2014-31
How do firms disclose environmental information
on climate change in aspects of both business risks
and opportunities?
Honami Sakaguchi Michiyuki Yagi
Katsuhiko Kokubu

How do firms disclose environmental information on
climate change in aspects of both business risks andopportunities?
Honami Sakaguchi1, Michiyuki Yagi2 and Katsuhiko Kokubu3
1Master Student, Graduate School of Business Administration, Kobe University(2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan)
E-mail: 131b011b@stu.kobe-u.ac.jp
2 Associate Professor, Interfaculty Initiative in the Social Sciences, Kobe University(2-1, Rokkodai-cho, Nada-ku, Kobe-city, 657-8501, Japan)
E-mail:yagi@ruby.kobe-u.ac.jp (corresponding author)
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

Corporate disclosure of environmental information has played an important role in the avoidance of
dangerous climate change. How firms choose to disclose environmental information about the business
opportunities and risks associated with climate change is important to policy makers and investors. In theliterature, there are two dominant theories of corporate disclosure: legitimacy theory and voluntary disclosure theory. Under legitimacy theory, firms are more likely to disclose information in response to theirrisks; under voluntary disclosure theory, firms are more likely to disclose information in response to theiropportunities. In certain industries, if firms disclose environmental information according to legitimacy
theory (voluntary disclosure theory), society may be unaware of the true risks (opportunities) of climatechange, and society, in these cases, we will need policies that mandate disclosure. Therefore, this study
examines the power of legitimacy theory and voluntary disclosure theory to explain corporate disclosure inthree industry groupings: manufacturing, non-manufacturing, and energy & utilities. We use Bloomberg’s
Carbon Disclosure Project (CDP) dataset of 3,861 firm level observations from 2008-2012, and regress thecorporate social disclosure score evaluated by Bloomberg on variables that indicate regulatory and physicalrisks and opportunities. We find that legitimacy theory does not explain corporate disclosure of regulatory
risks in any of the industries and that of physical risk in the energy and utilities industry. In addition,
voluntary disclosure theory does not explain disclosure of regulatory opportunities in the energy & utilities

1

industries. However, voluntary disclosure theory explains disclosure of opportunities in all of the industries.

Key Words: Legitimacy theory, Voluntary disclosure theory, disclosure score, climate change, CDP

2

1. Introduction
Amid concerns about the contribution of firms to adverse climate change, corporate disclosureof environmental information is more important than ever before. Information about risk management and business opportunities related to climate change is important not only to firms, butalso to investors and policy makers. In this study, we examine how recognition of risks and opportunities related to climate change affects a firm’s disclosure score.
Since Churchman (1971) 1) and Mobley, (1970) 2), there has been increasing research on social
environmental accounting 3). There are now two dominant theories of corporate disclosure in thesocial environmental accounting area: the voluntary disclosure theory 4), 5), and the legitimacy
theory 6), 7).
Voluntary disclosure theory, which is derived from information economics, considers the disclosure costs incurred when disclosing information. There is a partial disclosure equilibrium
wherein firms disclose information by, maximizing firm share-value net of expected proprietary
costs 5). Thus, according to the theory, firms decide to disclose information when it leads tocompetitive advantages and decide not to disclose the information when it harms the firm’s’
reputation.
Legitimacy theory, which is derived from social science, is often used in organizational theory.
Lindblom (1994)6) defined legitimacy as “a condition or status that exists when an entity’s value
system is congruent with the value system of the larger social system of which the entity is a part.
When a disparity, actual or potential, exists between the two value systems, there is a threat to theentity’s legitimacy.” Thus, legitimacy is a measure of society’s perceptions of the adequacy ofcorporate behavior 7). Criticisms by society are the primary way by which a firm’s legitimacy isharmed. When this occurs, it is necessary for the firm to repair its legitimacy, perhaps by improving communication with stakeholders. Disclosing information is also one of the most important means to repair legitimacy.
Both theories explain how firms consider external perception when choosing the information to3

