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The impact of internal and external governance mechanisms on the voluntary disclosure of financial and non-financial performance
Marleen Willekens Heidi Vander Bauwhede
Ann Gaeremynck Linda Van de Gucht
KULeuven
Correspondence: [email protected]
Abstract In this paper we examine the impact of internal and external governance mechanisms on the disclosure of financial and non-financial performance information in corporate annual reports. We used the balanced scorecard (Kaplan and Norton 1992, 1996) as a framework to create an index of the extent of disclosure of financial and non-financial performance information. We assessed the impact of internal and external governance mechanisms by creating a corporate governance index that includes the following governance information: board independence, audit committee existence, external audit quality and internal audit existence. We also examined the incremental and individual impact of each one of these governance mechanisms. Using unique survey data on corporate governance for a broad set of Belgian listed companies, we find that firms with a higher corporate governance index report more financial and non-financial performance information. External audit quality has the highest individual impact on disclosure of financial and non-financial performance information, followed by board independence and the existence of an internal audit department. We also report strong evidence that the extent of performance information increases with agency costs and information asymmetry problems. Our results suggest that governance mechanisms can aid in enhancing corporate disclosure of financial and non-financial performance information, and that disclosure of performance measures is used as a means to reduce information asymmetry and agency problems. Keywords: corporate governance, voluntary disclosure, non-financial performance measures, balanced scorecard.
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The impact of governance mechanisms on the voluntary disclosure of financial and non-financial performance measures
1. Introduction
Recent corporate reporting scandals (Enron, Worldcom, etc.) have put a discussion on reforms to the
current financial reporting model on the top of the political agenda (SEC, February 2002 and AICPA
March 2002). Disclosure of non-financial performance information may be one means to improve
companies' business reporting. The AICPA Special Committee on Financial Performance (Jenkins
Committee) already called for more forward-looking and non-financial disclosures in 1994, and results
of the FASB's Business Reporting Project, started in 1999 and finalized in 2001, indeed show that
leading companies voluntary disclose additional data - also non-financial data - in order to
communicate business performance more effectively1. Also the FASB is considering the usefulness of
non-financial performance measures in their deliberations on performance reporting (see Maines et al.
2002).
The current corporate reporting discussion does however not only pertain to specific disclosure and
reporting practices, but also to the role of the external auditor and other corporate governance
mechanisms (such as internal audit, the audit committee, board independence) in the business reporting
process. Financial accounting information has a corporate governance role, since other corporate
governance (henceforth CG) mechanisms such as, for example, takeovers and bank finance (see
Schleifer and Vishny 1997), make explicit or implicit use of this information (Bushman and Smith,
2001, Sloan 2001) to assure suppliers of finance a return on their investment (Schleifer and Vishny
(1997, p. 737). Financial accounting information - and information regarding a company's business in
general - is in itself however also a product of a governance process. We follow the latter approach in
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this paper, since we examine whether CG can help enhance business reporting and corporate disclosure
by examining the impact of some CG mechanisms (i.e. presence of an independent board, an audit
committee, a high quality auditor and an internal audit department) on voluntary disclosure of financial
and non-financial performance information by Belgian listed companies. We use the balanced
scorecard as a framework to develop our disclosure index, since this instrument gives a comprehensive
overview of measures of the companies' performance on some critical success factors.
We contribute to the CG literature and voluntary disclosure literature. Our research contributes to the
voluntary disclosure literature by studying the impact of CG on the extent of non-financial
performance disclosure. Prior research was limited to an examination of the impact of CG on the
comprehensiveness of financial disclosures (Ho and Wong2001) or on the disclosure of some specific
items, such as interim reporting (Leftwich et al. 1981) and share option disclosures (Forker 1992). We
also study a broad range of CG attributes, namely board independence, existence of an audit committee
and internal audit department and the quality of the external auditor.
Our second contribution to the voluntary disclosure literature is that we use the Balanced Scorecard
(Kaplan and Norton 1996) as a framework for the creation of a non-financial performance measure
disclosure index. Prior voluntary disclosure studies typically used researcher-created indices. Those
indices score whether companies disclose some well-specified items, such as for example discussion of
the competitive environment, cash flow forecast, the number of employees, etc.. The selection of the
disclosure score items is based on an analysis of investor needs (e.g. Jenkins’ report (AICPA 1994)) or
observed practices (see for example Robb et al. 2001, Chow and Wong-Borren 1987).
