation would be damaged by negative reports [
37
,
38
].
Also, the number of years since the first year that a firm initiated its public offering (AGE), firm nature
of whether it is state-owned (STATE), and whether it is in a heavy pollution industry (HP) are included.
3.2. Empirical Model
Environmental investment is disclosed in the Sustainable Development Report and Environmental
Report in the CSR Report. However, this disclosure is not compulsory and firms have great discretion
in whether their environmental information is disclosed. There are firms that invest in environmental
protection but have no disclosure of this. Therefore, the sample we collected is biased due to the
unobservable data. To solve this problem, the Heckman two-stage model was employed to correct
Sustainability 2019,11, 4226 6 of 19
the selectivity bias [
39
]. There are two stages in the environmental investment decision. The first is
a probit-type selection equation that describes the propensity to invest and to disclose an observed
environmental investment. The second is a regular regression equation with the additional inverse Mills
ratio (IMR) estimated in the first stage. With other fundamental factors, this model was employed to
examine the influence of the penalty for target firm environmental violation on peer firm environmental
investment in Hypothesis 1, and this model is shown in Equations (1) and (2):
probit(eni01it of peer firms =0, 1)
=α0+α1Penalties of target firms +α2ENI01it−1
+FundamentalFactorsΓit +IndDummies +YearDummies +εit
(1)
ENIit of peer firms
=β0+β1Penalties of target firms +β2ENIit−1+β3IMR
+FundamentalFactorsΓit +IndDummies +YearDummies +εit
(2)
Equation (1) is the first stage in the Heckman two-stage model,
probit
is the probability of a peer
firm i’s investment in year t. Equation (2) is the second stage in the Heckman two-stage model ENI
it
stands for the environmental investment of a peer firm iin year t. The difference in Equation (2) lies in
the additional IMR, which is estimated in the first stage. The selectivity bias is examined by a t-test on
the coefficient on
β3
, and if the coefficient is significant, it shows that the bias is corrected with the
necessary Heckman model.
The proxies for
Penalties of target firms
are Number of target firms: log-transformed the number
of target firms penalized for environmental violation in a particular industry and year plus one and
Ratio of target firms: the ratio of target firms penalized for environmental violation in a particular
industry and year. Hypothesis 1 predicts positive
β1
, suggesting that the peer firms will increase
environmental investment when observing that the target firms in the same industry are penalized.
FundamentalFactors
is a set of control variables defined in the above part of the sample and variables
which are supposed to have influence on corporate environmental investment.
When considering the influence of environmental regulation, the whole sample is divided into
two subsamples according to the categorical variable (EERid). The extent of environmental regulation
is high when EERid =1; the extent of environmental regulation is low when EERid =0. The tests are
done with the same regression in Equations (1) and (2) with the two subsamples and then the results
are compared to prove Hypothesis 2.
4. Results
4.1. Descriptive Statistics
Table 1presents the summary statistics of peer firms’ environmental investment, target firms’
penalties in the same industrial sector and other control variables. At first, there are 852 firm-year
observations with environmental investment selected manually. Considering that the penalized firm
may respond to the penalties by improving its environmental investment, and in order to only study
the deterrence effect of the penalized target firms on the peer firms, peer firms are distinguished from
target firms. The observations, which have environmental investment but also get penalized, were
excluded and then 550 firm-year observations were obtained. The mean of Number of target firms is
2.102, smaller than the median (2.197). The mean of Ratio of target firms is 0.168, which means that on
average 16.8% firms in an industrial sector are penalized, and the maximum of Ratio of target firms
indicates that in some industrial sectors 63.3% firms are penalized. From the summary statistics of
state and HP, we find that there are more state-owned firms and heavy-polluted firms involved in
environmental investment.
Table 2shows the correlations between the measures of the penalty for target firms, environmental
investment, and control variables. From this table, we can see that the Ratio of target firms is positively
Sustainability 2019,11, 4226 7 of 19
correlated with ENI, significant at the 0.01 level, which indicates that penalties for the target firms
in the same industry are related to peer firms’ environmental investment, which is consistent with
our expectation. The positive correlation between Number of target firms and ENI is not significant.
The Pearson correlation between Number of target firms and Ratio of target firms is relatively high,
because they are the proxies of the penalty for target firms and they capture a similar construct; we
report their effects on ENI separately. The positive correlation between SIZE and ENI indicates that
firms with more assets may have a better capacity to invest in environmental management, which is
similar to the positive correlation between SALES and ENI. However, it does not happen with ROA
and TQ. The positive correlation between STATE and ENI shows that state- owned firms intend to
invest more in environmental management. Overall, it seems that all the correlations between variables
are within acceptable limits, and there should be no concerns about multi-collinearity.
Table 1. Descriptive statistics.