be disclosed. From the standpoint of voluntary disclosure theory, we can assume that firms are
more likely to reveal information that points to business opportunity and competitive advantage intheir efforts to address climate change than information that points to the risks or disadvantages ofclimate change. On the other hand, according to the legitimacy theory, in order to repair legitimacy, firms disclose more when there is a possibility they may be criticized by society. Thus,
when they think they are facing risks of climate change, they disclose more and better.
Hence, the characteristics of the information disclosed depend on the purpose and motivationof the disclosure. Based on these characteristics, investors and policymakers change their own
behavior. For example, if in a partial disclosure equilibrium the firm discloses only the information that can benefit its reputation and not the information that could possibly harm its reputation, then in an environmental accounting context, the firm may hide information about its poor
performance on climate change prevention. This information asymmetry has a negative impact on
effective investment, because investors overestimate the firm’s value. Making matters worse, theymight end up invested in firms that contribute to adverse climate change. Thus, it is important tocreate incentives to disclose or to raise the cost of failing to disclose information that is not favorable to firms.
On the other hand, if firms disclose according to the underlying assumptions of legitimacy
theory, firms disclose when they recognize their legitimacy might be harmed and they under-disclose when they might do well. This also is an information asymmetry. As a result, investors cannot identify which firm is worthy of being invested in, in terms of sustainability.
According to recent guidance, the Securities and Exchange Commission (SEC) recognizes thatclimate change has become an important feature of physical and regulatory environments. According to Coburn et al., (2011) 8), firms also recognize the risks and opportunities of climatechange in their physical and regulatory aspects. However, the extent to which they recognize
those risks and opportunities is difficult to quantify. To solve this problem, Former Carbon Disclosure Project (CDP) made dummy variables that indicate whether the firm recognizes climate

4

change risks and opportunities in their physical and regulatory aspects. In this way, CDP is
helping to quantify the extent to which firms recognize climate change risks and opportunities
As mentioned above, it is important for investors and policymakers to get reliable information
about climate change risks and opportunities. Thus, many organizations that auditing and evaluate
firms’ eco-friendly behavior are forming and gaining power. CDP is one examples of such anorganization created by institutional investors. It sends questionnaires to firms, asking them todescribe their strategy for climate change and to disclose the amount of their greenhouse gas
emissions. In addition, CDP evaluates the responses it receives based on a set of criteria they callthe Climate Disclosure Leadership Index (CDLI). Approaches like these provide important information for firm valuation 9).
Considering this backgrounds, our study examines how the business risks and opportunities ofclimate change affect a firm’s disclosure score. In section 2, we introduce the model we test in ourstudy. The dependent variable is disclosure score and the independent variables are the CDP
dummy variables that indicate whether the firm recognizes climate change risks and opportunities. We divide risk and opportunity into regulatory and physical aspects, and we make four regression models, one for each industry grouping: Manufacturing, Non-manufacturing, and Energy & Utilities. In section 3, we explain the data and present some descriptive statistics. The data
we use in this study is CDP data provided by Bloomberg Environmental Social Governance(ESG) Professional Services from 2008 to 2012. It includes 45 countries and 20 industry groups.
In addition, we divided the industry groups into Manufacturing, Non-manufacturing, and Energy& Utilities. The analysis consists of two phases. The first step is a descriptive analysis thatcompares trends in the average disclosure score by industry. In section 4, we do the second phaseof analysis: regressions using the models we introduce in section 2. Section 5 concludes with a
discussion and interpretation of the results for decision making by firms’ and policymakers.

5

2. Model
The purpose of this study is to examine how risk and opportunity related to climate change
affects the quality of firms’ environmental disclosure. In other words, the objective of the study is
to identify whether voluntary disclosure theory and legitimacy theory are supported by firms’
environmental disclosures. In this section, we introduce the model we use in the regression
analysis.
The dependent variable is the indicator that reflects the quality of environmental disclosure. As
discussed, the quality of information affects the behavior of investors and policymakers. In orderto improve and maintain the quality of disclosure, there are organizations which evaluate thequality of the information disclosed. We use the disclosure score given by a third party organization as the dependent variable, and name it Score.
Independent variables include two opportunity variables, which are used for testing whether
voluntary disclosure theory is supported, and two risk variables, which are used for testingwhether legitimacy theory is supported. Since the two aspects of opportunity and risk that the firm
can recognize and disclose information about are regulatory and physical aspect. We use four
independent variables: regulatory aspects of risk (RegRisk), physical aspects of risk (PhysRisk),regulatory aspects of opportunity (RegOpp), and physical aspects of opportunity (PhysRisk).
In addition, we include control variables (Controls) to consider firms’ characteristics and tocontrol for firm. We also use fixed effect model. We examine the predictors of a firm’s disclosure
score using the following regression model:

