an investigation of the initial voluntary … · an investigation of the initial voluntary...
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AN INVESTIGATION OF THE INITIAL VOLUNTARYENVIRONMENTAL DISCLOSURES MADE IN KOREAN
SEMI-ANNUAL FINANCIAL REPORTS
Jong-Seo Choi
School of Commerce, Department of Accounting
Professor of Accounting, Pusan National University
Pusan, Korea
INTRODUCTION
The voluntary disclosure of social and environmental information by business firms has been the subject
of substantial academic interest for the past several decades. Surveys in a number of countries show an
increasing trend of disclosing such information in corporate reports. There has been an upsurge of
empirical studies on reporting practices in recent years, which provide insights into the number of
companies disclosing environment-related information, the subject matter included in those disclosures,
the trend in overall disclosure over time and the general relationship between corporate characteristics and
the propensity to disclose environmentally relevant information.
However, much of the literature to date has been focused on the experience of companies in the
industrialised countries, mostly of Europe and the United States. In a developing economy like Korea,
which has been experiencing rapid economic growth, the improvement of material welfare still tends to
receive top priority. On the other hand, the growth of the Korean economy in recent years has accelerated
the expansion and globalization of business enterprises, which in turn, has induced increased societal
demands for a cleaner environment. A few cases of river pollution caused by effluents discharged by some
Korean firms during the early 1990s have caused even greater concern about the undesirable
environmental outcomes of unregulated development.
Increasing pressure from a variety of sources including a number of private pressure groups led to
extensive environmental legislation during the early 1990s, which includes the Basic Environment Policy
Act (BEPA hereafter) of 1990, the Liability for Environment Improvement Expenses Act (LEIEA) of 1991,
the Environmental Pollution Damage Dispute Adjustment Act (EPDDAA) of 1990, and the Natural
Environment Preservation Act (NEPA) of 1991 among others. Specifically, article 5 of the BEPA stipulates
that all enterprisers shall take for themselves measures necessary for preventing any environmental
pollution caused by their industrial activities, and are required to participate in and cooperate with the
environmental preservation policy of the state or local government.1
1 Article 7 of the same act stipulates as the liability of person causing pollution for expenses as follows: any
person who causes an environmental pollution due to his act or business activities, shall in principle bear theexpenses for the prevention of such pollution, recovery of the contaminated environment and relief of damages,which is in line with the PPP (Polluters Pay Principle) in vein.
The Korean Securities Exchange Commission (KSEC) followed suit by enacting in 1996 a provision
in the Corporate Accounting Standards (CAS) which requires the inclusion of environmental information
in the form of accompanying footnote to the corporate financial reports. The amendment of CAS was
mainly intended to incorporate the International Accounting Standards (IAS) into the Korean CAS so that
the comparability of financial statements can be enhanced. From the viewpoint of corporate social
responsibility, the most noteworthy aspect of the amended CAS is that it provided a valuable momentum
for the Korean companies to incorporate social and environmental perspectives into their formal financial
statements for the first time in the public accounting history of Korea. Article 90 of the amended CAS
recommends the voluntary disclosure of the following information in the footnotes attached to the financial
reports.2
Clause 21: The company's environmental standards and policies, safety and
accident related matters, environment-related investments,
consumption of resources and energy, and matters related to
occurrence and treatment of by-products and scraps.
In June 1997, a group of companies listed on the Korean Stock Exchange (KSE) provided
environmental and employee information in their 1997 audited semi-annual financial statements, which
was to be recorded as the first incidence of corporate social and environmental disclosure (CSED
hereafter) in Korea. This incidence of initial disclosures, however, draws attention in that it was initiated
by only a small group of companies (64 out of 770 companies listed on the KSE as of 1997) from a limited
number of industries (14 out of 37 intermediate level of KSE industry classifications).
The purpose of this study is to examine the nature of this first environmental disclosure by Korean
corporations in terms of quantity and quality of the disclosure. It thus provides the first empirical evidence
in Korea regarding the environmental disclosure in corporate financial statements. Furthermore, initial
disclosures made in semi-annual financial reports provide unique information vis-a-vis ritual annual
reporting. This is because the disclosure is made under less attention and scrutiny. Once a new accounting
practice is broadly accepted and exercised, it is likely that individual members of the accounting society
will follow industry convention and are more likely to be exposed to many external confounding
influences. As firms are exposed to a broader range of constituencies, they are more likely to be subject to
interlocking factors affecting their accounting policies in various ways.
It is therefore conjectured that the semi-annual financial reports provide more purified information
wherein the underlying motivators are more closely related to the voluntary CSED decisions. In this sense,
another contribution of this study lies in investigating contextual implications surrounding the often
controversial voluntary disclosure issues on the basis of a semi-annual financial reports. Although annual
2 Korean CAS rules footnote disclosures in a dual system. A considerable amount of footnotes should be
disclosed on a mandatory basis, while some others are up to the individual company's disclosure policy, whichis commonly referred to as "additional footnotes". A number of items have been added to the inventory of theadditional footnotes by the 1996 amendments, which notably include two other clauses with social connotation.For example, clause 18 of the same article refers to the inclusion of names of accounts and amounts ofmanufacturing costs and selling and administrative expense which are necessary in calculating "value added".Clause 23 mentions employee welfare and contributions to society. Therefore the amendment made in 1996,from the viewpoint of social and environmental accounting, was dominated by employee welfare issues alongwith environmental consideration in tenor.
disclosures have been a topic of interest for a number of years, few studies have dealt with semi-annual
disclosures to my knowledge.
Specifically this study addresses three questions. First, what is the general nature of the voluntary
environmental disclosure made in 1997? This evidence may indicate the degree of willingness and
initiatives exercised by Korean companies on the environmental disclosure issue. Second, what corporate
characteristics are significant in distinguishing a disclosing firm from its non-disclosing counterpart?
Third, what corporate characteristics are either positively or negatively related to the amount and/or the
degree of specificity of disclosure? The answers to the second and third questions would provide an
additional clue to the long unsettled issue of the determinants of voluntary CSED. Moreover, this study on
semi-annual corporate reporting would hopefully provide an insight to the issue of CSED that
complements the related studies on annual corporate reporting.
The rest of the paper is organized as follows. The following section provides an overview of the
corporate environmental reporting issue including related previous research. Next, the sample of firms and
the methodology employed are discussed, where a systematic method for quantifying the content of firms'
disclosures is developed. Results of both the measured content and the statistical analyses are then
presented. The final sections of the paper contain a general discussion of the results, followed by a
summary of the conclusions. The implications for Korean environmental accounting practices,
recommendation for further research, and limitations of the study are also discussed.
PREVIOUS RESEARCH
The theory of social and environmental accounting has recently been the subject of much debate among
researchers. By articulating various theoretical perspectives including agency theory, legitimacy theory,
political economy of accounting theory and stakeholder theory, a number of researchers have studied why
corporations should and do disclose social information. However, there does not exist a universally
accepted theoretical framework for corporate social and environmental accounting.3
Gray et al. (1995) recently classified and discussed much of the literature in three groups: decision
usefulness studies, economic theory studies, and social and political theory studies. Each of these three
groups relies on different theoretical arguments and implies different motives for the corporate social and
environmental disclosures. It is not clear, however, whether these three groups of theories offer
overlapping or competing explanations. As Hackston and Milne (1995) point out, serious doubts exist
whether the empirical evidence available to date is enough to establish the superiority of one theory over
the others.
The basic argument for the decision usefulness approach is that companies release information on their
social and environmental activities because traditional user groups, mostly shareholders and creditors, find
it useful for their investment decision making.4 For the economic theory approach, the basic argument
relies on Watts and Zimmerman's (1978) agency theory. To avoid potential pressure from government
regulatory agencies which enforce corporate social responsibility, companies increase voluntary corporate
3 One of the reasons for the lack of theoretical coherence would be that CSED is not practiced systematically by
organizations nor able to claim either universal recognition or universal definition, presumably because it is notsupported by legislation equivalent to Companies Acts.
4 See for example, Spicer (1978), Belkaoui (1980, 1984), Dierkes and Antal (1985), etc.
social disclosures.5 Government regulation is seen as costly and restrictive on business decision making.
