2014-29
How Should Companies Implement
Environmental and Social Policies?
Kohei MATSUMOTO
Michiyuki YAGI Katsuhiko KOKUBU

Why Should Companies Implement Environmental
and Social Policies?
Kohei MATSUMOTO1, Michiyuki YAGI2, and Katsuhiko KOKUBU3
1

Master Student, Graduate School of Business Administration, Kobe University(2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan)
E-mail: 123b021b@stu.kobe-u.ac.jp
2
Associate Professor, Interfaculty Initiative in the Social Science, Kobe University(2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan)
E-mail: yagi@ruby.kobe-u.ac.jp
3
Professor, Graduate School of Business Administration, Kobe University(2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan)
E-mail: kokubu@kobe-u.ac.jp

Following the publication of the Report of the World Commission on Environment and Developmentin 1987, many managers, researchers, and investors have questioned how and why companies should
address environmental and social concerns. This article empirically investigates how environmental andsocial policies strengthen organizational capabilities, and how organizational capabilities subsequently
affect competitive benefits. In the energy and utilities industry, we found that social policies havesignificant positive relations with investments in one particular organizational capability. In the
manufacturing and non-manufacturing industries, however, we could not find significant positive
relations between environmental and social policies and investments in organizational capabilities,
indicating that managers are not implementing these policies in a way that strengthens organizational
capabilities. We also found that the pathways that connect organizational capabilities and competitive
benefits differ across industries. This study suggests that managers should consider industry
characteristics carefully while investing in organizational capabilities in order to make effective use of
these investments.

Key Words: environmental policy, social policy, organizational capability, competitive benefits

11. INTRODUCTION
In 1987, the Report of the World Commission on Environment and Development was
published, calling for countries, companies, and international communities to deal withenvironmental concerns and pursue sustainable development. Since then, many managers,
researchers, and investors have questioned how and why companies should addressenvironmental and social concerns. In order to respond to these questions, many management
scholars have tried to clarify the linkage between social responsibility and financial
performance.
In the extant literature, the relationship between social responsibility and financial
performance is controversial. Aupperle and Hatfield (1985)1), using an elaborate, forced-choice
instrument administered to CEOs, could not find any relationship between social responsibility
and profitability. They went so far as to say, “Perhaps this issue, whether or not corporate social
responsibility is related to profitability, will never be completely resolved” (Aupperle andHatfield 1985: 462)1).
On the other hand, an increasing number of researchers have found a positive relationshipbetween social responsibility and financial performance. McGuire et al. (1988)2) analyzed therelationship between firms’ perceptions of their social responsibility and measures of their
historical economic performance, and they found greater positive correlation between them.
Porter and van der Linde (1995)3) argued that properly designed environmental regulations
trigger innovations that reduce costs or improve product value, allowing companies to use
resources more productively and to become more competitive. Therefore, they insisted that how
an industry responds to environmental problems might be a major indicator of its overall
competitiveness.
With increasingly more researchers recognizing the positive relationship between social
responsibility and financial performance, scholars have started to focus on the role ofenvironmental management. Klassen and McLaughlin (1996)4) proposed a model that links

2environmental management with financial performance and empirically demonstrated thatstrong positive stock returns were measured for strong environmental management. Hart
(1995)5) theoretically predicted that innovative environmental strategies could lead to thedevelopment of firm-specific capabilities that could be sources of competitive advantage. His
theory added a resource-based perspective to the model provided by Klassen and McLaughlin(1996)4).
This study follows Sharma and Vredenburg (1998)6). They empirically demonstrated thatcompanies with proactive environmental strategies develop organizational capabilities and gain
competitive benefits, such as operational innovations, cost reductions, and improved reputations.
Sharma and Vredenburg (1998)6) focused on the Canadian oil and gas industry and conducted
their research through a mail survey and regression analysis. Using a straightforward model,
they illustrated the importance of environmental management for increasing competitive
benefits in an age when an increasing number of people recognize the importance of corporatesocial responsibility (CSR).
Although a number of empirical CSR studies have referred to Sharma and Vredenburg(1998)6), their research has two limitations. First, even though they sorted organizational
capabilities into three different types, they used only one integrated variable for organizational
capabilities in their regression analysis. Similarly, even though they identified several items that
constitute competitive benefits, they used only one integrated variable for competitive benefits
in their regression analysis. They did not separately consider each organizational capability andevery distinct item that constitutes competitive benefits; therefore, the pathways involving
proactive environmental strategy and the three organizational capabilities and those involving
the three organizational capabilities and the items of competitive benefits were not examined.
Second, their data was collected exclusively from the Canadian oil and gas industry. It would beof academic interest to extend their research to other industries and countries.
Following Sharma and Vredenburg (1998)6) but making up for these two limitations, this3