1 The objective of Business Reporting Project was to study how companies can make there business reporting more reliable and relevant. Apart from the report on voluntary disclosures, there were two other reports. One on the electronic distribution of business information, and one on redundancies between GAAP and SEC disclosure requirements.
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Our study contributes to the CG literature by examining whether stronger CG systems can enhance
corporate reporting by increasing the level of (voluntary) disclosure of non-financial performance
information, whereas prior studies on the relationship between CG and corporate reporting rather
examined whether CG can prevent low qualitiy financial statements ( i.e. fraud, earnings management
and audit qualifications) (see for example Becker et al. 1998, Francis et al. 1999, Beasley 1996 and
2000, Peasnell et al. 1999 and 2000, Dechow et al. 1996, Abbott et al. 2000a and 2000b, DeFond and
Jiambalvo 1991, Parker 2000, Carcello and Neal 2000, Reynolds and Francis 2000).
The paper is organized as follows. In the next section we give an overview of the CG regulation in
Belgium. We then develop the research questions in section 3. Section 4 describes our empirical model.
We discuss our sample and report the results in section 5. Section 6 concludes.
2. Corporate governance regulation and corporate reporting practices in Belgium
2.1 Corporate governance regulation in Belgium
Continental European accounting and auditing regulations can be found in company law and tax codes.
In addition, stock exchanges may impose additional auditing and reporting requirements as part of their
listing rules. At this moment, Belgian CG law is limited. Accounting law specifies for example that all
companies (listed and non-listed) that adopted a certain legal form and exceed some size criteria have
to appoint an external auditor for the audit of the statutory and consolidated financial statements.
The rule book of the Brussels Stock Exchange (now part of Euronext) does not include additional CG
requirements2. However, in 1998 the Brussels Exchanges (Commission Cardon), The Banking and
Finance Commission (CBF) as well as the Federation of Belgian enterprises (VBO – Verbond van
Belgische Ondernemingen) issued recommendations on good CG. The recommendations of the CBF
2 Note that Euronext has not yet a harmonised set of listing requirements (see The Euronext Rulebook) so that the requirements of the individual exchanges Paris, Brussels and Amsterdam apply.
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deal with the disclosure on corporate governance, whereas the recommendations of the Brussels
Exchanges and VBO - which are in fact quite similar - deal with the “content” of corporate
governance3. The latter recommendations pertain to the responsibilities, size, composition and working
procedures of the board of directors (BOD), the responsibilities and remuneration of non-executive
directors, remuneration of management and reporting and control. The Brussels Exchanges
recommend, for example, that the board should not consist of more than 12 directors, and that the
majority of the directors be non-executives. They also advise that part of the non-executive directors be
independent of management as well as of the majority shareholders and that the company establishes
an audit committee.
These Belgian requirements and recommendations differ in various respects from American rules and
regulations. A first observation is that the American rules and regulations issued by the Securities and
Exchange Commission (SEC) and New York Stock Exchange (NYSE), and which are applicable to
NYSE registrants, are not only larger in number, but are also mandatory, which contrasts with the mere
recommendations issued by the Brussels Stock Exchange and the Banking and Finance Commission.
We next also observe differences in specific CG rules or recommendations between the U.S. and
Belgium, which can often be explained by institutional differences between countries. A first example
is that in Belgium, and in contrast to the U.S., also the financial statements of non-listed companies
should be audited. One explanation for this extended audit requirement is that non-listed companies are
extremely important (both in number and in contribution to the GDP) to continental-European
economies. An additional explanation is that the use of earnings reported in statutory accounts for tax
assessment calls for audits of financial statements of listed and non-listed companies.
3 These are recommendations and or in no way mandatory regulations. The regulation of the internal audit function within credit banks (see CBF 1997) is an exception.
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A second example of differences between Belgian and American CG rules and regulations is that the
Belgian CG recommendations specify that some of the non-executive directors should not only be
independent from management, but also from majority shareholders. The reason is that ownership of
Belgian companies -listed as well as non-listed companies - is concentrated, and agency problems
between minority and majority shareholders become an important issue.