Watts and Zimmerman (1978) suggest that these restrictions adversely affect management's wealth by
imposing the political costs of reduced flexibility in the adoption of potentially profit maximizing policies.
Corporations respond by employing a number of devices including social responsibility disclosure
campaigns to counter these government interventions.
In the social and political theory group, Gray et al. (1995) include stakeholder theory, legitimacy theory,
and political economy theory. They see these three theories not as separate nor competing. Rather
stakeholder theory and legitimacy theory are viewed as overlapping perspectives within the framework
of political economy assumptions (Gray et al., p. 11). The social and political theory seeks to explain the
organization-society relationship including social disclosure from a perspective different from that of
economic theory. Corporations make social disclosures not just for their own economic self-interests, but
because they are pressured to exhibit social responsibility by employees, customers, suppliers, the general
public and other social activist groups. Such disclosures can be viewed as a medium for managing,
negotiating or manipulating stakeholders without whose support and approval the organization can no
longer exist (Roberts, 1992). Such disclosures can also be viewed as a means of establishing or protecting
the legitimacy of an organization by influencing public opinion and the public policy process (Preston and
Post, 1975; Patten, 1991, 1992). Changes in society have rearranged priorities and the social impact of
corporations has become increasingly important to the public. Rather than adding to the debate on the most
appropriate method of accounting for social information, this study attempts to provide empirical evidence
supporting or rejecting some operational hypotheses derived from various existing studies. As this is the
first study of the kind undertaken in the Korean context, it is purely exploratory, avoiding to favour any
normative stance.
Based on the survey of studies on corporate social disclosure in the U.S., the U.K., Australia and New
Zealand, Hackston and Milne (1995) describe a general pattern in corporate social disclosure. The pattern
of disclosure appears to be consistent across all countries, with human resources, environment, and
community receiving most attention in the disclosure. The amount of disclosure is reasonably consistent
across companies in the US, the UK, and Australia. On the other hand, a number of studies focused on the
determinants of social disclosure. The determinants typically studied include company size, profitability,
industry, country of ownership, reporting country, capital intensity, senior executive attitudes, company age,
and the existence of company social responsibility committees. In reviewing these studies, Gray et al.
(1995) tentatively concluded as follows (Gray et al., 1995, pp. 49-50). CSED is not related to profitability
in the same period6, but it may be related to lagged profits.7 CSED does appear to be related to company
size.8 Industry appears to affect CSED, but the studies are not clear or consistent enough to precisely
determine such effects.9 The country in which the company reports, and the country of company ownership
5 See for example, Epstein et al. (1976), Trotman and Bradley (1981), Belkaoui and Karpik (1989) etc.6 A number of studies failed to find a statistically significant relationship between profitability and CSED, which
include Abbott and Monsen (1979), Belkaoui and Karpik (1989), Cowen et al. (1987), Freedman and Jaggi(1988), Ingram (1978) and Singh and Ahuja (1983), among others.
7 The evidence has been provided in Roberts (1992) for instance.8 When other factors were taken into consideration, however, the results were not without some reliability. See
for example, Andrews et al. (1989), Belkaoui and Karpik (1989), Cowen et al. (1987), Singh and Ahuja (1983),and Trotman and Bradley (1981).
9 See for example, Aupperle (1984), Beresford and Cowen (1979), Cowen et al. (1987), Freedman and Jaggi(1988), Singh and Ahuja (1983), and Zeghal and Amed (1990).
appear to be related to CSED.10 In addition, capital intensity (Belkaoui and Karpik, 1989), age (Roberts,
1992), senior executive attitudes, and social responsibility committee (Cowen et al., 1987; Roberts, 1992;
Trotman and Bradley, 1981) may be related to CSED.
DISCLOSURE PATTERNS
Sample and Methodology
The initial population of firms comprised all of the 64 companies in the 14 industries that disclosed
some amount of environmental information in the supplementary footnotes of their 1997 audited semi-
annual financial statements. The data gathered from the 64 firms were used to evaluate the pattern of
disclosure. At the same time, a pair of firms from the same industry were selected randomly, each pair
consisting of a discloser firm and a non-discloser. Data for evaluating voluntary environmental disclosure
were collected from the footnotes to the financial statements of the 64 discloser firms and the data on firm
specific charateristics of total 128 firms were obtained from the Handbook on Korean Listed Companies,
1997 Fall edition.
Content analysis was used to provide an evaluation of the disclosers' environmental disclosures. Content
analysis is a method of codifying the text or the content of a piece of writing into various groups or
categories based on selected criteria (Weber, 1988). It involves the selection of analytical categories within
the context of the content material.11 Variables are defined by identifying criteria which will allow any item
to be judged as either belonging or not belonging to a particular category. Categories must be defined as
precisely as practicable so that different researchers could be expected to apply them to the same messages
and arrive at the same results. Systematic categories result from the consistent application of a set of rules
so that results could be replicated. Each set of related categories should be exhaustive and mutually
exclusive and defined in such a way that identifying an item with a category is not a discretionary process.
Following coding, quantitative scales are derived for further analysis. An indexing procedure based on the
content analysis similar to ones used by Wiseman (1982) was constructed to evaluate the content of
environmental disclosure. The purpose of this procedure was to objectively measure the information
contained in the disclosure and to provide a systematic numerical basis for comparing companies'
disclosures across different firm characteristics.
Items of information to be included in the index were selected from Wiseman (1982), which were
considered essential for complete environmental disclosure.12 From the list of essential categories and
items of information compiled by Wiseman (1982), 16 items of information were selected for inclusion in
the index.13 A rating sheet was developed to measure the extent of disclosure of the 16 index items. The
10 See for example, Gray et al. (1987), Guthrie and Parker (1989, 1990).11 In one form or another, content analysis has been widely adopted in many previous social responsibility
disclosure studies. See for example, Abbot and Monsen (1979), Guthrie and Mathews (1985), Guthrie andParker (1990), etc. This study borrows from the indexing procedure adopted by Wiseman (1982) and Ingramand Frazier (1980) in particular.
12 Wiseman included those items of information in the index selected through a review of the environmentalreporting literature (Dierkes and Preston, 1977; Estes, 1976; CEP, 1975), which provided proposed formats forenvironmental reports including items considered essential for complete environmental disclosure.
13 Wiseman (1982) selected 18 items of information for inclusion in the index. Of the 18 items, however, 2 itemsrelating to environmental litigation were omitted in this study, because no data on these items were availablefrom the sample data.
items on the rating sheet were classified into three categories. Category one represented items directly
related to economic factors. Category two represented items related to pollution abatement. Category three
represented other environmentally relevant items which did not fall into any of the previous categories. A
list of the 16 items by category is presented below:
(1) Economic factors (EF)
Past and current expenditure for pollution control equipment and
facilities.
Past and current operating costs of pollution control equipment and
facilities.
Future estimates of expenditure for pollution control equipment and
facilities.
Future estimates of operating costs for pollution control equipment and
facilities.
Financing for pollution control equipment or facilities.
(2) Pollution abatement (PA)
Air emission information.
Water discharge information.
Solid waste disposal information.
Control, installations, facilities or processes described.
Compliance status of facilities.
(3) Other general information (GI)
Discussion of regulations and requirements.
Environmental policies or company concern for the environment.
Conservation of natural resources.
Awards for environmental protection.
Recycling.
Departments or offices for pollution control.