study separately incorporates the three organizational capabilities and the items of competitive
benefits in an attempt to empirically reveal the pathway of how environmental and social
policies strengthen the three organizational capabilities, and how these organizational
capabilities then affect the items of competitive benefits. This research explores the significant
correlations—but not the causal relations—between environmental and social policies andorganizational capabilities as well as those between organizational capabilities and competitive
benefits. This method would enable managers to determine the priority of their environmental
management practices to gain competitive benefits effectively. We use global company datasets
covering firms in every industry during the period 2005–2013.
Our results partly support the findings of Sharma and Vredenburg (1998)6). For example, ourregression results conducted for the energy and utilities industry clarify the pathway betweensocial policies and one particular organizational capability and that between organizational
capabilities and competitive benefits. The pathway between environmental and social policies
and organizational capabilities, however, is less clear than what was expected; in the
manufacturing and non-manufacturing industries, there are no significant positive relations
between these policies and organizational capabilities.
The next section presents a detailed explanation of our model and data. This paper thendescribes the results of the regression model and ends with conclusion and a discussion of theimplications.

2. MATERIALS AND METHODS(1) Model
The regression model used in Sharma and Vredenburg (1998)6) has two steps. In the first step,
the model predicted that companies that were ranked higher on environmental strategy would
develop higher organizational capabilities. Organizational capabilities are divided into threegroups: capability for stakeholder integration, capability for higher-order learning, and4

capability for continuous innovation. The capability for stakeholder integration involves theability to establish good relationships with stakeholders. The capability for higher-order learning
involves the ability to develop different interpretations of new and existing information and todeal with ambiguities and lack of information. The capability for continuous innovation
involves the ability to continuously generate a stream of innovations, such as changes inorganizational activities, processes, specifications, inputs, and products. The second step of themodel predicted that companies that developed higher organizational capabilities would gethigher levels of competitive benefits.
The regression model in Sharma and Vredenburg (1998)6) is as follows:

CAPABLTY

ENVSTRGY(1)

BENEFITCAPABLTY(2)

where ENVSTRGY, CAPABLTY, and BENEFIT denote environmental responsive strategy,organizational capability, and competitive benefits, respectively, anddenotes an error term.

Equation (1) describes the first step of the regression model, and equation (2) describes thesecond step.
Similar to the model used in Sharma and Vredenburg (1998)6), the model used in this study
has two steps. In the first step, the model examines how environmental and social policies affectthe three organizational capabilities (Fig. 1). We decided to examine environmental policies
because these policies reflect the environmental practices of companies. We also add social
policies to the model in order to measure the overall social responsibility of companies. In thesecond step, the model examines how each organizational capability affects competitive benefits(Fig. 2). In this study, the transparency, profitability, and market value of firms serve as
indicators of competitive benefits. The three indicators of competitive benefits correspond to the5competitive benefits from three different perspectives: transparency is specifically beneficial forsociety, profitability is specifically beneficial to the company itself, and market value isspecifically beneficial for outside investors.
The first step of the regression model used in this study is as follows:

lnCmSpelnETCstlnR&DEx

where

and

EPolicy

SPolicy

Controls

denote the fixed effects of firm

and year(3)

respectively, anddenotes

the error term. EPolicy indicates the degree of implementation of environmental policies andSPolicy indicates the degree of implementation of social policies. The dependent variables
represent the investments in organizational capabilities: lnCmSpe denotes investments incommunity-building activities, which are related to the capability for stakeholder integration;
lnETCst denotes investments in employee training, which is related to the capability forhigher-order learning; and lnR&DEx denotes investments in R&D activities, which are related
to the capability for continuous innovation. Controls denotes the control variables that control
for firm-specific characteristics.
The sign of EPolicy is expected to be positive for investments in community-building
activities, employee training, and R&D activities. In implementing environmental policies, thecompany has incentives to make the members of the local community aware about these
activities and to establish a good image for the company, which would enable the company topursue its business more smoothly. Moreover, environmental policies involve employee training
for effectively achieving the targets described in the policies. In addition, implementing
environmental policies forces a company to operate more efficiently, thereby increasing theinvestments in R&D activities. The sign of SPolicy is also expected to be positive for the three
investments in organizational capabilities because each social policy is related to employees,

6and companies that place importance on their employees spend more money on employee
training and on the local communities where their employees live. Moreover, social policies
attract competent employees, thereby facilitating innovative activities.
The second step of the regression model used in this study is as follows:

Score
ROA
TobinqlnCmSpelnETCstlnR&DEx

Controls(4)