Variable Mean Sd P50 Min Max N
ENI 16.01 2.234 16.18 8.007 25.53 550
Number of target firms 2.102 0.947 2.197 0 4.078 550
Ratio of target firms 0.168 0.128 0.145 0 0.633 550
SIZE 22.94 1.325 22.80 19.86 26.89 550
ROA 0.045 0.052 0.037 -0.244 0.228 550
TQ 2.111 1.342 1.728 0.744 9.894 550
SALES 0.864 0.575 0.745 0.061 4.272 550
LEV 0.503 0.191 0.515 0.048 1.163 550
MFR 0.071 0.041 0.064 0.010 0.299 550
INDDIR 0.374 0.063 0.333 0.250 0.667 550
AGE 2.450 0.555 2.639 0.693 3.219 550
STATE 0.695 0.461 1 0 1 550
HP 0.785 0.411 1 0 1 550
Note: ENI stands for the environmental investment; Number of target firms is defined as the log-transformed the
number of target firms penalized for environmental violation in a particular industry and year plus one; Ratio of
target firms is defined as the ratio of target firms penalized for environmental violation in a particular industry and
year; SIZE is the log-transformed asset; LEV is the asset-liability ratio; ROA is the return on total assets; SALES
is total sales scaled by total assets; TQ is Tobin’s Q; MRF is the management fees scaled by the sales; INDDIR is
the ratio of independent directors in board; AGE is the number of years since the first year that a firm initiated its
public offering; STATE represents firm nature of whether it is state-owned; HP indicates that whether it is in a heavy
pollution industry.
Table 2. The Pearson correlation matrix.
1 2 3 4 5 6
ENI 1
Number of target firms 0.060 1
Ratio of target firms 0.212 *** 0.607 *** 1
SIZE 0.392 *** −0.160 *** −0.040 1
ROA −0.092 ** −0.0410 −0.123 *** −0.111 *** 1
TQ −0.258 *** −0.0620 −0.110 *** −0.401 *** 0.459 *** 1
SALES 0.095 ** 0.074 * 0.027 0.044 0.174 *** 0.070
LEV 0.256 *** 0.082 * 0.127 *** 0.481 *** −0.526 *** −0.411 ***
MFR −0.134 *** 0.022 −0.066 −0.350 *** 0.066 0.220 ***
INDDIR 0.060 −0.013 −0.054 0.206 *** −0.031 0.022
AGE −0.017 0.105 ** 0.015 0.202 *** −0.115 *** −0.107 **
STATE 0.086 ** −0.055 0.061 0.309 *** −0.093 ** −0.160 ***
HP 0.014 0.516 *** 0.414 *** −0.315 *** 0.037 0.082 *
7 8 9 10 11 12
SALES 1
LEV 0.146 *** 1
MFR −0.332 *** −0.365 *** 1
INDDIR −0.053 0.063 −0.043 1
AGE −0.027 0.174 *** −0.038 −0.033 1
STATE −0.027 0.147 *** −0.063 −0.057 0.337 *** 1
HP 0.141 *** −0.117 *** 0.173 *** −0.103 ** 0.010 −0.077 *
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
Sustainability 2019,11, 4226 8 of 19
4.2. Tests Results
Table 3presents the results of estimating Equations (1) and (2) when the independent variables are
Number of target firms and Ratio of target firms. The columns (1) and (3) report the estimation results
of the first stage of the Heckman model, and the columns (2) and (4) report the estimation results of
the second stage. From the columns (2) and (4) we can see that the coefficients
β1
on the variables
Number of target firms and Ratio of target firms are 0.366 and 3.425 respectively, both are significant at
the level of 0.01, implying that after learning about the penalties of the target firms, the peer firms
check their environmental performance, such as whether the emission-control equipment is installed
or not, leading to the deterrence effect on environmental investment. The number and proportion of
the penalized firms in an industry when increasing, indicates that the certainty of the environmental
regulation is strengthened, and is more likely to cause peer firms to invest more in environmental
protection, leading to the deterrence effect increasing, which proves Hypothesis 1.
Table 3. The influence of target firms’ penalties on peer firms’ environmental investment.
Peer Firms’ Environmental Investment
(1) (2) (3) (4)
ENI01 ENI ENI01 ENI
Number of target firms 0.029 (0.82) 0.366 *** (2.89)
Ratio of target firms 0.227 (0.91) 3.425 *** (4.09)
l.ENI01 1.577 *** (22.20) 1.574 *** (22.15)
l.ENI 0.180 *** (3.31) 0.159 *** (2.93)
IMR 1.954 *** (2.60) 1.679 ** (2.24)
SIZE 0.276 *** (9.77) 0.900 *** (5.49) 0.276 *** (9.78) 0.850 *** (5.24)
ROA 1.352 ** (2.17) 0.921 (0.36) 1.397 ** (2.24) 1.711 (0.67)
TQ −0.011 (−0.57) −0.189 ** (−2.05) −0.011 (−0.55) −0.189 ** (−2.07)
SALES −0.036 (−0.77) 0.351 * (1.89) −0.036 (−0.76) 0.370 ** (2.01)
LEV −0.333 * (−1.88) 0.212 (0.30) −0.331 * (−1.88) 0.296 (0.42)
MFR −1.784 *** (−2.98) −2.988 (−1.00) −1.696 *** (−2.83) −0.734 (−0.25)
INDDIR 0.230 (0.51) 1.042 (0.69) 0.238 (0.53) 1.122 (0.75)
AGE 0.063 (1.16) −0.391* (−1.93) 0.063 (1.14) −0.380* (−1.90)
STATE 0.264 *** (4.41) 0.091 (0.35) 0.264 *** (4.40) 0.025 (0.10)
HP −0.006 (−0.02) −0.250 (−0.22) 0.001 (0.00) −0.387 (−0.35)
constant −8.332 *** (−12.19) −9.019 * (−1.68) −8.349 *** (−12.21) −7.410 (−1.39)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 11,197 506 11,197 506
r2_p 0.306 0.306
r2_a 0.254 0.265
F 6.718 7.065
Note: * p<0.1, ** p<0.05, *** p<0.01; tstatistics in parentheses.