Rating of the disclosure was based on the presence or absence of the degree of specificity of each of the
information items. A score of three was assigned to an item if it was present in the disclosure and was
described in monetary or quantitative terms. A score of two was assigned to an item if it was present in the
disclosure with company specific information, but in non-quantitative terms. A score of one was assigned
to items mentioned only in general terms. A score of zero was assigned if the item was not present in the
disclosure. The scores of individual items in each category were added for three category scores (EF, PA
and GI), which in turn were added to yield the total score for each firm (SCORE). Since any rating
procedure is subject to a certain degree of arbitrariness, a rating sheet was completed independently by the
author and two other coders. Any disagreement was thoroughly examined and reconciled by careful re-
evaluation of the disclosure in question.14
14 The objectivity criteria for the categories can be verified by computing computing the coefficient of reliability
(Holsti, 1969), which is the ratio of the number of interjudge agreements to the total number of judgementsmade. Also, the index of reliability (Scott, 1955) can be used as a refinement of the the Holsti's coefficient. Theindex of reliability is computed as: IR=(OA-EA)/(1-EA), where OA is the percentage of observed agreement,
Also, as an alternative measure of the degree of sufficiency of disclosure, an ordinal scale was
developed. Based on the quantification, specificity and the amount of disclosure, a score between zero and
three was assigned to an individual firm as an overall measure for the evaluation of disclosure. A score of
three was assigned if the disclosure provided detailed factual information in monetary terms. The firms
belonging to this category are hereafter referred to as excellent disclosers. A score of two was assigned to
the disclosure which is quantitative in nature but lacks specificity. This category is referred to as good
discloser group. A score of one was assigned if the disclosure was qualitative overall but includes highly
condensed quantified measures. These firms are grouped as intermediate disclosers. A score of zero was
assigned if the disclosure is declarative and reflects only personal opinion, which is classified as poor
disclosure. This score is used as an alternative measure of disclosure and is referred to as LEVEL
throughout the paper. More specific definitions of the content categories used for this index are provided
below:
(1) Monetary (LEVEL=3): a disclosure expressing factual information concerning
a firm's environmental activities expressed in monetary terms.
(2) Quantitative (level=2): a disclosure expressing factual information
concerning a firm's environmental activities expressed in quantitative terms.
(3) Qualitative (level=1): a disclosure expressing factual information concerning a firm's environmental
activities expressed in qualitative terms.
(4) Declarative (level=0): a disclosure of opinion or unsupported declaration
concerning a firm's environmental activities.
Finally, the amount of disclosure per company is measured by the number of lines. In many earlier
studies, the total amount of disclosure per company was measured in numbers of pages, paragraphs,
sentences, words or by the proportion of the absolute amount of social disclosure to the total amount of
reports. However, Korean language differs from English in its sentence structure and/or the amount of
information conveyable by each word, sentence, or paragraph. Moreover the sample firms used in this
study disclosed only meager amount of information in general so that the absolute number of line counts
was considered adequate for objective inter-firm comparison purposes. This variable will be referred to as
LINES.
Evaluation of the Contents
The results from the descriptive analysis of the various measures defined in the previous section are
presented in Table 1 by industry type. The industry classification used for this study was based on the KSE
industry classifications. Following many earlier studies, I have divided the KSE industry categories into
high-profile and low-profile industries. Those industries identified as high profile include food and
beverage, pulp and paper, chemical, petroleum, rubber and plastics, non-metallic mineral, and basic metals
industries. The low profile category includes textile, leather, publishing, fabricated metal, machinery,
and EA is the percentage of expected agreement computed as the sum of squared proportion of the total itemsin each category. The coefficient was calculated to be 0.831, and the index of reliability was calculated as0.801. These results indicate that the categories selected for analysis were objective in the sense thatindependent judges could use them to arrive at similar decisions.
electronics, and wholesale industries.15 The columns labeled 'Mean' show individual category scores (EF,
PA, and GI), the total environmental disclosure index scores (SCORE) and the line count scores (LINES)
for each industry. The frequency of reporting incidence is shown in the columns labeled 'LEVEL'.
(Insert Table 1 About Here)
Several observations are immediate from Table 1. First, there appears to be a systematic relationship
between the nature of industry and CSED: the values of various measures of environmental disclosure are
consistently higher in high-profile than in low-profile subsample. This is consistent with common belief
that some industries are considered to feel greater pressure from government to provide information in
certain areas of social responsibility and thus are more likely to disclose in those areas to avoid attention
and scrutiny. Similarly, industries whose businesses have direct bearings upon environment such as those
in the high-profile category may be considered more likely to disclose environmentally relevant
information.
Second, there is a distinct lack of specificity in disclosed information. This is reflected not only in the
low frequency found in excellent (LEVEL=3) and good (LEVEL=2) categories but also in the extremely
low mean scores observed across the board. Since the highest score assigned to each of the 16 items used
for indexing was three, the maximum possible scores for the index categories of EF, PA, and GI are 15, 15,
and 18 respectively, and the maximum possible total score is 48 for SCORE. The mean values of SCORE
hardly exceed 7, with the highest score of 7.3 found in pulp and paper industry.
Third, the length of environmental disclosure is deemed to represent the quality of information disclosed.
This can be seen by comparing the values of LINES and SCORE. In terms of the amount disclosed, the
line counts exhibited a mean value of 7, which is roughly equivalent to one third of a page in typical
Korean semi-annual reports. The mean line counts for pulp and paper industry was 12.7, and the total
index score for the same industry had a mean value of 7.3, which was the highest of all industries. The
shortest disclosure was found in textile, publishing and fabricated metal industries, where the total index
scores were also relatively lower. This pattern of rough association between the length and the index score
of disclosure is consistent for firms in other industries as well.16
For a more formal comparison of the two industry categories, a parametric t-test and a non-parametric
Wilcoxon rank sum test were conducted. The results from these tests are provided in Table 2. The
association between the nature of industry and CSED is again confirmed. Table 2 shows that the scores for
various measures are significantly higher for high profile industries. A direct implication from this analysis
is that the industry type is an essential factor influencing CSED. Therefore a resonable basis for inference
would be to treat industry type as an inherent controller variable rather than as one of the potential
motivator variables when one investigates the determinants of the CSED.
(Insert Table 2 About Here)
15 This classification of industries is not without subjectivity. But the author made best efforts to reflect the
Korean context in terms of which industries can be expected to exhibit greater concern with environmentalissures when compared to the others.
16 Although not reported in Table 1, past and current expenditure for pollution control equipment and facilitieswas the item most frequently reported, followed by solid waste disposal information, and then environmentalpolicies or company concern for the environment.
DETERMINANTS OF ENVIRONMENTAL DISCLOSURE
HYPOTHESES AND VARIABLES
This section studies the relationship between environmental disclosure and corporate characteristics in
two steps. First, a series of tests are conducted to see if there is a significant difference in selected
attributes between discloser and non-discloser companies. Both parametric and nonparametric two sample
tests as well as logistic regressions are used. Second, for discloser companies, multiple sample tests and a
variety of regression models are adopted to test the association between relevant variables and various
index scores developed in the previous section.
Many of the previous studies on the determinants of CSED identify at least four groups of corporate
characteristics as having a certain degree of relationships with CSED. These are corporate financial
performance, firm size, stakeholder power and others. These motivator groups, however, may not
necessarily be mutually exclusive nor completely free from ambiguity. Nonetheless, in this exploratory
attempt to search for possible determinants of CSED, I choose these frequently used determinants along
with some additional variables deemed to reflect the Korean corporate disclosure context fairly well.
Coporate Financial Performance
The relationship between corporate financial performance and CSED is arguably one of the most
controversial issues yet to be resolved. Various arguments suggest conflicting implications. On the one
hand, some argue that there are significant additional costs and foregone profit opportunities to being
especially responsible, so that profitability is depressed.17 On the other hand, many contend that pursuing a
proactive social policy requires superior management and so more responsible companies are likely to
have more skillful management, hence better economic performance.18 Empirical research to date on the
profitability-CSED relationship also shows very mixed results. Bowman and Haire (1975), for instance,
report significant differences for a five year average return on equity between disclosing and non-
disclosing companies. Preston (1978) also finds a higher single-year return on equities for high disclosers
than for others. On the other hand, Cowen et al. (1987) failed to support any profitability-CSED
relationship. Belkaoui and Karpik's (1989) findings are more intriguing. They report a significantly
positive pairwise correlation, yet an insignificant, negative regression coefficient for return on assets and
disclosure. While Roberts (1992) has found evidence for a positive relationship between lagged profits and
CSED, Patten (1991) fails to find any relationship between profitability and CSED when multiple
measures of profitability including lagged profits are used.