The dependent variables represent the items of competitive benefits used in thisstudy—transparency, profitability, and market value of firms. Disclosure score (Score), Returnon Assets (ROA), and Tobin’s q (Tobinq) serve as the corresponding indicators of the three itemsof competitive benefits.
The sign of lnCmSpe is expected to be positive for the three benefit indicators (disclosure
score, ROA, and Tobin’s q). This is because companies have an incentive to disclose
information about community-building activities in order to improve firm image. Moreover, if acompany has established a trust-based collaborative relationship with a community, thecompany can pursue its business more easily and efficiently, which is a positive factor forinvestors as well. The sign of lnETCst is also expected to be positive for the three benefit
indicators because companies have an incentive to disclose information about employee training
to improve firm image and because employee training improves the ability of each employee
and strengthens the company’s overall competitiveness. The sign of lnR&DEx is also expected
to be positive for the three benefit indicators because investments in R&D activities facilitate
the company’s innovation, making its operation more efficient; moreover, companies have an
incentive to disclose this information in order to let investors perceive the possibility for future
growth.
We first conduct the regression analysis for all the industries together and then for the three7

industry categories separately: energy and utilities industry, manufacturing industry, andnon-manufacturing industry. Each regression test includes only ONE independent variable; thisis primarily intended to increase the observation numbers.

(2) Data
This study uses the global firm dataset from the Bloomberg professional service. The numberof observations in the original dataset is 49,915 for the 2005–2013 period. Depending on thevariable, the number of observations in each specification of the regression model varies from
389 to 24,115.
Epolicy and SPolicy are the total number of environmental policies and social policies,
respectively, implemented by a company out of all the policies included in the dataset. Table 1
and Table 2 present the types and definitions of environmental and social policies. Each item isa dummy variable; therefore, if a company has implemented five of the seven environmental
policies in the dataset, the company’s EPolicy score is five.
In order to measure the investments in community-building activities, employee training, andR&D activities, we use the logs of community spending (lnCmSpe), employee training cost(lnETCst), and R&D expenditure (lnR&DEx), respectively.
To measure the three benefit indicators (transparency, profitability, and market value), we use
disclosure score (Score), Return on Assets (ROA), and Tobin’s q (Tobinq), respectively. The
disclosure score we use is Bloomberg’s proprietary ESG Disclosure Score based on the extent
of a company’s environmental, social, and governance disclosure. To calculate ROA and Tobin’s
q, we divide earnings before interest and taxes (EBIT) and enterprise value (calculated as
market cap plus preferred equity, debt, and minority interest, minus total cash and cash
equivalents), respectively, by the total assets. Due to data constraints, we use enterprise value
instead of market cap and total liabilities for calculating Tobin’s q. Therefore, our Tobin’s q is alittle lower than the true one would be.

8The control variables in this study are the log of the total assets representing the size of firms(Size), the log of capital labor ratio (lnKL), and the log of labor efficiency (lnLEFF). To
calculate capital labor ratio and labor efficiency, we divide net fixed assets and revenue,
respectively, by the number of employees. Table 3 and Table 4 present the descriptive statistics
and correlation table, respectively.1

3. RESULTS AND DISCUSSION(1) Regression Results
Table 5 to Table 8 present the detailed regression results. Table 9 summarizes the results,
showing the coefficients that are important to this study. The ‘ ’ symbol indicates positivecoefficients at a statistically significant level, and ‘ ’ indicates negative coefficients at a
statistically significant level. If a coefficient is not statistically significant, the column is left
blank.
The result of the analysis involving all the industries together (Table 5) shows that thecoefficient of EPolicy is statistically significant from zero and positive for lnCmSpe in the firststep. Contrary to our expectations, the effects of environmental and social policies for lnETCst
and lnR&DEx are non-significant. In the second step, the coefficient of lnCmSpe is statisticallysignificant from zero and positive for all the three benefit indicators (Score, ROA, and Tobinq).
Further, the coefficient of lnETCst is statistically significant from zero and positive for ROA and
Tobinq.
Table 6 shows the regression results for the energy and utilities industry. In the first step, thecoefficient of SPolicy is statistically significant from zero and positive for lnCmSpe and is
statistically significant from zero and negative for lnR&DEx. This implies that implementing

1

Note that as shown in Table 3, the dataset could include some outliers, especially in terms of
ROA and Tobin's q.
9social policies takes money away from the R&D division in this industry. In the second step, thecoefficient of lnETCst is statistically significant from zero and positive for Score and ROA,
indicating that employee training may have an important role in this industry. The coefficient of
lnCmSpe is statistically significant from zero and positive for Score but is non-significant for
ROA and Tobinq. None of the three capabilities measures improves Tobin’s q at a statisticallysignificant level in this industry. This finding indicates that investors are skeptical about theenergy and utilities industry because they tend to associate this industry with environmental
destruction, as Klassen and McLaughlin (1996)4) suggested.
Table 7 presents the regression results for the manufacturing industry. In the first step, theenvironmental and social policies have no significant relations with any of the three capability
indicators. In the second step, the coefficient of lnCmSpe is statistically significant from zeroand positive for ROA, and the coefficient of lnETCst is statistically significant from zero andpositive for ROA and Tobinq. These results indicate that employee training may have an
important role in this industry.
Finally, Table 8 shows the regression results for the non-manufacturing industry. In the firststep, the coefficient of EPolicy is statistically significant from zero and negative for lnETCst.
This indicates that implementing environmental policies takes money away from employee
training in this industry. In the second step, while the coefficient of lnCmSpe is statisticallysignificant from zero and positive for ROA and Tobinq, the coefficients of lnETCst andlnR&DEx are both statistically significant from zero and negative for ROA. This indicates that
employee training and R&D activities do not differentiate one company from another in the
non-manufacturing industry, and the expenditure on such activities simply leads to loss of
profitability.