Turning to the control variables, in the first stage the variables l.ENI01 and ENI01 in the year
t
−
1, are included in columns (1) and (3) and in the second stage the variable l.ENI, ENI in the
year t
−
1, is included. The coefficients of the above-included variables are significantly positive,
suggesting that the decision of environmental investment is inertia, affected by the decision at year t
−
1. According to Equation (1) of the first stage of the Heckman model, the IMR (inverse Mills ratio) is
calculated and the coefficients on IMR in columns (2) and (4) are significant, proving that the sample
selection bias is effectively corrected. Besides, a firm’s environmental investment increases with its
SIZE, return on assets (ROA), SALES, the percentage of independent directors on boards (INDDIR), and
the nature of the firm (STATE). It decreases with its Tobin’s Q (TQ), asset-liability ratio (LEV), and the
management fees scaled by the sales (MRF). These associations seem intuitive, as a powerful firm with
good corporate governance has the capability for environmental investment. Usually,
a state-owned
firm is stronger than other firms, and under its diverse political aims, it will intend to invest more in
environmental investment. Additionally, a firm facing good investment opportunity, with high liability
or high agency cost, will reduce the investment in environmental management in order to increase the
investment in production. Whether the firm is exposed to heavy pollution has no significant influence.
Finally, the year effect and the industry effect are controlled in all regressions.
Sustainability 2019,11, 4226 9 of 19
Table 4summaries the results of Table A1 in the Appendix Aand presents the estimated results
of two samples: one with a low extent of environmental regulation and one with a high extent of
environmental regulation. When the independent variable is Number of target firms, comparing the
columns (2) between Panel A and Panel B, the coefficient in the sample of low extent of environmental
regulation is 0.367, significant at a level of 0.1 and the coefficient in the sample of low extent of
environmental regulation is 0.451, significant at a level of 0.01. When the independent variable is Ratio
of target firms, comparing the columns (4) between Panel A and Panel B, the coefficient in the sample
of low extent of environmental regulation is 2.955, significant at a level of 0.05 and the coefficient in the
sample of low extent of environmental regulation is 3.465, significant at a level of 0.01. We can see that
the significance is also obviously improved. All these results show that the influence of target firms’
penalties on peer firms’ environmental investment in the sample of the high extent of environmental
regulation is much stronger than that in the sample of the low extent of environmental regulation.
In regions with high-level environmental regulation, the penalties for the target firms have a stronger
deterrence effect.
Table 4. The influence of environmental regulation.
Peer Firms’ Environmental Investment
Panel A: The Low Extent of Environmental Regulation
(1) (2) (3) (4)
ENI01 ENI ENI01 ENI
Number of target firms 0.017 0.367 *
(0.33) (0.33)
Ratio of target firms −0.061 2.955 **
(−0.16) (2.01)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 5120 244 5120 244
r2_p 0.315 0.315
r2_a 0.244 0.251
F 3.711 3.801
Panel B: the high extent of environmental regulation
(1) (2) (3) (4)
ENI01 ENI ENI01 ENI
Number of target firms 0.044 0.451 ***
(0.92) (2.77)
Ratio of target firms 0.511 3.465 ***
(1.52) (3.17)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 5910 262 5910 262
r2_p 0.304 0.305
r2_a 0.284 0.294
F 4.562 4.749
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
5. Robust Tests
Several tests were conducted to challenge the robustness of our results. First, the Heckman
two- stage model was employed to correct the selectivity bias based on the consideration that the
observations we collected were biased due to the unobservable data. However, if there is no selectivity
bias in the sample, the ordinary least squares regression could be the best method to use to do the
empirical test. Panel A of Table 5shows the summary of the test results with the regression method
Sustainability 2019,11, 4226 10 of 19
of ordinary least squares (OLS) in Tables A2 and A3. From the columns (2) and (4) in Panel A in
Table 5, we can see that the coefficients on the variables Number of target firms and Ratio of target
firms are 0.325 and 3.336 respectively, which are significant at the level of 0.05 and 0.01 respectively.