In this study, financial performance was measured using various accounting and market indicators: (1)
net margin (NM: net profit before extraordinary items and taxes, as a percentage of sales), (2) return on
equity (ROE: after-tax profit as a percentage of the book value of stockholders' equity), (3) earnings per
share (EPS: after-tax profit divided by average number of common stocks outstanding), (4) cash flow per
share (CFPS: sum of after-tax profit and depreciation divided by average number of outstanding common
17 See for example, Bragdon and Marlin (1972), Vance (1975), Ullman (1976), Aupperle et al. (1985) among
others.18 See for example, Moskowitz (1972), Bowman and Haire (1975), Alexander and Buchholz (1978), and Spicer
(1978) etc.
stocks), (5) average price earnings ratio (PER: average of the highest and the lowest stock prices divided
by EPS), (6) sales growth rate (SGR: current year's sales minus previous year's sales as a percentage of
the latter), (7) profit growth rate (PGR: current year's after-tax profit minus previous year's profit as a
percentage of the latter). (8) reserve ratio (RES: cumulative retained earnings as a percentage of paid-in
capital). Following previous studies it is initially hypothesized that the higher the financial performance,
the more likely the company will disclose environmental information.
(H1) Profitability Hypothesis: Other things being equal, there is a positive
association between financial performance of a company and the propensity to
disclose environmental information.
Corporate Size
Various theories suggest a positive relationship between the firm size and CSED.19 Eilbert and Parket
(1973) argued that larger firms may simply be or feel themselves to be targets of attention and thus find it
necessary to make visible efforts to establish social responsibility credentials. According to the agency
theory of Watts and Zimmerman (1978), social responsibility disclosure campaigns can be used to reduce
political costs which otherwise could reduce management wealth. As the magnitude of political costs is
highly dependent on firm size, it is inferred that there will be a positive size-CSED relationship. Not all
empirical studies have supported a positive size-CSED relationship, however. Roberts (1992) found no
relationship with a U.S. sample nor did Davey (1982) and Ng (1985) with a sample from New Zealand.20
In previous studies, firm size has been typically measured by the number of employees, total asset value,
sales volume, or an index rank such as Fortune 500 although the former three are highly correlated. Given
that there is no theoretical reasoning in favor of any particular measure of size in disclosure studies, I have
chosen the following two variables: (1) number of employees (EMP: total number of employees including
executives as of the latest financial reporting date). (2) sales volume (SALE: net sales during the first
semi-annual accounting period of 1997 measured in 100 million won).
(H2) Size Hypothesis: Other things being equal, there is a positive association
between the size of a company and the propensity to disclose environmental
information.
Stakeholder Influence
A stakeholder is defined as any group or individual who can affect or is affected by the achievement of
the firm's objectives (Freeman, 1984). Stakeholders of the firm include stockholders, creditors,
employees, customers, suppliers, public interest groups and governmental bodies. The stakeholder theory
as initiated by Ansoff (1965) contends that the major objective of the firm is to attain the ability to balance
19 See for example, Kelly (1981), Trotman and Bradley (1981), Pang (1982), Cowen et al. (1987), Belkaoui and
Karpik (1989), Patten (1991, 1992), etc.20 Cowen et al. (1987) argues on the other hand, that larger companies (1) may have more shareholders
interested in corporate social activity, and (2) are more likely to use formal communication channels to relateresults of social endeavors to interested parties.
the conflicting demands of various stakeholders in the firm, a dynamic implication of which has been
further developed by Freeman (1983). Ullman (1985) proposed a conceptual model for studying
corporate social responsibility activities in a stakeholder framework, concluding that stakeholder theory
provides an appropriate justification for incorporating strategic decision making into studies of corporate
social responsibility activities. Based on the stakeholder theory, one could argue that, if social
responsibility activities including CSED are useful for satisfactory relationships with stakeholders, then
developing corporate reputation as being socially responsible through performing and disclosing social
responsibility activities is part of a strategic plan for managing stakeholder relationships.
This study considers four sources of stakeholder influence: owners, creditors, governmental bodies, and
auditors. The proxy variables used in this study to capture these stakeholder influence are as follows: (1)
owners' influence (OWN: percentage of outstanding common stocks held by major stockholders who own
1% or more of the stocks); (2) creditors' influence (LEV: total liability as a percentage of owners'
equity; INT: interests as a percentage of sales); (3) regulators' influence (SECT: assumes the value of 1 if
the company belongs to section 1 of KSE, and 0, otherwise; CHBL: assumes the value of 3 if the company
belongs to top 10 conglomerates, 2 if 11-30th largest conglomerates, 1 if remaining conglomerates, and 0
if independent); (4) auditors' influence (CPA: the relative size of the auditing firm in terms of the number
of client companies as of June 1997. The numbering scheme follows: 1 = Samil, 2 = Ankeon, 3 = Chungun,
4 = Sandong, 5 = Younghwa, 6 = Samdeok, 7 = Seidong, 8 = Sinhan, 9= Ahnjin, and 10 = others). For
each of these proxy variables, hypotheses regarding the relationship with CSED are developed below.
Keim (1978) augues that, as the distribution of ownership of a corporation becomes less concentrated,
the demands placed on the corporation by shareowners become broader. Disperse corporate ownership,
especially by investors concerned with corporate social activities, increases pressure for management to
disclose social responsibility activities. Therefore it is hypothesized that the wider the dispersion of
corporate ownership, the better the corporation's social responsibility disclosures.
(H3) Owner's Influence Hypothesis: Other things being equal, there is a negative
association between percentage of ownership held by principal shareholders and
the propensity to disclose environmental information.
Creditors control access to financial resources that may be essential for continued operation of a
corporation. This is especially true in Korea as the long history of fund shortage has had significant
influence on its accounting practice. It could be argued then that the greater the corporation's reliance on
debt financing, the greater the degree to which corporate management would be expected to respond to
creditor expectations concerning the corporation's role in social responsibility activities (Ullman, 1985).
Under the assumption that creditors exhibit serious concern about the social and environmental
responsibility of Korean companies, the variables LEV and INT are expected to have a positive
relationship with the level of CSED.
(H4) Creditors' Influence Hypothesis: Other things being equal, there is a
positive association between creditors' influence and the propensity to
disclose environmental information.
Whether one relies on the stakeholder theory of Freeman (1984) or the political costs theory by Watts
and Zimmerman (1978), one can reach a general conclusion that firms may use social responsibility
disclosures to satisfy government demands. While Korea had a long tradition of close and mutually
beneficial relationship between large corporations and government, such implicit entitlements are
gradually disappearing. Since the early 1990s, the larger corporations in Korea, or chaebols became to be
pointed at as a major culprit for the unfair distribution of wealth and a stumbling block to the restructuring
of domestic economy. Accordingly, a certain degree of tensions and conflicts cave in between the
government and businesses, justifying either of the above theories to describe the government-business
relationship. In many previous studies, corporate size has often been used as a proxy for political exposure,
but subject to criticism that it is correlated with many other corporate characteristics. Therefore this study
uses the relative size of the chaebol group to which an individual company belongs as a proper proxy for
political exposure. As a supplementary proxy for the degree of political exposure, SECT variable is also
used - companies belonging to the first section of KSE are in general under tougher regulation than those
in the second section. It could then be hypothesized that CHBL and SECT are expected to have a positive
relationship with the level of CSED.
(H5) Government's Influence Hypothesis: Other things being equal, there is a
positive association between a government's regulatory influence and the
propensity to disclose environmental information.
It is needless to say that auditors play an important role for a company's accounting policy, especially in
initiating new accounting practices. Moreover, it is believed to be an important responsibility of auditors to
recommend their client companies to practice socially responsible accounting policies. For fair and
impartial audit opinions, the auditor independence is crucial. Since the auditor-firm match in Korea is
generally determined by a free choice contracting system whereby firms can choose auditors or audit firms
freely, it is often criticized to have negative impact on auditor independence. The fixed audit fee often
determined by the asset size of a client company, it is argued, may further impair the audit quality. If we
accept that larger audit firms are less subject to the influence from clients, then one could argue that larger
audit firms are in a position to exercise more discretion over the accounting policies of their client firms. It
is thus hypothesized that companies under the contract with larger audit firms are likely to disclose more
environmental information.
(H6) Auditor's Influence Hypothesis: Other things being equal, there is a positive
association between the auditor's relative independence as measured by the
size of audit firm and the propensity for a client company to disclose
environmental information.