104. CONCLUSIONManagers have increasingly begun to recognize the importance of a firm’s environmental andsocial responsibility, but they are yet to realize how environmental and social management canlead to competitive benefits. Following the empirical model of Sharma and Vredenburg (1998)6),
which used data from the Canadian oil and gas industry, this study empirically examines how
environmental and social policies improve the three organizational capabilities andsubsequently affect the items of competitive benefits.
Our results partly support the findings of Sharma and Vredenburg (1998)6). The results of theanalysis of all the industries together show that environmental policies have significant positive
relations with community spending; subsequently, community spending has significant positive
relations with the disclosure score. Similarly, the results of the analysis of the energy andutilities industry show that social policies have significant positive relations with community
spending; subsequently, community spending has significant positive relations with thedisclosure score. Sharma and Vredenburg (1998)6) examined the Canadian oil and gas industry;
therefore, our results strengthen their findings in the context of this industry.
The pathways that connect environmental and social policies and organizational capabilities,
however, are quite limited. Contrary to our expectations, these policies have significant positive
relations only with community spending in the regression results in two contexts
only—involving all the industries together and in the energy and utilities industry. This finding
suggests that even though companies implement environmental and social policies, they are notdoing so in a way that strengthens organizational capabilities.
The pathways that connect organizational capabilities and competitive benefits differ fromindustry to industry. For example, in the manufacturing industry, investments in employee
training are effective in increasing ROA and Tobin’s q, while in the non-manufacturing industry,
investments in community-building activities are effective in increasing them. In the energy andutilities industry as well as the manufacturing industry, investments in employee training are

11

effective in increasing ROA. However, in the non-manufacturing industry, investments in
employee training actually have a negative impact on ROA. These results suggest that managers
should carefully examine the industry characteristics and determine how policies could improveorganizational capabilities and competitive benefits most effectively.
Surprisingly, R&D expenditure does not positively affect any of the three benefit indicators at
a statistically significant level. This implies that companies do not spend money on R&D
activities in a way that increases the transparency, profitability, and market value of the firms. In
the non-manufacturing industry, R&D expenditure actually has a significant negative
association with ROA, which implies that there is almost no room for investments in R&D
activities in this industry.
A significant implication for increasing corporate competitiveness based on the results of thisstudy is that managers should consider industry characteristics while spending money oncommunity-building activities, employee training, and R&D activities. For example, in theenergy and utilities industry, investments in employee training would be effective in improving
the firm’s transparency and profitability. Therefore, managers in this industry should take alonger-term perspective specifically in the case of employee training for strengtheningorganizational capabilities. Further, in some industries, managers can strategically implement
environmental and social policies to facilitate community-building activities.
This research has several limitations. Due to constraints of data availability, we examined
only community spending, employee training costs, and R&D expenditure as indicators ofinvestments in organizational capabilities. Other indicators that reflect the three organizational
capabilities more precisely would produce results that are more reliable. In addition, because
our model is based on a one-way approach, the direction of causality is not examined. For
example, we found that community spending is positively associated with ROA at a statisticallysignificant level; however, it is possible that companies with higher ROA increase community
spending.

12

ACKNOWLEDGEMENTS: This study was carried out as part of the SESAMI project inKobe University and was supported by research grant from the Research Institute of Advanced
Management.

REFERENCES
1)

Aupperle, K. E., Carroll, A. B., and Hatfield, J. D. : An empirical examination of the relationship between
corporate social responsibility and profitability, Academy of Management Journal, Vol. 28, No.2, pp. 446-463,
1985.

2)

McGuire, J. B., Sundgren, A., and Schneeweis, T. : Corporate social responsibility and firm financial
performance, Academy of Management Journal, Vol. 31, No. 4, pp. 854-872, 1988.

3)

Porter, M. E., and Van der Linde, C. : Green and competitive: ending the stalemate, Reader In Business And
The Environment, pp. 61-77, 1995.

4)

Klassen, R. D., and McLaughlin, C. P. : The impact of environmental management on firm performance,
Management Science, Vol. 42, No. 8, pp. 1199-1214, 1996.