The results show that the penalties for target firms can be significantly associated with the peer firms’
environmental investment, which are the same as the results with the method of Heckman two-stage.
Similarly, with the environmental regulation’s influence being considered, when comparing columns
(3) and (5), the coefficient on the variable Number of target firms becomes significant with high extent
of environmental regulation from being insignificant with low extent of environmental regulation.
Also, when comparing columns (4) and (6), the coefficient of the variable Ratio of target firms becomes
significant at a level of 0.01 with high extent of environmental regulation from being significant at
a level of 0.05 with low extent of environmental regulation. The results show that environmental
regulation increased the deterrence effect of the penalty. Results obtained using OLS regression are
consistent with our main findings.
Table 5. The robust tests with different regression methods.
Peer Firms’ Environmental Investment
Panel A: Robust Tests with OLS Regression
Whole Sample
Low Extent of
Environmental
Regulation
High Extent of
Environmental
Regulation
(1) (2) (3) (4) (5) (6)
ENI ENI ENI ENI ENI ENI
Number of target firms 0.325 ** 0.340 0.448 ***
(2.57) (1.60) (2.85)
Ratio of target firms 3.336 *** 3.157 ** 3.567 ***
(3.97) (2.14) (3.41)
Year effect Yes Yes Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes Yes Yes
N 506 506 244 244 262 262
r2_a 0.245 0.259 0.234 0.241 0.287 0.297
F 6.636 7.075 3.658 3.762 4.745 4.934
Panel B: Robust Tests with 2SLS Regression
(1) (2) (3) (4) (5) (6)
ENI ENI ENI ENI ENI ENI
Number of target firms 0.408 *** 0.481 * 0.479 ***
(2.70) (1.85) (2.71)
Ratio of target firms 4.932 *** 4.428 ** 5.263 ***
(4.57) (2.09) (4.35)
Year effect Yes Yes Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes Yes Yes
N 425 425 201 201 224 224
r2_a 0.257 0.275 0.250 0.262 0.297 0.313
Note: * p<0.1, ** p<0.05, *** p<0.01; tstatistics in parentheses.
Second, when considering the two-way causal relationship between the penalties for target firms
and peer firms’ environmental investment, on the one hand, it could be the process of the penalties
for target firms leading to peer firms’ environmental investment, during which after the penalties for
target firms are observed, the peer firms perceive that the risk and cost for environmental violation
are increasing and so they increase environmental investment to avoid potential violation. On the
other hand, it also may be the other process of peer firms’ environmental investment leading to the
penalties for target firms, during which the environmental investment of peers’ firms reduces their
risk of being penalized, and then the possibility of penalties for target companies increases, because
penalties always target at firms with relatively poor environmental performance. In order to avoid the
relationship between the penalties for target firms and peer firms’ environmental investment in the
second case, this paper introduces instrumental variables to solve possible endogenous problems.
The instrumental variables introduced in this paper are penalties for target firms in the year t +1.
The penalized target firms will be on the blacklist of the regulators and receive continuous attention
Sustainability 2019,11, 4226 11 of 19
from them. The penalties for target firms in the year t are highly related to penalties for target firms in
the year t +1, however, for peer firms’ environmental investment in the year t, penalties for target firms
in the year t +1 have not happened yet and cannot be the information of peer firms’ environmental
investment decisions. Therefore, the instrumental variable is highly correlated with the explanatory
variable but is not related to the random error term.
As an additional robustness test, we repeated our analysis with an instrumental variable by the
method of 2SLS and observed results consistent with the main findings. Using the two-stage least
squares method (2SLS), the instrumental variable replaces the endogenous variable itself with the
predicted value of the endogenous variable. The results are shown in Panel B in Table 5, which are the
summary of Tables A4 and A5. With penalties for target firms in the year t +1 as the instrumental
variable, the estimated estimators are still significant in the second-stage regression, and the coefficient
directions are consistent with before. The Hausman test was used for the endogeneity test, and the
exogenous hypothesis was rejected, indicating that the two-stage least squares method is more effective.
Finally, another proxy for environmental regulation, measured by the number of local laws and
regulations (GOV) promulgated in the environmental law work in various regions from the China
Environmental Yearbook, was adopted. The whole sample was divided into two subsamples according
to the categorical variable (GOVid). The extent of environmental regulation is high when GOVid =1;
the extent of environmental regulation is low when GOVid =0. The tests were done with the same
regression method based on the two subsamples and are shown in Table 6and the whole table with all
the control variables is presented as Table A6. The significance of coefficient on Number of target firms
increases from column (1) of Panel A to column (1) of Panel B, and the significance of coefficient on
Ratio of target firms increases from column (2) of Panel A to column (2) of Panel B. Therefore, with
another proxy of environmental regulation, our main findings are still robust.
Table 6. The robust tests with environmental regulation measured as GOV.