This study also considered several other control variables used in a number of previous studies and
deemed relevant for the enhancement of interpretability. The first one is the systematic risk measured by
market-model measure of beta (BETA). Corporations with lower systematic risk are expected to have
higher levels of CSED for at least two reasons. First, a lower systematic risk implies a more stable pattern
of stock market returns, which could enhance a firm's involvement in social responsibility endeavors.
Second, because social responsibility activities may improve a firm's access to capital and increase
employee morale and productivity (Moskowitz, 1972; McGuire et al., 1988), market participants may view
socially responsible firms as better managed and thus, less risky (Roberts, 1992).
(H7) Systematic Risk Hypothesis: Other things being equal, there is a negative
association between the systematic risk of companies and the propensity to
disclose environmental information.
The second one is the age of corporation as measured by the number of years of operation since
foundation (AGE). The more mature a corporation is, the higher can be the value of reputation and the
history of involvement in social responsibility activities. As the opportunity cost of socially irresponsible
activities could be dear for mature firms, it thus follows that AGE is expected to be positively related to
CSED.
(H8) Corporate Age Hypothesis: Other things being equal, there is a positive
association between a corporation's age and the propensity to disclose
environmental information.
The last variable is foreign customers' influence as measured by the amount of sales exported as a
percentage of total sales realized during the first half of 1997 (EXP). As the larger proportion of sales is
exported, the company is exposed to a broader spectrum of stakeholder influences from abroad. Given the
ever intensifying trend of pro-environmental international regulations, the influence from foreign
customers is likely to lead to more proactive corporate initiatives with respect to the environmental issues.
(H9) Foreign Influence Hypothesis: Other things being equal, there is a positive
association between foreign influence and the propensity to disclose
environmental information.
A complete description of the variables used in the subsequent analyses is in Table 3.
(Insert Table 3 About Here)
ANALYSES AND RESULTS
Discriminating Disclosers from Non-disclosers
The first step of analyses is the dichotomous comparison of corporate characteristics between discloser
and non-discloser samples. Two procedures were used to test whether the selected corporate characteristic
variables are associated with the voluntary CSED decisions of the sample firms. First, both parametric and
non-parametric two sample tests were carried out to determine statistical significance of the differences in
mean raw and rank scores between discloser and non-discloser samples. Second, logistic regression
analyses were used to determine whether the decision to disclose was related to selected corporate
characteristic variables. The dependent variable used in these procedures is defined as follows:
DISC=1 if a discloser, 2 if a non-discloser.
Following the hypotheses developed in previous sections, the disclosing companies are expected to have
on average (1) better financial performance, (2) larger company size, (3) more stakehoders' influence, (4)
lower systematic risk, and (5) longer corporate history than their non-disclosing counterparts. Two
complementary univariate tests were used initially to test these hypotheses: (1) a parametric two sample t-
test for the statistical significance of the difference in means of the variables for the two samples in
question; (2) a non-parametric Wilcoxon rank sum test for the comparison of the magnitude of the rank
sums of the variables for the two samples. Additional preliminary studies of the independent variables
showed that the distributions of most size variables and profitability-related variables were markedly
nonnormal as confirmed by Shapiro Wilks' W statistics. Given this nonnormality and the presence of
ordinally scaled variables such as SECT, CPA, and CHBL, nonparametric test results are presented along
with parametric test results in all subsequent analyses. If these two statistical tests yield essentially
equivalent results, then greater confidence can be placed upon the results of the tests than if only one test
was used. In those cases where they produce inconsistent results, priority will be given to the
nonparametric evidence, considering the existence of possible outliers.
Table 4 presents the t statistics as well as Z statistics obtained by comparing the relative magnitude of
the independent variables between the two subsamples. The significance level associated with each
statistic is represented by the superscript asterisk symbol. With a few exceptions, most of the hypothesized
relationships were confirmed, although it was not possible to reject the null hypothesis of no difference in
many cases. The variables SGR, EMP and SALE exhibited siginificance in the predicted direction,
whereas the CPA variable showed an opposite sign in the t-test. However, the results for EMP, SALE and
CPA are not consistently significant across the parametric and nonparametric tests. This evidence suggests
that, relative to non-disclosing counterparts, the disclosers have higher sales growth rates and are larger in
size as measured by the number of employees and sales volume. However, the relative size of audit firms
are, on average, smaller for the discloser firms than nondisclosers.
(Insert Table 4 About Here)
Multiple logistic regressions were used next to examine the multivariate association between the
dichotomous disclosure variable and potential determinant variables. The first step in this procedure,
however, was to reduce the set of independent variables to a set of uncorrelated variables. Multicollinearity
would not be a problem in analyzing the descriptive statistics to determine the significant predictor
variables, but could diminish the statistical significance of individual variables under the context of
hypothesis testing. Using varimax rotated factor analysis, six factors with the eigen values greater than 1.0
were identified which significantly explained intercorrelations in the 19 variables, accounting for 74.2
percent of the total variance. Factor loadings greater than 0.40 are shown in Table 5. It is apparent that
factors 1 through 6 represent profitability, size, financial stability, export, growth and outsider influence,
respectively. The highest loading variable in each factor, as are indicated by superscipt asterisk symbols -
ROE, SALE, RES, EXP, SGR, and CPA -, was used in the subsequent multiple logistic analysis.
(Insert Table 5 About Here)
Table 6 shows the results of the logistic regression analysis. Three sets of regression models were run
which included regressions based on original scores, rank scores and factor scores. The regression results
presented in the table were obtained first by including all six variables as predictors (M1, M6, and M11),
then by including profitability and size-related variables in three possible combinations (M2, M7, M12
include both; M3, M8, M13 include profitability only; M4, M9, M14 include size only), and finally by
performing stepwise procedure for variable selection (M5, M10). Special attention has been paid to the
profitability and size variables, as these were among the most frequently examined variables in earlier
studies. The results presented in the three panels of Table 6 are not always consistent, however. As
mentioned earlier, more emphasis is thus given to the results based on rank scores test which is essentially
nonparametric. Parameter estimates are accompanied by corresponding chi-square statistics in the brackets
below each variable. The -2 log likelihood chi-square statistics evaluating the overall significance of the
model are shown in the last column.
The noteworthy variables are SALE, SGR and CPA, each representing corporate size, financial
performance, and auditor's influence respectively. Again, size and financial performance as measured by
sales volume and sales growth rate are significantly positively associated with DISC as hypothesized, but
audit firm's size is negatively associated contrary to the hypothesis. The traditional profitability variable
ROE only weakly supports the positive profit-CSED relationship. One possible explanation for the results
might be that sales rather than profits tend to play a major role as an important indicator for financial
success of companies in Korea. Therefore it might be concluded that financial performance, especially
when a company is growing rather than stable, affects environmental disclosure decisions in some ways.
Size effect looks relatively evident, even though size could act as a surrogate for many other factors. It is
not clear, however, whether or not this is because of greater public pressure upon larger firms as suggested
by legitimacy theory and/or political cost theory. The propensity of larger firms to disclose more
environmental information might be a reflection of superior system and quality of management or simply
due to inertia to provide whatever information more. One needs, therefore, to exercise due caution to
interpret the above results as a confirming evidence in favor of either agency or stakeholder theories.
The results from CPA variable warrant explanations. Given that Korean business society still tends to
emphasize the importance of immediate material wealth rather than social issues, the winners in the
Korean business arena might well be those placing more importance on economic performance. And
corporate social concerns are likely to be monitored through public-policy arena rather than through the
market place. Moreover, larger audit firms are the winners in this highly competitive Korean audit service
market under the free choice contracting system. The negative impact on auditor independence of the
Korean audit practice together with the short history of environmental disclosure practice could imply that
the major audit firms might have tended to neglect the potential importance of social and environmental
responsibility of business enterprises. This explanation, of course, is subject to the limitation that the
current analysis dealt only with the initial environmental disclosure in semi-annual reports. Being a little
bit slow in responding may not necessarily mean a lack of understanding nor willingness.