5)

Hart, S. L. : A natural-resource-based view of the firm, Academy of Management Review, Vol. 20, No. 4, pp.
986-1014, 1995.

6)

Sharma, S., and Vredenburg, H. : Proactive corporate environmental strategy and the development ofcompetitively valuable organizational capabilities, Strategic Management Journal, Vol. 19, No. 8, pp. 729-753,
1998.

13

Table 1 Definitions of the environmental policies
Policy Name
1

Emission reduction
policy2

Green building policy

3

Climate changing policy

4

Environmental quality
management policy

5

Biodiversity policy

6

Energy efficiency policy

7

Waste reduction policy

Definition
Indicates whether the company has implemented any initiatives toreduce its environmental emissions to air.
Indicates whether the company has taken any steps towards using
environmental technologies and/or environmental principles in thedesign and construction of its buildings.
Indicates whether the company has outlined its intention to help
reduce global emissions of the Greenhouse Gases that cause
climate change through its ongoing operations and/or the use of its
products and services.
Indicates whether the company has introduced any kind ofenvironmental quality management and/or environmental
management system to help reduce the environmental footprint of
its operations.
Indicates whether the company has implemented any initiatives toensure the protection of biodiversity.
Indicates whether the company has implemented any initiatives tomake its use of energy more efficient.
Indicates whether the company has implemented any initiatives toreduce the waste generated during the course of its operations.

14

Table 2 Definitions of the social policies
Policy Name
1

Health and safety policy

2

Equal opportunity policy

3

Human rights policy

4

Training policy

5

Fair remuneration policy

Definition
Indicates whether the company has recognized its health and safety
risks and responsibilities and is making any effort to improve themanagement of employee health and/or employee safety.
Indicates whether the company has made a proactive commitment
to ensure non-discrimination against any type of demographic
group.
Indicates whether the company has implemented any initiatives toensure the protection of the rights of all people it works with.
Indicates whether the company has implemented any initiatives totrain new and existing employees on career development,
education or skills.
Indicates if the company has demonstrated a group widecommitment to ensure payment of a fair wage to all group
employees, even in those countries that do not legally require aminimum wage.

15

lnCmSpe

Table 3 Descriptive statistics
description
obs
mean
Indicates the number of
31,1782.74
environmental policies thecompany has implemented.
Indicates the number of social
29,068
1.99
policies the company has
implemented.
Log of community spending
11,70813.18

lnETCstLog of employee training cost

2,646

15.19

2.62

0

24.95

lnR&DExLog of R&D expenditure

31,068

10.83

8.17

0

23.15

Score

ESG Disclosure Score

49,915

19.65

12.68

0.82

85.12

ROA

Return on Assets

44,0771.69

293.1961,002.1

2.01

TobinqTobin’s q

48,412

11.56

1,694.08

0.61

369,131.3

Controlvariables
Size

Log of total assets

49,860

21.11

2.22

2.63

28.92

lnKL

Log of capital labor ratio

38,098

11.47

1.71

2.19

20.81

lnLEFF

Log of labor efficiency

38,267

12.73

1.11

1.79

19.77

variable
EPolicy

SPolicy

16

s.d
2.24

min

max
0

7

1.57

0

5

3.36

0

23.19

Table 4 Correlation table
EPolicy
SPolicylnCmSpelnETCstlnR&DEx
Score
ROA
TobinqSize
lnKL
lnLEFF

EPolicy
1
0.69
0.22
0.23
0.29
0.78
0.00
0.00
0.34
0.10
0.06

SPolicylnCmSpelnETCstlnR&DEx

Score

ROA

TobinqSize

lnKL

lnLEFF

1
0.24
0.16
0.18
0.72
0.01
0.00
0.34
0.00
0.01

1
0.54
0.06
0.39
0.15
0.01
0.60
0.13
0.16

1
0.25
0.37
0.05
0.07
0.62
0.08
0.02

1
0.28
0.00
0.01
0.16
0.12
0.05

1
0.00
0.00
0.47
0.12
0.13

1
0.08
0.04
0.01
0.06

1
0.03
0.05
0.07

1
0.25
0.30

1
0.54

1

17

Table 5 Regression result: All industries(1)
lnCmSpe0.03*(0.01)

EPolicy(2)
lnCmSpe(3)
lnETCst0.04(0.03)(4)
lnETCst0.00(0.02)

SPolicy(5)lnR&DEx
0.01(0.01)(6)
lnR&DEx

0.02(0.05)

(7)
Score

(8)
Score

0.02
(0.02)
0.06*(0.03)

lnCmSpe0.16(0.10)

lnETCstlnR&DExSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.51***(0.06)
0.06(0.05)
0.00(0.06)
2.01(1.61)
Yes
Yes
10,577
2005-2013
0.02
0.37