A third example of differences between American and Belgian CG requirements is that Belgian
companies, unlike registrants on the NYSE, are not obliged to have an audit committee and internal
audit function. AC formation and the establishment of an internal audit function are still voluntary in
Belgium4.
We mention as a final example that American (NYSE) rules are more severe concerning the
composition of the BoD: they require that the majority of the board consists of independent (and not
only non-executive) directors.
2.2 Corporate reporting practices in Belgium
Belgian accounting law requires that all companies - listed as well as non-listed companies- that exceed
certain legal form and size criteria submit financial statements with the Belgian national Bank. These
financial statements include the companies statutory (and if appropriate consolidated) balance sheet,
profit and loss account and notes to the financial statements. Only few studies have examined the
reporting practices of Belgian companies. Some of those studies deal with earnings management
behaviour of listed and non-listed Belgian companies (see for example Sercu et al. 2002, Vander
Bauwhede et al. 2003) or the use of accounting methods (such as for example the revaluation of assets)
as a signalling device (Gaeremynck and Veugelers 1999). A recent study (Willekens et al. 2003) also
examined value and risk reporting practices among listed companies in Belgium. Using an index based
4 Except for Credit companies and Insurance companies. The Banking and Finance Commission and Supervisory body for Insurance Companies (“Office de Contrôle des Assurances”) demand that they set up an internal audit function.
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on PricewaterhouseCoopers' ValueReportingTM framework and ValueReporterTM diagnostic tool, they
find that Belgian firms perform good on disclosure of items from the categories Management &
Performance and Organization & Strategy, while they perform poor on disclosure of Value Drivers and
Risk Management. Their results also show that manufacturing firms do best, followed by
retail/distribution/media and then technology.
3. Literature review and research questions
The board of directors, the audit committee, the external auditor as well as the internal audit department
within an organization all have an impact on corporate reporting. While corporate law explicitly
requires that financial statements be audited by an external auditor, the board of directors actually bears
first responsibility for the truthfulness of the financial statements (see for example article 92 of the
Belgian Company Law) and the underlying financial reporting process. Boards may however delegate
the actual review of the financial reports to an audit committee. Boards may also install an internal
audit department that examines the adequacy of the internal control system to preserve the integrity of
(financial) information that will be used for external reporting purposes. It follows that more effective
boards, external audits of higher quality, the existence of an audit committee and the existence of an
internal audit department may positively influence corporate reporting in general and the extent of
disclosure of financial and non-financial performance measures in particular.
Board effectiveness is itself influenced by its size and its composition (see for example Jensen 1993
and various reports on corporate governance). It is generally believed that a higher proportion of
independent directors increases board effectiveness. There is however less consensus on the impact of
size on board effectiveness. Some argue that a minimum number of board members is necessary for the
board to have some monitoring ability (see, for example Jensen 1993 and Lipton and Lorsch 1992).
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Other reason, however, that as boards grow larger, communication and coordination problems arise
which reduces board effectiveness. The quality of the external audit (audit quality) is believed to
increase in audit firm size (see for example DeAngelo 1981, Klein and Leffler 1981).
The evidence on the impact of various corporate governance elements on disclosure is scarce. Chen an
Jaggi (2001) found for example that the proportion of independent non-executive directors on boards
has a positive impact on the comprehensiveness of financial disclosures and that this impact is smaller
for family-controlled firms compared to non-family controlled firms. Similarly, Leftwich et al. (1981)
and Forker (1992) report evidence of a positive association between board independence and interim
reporting and share options disclosure respectively. Ho and Wong (2001) find that the extent of
voluntary disclosure by listed Hong Kong firms that have an audit committee is significantly higher
than for firms that have no such audit committee. Forker (1992) found evidence of a weak significant
and positive relationship between the existence of an audit committee on the share option disclosures.
We add to this literature by examining the impact of five corporate governance elements - board size,
board independence, audit quality, audit committee existence and internal audit department existence -
on the level of disclosure of both financial and non-financial performance measures in annual reports.
We will first examine whether companies with a stronger corporate governance structure disclose more
financial and non-financial performance information. Following the discussion above, we conclude that
an independent board, a high quality external auditor, the existence of an audit committee and the
existence of an internal audit department are all attributes of a strong corporate governance system. We
then measure the strength of the corporate governance system by a corporate governance index that
indicates how many of the attributes of a strong system are present. Note that we do not include board
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size in the corporate governance index, since there is no consensus on the impact of board size on
board effectiveness.