Peer Firms’ Environmental Investment
Panel A: Low Extent of Environmental Regulation
(1) (2)
ENI ENI
Number of target firms 0.274 (1.34)
Ratio of target firms 2.809 ** (2.25)
Year effect Yes Yes
Industry effect Yes Yes
N 239 239
r2_a 0.308 0.319
F 4.927 5.122
Panel B: Low Extent of Environmental Regulation
(1) (2)
ENI ENI
Number of target firms 0.338 ** (2.07)
Ratio of target firms 4.021 *** (3.36)
Year effect Yes Yes
Industry effect Yes Yes
N 267 267
r2_a 0.179 0.203
F 3.078 3.412
Note: ** p<0.05, *** p<0.01; tstatistics in parentheses.
Sustainability 2019,11, 4226 12 of 19
6. Discussion and Conclusion
6.1. Discussion
To clarify the penalty mechanism for corporate environmental violation in prior literature, this
study investigated the deterrence effect of the penalty by examining the relationship between the target
firm’s penalty and peer firm’s environmental investment. The research makes valuable advances
contributing to academic research and regulatory officials.
6.1.1. Theoretical Implications
Our research highlights a new possible way of understanding the influence of penalties for an
environmental violation as a driving force that originated from the deterrence effect of such penalties.
Growing literature examines the influence of penalties on the penalized firms themselves. It has
been found that firms suffer drops in market value [
37
–
39
], damage in corporate reputation [
40
,
41
],
even boycotts from consumers [
42
], as evidence that product markets and capital markets discourage
corporate environmental misconduct. However, the debate over the environmental penalty constantly
occurs amidst a vacuum of evidence from empirical evidence. There is surprisingly little research
that investigates the importance of the threat of a legal penalty in motivating other firms to comply
with the law and little is known about the actual effect of the penalties imposed on firms that violate
environmental regulations and how such penalties can influence other firms.
We combined deterrence theory to predict that the information of the target firm’s penalty is one
of the determinants of the receivers’ reactions. Our study shows that only to study the direct effects of
penalties for the environmental violation on the penalized firms themselves cannot fully evaluate the
effectiveness of legal sanctions against violators. Together with the association between the observation
of a target firm’s penalty and risk perception of the peer firm, we extended the focus to whether peer
firms in the same industrial sector are affected when the target firms are penalized for environmental
violations. Furthermore, we used environmental investment as a measure of behavior change taken by
the peer firms in response to deterrence signals. This study shows that the information conveyed by
environmental violation and the related penalty is not confined to the focal firms but is also related
with environmental investment decisions within the same industrial sector, including peer firms that
do not conduct such environmental misbehavior.
By extending current research from the content perspective, our work contributes to a better
understanding of the nature of peer effects in the study of environmental issues. A growing
body of literature on peer effects shows that corporate decisions are often made after learning
and considering other firms’ actions [
14
,
17
,
36
], and this exists in many corporate events, such as
corporate liquidations [
43
], earning management [
23
], and tax [
14
]. Applying the peer effect in the field
of corporate environmental violation is relatively novel. While other studies on peer effect focus on
contagion and imitation of behavior, this paper investigated the deterrence also arising from other firm’s
observed behavior, which in this case is the penalty for an environmental violation.
We proved
that
the penalty of the target firm poses a potential legitimacy threat to peer firms, eventually motivating
them to do subsequent environmental investment. In this way, the environmental violation setting
provides us with suggestive evidence of the deterrence mechanism that is used as incentive to peer
firms to respond to the actions of focal firms.
6.1.2. Practical Implications
Our study has practical implications and important guidance for regulatory officials and regulatory
scholars, especially in an emerging country. China, as a typical representation of a transition and
developing economy, faces serious environmental problems. The corporate approach to environmental
protection is mainly a regulation-driven reactive mode, which is pushed by legal penalties imposed
by the government. With the limits of governmental capacity and information asymmetry, officials
formally prosecute and obtain legal sanction against violators in only a small proportion. Also, at the
Sustainability 2019,11, 4226 13 of 19
current stage, most firms just respond to changing environmental policies by committing minimal
resources towards environmental issues, which is a kind of reactive environmental practice emphasizing
ex-post actions. Only based on actual corporate reactions, can the policies of environmental protection
be well-designed and have an effect. Thus, it is crucial to know the extent and the way in which
the implementation of regulatory norms will actually lead to improvements toward environmental
protection, and whether the penalty has deterrence effect is important in the implementation of a
penalty for environment violation. Our study proves that the existence of any penalty is crucial for
environmental public policy and it is important to keep the penalty to be effective in spurring specific
and general deterrence, which by increasing the violation cost and perceived risks of the penalized
firms and reducing the potential expected earnings from violation, eventually achieves the goal of
protecting the environment and maximizing social welfare.
6.2. Limitations and Future Study
This study is subject to some limitations which should be rectified by future research. First, the
deterrence effect on peer firms could not only come from the fear of a formal legal penalty but also
from the potential drops in market value, damage in corporate reputation or manager’s job, and a
boycott from consumers. Environmental investment could be a form of compliance stemming more
from fear of these informal sanctions. The theatrical framework should be completed by adding these
factors and related mediators should be used in empirical tests in the future. Plus, the observation
of penalty might lead to other reactive environmental practices, such as information disclosure of
environmental responsibility, or other changes in corporate governance, eventually leading to improved
environmental performance.