(Insert Table 6 About Here)
Association of Corporate Characteristics with the Level of Environmental Disclosure
The next step of analysis consists basically of replicating the first step using only the discloser
subsample. In the initial phase of the analysis, LEVEL instead of DISC was used as the dependent variable
to determine the univariate association between firm specific characteristics and the level of environmental
disclosure. Since LEVEL has four ordinal levels, parametric one-way ANOVA and nonparametric
Kruskal-Wallis tests were used in lieu of the two sample tests. The results from both tests are shown in
Table 7. However, there does not exist any clear evidence supporting hypothesized relationships between
the potential determinants and the level of disclosure. One exception was the nonparametric test results for
PER of which value monotonically decreased as the level of disclosure declined. None of the remaining
variables showed statistical significance nor were the patterns consistent with hypotheses.
(Insert Table 7 About Here)
In order to apply multiple regressions as the next phase of the analysis, varimax rotated factor analysis
was applied to reduce the set of independent variables as in the previous analysis. The results of the factor
loading pattern are presented in Table 8. Again six factors were identified by the selection criterion of
minimum eigen value of 1.0 or greater. They explained 76.5 per cent of the intercorrelations in the 19
variables included in the analysis. Factors 1 through 6 were interpreted to represent profitability, size,
financial structure, growth, reserve, and stakeholder influence respectively. The highest loading variable
from each factor - ROE, SALE, LEV, SGR, RES, and CPA - was used in a variety of subsequent regression
analyses. While the other variables were highly correlated with one or another of these six, the six
variables were not intercorrelated, thus avoiding the potential multicollinearity.
(Insert Table 8 About Here)
A couple of multiple regression approaches were employed as follows. First, based on both the original
scores and rank scores, generalized logit models were estimated for the dichotomous PROFILE variable
and the variables representing each factor as suggested from the above factor analysis. For these
regressions, LEVEL was used as a dependent variable. The inclusion of PROFILE as an extra independent
variable was to recheck the industry effect. Next, OLS stepwise regression models were estimated by high
and low-profile industry types. For these regressions, SCORE, LINES and LEVEL were used as
alternative dependent variables while all of the 19 independent variables were transformed into rank scores.
Rank transformation was intended to avoid possible violation of classical OLS regression assumptions due
to the skewness of the sample distribution or the existence of outliers in the original variables included.
The results from the logistic tests are provided in Table 9, and those from stepwise regressions are shown
in Table 10.
From Table 9, it is evident that the industry classification is a significant explanatory variable. High-
profile industries reveal consistently better environmental information vis-a-vis low-profile industries. The
results for all other variables are either mixed or ambiguous, however. SALE exhibits only a weak support
for the size hypothesis while RES is significant in the direction opposite to the hypothesized relationship.
This ambiguity could have been due to the inadequacy of LEVEL as a proxy for the quality of disclosure.
It thus seems necessary to have further evidence based on a variety of other variables as a proxy for the
level of disclosure.
(Insert Table 9 About Here)
A stepwise regression is a shortcut in arriving at a parsimonious model when one has to choose a best
possible combination of independent variables in the absence of well-specified theoretical foundation. It is
generally believed that the stepwise regression provides an optimal regression model by selecting relevant
independent variables from a large set of candidates in a way to minimize the loss of information while
simplifying the model specification. As noted earlier, industry type was deemed to serve as an inherent
controller rather than as an independent variable. Thus the discloser subsample was further partitioned into
high and low-profile subgroups for each of which a stepwise regression was run independently. The results
are summarized in Table 10.
For the high-profile subgroup, LEV and CPA are found to be the best possible choice of independent
variables among 19 variables considered. The coefficient estimates exhibit expected signs and the
regression model resulted in the highest statistical significance when line counts were used as the
dependent variable. The significance of LEV supports the contention that environmental disclosures may
be viewed by management as a way of fulfilling creditor expectations. Also, the significance of CPA,
albeit weak, supports the proposition that, in high-profile industries at least, larger audit firms are aware of
the importance of environmental issues, presumably in light of more pressures from creditors. It might well
be that the creditors of the companies in pollution-prone industries are more sensitive to potential
environmental contingencies and as such, the auditors of these companies may have tendency to
recommend their clients to provide improved environmental disclosure.
The regression results obtained from low-profile industries depict different picture. The variables LEV
and AGE, along with the two economic performance variables RES and EPS are found to be statistically
significant. While the same line of logic may hold for LEV, the significance of AGE may be explained in
part by the argument that age is a macro-level proxy for stakeholder influence, or economic performance.
As for RES and EPS, the results are conflicting, which needs further exploration. In sum, one important
finding from the above results is that the high and low-profile industries exhibit differential behaviour with
respect to the relationship between the firm characteristics and the amount and/or the specificity of
environmental disclosures.
(Insert Table 10 About Here)
SUMMARY AND DISCUSSIONS
Using the initial voluntary CSED experience in Korea, this paper has studied the relationship between
various firm characteristics and the quality and quantity of environmental disclosure. Three main findings
emerged: (1) Although no attempt has been made in this study to compare the index measures of
environmental disclosure with the actual environmental performance evaluated by any reliable outside
parties, the initial voluntary environmental disclosure was largely incomplete, providing self-serving,
inaccurate accounts of corporate environmental performance. Consistent with many other studies, however,
the type of industry was shown to be significantly related to both the quality and the quantity of
disclosure. The firms in high profile industries disclosed systematically more and better information than
their counterparts in low-profile industries.
(2) When the type of industry was controlled to discriminate discloser firms from nondisclosers, it was
found that firm size and auditors' influence are significantly associated with disclosure decisions, while
financial performance showed only weak and sporadic evidence in support of the positive relationship.
Corporate size was found to be positively associated with the propensity to disclose, whereas the relative
size of auditing firms was negatively associated, which contradicts the implication of the stakeholder
theory. Sales growth rate was also found to be moderately associated with the disclosure decisions.
(3) When the analysis was confined within the discloser group only, financial leverage and corporate age
emerged as significant variables for the level of environmental disclosure. As for high-profile industries,
leverage ratio was found to be the single most important explanatory variable, implying that the creditors'
influence matters for corporate environmental disclosure in this type of industry. In low profile industries,
it was found that as corporate age increases, the quality of environmental disclosure tends to improve as
well.
It is to be cautioned, however, that these findings are subject to the limitations of this study. Not only
was only a limited amount of sample available, but also the fact that this was the first voluntary CSED
experience in Korea might have resulted in poor quality and quantity of overall disclosure. The latter is
even more confounded by the voluntary nature of disclosure. It is conceivable that a majority of Korean
listed firms decided to wait rather than take proactive measures from the very inception. Earlier studies on
the experience of other countries suggest similar difficulties. For example, in their study of environmental
disclosure practice in earlier periods of the U.S., Ingram and Frazier (1980) found only a weak association
between quantitative measures of disclosure content and independent measures of performance. They
argued that because management is free to use its own discretion in selecting information to be reported, it
is possible for poorer performers to bias their selections in order to appear like the better performers.
Similarly, Wiseman (1982) concluded that voluntary environmental disclosures were incomplete,
providing inadequate information for most of the environmental performance items, and that no
relationship existed between the measured contents of the firm's environmental disclosure and
environmental performance. It may be thus rash to assume that the findings from these study may be
generalized to future experience.
It is also important to note that the interpretation of the results is conditional on the assumption that the
proxy variables used are reliable indicators of the determinants underlying corporate environmental
disclosure decisions. Despite extensive efforts made to enhance the accuracy of proxy variables, data
availability might have limited their validity. For example, Herremans at al. (1993) suggest that they have
found a positive relationship between reputation for social responsibility and profitability over an extended
period of time rather than over a contemporaneous period. Roberts (1992) reports a lagged relationship
between economic performance and the level of social disclosure. These problems are hoped to be
resolved in future research as more data become available.
A final point is the discrepancy between empirical findings and theoretical predictions. While
considerable efforts were made to derive testable hypotheses from various theories, a closer look at the
theories and empirical evidence reveals a significant gap between them. For example, size appears to be
related to environmental disclosure, yet as argued by Hackston and Milne (1995), it is not clear whether
this is evidence for agency theory, legitimacy theory, both theories or neither of these. Therefore, this study
did not attempt to draw any inference from empirical evidence in support of or against existing
explanations for social and environmental disclosures.