Score =β0 +β1·Reg Risk +β2·Phys Risk +β3·Reg Opp +β4·Phys Opp(1)+β5·Controls +αi+αt+ewhere i and t denote firm and year, and e is the error term.
β1 and β2 are coefficients to test legitimacy theory. If these coefficients are significantly posi-

6

tive, firms makes better disclosure when they recognize more risks from climate change. If thesecoefficients are significantly negative, firms make poorer disclosure when they recognize morerisks, a contradiction of legitimacy theory. In addition, β1 and β2 indicate regulatory aspects andphysical aspects of the risks respectively. Hence, if β1 is significantly positive (negative), firms
makes better (poorer) disclosure when they recognize risks that their business might soon be
regulated or that they might have violated some regulation. If β2 is significantly positive (negative), firms makes better (poorer) disclosure when they recognize climate change risks in theirfundamental business.
β3 and β4 are coefficients to test voluntary disclosure theory. If these coefficients are significantly positive, firms makes better disclosure when they recognize more opportunities fromclimate change. If these coefficients are significantly negative, firms make poorer disclosure
when they recognize more opportunities, a contradiction of voluntary disclosure theory. In addition, β3 and β4 indicate regulatory aspects and physical aspects of the opportunities respectively.
Hence, if β3 is significantly positive (negative), firms makes better (poorer) disclosure when theyrecognize opportunities, to make a business plan in the expectation of future regulation or to takea leadership position in creating a regulatory standard. If β4 is significantly positive (negative),firms makes better (poorer) disclosure when they recognize climate change opportunities directly,
by emitting less CO2 or by offering ecofriendly products or services that best the offerings of otherfirms’

3. Data and descriptive statistics
In this section, we explain the data used in regression analysis, and we investigate descriptive
statistics in order to see trends and to see how disclosure scores differ from industry to industry.
The data is CDP data (2008-2012) provided by ESG Professional Services. The number of observations is 3,806. The data includes 45 countries and 20 industry groups(1) Data

7

Data used for this study was based on Bloomberg ESG data, which was collected by Bloomberg
Professional Service. The data includes ESG data, financial data, ESG ratios, and CDP data
(2008-2012). The number of observations is 3,806. The data includes 45 countries and 20 industry
groups.
The dependent variable is Score that is The Climate Disclosure Leadership Index (CDLI) scorethat reflects the comprehensiveness of a company’s response to the CDP questionnaire. The CDPquestionnaire includes three kinds of questions: management, risk and opportunity, and emission.
The response to each question is equally weighted in the CDLI. The score is normalized to a
100-point scale. Generally, companies scoring within a particular range suggest comparable
levels of commitment to, and experience of, carbon disclosure. Thus, the higher CDLI score therespondent gets, the better reputation reports they receive from CDP.
The model includes four independent variables, which are RegRisk, PhysRisk, RegOpp, andPhysOpp. It is difficult to quantify and compare the risk and opportunity by dividing it into regulatory aspects and physical aspects. Thus, CDP provides dummy variables that indicate whether
the company considers itself exposed to climate change regulatory risk, physical risk, regulatory
opportunity, and physical opportunity. We use the dummy variables as independent variables.
Although these dummy variables are part of the disclosure score, the score also includes otheraspects. Thus, they can contribute to the examination of the relationship between indicators of risk
and opportunity and the disclosure score.
We include the following control variables–ROA, lnSize, lnCP and lnLP that indicates firm’s’characteristics. ROA (return of asset) is calculated by EBIT divided by total assets. lnSize is thelogarithm of total assets. lnCP is the logarithm of the capital labor ratio, which is net fixed assets
divided by the number of employees. lnLP is the logarithm of labor productivity, which is revenue
divided by the number of employees.
Next, we explain the classification of industries. We categorize industries using two classification schemes. First, the industries are divided into three broad groupings: Manufacturing,8

Non-manufacturing, and Energy & Utilities. This is because the central tendency of a firm’s scoreis expected to depend on how much its industry emits carbon dioxide. For example, Manufacturing and Energy & Utilities are more likely to emit CO2 than Non-manufacturing. Also, Energy& Utilities are more greatly influenced by the government and policy makers. We excluded thefinancial industry because the number of observations was comparatively small.
The second classification scheme is based on the Global Industry Classification Standard(GICS), an industry classification standard developed by MSCI in collaboration with Standard &Poors (S&P). GICS consists of 10 sectors, 24 industry groups, 62 industries, and 132
sub-industries. The GICS classification assigns an industry group name to each company according to its principal business activity. The GICS industry group classification is the largest
classification that can define whether the industry is categorized as manufacturing or
non-manufacturing.