Second, the sample in this paper contains only public listed firms in China. The fact is that the
penalty for environmental violation is not only imposed on public listed firms. Thus, the target firms
could be non-listed firms, and behaviors are observed by peer firms, which could also be listed and
non-listed firms. Moreover, the listed firms mostly are larger firms, which are more visible and easier
to be the target of inspection. The results for the non-listed or small firms could not be the same.
However, the data of non-listed and small firms is hard to access. In future research, a case study may
be used to investigate the reaction of non-listed firms to the penalty of target firms. Also, the research
context could be extended on a wider scale in different countries.
Third, the announcements of penalties for environmental violation are gathered using web crawler
technology from the websites of IPE and others, and they are not uniform. Detailed information, such
as the specific time in a year when the violation happens and how serious it could be, is hard to extract.
Therefore, the differentiated influence of a penalty for various environmental violations should be
further investigated when detailed information can be obtained and processed.
Additionally, the importance of peer effect on the environmental penalty may encourage future
research to explore other specific mechanisms. As peer effect is closely related to the social network,
people observe and learn through economic links among them. In future research, the social network and
the economic link should be considered in the framework of this study. We explored the environmental
regulation level as a contextual factor, further study may take other potentially important contextual
factors into consideration.
6.3. Conclusions
Based on the sample of Chinese listed companies, the firm-level environmental investment
and penalty for environmental violation were collected by hand in the period between 2007 and
2015 from multiple resources, the deterrence effect of the penalty for target firms on peer firms was
investigated. With the Heckman two-stage model, the empirical tests showed that the penalty for the
target firms was positively related to the peer firms’ environmental investment, no matter whether
the penalty for the target firms is measured by Number of target firms or Ratio of target firms,
which proves that environmental regulation has a deterrent effect. Further, the formal institution of
Sustainability 2019,11, 4226 14 of 19
environmental governance was considered, and we found that with a high level of environmental
regulation, the deterrence effect of the penalty for target firms on peer firms is stronger than that with
a low level of environmental regulation. The results of this study are consistent with the anecdotal
evidence that using legal means to reduce environmental pollution is necessary and that deterrence
theory applies to the penalty of environmental violation.
Author Contributions:
Data curation, Z.M.; Investigation, Z.M.; Supervision, J.S.; Writing—original draft, Y.W.;
Writing—review & editing, Y.L.
Funding:
This research was funded by the Major Program of the National Fund of the Philosophy and Social
Science of China (Grant No. 18ZDA095).
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A1. The influence of environmental regulation.
Peer Firms’ Environmental Investment
Low Extent of Environmental Regulation High Extent of Environmental Regulation
(1) (2) (3) (4) (5) (6) (7) (8)
ENI01 ENI ENI01 ENI ENI01 ENI ENI01 ENI
Number of target firms 0.017 0.367 * 0.044 0.451 ***
(0.33) (1.73) (0.92) (2.77)
Ratio of target firms
−0.061 2.955 ** 0.511 3.465 ***
(−0.16) (2.01) (1.52) (3.17)
l.ENI01 1.566 *** 1.566 *** 1.571 *** 1.564 ***
(15.41) (15.40) (15.63) (15.53)
l.ENI 0.196 ** 0.190 ** 0.050 0.015
(2.46) (2.39) (0.61) (0.18)
IMR 2.127 * 2.052 * 0.092 −0.389
(1.96) (1.90) (0.08) (−0.34)
SIZE 0.262 *** 0.948 *** 0.261 *** 0.980 *** 0.296 *** 0.290 0.293 *** 0.147
(6.60) (3.97) (6.56) (4.11) (6.90) (1.10) (6.84) (0.57)
ROA 1.013 2.073 0.998 1.999 1.676 * −1.520 1.789 ** −0.040
(1.08) (0.48) (1.06) (0.46) (1.95) (−0.43) (2.09) (−0.01)
TQ −0.013 −0.242 −0.012 −0.226 −0.022 −0.158 −0.020 −0.173
(−0.47) (−1.52) (−0.45) (−1.42) (−0.74) (−1.36) (−0.70) (−1.51)
SALES −0.038 0.426 −0.039 0.453 −0.038 0.249 −0.036 0.265
(−0.54) (1.32) (−0.55) (1.41) (−0.58) (1.10) (−0.55) (1.18)
LEV −0.437 0.854 −0.436 0.648 −0.238 0.966 −0.231 1.287
(−1.63) (0.67) (−1.63) (0.51) (−0.97) (1.10) (−0.94) (1.49)
MFR −1.768 ** 5.274 −1.783 * 6.153 −1.815 ** −5.578 −1.618 ** −1.917
(−1.96) (1.03) (−1.96) (1.20) (−2.22) (−1.41) (−2.00) (−0.48)
INDDIR 0.757 2.278 0.759 2.167 −0.244 1.169 −0.209 1.354
(1.22) (0.87) (1.22) (0.83) (−0.36) (0.56) (−0.31) (0.65)
AGE 0.033
−0.999
*** 0.036
−0.926
*** 0.085 0.182 0.082 0.199
(0.42) (−3.05) (0.46) (−2.88) (1.08) (0.65) (1.05) (0.72)
STATE 0.342 *** 0.714 0.344 *** 0.571 0.191 ** −0.813 ** 0.191 **
−0.814
***
(3.67) (1.52) (3.68) (1.21) (2.34) (−2.59) (2.35) (−2.63)
HP −0.127 −0.414 −0.064 −0.384 0.059 0.272 0.027 0.203
(−0.27) (−0.22) (−0.14) (−0.21) (0.16) (0.19) (0.08) (0.14)
constant −8.018 *** −10.329 −8.001 *** −10.853 −8.675 *** 7.392 −8.653 *** 11.301
(−8.04) (−1.34) (−8.01) (−1.42) (−8.54) (0.85) (−8.53) (1.33)
Year effect Yes Yes Yes Yes Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes Yes Yes Yes Yes
N 5120 244 5120 244 5910 262 5910 262
r2_p 0.315 0.315 0.304 0.305
r2_a 0.244 0.251 0.284 0.294
F 3.711 3.801 4.562 4.749
Note: * p<0.1, ** p<0.05, *** p<0.01; tstatistics in parentheses.