Despite these limitations, this study could provide some preliminary evidence on corporate
environmental reporting practice. A continued research in this line based on further evidence seems
essential for any generalized picture about the long-term trend of the CSED practice and the possible
association between potential determinants and the level of CSED practice in Korea.
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TABLE 1. Environmental Disclosure Index Scores by Industry TypeIndustries Proportion of
disclosersEF PA GI SCORE LEVEL LINES
Mean(S.D.)
Mean(S.D.)
Mean(S.D.)
Mean(S.D.)
3 2 1 0 Mean(S.D.)
Panel A: Low-Profile Industriestextile 1/49 0.0
(n.a)3.0
(n.a.)0.0
(n.a.)3.0
(n.a.)0 0 1 0 2.0
(n.a.)leather 2/11 1.5
(2.12)2.5
(2.12)0.0
(0.00)4.0
(0.00)0 0 2 0 5.0
(1.41)publishing 1/3 0.0
(n.a.)0.0
(n.a.)1.0
(n.a.)1.0
(n.a.)0 0 0 1 2.0
(n.a.)fabricated
metal3/14 2.0
(1.73)1.0
(1.00)0.3
(0.58)3.3
(2.52)0 0 2 1 3.7
(2.89)machinery 6/28 1.0
(1.55)1.5
(0.84)1.3
(1.21)3.8
(2.56)0 1 4 1 9.0
(4.64)electronics 5/59 2.4
(2.51)1.0
(1.41)1.0
(0.71)4.4
(3.20)0 1 4 0 9.0
(4.64)wholesale 2/36 1.5
(2.12)1.0
(1.41)2.0
(2.83)5.0
(5.66)0 1 0 1 7.0
(5.66)Subtotal 20/248 1.5
(1.82)1.4
(1.23)1.0
(1.15)3.9
(2.64)0 3 13 4 6.0
(3.90)Panel B: High-Profile Industries
foods &beverages
11/48 2.9(1.94)
2.3(2.10)
1.4(1.36)
5.4(3.88)
2 1 8 0 8.0(7.99)
pulp & paper 6/27 1.7(1.51)
4.0(3.46)
1.7(1.63)
7.3(4.46)
2 1 2 1 12.7(9.35)
chemicals 10/90 1.8(2.10)
2.1(2.51)
1.0(1.15)
4.9(5.02)
2 1 4 3 6.4(6.82)
petroleum 2/6 0.0(0.00)
2.0(1.41)
1.5(0.71)
3.5(2.12)
0 0 2 0 4.0(0.00)
rubber &plastics
6/19 3.0(1.90)
0.5(0.84)
1.5(1.05)
5.0(2.45)
0 1 5 0 6.3(3.20)
non-metalicmineral
3/27 2.0(1.73)
2.7(3.79)
1.3(1.53)
6.0(6.24)
1 0 2 0 13.7(17.67)
basic metals 6/42 3(1.9)
2(1.79)
1.17(1.47)
6.17(3.25)
1 1 3 1 7.3(4.68)
Subtotal 44/211 2.1(1.88)
2.2(2.41)
1.3(1.25)
5.5(3.97)
8 5 26 5 8.2(7.75)
Total 64/459 1.9(1.87)
1.9(2.14)
1.2(1.22)
5.0(3.68)
8 8 39 9 7.5(6.83)
Table 2. Comparison of Index Scores between High vs Low-Profile Industry
Indexes Firm Level Comparisons Industry Level Comparisons
High Low T-test Wilcoxon High Low T-test Wilcoxon
Mean
(S.D.)
Mean
(S.D.)
t-stat
(sig. t)
Z-stat
(sig. Z)
Mean
(S.D.)
Mean
(S.D.)
t-stat
(sig. t)
Z-stat
(sig. Z)
EF 2.14
(1.88)
1.50
(1.82)
1.27
(0.209)
1.31
(0.190)
1.95
(1.01)
1.20
(0.93)
1.44
(0.175)
1.48
(0.139)
PA 2.20
(2.41)
1.35
(1.23)
1.88*
(0.065)
0.98
(0.325)
2.22
(1.04)
1.43
(1.02)
1.44
(0.175)
1.16
(0.248)
GI 1.32
(1.25)
0.95
(1.15)
1.12
(0.268)
1.22
(0.224)
1.36
(0.23)
0.80
(0.74)
1.90*
(0.098)
1.74*
(0.082)
SCORE 5.55
(3.97)
3.85
(2.64)
2.02**
(0.049)
1.66*
(0.098)
5.47
(1.20)
3.50
(1.29)
2.95**
(0.012)
2.37**
(0.018)
LINES 8.16
(7.75)
5.95
(3.90)
1.52
(0.135)
0.92
(0.358)
8.34
(3.55)
4.93
(2.59)
2.05*
(0.062)
1.79*
(0.073)
LEVEL 1.36
(0.92)
0.95
(0.60)
2.14**
(0.037)
1.57
(0.116)
1.40
(0.34)
0.84
(0.40)
2.84**
(0.015)
2.55**
(0.011)
*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level
Table 3. Description of Variables
Name Description Operational definitions
NM(+) net margin ordinary income/sales * 100
ROE(+) return on equity net income/capital * 100
EPS(+) earnings per share net income/avg(# of common stocks)
CFPS(+) cash flow per share (net income+depreciation)/avg(# of common stocks)
PER(+) price earnings ratio avg(stock price)/EPS
SGR(+) sales growth rate (salest -salest-1)/salest-1 * 100
PGR(+) profit growth rate (net incomet - net incomet-1)/net incomet-1 * 100
RES(+) reserve rate retained earnings/capital * 100
EMP(+) employee number total # of full-time employees inc. executives
SALE(+) sales volume net sales during the first half accounting period 1997
OWN(-) owners' share major stockholders' share/capital * 100
LEV(+) leverage total liability/capital * 100
INT(+) interest charge interest expenses/sales * 100
SECT(+) section in KSE SECT=1 if in section 1; 0 otherwise
CHBL(+) chaebol ranking CHBL=3 if top 10; 2 if top 30; 1 if others; 0 if none
CPA(+) auditor firm ranking AUD=1 if the largest; 2 if the second etc.
BETA(-) beta coefficient market model beta using monthly return'93 thru '96
AGE(+) corporate age age of corporation as of 1997
EXP(+) foreign influence sales exported/sales * 100
Table 4. Descriptive Statistics of Independent Variables for Two Sample Comparisons
Variable Discloser(DISC=1)
(n=64)
Non-discloser(DISC=2)
(n=64)
T-test Wilcoxon
Test
Mean(S.D.) Mean rank Mean(S.D.) Mean rank t-stat. Z-stat.