(2) Descriptive statistics
The means, standard deviation, maximum, and minimum of all variables are shown in Table 1.
The average value of Score is 68.81. The mean of PhysOpp is relatively lower than the means of
the other measures of risks and opportunities.
Table 2 shows the average disclosure score and growth rate of each industry groups by year.
The growth rate is calculated by the difference of the present year’s average and the previous
year’s average, divided by the present year’s average, multiplied by 100. The rightmost column
contains simple average from 2008 to 2012.
As a whole, the average of all industries by year is monotonically increasing. We can find over
10 increases in four years when we see the average of all industries is 59.99 in 2008 and 73.18 in
2012. Notably, in 2009 and 2011, the growth rate exceeds 7%. For manufacturing, the averagescore is higher than the average of all industry groups from 2008 to 2012. Automobiles andComponents and commodity industries like Food, Beverages & Tobacco and Household &9Personal Products got higher scores than other manufacturing industries. Although Consumer
Durables & Apparels got a relatively low score, it has grown since 2009 and reached 73.7, which
is higher than the average of all industries. On the other hand, Healthcare Equipment & Services,which also got a lower score than other manufacturing, had an average score in 2012 that was
lower by about 10 than the average of all industries.
With regards to the Non-manufacturing industry, the average disclosure score is lower than
Manufacturing and Energy & Utilities. However, it increased rapidly compared with other industries. The scores of Consumer Services, Retailing and Food & Staples Retailing increasedaround 20. Food & Staples Retailing got 79.21, which was the third highest score in 2012. We canassume that individual consumers have become more interested in environmental issues. In addition, we can assume that managing CO2 emissions in the supply chain has become increasingly
important. However, some industries like Media got a low score because they are not emittingmuch CO2.
In addition, Energy & Utilities scored higher than the average of all industries. These industries
are regulated by the government; thus, they are under pressure from governments to disclose
information.
4. Results
In this section, we explain the regression results in Table 3. Regression model (1) includes allindustries. Regression models (2), (3), and (4) include only Manufacturing, Non-manufacturing,and Energy & Utilities, respectively. Table 4 is a summary of the coefficients from Table 3, with+ denoting positive significance and blank denoting insignificance.
First, we explain the results on risk. Regression models (1), (2), (3), and (4) show that RegRisk
is statistically insignificant. On the other hand, PhysRisk is positively significant in models (1),(2), and (3). However, PhysRisk is statistically insignificant in model (4). From these results,
firms, other than those in the Energy & Utilities industry, make environmental disclosures bylegitimacy theory with regard to physical aspects, but not with regard to regulatory aspects.

105. Conclusions
The purpose of the study is to examine how climate change risks and opportunities

affectdisclosure score and to deepen the understanding of whether or not firms’ environmental disclosure is adequately explained by voluntary disclosure theory and legitimacy theory.
We provide an overview of the trends in disclosure score by industry, presenting an average of
disclosure score by industry group. We find that the Manufacturing industry and Energy & Utilities, which are more likely to emit CO2, get higher scores than the Non-manufacturing industry.
However, within the Non-manufacturing industry, the average score of Food & Beverage Retailing is higher than the average score of all industries. In addition, the average growth rate of theNon-manufacturing industry from 2008 to 2012 is higher than other industries. It can be said thatthe industry that is most directly related to the issue of climate change makes better disclosure,and the industry that is not directly related does not pay much attention to environmental disclosure. However, even if this industry does not affect climate change directly, the possibility
exists that its activities affect climate change indirectly. Thus, before making policy based on sthese indirect relations, policymakers should consider how to make these industries pay attention
to environmental disclosure.
By regression analysis, we tested whether voluntary disclosure theory and legitimacy theory
are supported. We find that voluntary disclosure theory is supported in almost all regression
models. Regulatory aspect of voluntary disclosure theory is supported except in Energy & Utilities. The physical aspect is supported in all models. On the other hand, legitimacy theory is only
partially supported in this study. Physical risk has significantly positive impact on disclosure
score in all models except the Energy & Utility industry, whereas Regulatory aspect is not supported in all models. When taken together, voluntary disclosure theory is strongly supported inalmost all models, compared to legitimacy theory. In addition, we can conclude that the physical11