0.74***(0.07)
0.08(0.06)
0.00(0.07)
2.68(1.72)
Yes
Yes
9,813
2005-2013
0.03
0.38
(9)
Score

0.42***(0.13)
0.02(0.07)
0.12(0.12)
6.60**(3.21)
Yes
Yes
2,298
2005-2013
0.02
0.38
(10)
ROA

0.51***(0.14)
0.03(0.09)
0.12(0.13)
5.21(3.45)
Yes
Yes
2,205
2005-2013
0.02
0.40
(11)
ROA

0.49***(0.07)
0.05(0.06)
0.01(0.06)
1.07(1.60)
Yes
Yes
16,298
2005-2013
0.00
0.03
(12)
ROA

0.51***(0.07)
0.07(0.06)
0.02(0.07)
0.60(1.68)
Yes
Yes
15,288
2005-2013
0.01
0.02
(13)
Tobinq0.44**(0.20)
0.26(0.15)
0.10(0.20)
10.13**(4.69)
Yes
Yes
10,664
2005-2013
0.33
0.06
(14)
Tobinq0.58(0.55)
0.21(0.32)
0.25(0.52)
28.58**(13.28)
Yes
Yes
2,319
2005-2013
0.38
0.00
(15)
TobinqEPolicy
SPolicy
0.001***(0.00)

lnCmSpeSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.02***(0.00)

0.002*(0.00)

lnETCstlnR&DEx0.01***(0.00)

0.03(0.02)
0.36**(0.15)
0.07(0.12)
0.04(0.13)
3.09(3.44)
Yes
Yes
24,115
2005-2013
0.24
0.00

0.01***(0.00)
0.02***(0.00)
0.05***(0.00)
0.03(0.04)
Yes
Yes
9,712
2005-2013
0.13
0.04

0.01***(0.00)
0.01***(0.00)
0.08***(0.00)
0.34**(0.16)
Yes
Yes
1,937
2005-2013
0.17
0.04

0.00
(0.00)
0.35***(0.02)
0.12***(0.01)
0.27***(0.01)
9.16***(0.50)
Yes
Yes
24,1102005-2013
0.02
0.01

0.61***(0.02)
0.00(0.02)
0.05**(0.02)
14.28***(0.65)
Yes
Yes
10,476
2005-2013
0.14
0.05

0.24***(0.04)
0.10***(0.03)
0.02(0.04)
7.26***(1.13)
Yes
Yes
2,277
2005-2013
0.14
0.06

0.00
(0.00)
1.73***(0.07)
0.47***(0.05)
0.49***(0.06)
38.42***(1.60)
Yes
Yes
23,814
2005-2013
0.04
0.01

Notes: ***, **, * denote significances at the 1%, 5%, and 10% level (two-tailed), respectively.
Coefficients are without parentheses, and standard errors are in parentheses.

18

Table 6 Regression result: Energy and utilities industry(1)
lnCmSpe0.00(0.04)

EPolicy(2)
lnCmSpe(3)
lnETCst0.08(0.07)(4)
lnETCst0.12*(0.06)

SPolicy(5)lnR&DEx
0.06(0.07)(6)
lnR&DEx

0.03(0.12)

(7)
Score

(8)
Score

0.23**(0.10)
0.33**(0.14)

lnCmSpe0.66***(0.24)

lnETCstlnR&DExSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.78***(0.21)
0.07(0.09)
0.00(0.14)
4.69(4.96)
Yes
Yes
1,157
2005-2013
0.04
0.44

0.96***(0.23)
0.05(0.10)
0.10(0.16)
10.20*(5.42)
Yes
Yes
1,091
2005-2013
0.06
0.46
(9)
Score

0.73**(0.33)
0.29**(0.12)
0.64*(0.36)
14.62*(8.87)
Yes
Yes
389
2005-2013
0.15
0.25
(10)
ROA

0.84**(0.38)
0.20(0.17)
0.69*(0.41)
17.05*(10.33)Yes
Yes
369
2005-2013
0.13
0.29
(11)
ROA

0.29
(0.37)
0.79***(0.23)
0.20(0.27)
5.90(8.82)
Yes
Yes
1,515
2005-2013
0.02
0.00
(12)
ROA

0.28
(0.37)
0.85***(0.23)
0.33(0.27)
4.18(8.87)
Yes
Yes
1,465
2005-2013
0.03
0.01
(13)
Tobinq0.95(0.88)
0.31(0.37)
0.00(0.59)
5.64(20.38)
Yes
Yes
1,179
2005-2013
0.38
0.15
(14)
Tobinq0.42(1.43)
0.61(0.53)
0.48(1.54)
29.17(37.55)Yes
Yes
395
2005-2013
0.51
0.02
(15)
TobinqEPolicy
SPolicy
0.00(0.00)

lnCmSpeSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.00(0.01)