NM 2.5(8.6) 64.27 -5.3(47.7) 61.90 1.32 0.81
ROE 8.6(17.2) 65.47 -18.3(140.8) 57.91 1.51 1.18
EPS 2867.3(7832.8) 68.31 1239.2(3065.9) 62.77 1.55 0.84
CFPS 4134.6(8508.8) 69.77 2461.9(4812.7) 61.36 1.37 1.27
PER 34.0(66.7) 51.37 31.0(36.5) 55.79 0.28 -0.74
SGR 13.8(23.8) 72.02 5.4(25.8) 57.21 1.91* 2.26**
PGR 49.8(252.7) 52.51 12.6(191.5) 44.32 0.81 1.44
RES 369.0(596.9) 66.56 285.7(309.6) 64.47 1.25 1.26
EMP 2563.6(5287.6) 71.83 1805.7(7497.8) 59.36 0.67 1.88**
SALE 3722.0(8296.3) 72.98 3260.2(11991.4) 58.24 0.26 2.23**
OWN 31.0(16.4) 66.63 29.8(14.1) 64.41 0.45 0.33
LEV 288.0(265.2) 61.92 666.9(1823.1) 62.08 -1.64 -0.02
INT 7.4(5.1) 65.88 10.1(16.9) 65.14 -1.23 0.11
SECT 1.6(0.56) 66.08 1.6(0.56) 64.94 0.18 0.20
CHBL 1.18(1.07) 69.05 0.9(1.03) 62.05 1.08 1.12
CPA 3.6(2.43) 61.27 4.7(3.31) 69.60 -2.02** -1.27
BETA 1.0(0.4) 56.80 1.1(0.5) 63.25 -1.33 -1.02
AGE 31.6(12.6) 69.74 28.9(12.0) 61.39 1.25 1.26
EXP 24.7(27.1) 67.30 21.5(25.0) 62.73 0.68 0.69*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level
Table 5. Independent Variables Rotated Factor Loadings (Total Sample)
Variables Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
NM 64
ROE 89*
EPS 83
CFPS 71
PER -86
SGR 89*
PGR 64
RES 73*
EMP 91
SALE 93*
OWN 67
LEV 58 -54
INT -52
SECT 71
CHBL 81
CPA 77*
BETA -42
AGE -58
EXP 87*
Eigenvalue 3.67 3.55 2.46 1.69 1.36 1.36
Table 6. Logistic Regression Results for the Environmental DisclosureDependent Variable: DISC=1 if a discloser, 2 if a non-discloser
Panel A: Results Based on Original Scores
Model ROE SALE RES EXP SGR CPA Chi-square
M1 0.02(2.50)* -0.00(0.34) 0.00(1.89) 0.00(0.05) 0.01(0.15) -0.21(8.01)*** 18.56***
M2 0.01(2.03) 0.00(0.04) 4.68**
M3 0.01(2.03) 4.64**
M4 0.27(1.60) 1.63
M5 -0.12(3.89)** 4.03**
Panel B: Results Based on Rank Scores
Model Rk(ROE) Rk(SALE) Rk(RES) Rk(EXP) Rk(SGR) Rk(CPA) Chi-square
M6 0.01(1.94) 0.01(2.83)* 0.00(0.15) -0.0(0.09) 0.01(1.75) -0.01(4.02)** 12.32***
M7 0.01(1.99) 0.01(5.0)** 6.62**
M8 0.01(1.39) 1.41
M9 0.01(4.90)** 5.07**
M10 0.01(4.12)** -0.01(4.62)** 9.98***
Panel C: Results Based on Factor Scores
Model Factor1 Factor2 Factor3 Factor4 Factor5 Factor6 Chi-square
M11 0.11(0.25) 0.29(0.75) 0.30(1.74) 0.35(2.36) 0.43(3.57)* 0.03(0.02) 9.76*
M12 0.10(0.23) 0.27(1.61) 1.87
M13 0.10(0.23) 0.23
M14 0.27(1.60) 1.63
*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level
Table 7. Descriptive Statistics of Independent Variables for Multiple Sample Comparisons
Variables LEVEL One-way
ANOVA
Kruskal-
Wallis Test
3(n=8) 2(n=8) 1(n=37) 0(n=9)
Mean(S.D.) Mean(S.D.) Mean(S.D.) Mean(S.D.) F-stat. Chi-square
NM 1.5(9.2) 2.7(1.9) 2.1(9.3) 5.2(9.1) 0.34 2.51
ROE 5.2(9.0) 7.0(0.5) 9.7(19.0) 6.4(22.5) 0.18 0.75
EPS 671(1773) 3651(6613) 2289(7963) 6627(10859) 1.00 3.13
CFPS 2418(2468) 5175(8669) 3449(8581) 7703(11338) 0.75 0.51
PER 89.2(166.7) 33.3(62.3) 29.4(38.6) 6.2(4.5) 1.85 8.15**
SGR 6.1(13.1) 10.2(7.2) 16.6(29.1) 11.9(12.3) 0.53 1.11
PGR 91.4(134.4) -15.6(45.3) 58.8(310.9) 44.5(89.8) 0.20 3.54
RES 314.3(202.7) 393.4(492.1) 313.1(460.3) 637.9(1205) 0.74 0.55
EMP 2351(4299) 5302(11633) 2473(3943) 710(531) 1.10 2.67
SALE 4450(7163) 7253(14957) 3464(7665) 1052(967) 0.82 1.50
OWN 37.3(42.3) 26.8(17.1) 30.7(16.5) 30.4(17.8) 0.56 2.83
LEV 369.9(373.0) 279.2(116.0) 277.0(284.4) 262.8(167.8) 0.29 1.30
INT 8.3(4.1) 6.5(4.5) 7.8(5.8) 5.8(2.2) 0.56 1.88
SECT 1.8(0.5) 1.8(0.5) 1.6(0.5) 1.8(0.5) 0.80 2.42
CHBL 1.4(1.2) 1.1(1.4) 1.1(1.1) 0.7(0.9) 0.65 1.91
CPA 3.1(2.2) 4.4(2.7) 3.6(2.2) 3.8(3.6) 0.38 1.59
BETA 1.0(0.3) 0.9(0.3) 1.0(0.5) 1.0(0.6) 0.38 1.37
AGE 37(12.9) 34.1(9.4) 30.1(14.1) 31.6(5.7) 0.78 3.97
EXP 9.9(11.8) 27.3(24.6) 27.6(29.9) 22.9(25.2) 0.97 2.51
*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level
Table 8. Independent Variables Rotated Factor Loadings (Discloser Sample)
Variables Factor1 Factor2 Factor3 Factor4 Factor5 Factor6
NM 82
ROE 87*
EPS 84
CFPS 70
PER -85
SGR 84*
PGR 64
RES 71*
EMP 89
SALE 93*
OWN -41
LEV 73*
INT 63
SECT 70
CHBL 80
CPA 78*
BETA 50
AGE -63
EXP -65
Eigenvalue 3.95 3.67 2.31 1.69 1.54 1.38
Table 9. Logistic Regression Results for the Level of Environmental DisclosureDependent Variable: LEVEL=0 if excellent, 1 if good, 2 if medium, 3 if poor
Panel A: Results Based on Original Score
Model Profile ROE SALE LEV SGR RES CPA Chi-
square
(+) (+) (+) (+) (+) (+) (+)
M1 1.9(7.0)*** -0.0(0.0) 0.0(3.1)* 0.0(0.7) -0.0(0.1) -0.0(4.3)** 0.2(2.0) 13.04*
M2 1.0(3.1)* -0.0(0.1) 0.0(1.4) 4.20
M3 0.9(2.6) -0.0(0.1) 2.76
M4 1.0(2.9)* 0.0(2.0) 4.59
Panel B: Results Based on Rank Score
Model Profile Rk(ROE
)
Rk(SALE) Rk(LEV) Rk(SGR) Rk(RES) Rk(CPA) Chi-
square
(+) (+) (+) (+) (+) (+) (+)
M5 1.3(3.4)* 0.0(0.0) 0.0(0.0) 0.0(0.4) -0.0(0.5) -0.0(0.5) 0.0(0.6) 5.70
M6 0.9(2.5) -0.0(0.9) 0.0(0.0) 2.73
M7 0.9(2.5) -0.0(0.0) 2.70
M8 0.9(2.3) 0.0(0.9) 3.41
*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level
Table 10. OLS Regression Results Using Models Selected by Stepwise Procedure with DependentVariables: SCORE(Total index score), LINES(Number of line counts) LEVEL=3 if excellent, 2 if
good, 1 if medium, 0 if poor
Dep. Var. Rk
(LEV)
Rk
(CPA)
Rk
(AGE)
Rk
(PER)
Rk
(RES)
Rk
(EPS)
Rk
(BETA)
Rk
(NM)
F-stat.
(+) (+) (+) (+) (+) (+) (-) (+)
Panel A: Results based on High-Profile Sample
SCORE 0.09
(9.24)***0.05
(3.31)*5.72***
LINES 0.22
(8.59)***8.59***
LEVEL 0.02
(4.29)**4.29**
Panel B: Results Based on Low-Profile Sample
SCORE 0.11
(7.31)**0.09
(2.82)
3.68*
LINES 0.26
(22.23)***0.29
(12.87)**
*
-0.15
(7.15)**-0.07
(2.90)
8.93***
LEVEL 0.02
(4.02)*-0.02
(2.98)
2.94*
*** significant at 0.01 level; ** significant at 0.05 level; * significant at 0.10 level