aspect has a stronger relationship to the disclosure score than the regulatory aspect does.
From here, we discuss the implication of the results and make suggestions for firms and policymakers. As Dhaliwal et al., (2011) 5) discuss, if firms disclose according to voluntary disclosure
theory, they will want to be competitive and differentiate themselves by disclosing. Thus, theevidence supporting voluntary disclosure theory in this study indicates that a disclosure score canwork as an incentive for the firm to treat environmental problems proactively.
However, there is one problem investors and policymakers have to consider when firms’ disclosure behavior is governed by voluntary disclosure theory. As Dhaliwal et al., (2011) 5) mentioned, firms decide to disclose when it maximizes profits net of disclosure costs and they decide
not to disclose when it harms their reputation. This might be very dangerous if the firm is destroying the environment without being noticed by anyone, because the pollution has not yet
caused an observable problem. Obviously, that is not good for the environment. It is also not goodfor the firm because they miss the opportunity to notice the risk in their business and therefore
they cannot prepare for it. As a result, they might be punished with a negative impact to theirreputation or perhaps an obligation to pay compensation. In addition, information asymmetrycauses inefficient investment. Investors cannot identify which firms really do well in terms ofbusiness sustainability. In order to prevent this problem, policymakers should create incentives
that ensure the disclosure of information that is not favorable to the firm. It is necessary to create
some mandatory standard of disclosure or to create cost of not disclosing.
Another finding is that, except for the physical risk in the Energy & Utilities industry, allphysical indicators are significant predictors of the disclosure score. In some countries, increasing
attention is being paid to the importance of disclosing environmental liability and of meeting
global standards for environmental accounting. In February 2010, the SEC decided to issue
guidance for companies on the climate change-related information they should be disclosing toinvestors 8). The Climate Change Disclosure Guideline was published in 2013. According to thisguideline, the disclosure of physical risk and opportunity is mandatory for all publicly traded12companies. This is controversial because the rule applies to all companies, not just those withenvironmental issues. Considering these trends, the initiatives being taken to quantify environmental performance and the investments being made to meet these standards will result in more
effective decision making on climate change.
Finally, the regulatory aspect does not show a clear relationship to the disclosure score. For one
thing, unlike the physical aspect, there are already some legal standards for regulatory aspects inparticular countries. According to Table 1, the average of regulatory risk is not very low compared with other aspects. This may also explain why, with the exception of physical opportunity,
the independent variables of Energy & Utilities do not show a significant relationship to disclosure score. Because Energy & Utilities are regulated by the government, we can assume that theirdisclosures about regulation do not make much difference.
To summarize our conclusions, voluntary disclosure theory is supported in almost all models
and legitimacy theory is partially supported. Policymakers should redesign a system that discloses
information that is not favorable for firms, in order to avoid adverse climate change. In addition,although the average level of disclosure of physical opportunity is the lowest for risk and opportunity, it has the strongest relationship with disclosure score. Thus, not only are quantitative
standards important for quantifying firm’s bad impacts on the environment, but they are also
important for quantifying firms’ good impacts on the environment, both of which might affectfirm value.
Finally, we identify some limitations of this study and suggestions for future research. First, theindicators of risk and opportunity are dummy variables; thus, we cannot reveal the relationshipbetween the amount of information about risk and opportunity and disclosure score. Although thisis a problem, it is worth attention because it is difficult to compare regulatory aspects and physicalaspects. Second, we used CDLI as a dependent variable. Thus, our conclusion is only applicable
to CDP. However, non-environmental organizations such as the SEC have been more interested inenvironmental issues, and they have tried to apply the methods of environmental organization. In

13terms of this trend, our study can contribute to further research. Finally, we cannot identify thereason why legitimacy theory is not supported. Thus, further analysis will help improve understanding of environmental disclosure.

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

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REFERENCES
1)

Churchman, C.W. : On the facility, felicity, and morality of measuring social change, Accounting Review, Vol.
46, No. 1, pp. 30-5, (1971)

2)

Mobley, S.C. : The challenges of socio-economic accounting, Accounting Review, Vol. 45, No. 4, pp. 762-8,
1970.