Sustainability 2019,11, 4226 15 of 19
Table A2.
The influence of target firms’ penalties on peer firms’ environmental investment with OLS.
Peer Firms’ Environmental Investment
(1) (2)
ENI ENI
Number of target firms 0.325 ** (2.57)
Ratio of target firms 3.336 *** (3.97)
l.ENI 0.042 *** (3.68) 0.040 *** (3.52)
SIZE 0.567 *** (5.50) 0.566 *** (5.56)
ROA −1.819 (−0.76) −0.681 (−0.29)
TQ −0.167 * (−1.80) −0.170 * (−1.85)
SALES 0.392 ** (2.10) 0.406 ** (2.20)
LEV 0.555 (0.79) 0.578 (0.83)
MFR −0.311 (−0.11) 1.544 (0.55)
INDDIR 0.447 (0.30) 0.615 (0.41)
AGE −0.523 *** (−2.66) −0.493 ** (−2.53)
STATE −0.307 (−1.45) −0.316 (−1.51)
HP −0.215 (−0.19) −0.424 (−0.38)
constant 3.367 (1.34) 3.201 (1.28)
Year effect Yes Yes
Industry effect Yes Yes
N 506 506
r2_a 0.245 0.259
F 6.636 7.075
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
Table A3. The influence of environmental regulation with OLS.
Peer Firms’ Environmental Investment
Low Extent of Environmental Regulation High Extent of Environmental Regulation
(1) (2) (3) (4)
ENI ENI ENI ENI
Number of
target firms 0.340 (1.60) 0.448 *** (2.85)
Ratio of target
firms 3.157 ** (2.14) 3.567 *** (3.41)
l.ENI 0.044 ** (2.31) 0.043 ** (2.28) 0.043 *** (3.09) 0.042 *** (2.99)
SIZE 0.616 *** (3.62) 0.664 *** (3.86) 0.272 * (1.96) 0.221 (1.59)
ROA −0.116 (−0.03) −0.102 (−0.02) −1.680 (−0.57) 0.665 (0.23)
TQ −0.215 (−1.35) −0.200 (−1.26) −0.156 (−1.37) −0.181 (−1.62)
SALES 0.503 (1.55) 0.527 (1.64) 0.251 (1.12) 0.256 (1.15)
LEV 1.370 (1.10) 1.131 (0.91) 0.976 (1.13) 1.245 (1.46)
MFR 8.625 * (1.78) 9.420 * (1.94) −5.454 (−1.51) −2.418 (−0.66)
INDDIR 0.514 (0.21) 0.440 (0.18) 1.179 (0.56) 1.314 (0.63)
AGE −1.115 *** (−3.44) −1.043 *** (−3.29) 0.175 (0.66) 0.226 (0.85)
STATE 0.167 (0.44) 0.036 (0.09) −0.826 *** (−3.17) −0.757 *** (−2.92)
HP 0.021 (0.01) −0.089 (−0.05) 0.260 (0.18) 0.261 (0.19)
constant 2.314 (0.55) 1.259 (0.30) 8.022 ** (2.38) 8.654 ** (2.60)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 244 244 262 262
r2_a 0.234 0.241 0.287 0.297
F 3.658 3.762 4.745 4.934
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
Sustainability 2019,11, 4226 16 of 19
Table A4.
The influence of target firms’ penalties on peer firms’ environmental investment with 2SLS.