We next examine the individual impact of each one of the corporate governance attributes - board size,
board independence, audit committee existence, quality of the external auditor, and the existence of an
internal audit department - since some corporate governance elements may have a stronger impact on
disclosure than the other ones.
4. Model Specification and Variable Measurement
We estimate the following multiple regression models (OLS) to test our hypothesis on the relationship
between corporate governance and the disclosure of non-financial performance information.
DISCL = β0 + β1 CGIND+ β2 LNBS + β3 LEV + β4 YQUOTED + β5 LNTA
+ β6 ROA + β7 NYSE + β8 FIN + ε (1)
DISCL = β0 + β1 PBINDD + β2 AC + β3 B5 + β4 INTAUD + β5 LNBS + β6 LEV + β7 YQUOTED
+ β8 LNTA + β9 ROA + β10 NYSE + β11 FIN + ε (2)
Where:
DISCL = disclosure index;
CGIND = corporate governance index;
PBINDD = dummy, =1 if more than half of the board members are independent from
management and the majority shareholders, zero otherwise;
AC = dummy, =1 if an audit committee exists, zero otherwise;
B5 = dummy, =1 if the company is audited by a Big5 auditor, zero otherwise;
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INTAUD = dummy, =1 if the company has an internal audit department, zero otherwise;
LNBS = natural logarithm of board size;
LEV = leverage measured as long-term debt over total assets;
YQUOTED = number of years the company has been quoted on the Brussels Stock
Exchange;
LNTA = natural logarithm of total assets;
ROA = return on assets;
FIN = dummy =1 if the company is a holding company, a real estate business or a
bank, zero otherwise.
Table 2 gives an overview of the measurement of the variables and the predicted sign on the
coefficients.
[Insert Table 1 about here]
4.1 Disclosure proxy
We use the Balance Scorecard (BSC) – a set of measures that gives top management a fast but
comprehensive view of the business (Kaplan and Norton 1992, p. 71) - as a framework to create a non-
financial performance disclosure index. We use the BSC as a framework because it has been shown
that using the BSC does not only enhance company performance (see for example Banker et al. 1998,
Anderson et al. 1994 and 1997, Hoque and James 2000) but may also be useful for market participants.
Ittner and Larcker (1996) find, for example, that if customer satisfaction measures (i.e. an element of
the BSC information) are used to form hedge portfolios, then those portfolios outperform the market.
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The BSC does, in contrast to current practice, not only include financial but also operational measures
of performance. Financial performance measures only capture results of actions already taken. Non-
financial (operational) measures are by contrast drivers of a firm’s future financial performance.
Kaplan and Norton (1992) argue therefore that a firm’s performance should not only be evaluated from
a financial perspective, but also from the following three operational perspectives: a customer
perspective, an internal processes perspective and a learning and growth perspective. These
perspectives are related to the following questions (Kaplan and Norton 1996, p. 9): To achieve our
vision, how should we appear to our customers?; To satisfy our shareholders and customers, what
business processes must we excel at?; To achieve our vision, how will we sustain our ability to change
and improve?. The BSC includes measures that give indications of a firm’s performance in each one of
these three areas. Customer satisfaction measures are typically operational measures that are related to
the customer perspective. The number of new products developed and defect rates are examples of
operational measures with respect to innovation and production, two internal business processes.
Typical examples of operational measures that belong to the learning and growth perspectives are
employee satisfaction and employee retention (see Kaplan and Norton 1996, Kaplan and Atkinson
1998, Horngren et al. 2002).
Our disclosure proxy is an index that measures whether a company reports information on each one of
the balanced scorecard perspectives, i.e. the financial, customer, internal business processes and
learning and growth perspectives. Following the BSC literature we further divided the internal business
processes perspective in the innovation and production perspectives. We then identified for each
company and for each perspective (i.e. financial, customers, innovation, production and learning and
growth) whether the company reports:
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1. quantitative historic information;
2. qualitative historic information;
3. segment information;
4. quantitative forward-looking information;
5. qualitative forward-looking information.
We give the company a score of 1 for each item for which performance measures are available. We
then compute an average score per company by summing the item scores and then scaling this value by
the maximum score, i.e. 25 (5 perspectives * 5 items).