3)

Mathews, Martin Reginald. : Twenty-five years of social and environmental accounting research: is there a silver
jubilee to celebrate? , Accounting, Auditing & Accountability Journal, 10, 4, pp. 481-531, 1997.

4)

Barth, Mary E., Maureen F. McNichols, and G. Peter Wilson. : Factors influencing firms' disclosures about
environmental liabilities, Review of Accounting Studies, 2.1, 35-64, 1997.

5)

Dhaliwal, D. S., Li, O. Z., Tsang, A., & Yang, Y. G.: Voluntary nonfinancial disclosure and the cost of equity
capital: The initiation of corporate social responsibility reporting. The Accounting Review, 86(1), 59-100, 2011.

6)

Lindblom, C.K. : The implications of organizational legitimacy for corporate social performance and disclosure,
Critical Perspectives on Accounting Conference, New York, 1994.

7)

Deegan, Craig. : Organizational legitimacy as a motive for sustainability reporting, Sustainability Accounting
and Accountability, J.Unerman, J. Bebbington, and B.O’Dwyer, Eds. New York, Routledge, pp.127-149, 2007.

8)

Coburn,Jim, Donahue H.Sean, and Jayanti Suriya . : Disclosing climate risks and opportunities in SEC filings, A
guide for corporate executives, attorneys & directors, Ceres, 2011.

9)

Shorter, Gary. : SEC Climate Change Disclosure Guidance: An Overview and Congressional Concerns, CRS
report for Congress, 2013.

15

Table 1 Descriptive statistics of all variables
Variable
Dep. variable
Score

Description

Obs Mean

s.d

Min

Max

The Climate Disclosure Leadership Index (CDLI) score
which reflects the comprehensiveness of a company’s
response in terms of the depth and breadth of its answers
to the CDP questionnaire

3,807 68.94

18.41

0

100

3,807 0.87

0.34

0

1

3,807 0.78

0.41

0

1

3,807 0.86

0.35

0

1

3,807 0.64

0.48

0

1

3,807 0.09

0.08 −0.36

2.014

3,807 11.85

1.89

5.94

20.82

3,807 23.00
3,807 12.96

1.44 18.00
0.915 9.77

27.39
18.95

Indep. variable
Indicates if the company considers itself exposed toclimate change regulatory risk.
Indicates if the company considers itself exposed toclimate change physical risk.
Indicates if the company considers itself exposed topotential climate change regulatory opportunity.
Indicates if the company considers itself exposed topotential climate change physical opportunity.

Reg Risk
Phys Risk
Reg Opp
Phys Opp
Control
bles
ROA
lnCL
lnSizelnLPvariaReturn on Assets
Capital labor ratio=Net Fixed Assets/ Number of employees
Total Assets
Labor productivity=Sales/ Number of employees

16Table 2 Average disclosure score and growth rate of score by industry group and year
Type of industry
Manufacturing

Industry group

2008
63.38

Automobiles and Components

70.67

Consumer Durables and Apparels

56.57

Food Beverage and Tabaco

59.79

Healthcare Equipment and Service

59.14

Households and Personal Products

64.5

Materials

61.79

Pharmaceutical, Biotechnology and Life
Science
Semiconductors and Semiconductor Equipment
Technology hardware and Equipment

61.79

75.35

60.79

Non-Manufacturi
ng

55.02
Commercial & Professional Services

49.62

Consumer Services

53

Food & Staples Retailing

54.91

Media

51

Real Estate

58.69

Retailing

59.43

Software & Services

54.58

Telecommunication Services

57.3

Transportation

56.68

Energy & Utilities

65.29
Energy62.78

Utilities

67.8

17

2009
67.65
(6.32)
74.38

2010
67.76
(0.16)
67.83

56.13

63.4

2011
72.87
(7.01)
77.06
(11.9
8)
65.6

(4.99)(-9.66)(-0.78)(11.47)(3.35)

86.11

76.63

(30.57)(-12.37)

2012
74.64
(2.38)
77.03(-0.04
)
73.7
(10.9
9)
77.19

63.44

64.9

76.41
(-0.29)68(6.78)(2.25)(4.56)

69.53(7.23)
67.41

73.13(4.92)
68.76

79.5(8.01)
74.71(8.34)(1.96)(7.96)