Peer Firms’ Environmental Investment
(1) (2)
ENI ENI
Number of target firms 0.408 *** (2.70)
Ratio of target firms 4.932 *** (4.57)
l.ENI 0.051 *** (4.56) 0.045 *** (4.09)
SIZE 0.546 *** (5.44) 0.563 *** (5.68)
ROA −2.264 (−0.97) −0.918 (−0.40)
TQ −0.049 (−0.49) −0.037 (−0.38)
SALES 0.195 (1.12) 0.225 (1.31)
LEV 0.857 (1.28) 0.802 (1.21)
MFR −1.094 (−0.40) 1.798 (0.65)
INDDIR 0.601 (0.41) 0.749 (0.52)
AGE −0.329 * (−1.76) −0.282 (−1.52)
STATE −0.256 (−1.25) −0.271 (−1.34)
HP −0.610 (−0.57) −1.042 (−1.01)
constant 3.254 (1.34) 2.658 (1.11)
Year effect Yes Yes
Industry effect Yes Yes
N 425 425
r2_a 0.257 0.275
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
Table A5. The influence of environmental regulation with 2SLS.
Peer Firms’ Environmental Investment
Low Extent of Environmental Regulation High Extent of Environmental Regulation
(1) (2) (3) (4)
ENI ENI ENI ENI
Number of
target firms 0.481 * (1.85) 0.479 *** (2.71)
Ratio of target
firms 4.428 ** (2.09) 5.263 *** (4.35)
l.ENI 0.058 *** (3.21) 0.055 *** (3.08) 0.050 *** (3.77) 0.045 *** (3.39)
SIZE 0.576 *** (3.52) 0.662 *** (3.80) 0.287 ** (2.22) 0.231 * (1.79)
ROA 1.296 (0.33) 0.867 (0.22) −3.869 (−1.32) −1.123 (−0.39)
TQ −0.130 (−0.81) −0.096 (−0.60) −0.004 (−0.03) −0.007 (−0.06)
SALES 0.106 (0.36) 0.186 (0.64) 0.220 (1.08) 0.241 (1.19)
LEV 1.734 (1.52) 1.246 (1.08) 1.141 (1.40) 1.445 * (1.80)
MFR 6.042 (1.36) 7.179 (1.62) −3.436 (−0.95) 1.629 (0.43)
INDDIR 0.506 (0.22) 0.075 (0.03) 0.739 (0.38) 1.277 (0.67)
AGE −0.866 *** (−2.89) −0.759 *** (−2.60) 0.264 (1.05) 0.380 (1.51)
STATE 0.335 (0.95) 0.142 (0.40) −0.856 *** (−3.41) −0.781 *** (−3.14)
HP −0.802 (−0.48) −0.824 (−0.50) 0.331 (0.25) 0.003 (0.00)
constant 3.173 (0.80) 1.402 (0.34) 6.915 ** (2.21) 7.132 ** (2.31)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 201 201 224 224
r2_a 0.250 0.262 0.297 0.313
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
Sustainability 2019,11, 4226 17 of 19
Table A6. The influence of environmental regulation measured as GOV.
Peer Firms’ Environmental Investment
Low Extent of Environmental Regulation High Extent of Environmental Regulation
(1) (2) (3) (4)
ENI ENI ENI ENI
Number of target firms 0.274 (1.34) 0.338 ** (2.07)
Ratio of target firms 2.809 ** (2.25) 4.021 *** (3.36)
l.ENI 0.048 *** (2.78) 0.045 *** (2.64) 0.038 ** (2.39) 0.036 ** (2.29)
SIZE 0.883 *** (5.45) 0.893 *** (5.57) 0.503 *** (3.38) 0.488 *** (3.33)
ROA −8.103 ** (−2.36) −7.049 ** (−2.07) 1.327 (0.35) 2.039 (0.55)
TQ −0.109 (−0.75) −0.114 (−0.80) −0.191 (−1.52) −0.173 (−1.40)
SALES 0.351 (1.22) 0.294 (1.02) 0.518* (1.97) 0.660 ** (2.51)
LEV −1.394 (−1.25) −1.384 (−1.25) 1.335 (1.39) 1.255 (1.33)
MFR −4.350 (−1.00) −2.908 (−0.67) 4.316 (1.05) 6.745 (1.64)
INDDIR 1.734 (0.80) 1.763 (0.82) −0.572 (−0.25) −0.032 (−0.01)
AGE −0.582 * (−1.82) −0.553 * (−1.74) −0.570 ** (−2.08) −0.579 ** (−2.14)
STATE −0.309 (−0.94) −0.412 (−1.25) −0.269 (−0.90) −0.143 (−0.48)
HP 3.924 * (1.68) 3.854 * (1.67) −0.469 (−0.38) −0.755 (−0.63)
constant −6.894 (−1.58) −7.239 * (−1.68) 4.887 (1.36) 4.742 (1.34)
Year effect Yes Yes Yes Yes
Industry effect Yes Yes Yes Yes
N 239 239 267 267
r2_a 0.308 0.319 0.179 0.203
F 4.927 5.122 3.078 3.412
Note: * p<0.1, ** p<0.05, *** p<0.01; t statistics in parentheses.
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