0.006***(0.00)

lnETCstlnR&DEx

0.00(0.01)

0.03(0.05)
1.56**(0.64)
0.04(0.38)
0.26(0.44)
24.83*(14.33)
Yes
Yes
1,993
2005-2013
0.27
0.20

0.03***(0.00)
0.01***(0.00)
0.02***(0.00)
0.64***(0.14)
Yes
Yes
1,179
2005-2013
0.24
0.05

0.04***(0.01)
0.00(0.00)
0.05***(0.01)
0.35(0.32)
Yes
Yes
395
2005-2013
0.25
0.14

0.00
(0.00)
0.02***(0.00)
0.00(0.00)
0.03***(0.00)
0.91***(0.16)
Yes
Yes
1,993
2005-2013
0.12
0.03

0.54***(0.06)
0.00(0.02)
0.09**(0.04)
15.08***(1.48)
Yes
Yes
1,167
2005-2013
0.32
0.06

0.29***(0.09)
0.00(0.03)
0.09(0.10)
6.66***(2.40)
Yes
Yes
389
2005-2013
0.38
0.11

0.00
(0.00)
0.75***(0.06)
0.03(0.03)
0.21***(0.04)
15.39***(1.34)
Yes
Yes
1,970
2005-2013
0.25
0.01

Notes: ***, **, * denote significances at the 1%, 5%, and 10% level (two-tailed), respectively.
Coefficients are without parentheses, and standard errors are in parentheses.

19

Table 7 Regression result: Manufacturing industry(1)
lnCmSpe0.04(0.02)

EPolicy(2)
lnCmSpe(3)
lnETCst0.01(0.05)(4)
lnETCst(6)
lnR&DEx

0.12(0.09)

0.03(0.04)

SPolicy(5)lnR&DEx
0.01(0.01)

(7)
Score

(8)
Score

0.00
(0.02)
0.00(0.03)

lnCmSpe0.03(0.16)

lnETCstlnR&DExSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.49***(0.10)
0.17(0.11)
0.18(0.12)
1.42(2.43)
Yes
Yes
5,793
2005-2013
0.01
0.33

0.81***(0.11)
0.26**(0.11)
0.24*(0.12)
4.92*(2.59)
Yes
Yes
5,275
2005-2013
0.02
0.35
(9)
Score

0.45*
(0.25)
0.28(0.19)
0.10(0.22)
9.19*(5.43)
Yes
Yes
970
2005-2013
0.02
0.39
(10)
ROA

0.59**(0.26)
0.27(0.20)
0.18(0.23)
6.76(5.77)
Yes
Yes
929
2005-2013
0.03
0.39
(11)
ROA

0.61***(0.08)
0.09(0.07)
0.06(0.07)
2.04(1.73)
Yes
Yes
10,965
2005-2013
0.01
0.12
(12)
ROA

0.64***(0.08)
0.10(0.08)
0.08(0.08)
1.24(1.83)
Yes
Yes
10,232
2005-2013
0.01
0.13
(13)
Tobinq0.00(0.27)
0.45(0.27)
0.47(0.30)
23.46***(6.15)
Yes
Yes
5,814
2005-2013
0.34
0.00
(14)
Tobinq0.42(1.05)
0.97(0.82)
0.79(0.91)
30.10(22.52)
Yes
Yes
974
2005-2013
0.41
0.00
(15)
TobinqEPolicy
SPolicy
0.0007*(0.00)

lnCmSpe0.004**(0.00)

lnETCstlnR&DExSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.00(0.00)

0.03(0.02)
0.30(0.20)
0.02(0.17)
0.21(0.16)
6.03(4.34)
Yes
Yes
15,692
2005-2013
0.24
0.00

0.00
(0.00)
0.05***(0.00)
0.09***(0.00)
0.50***(0.06)
Yes
Yes
5,813
2005-2013
0.20
0.01

0.00
(0.01)
0.05***(0.01)
0.13***(0.01)
1.19***(0.27)
Yes
Yes
974
2005-2013
0.25
0.02

0.05***(0.01)
0.00(0.00)
0.52***(0.03)
0.27***(0.03)
0.44***(0.02)
13.12***(0.75)
Yes
Yes
15,692
2005-2013
0.03
0.01

0.43***(0.03)
0.10***(0.04)
0.09**(0.04)
10.80***(0.88)
Yes
Yes
5,767
2005-2013
0.15
0.03

0.17
(0.11)
0.34***(0.08)
0.13(0.10)
6.52***(2.48)
Yes
Yes
960
2005-2013
0.18
0.01

0.00
(0.01)
2.21***(0.11)
0.82***(0.09)
0.71***(0.09)
46.89***(2.39)
Yes
Yes
15,525
2005-2013
0.04
0.01

Notes: ***, **, * denote significances at the 1%, 5%, and 10% level (two-tailed), respectively.
Coefficients are without parentheses, and standard errors are in parentheses.