67.41

68.67

74.71

73.86

62.6
(-8.63)86.67(8.27)
73.86(-1.15)(8.34)(1.83)(8.08)

58.54

59.33

70.93

75.34

(-28.72)(1.33)

54.38

60(6.22)(9.37)(8.19)

65.89

67.17

(7.74)(1.91)

62.39

66.36

(11.81)
60.43(17.89)65.72(5.98)
65.86(8.25)
60(19.35)(-9.53)

65.72

73.14

(16.45)(10.14)

61.12

68.06

71.53

(3.98)(10.20)(4.85)

63.77

66.96

(6.81)(4.76)

61

67.06

75
(10.7
2)
71.12

(10.52)(9.04)(5.71)

64.81

67.81

(11.59)(4.42)

64.56(12.21)
64.96

68.32
(5.50)
68.63

78.3
(13.4
0)
72.3
(5.50)
75.77(-0.52)(5.35)(9.42)
73.11
(11.2
6)
78.42
(7.70)

62.43

64.88

(-0.56)(3.78)

67.48(-0.47)72.38(6.77)

73.39
63.08
75.23

(1.01)(-1.15
)

(16.3
5)
68.87
(2.4
7)
71.0
8
(6.65)
68.43(3.76)
69.47
(13.6
3)
68.23
(-7.20
)
65.35

08-12
69.26

63.62
74.67
69.31

69.29

67.90

(5.85)
71.53

66.85

(3.72)
71.29(0.30)
73.06(6.34)
73.03

65.23
63.48
64.24

(4.87)
79.21
(13.8
6)
62.88
(-3.93)
67.86(-5.41)
71.92(-4.28)67.78(-4.93)73.17(-7.01)72.72(0.58)
74.59(-1.58)69.03(-5.91)80.14(2.15)

68.24
58.72
65.45
67.42
64.31
68.28
66.92
69.85
66.45
73.24

Total

59.99

65.01
(7.72)

67.26(3.35)

72.47(7.18)

73.18(0.97)

67.58

Notes: Average disclosure score is without parentheses, and growth rate of disclosure score is in parentheses. Growthscore are calculated by the difference of present average and previous average divided by the present average
multiplied 100.

18

Table 3 Regression result

Reg Risk
Phys Risk
Reg Opp
Phys Opp
ROA
lnCL
lnSizelnLPConstant
Firm fixed effects
Year fixed effects
Obs
The number of industry groupYear
Within R-squared
Overall R-squared

(1)
Score

(2)
Score

(3)
Score

(4)
Score

All industry

ManufacturingNon-manufacturing

Energy and Utilities

1.10(0.90)
2. 58***(0.77)
3.93***(0.77)
3.65***(0.64)-6.04(6.43)-1.01(1.07)
0.71(1.30)
0.67(1.34)
35.09(32.66)
Yes
Yes
3,806
20
2008-2012
0.47
0.16

0.03
(1.15)
2.39**(0.96)
3.01***(1.15)
3.55***(0.86)-1.88(7.99)
0.58(1.40)
1.31(1.70)-0.80(1.89)
23.77(44.74)
Yes
Yes
2,083
9
2008-2012
0.48
0.17

2.05
(1.637)
3.32**(1.507)
5.15***(1.496)
3.13*(1.215)-7.21(14.797)-0.75(2.441)-0.18(2.599)-0.92(2.79)
67.31(63.08)
Yes
Yes
1,186
9
2008-2012
0.50
0.18

1.74
(3.74)-.042(2.30)
3.84(3.33)
5.46***(1.50)-33.13(1.50)-2.59(2.34)
4.54(3.57)
6.44(2.88)-104.12(85.95)
Yes
Yes
537
2
2008-2012
0.41
0.22

Notes: Columns 1 and 2 shows results of regression model. ***, **, and * denote significances at the 1%, 5%, and 10%
level, respectively. Coefficients are without parentheses, and standard errors are in parentheses.

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Table 4 Results summary
RegRiskPhys Risk
RegOpp
PhysOpp

Score(1)

Score(2)Score(3)Score(4)

+
+
+

+
+
+

+
+
+

+

Notes: The table is summary of the coefficients which mark significant on Table 3. + donates positively significant andblank donates insignificant.

Acknowledgement This research is supported by JSPS KAKENHI Grant Number 25285138.

[2014.10.22 1178]

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