20

Table 8 Regression result: Non-manufacturing industry(1)
lnCmSpe0.00(0.03)

EPolicy(2)
lnCmSpe(3)
lnETCst0.12*(0.07)(3)
lnETCst(6)
lnR&DEx

0.01(0.10)

0.01(0.05)

SPolicy(5)lnR&DEx
0.05(0.03)

(7)
Score

(8)
Score

0.03
(0.04)
0.07(0.06)

lnCmSpe0.03(0.17)

lnETCstlnR&DExSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.63***(0.13)
0.06(0.09)
0.14(0.13)
0.37(3.08)
Yes
Yes
2,575
2005-2013
0.04
0.28

0.88***(0.14)
0.00(0.11)
0.09(0.14)
5.67*(3.30)
Yes
Yes
2,439
2005-2013
0.05
0.30
(9)
Score

0.01
(0.27)
0.11(0.15)
0.28(0.30)
20.20***(6.96)
Yes
Yes
546
2005-2013
0.04
0.02
(10)
ROA

0.09
(0.29)
0.21(0.19)
0.23(0.34)
19.02***(7.36)
Yes
Yes
527
2005-2013
0.04
0.16
(11)
ROA

0.24
(0.15)
0.01(0.11)
0.06(0.14)
1.02(3.67)
Yes
Yes
3,651
2005-2013
0.00
0.01
(12)
ROA

0.30*
(0.16)
0.05(0.13)
0.12(0.16)
0.23(3.91)
Yes
Yes
3,431
2005-2013
0.00
0.00
(13)
Tobinq0.35(0.35)
0.09(0.27)
0.04(0.35)
12.22(8.41)
Yes
Yes
2,612
2005-2013
0.32
0.07
(14)
Tobinq1.65*(0.94)
0.23(0.54)
0.36(1.04)
61.44**(24.12)
Yes
Yes
555
2005-2013
0.36
0.03
(15)
TobinqEPolicy
SPolicy
0.004***(0.00)

lnCmSpeSize
lnKL
lnLEFF
Constant
Firm fixed effects
Year fixed effects
obs
year
Within R-squared
Overall R-squared

0.00(0.01)

0.003*(0.00)

lnETCstlnR&DEx0.06***(0.01)

0.02(0.03)
0.28(0.29)
0.22(0.19)
0.11(0.24)
13.48**(6.68)
Yes
Yes
6,164
2005-2013
0.26
0.02

0.00**(0.00)
0.01***(0.00)
0.03***(0.00)
0.02(0.09)
Yes
Yes
2,608
2005-2013
0.08
0.07

0.02***(0.00)
0.01***(0.00)
0.04***(0.01)
0.33(0.24)
Yes
Yes
551
2005-2013
0.13
0.03

0.002*(0.00)
0.10***(0.01)
0.08***(0.00)
0.08***(0.00)
1.87***(0.26)
Yes
Yes
6,164
2005-2013
0.05
0.00

0.66***(0.06)
0.00(0.05)
0.11(0.07)
13.71***(1.64)
Yes
Yes
2,574
2005-2013
0.13
0.08

0.20***(0.06)
0.06(0.05)
0.00(0.07)
6.27***(1.70)
Yes
Yes
548
2005-2013
0.15
0.05

0.00
(0.00)
0.91***(0.05)
0.15***(0.03)
0.24***(0.04)
21.99***(1.32)
Yes
Yes
6,072
2005-2013
0.10
0.03

Notes: ***, **, * denote significances at the 1%, 5%, and 10% level (two-tailed), respectively.
Coefficients are without parentheses, and standard errors are in parentheses.

21

Table 9 Regression result: Summary
All Industries

Energy &Utilities Industry

Manufacturing
Industry

NonManufacturing
Industry

1st step: Policy to Capability(1) EPolicy→lnCmSpe(2) SPolicy→lnCmSpe(3) EPolicy→lnETCst(4) SPolicy→lnETCst(5) EPolicy→lnR&DEx(6) SPolicy→lnR&DEx
2nd step: Capability to Benefits(7) lnCmSpe→Score(8) lnETCst→Score(9) lnR&DEx→Score(10) lnCmSpe→ROA(11) lnETCst→ROA(12) lnR&DEx→ROA(13) lnCmSpe→Tobinq(14) lnETCst→Tobinq(15) lnR&DEx→Tobinq

Notes: This table summarizes results from Table 5 to 8. Positive coefficients at a statisticallysignificant level are indicated as ‘ ’, and negative coefficients at a statistically significant levelare indicated as ‘ ’. If a coefficient is not statistically significant, the cell is left blank.

Acknowledgement25285138.

This research is supported by JSPS KAKENHI Grant Number[2014.10.22 1176]

22