4.2 Corporate governance index and individual corporate governance variables
Our first goal is to assess the joint impact of the alternative corporate governance systems on the extent
of disclosure of financial and non-financial performance information. We therefore created a
“corporate governance index” which measures the overall the strength of a company’s corporate
governance system. Theory predicts that board independence, the existence of an audit committee,
higher audit quality as well as the existence of an internal audit department can enhance corporate
reporting. We therefore use as a corporate governance index a measure of the number of (the four)
corporate governance components that are present within the company. That is, we identify for each
company the quality of the external auditor (Big5 vs. non-Big5 auditor) and whether an independent
board, an audit committee and/ or internal audit department are present. We give a score of one for
each item that is present and scale by the total number of items, i.e. 45. Higher scores indicate stronger
corporate governance systems. We thus predict a positive coefficient on CGIND. Note that board
independence is not a dichotomous variable. We identified a board as being independent when more
5 We do not use a weighted index because there is no theory that predicts that one CG component will increase the strength of the CG system more than another component.
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than half of the board members are independent. We do not include board size in our corporate
governance index because there is no consensus on the direction of the impact on board effectiveness
(see section 3). We therefore preferred to include it as a separate control variable in our multiple
regression model (cf. infra).
Our second objective is to assess the individual impact of board independence, audit committee
existence, quality of the external audit and existence of an internal audit department on disclosure of
financial and non-financial performance information. We replace therefore the variable CGIND by four
indicator-variables: PBINDD, AC, B5 and INTAUD. Those variables take the value of 1 if more than
half of the board members of the company are independent, there exists an audit committee, the
company has a Big5 auditor and the company has an internal audit department, respectively, and the
value of zero otherwise.
4.3 Control variables
Board size. We include the natural log of the size of the board of directors (LNBS) as a first control
variable because prior studies argue that board size affects board effectiveness and hence corporate
reporting (see for example Dechow et al. 1996 and Beasley 1996). We prefer the natural log to control
for non-linearities in the relation between of board size and board effectiveness (see also Yermack
1996). We do not predict the sign of the coefficient since there is no consensus on the direction of its
impact on board effectiveness (cf. infra).
Other control variables were derived from a review of the voluntary disclosure literature. Those control
variables proxy for the potential costs and the benefits of disclosure. Those costs and benefits reduce,
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respectively increase, the propensity of disclosure. Costs include direct costs of information production,
political costs as well as proprietary costs of information dissemination. The benefits of disclosure stem
from reductions in agency costs and information asymmetry problems. We include the following
proxies to control for these effects.
Agency costs. Agency costs may arise from both equity and debt financing. We do not include a proxy
for the agency costs of equity since ownership is - even for listed companies - concentrated in Belgium.
We include leverage (LEV), measured as long-term debt over total assets, as a proxy for the agency
costs between a company and its debtholders (see Watts and Zimmerman 1986 and 1990) and predict a
positive relationship with the extent of disclosure.
Information asymmetry. We proxy information asymmetry by a measure of firm age (YQUOTED)
(see for example Helwege and Liang 1996, Petersen and Rajan 1994), and more specifically by the
number of years the company is quoted on the Brussels Stock Exchange. Information asymmetry is
inversely related with firm age because there is more information available on companies that are
longer quoted on the stock exchange. We therefore predict a negative coefficient on YQUOTED.
Disclosure costs. We include firm size (LNTA) as a proxy for the cost of information production,
proprietary costs and political costs (see for example Watts and Zimmerman 1986, Lang and Lundholm
1993, Wallace and Naser 1995). We measure company size by the natural logarithm of total assets. We
prefer the natural logarithm in order to control for non-linearities in the relationship between audit firm
size and the extent of disclosure. We do not predict the direction of the relationship between firm size
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and disclosure since the hypothesised direction depends on which particular disclosure cost firm size is
proxying6.
Profitability. We next include profitability (ROA), measured as return on assets, as a control variable.
There is no consensus on the direction of the relationship between a company's profitability and its
disclosure behaviour. Some theoretical studies (see for example, Dye 1985 and 1986, Verrecchia 1983
and 1990, Darrough and Stoughton 1990) suggest that profitable companies will disclose more, while
other studies argue that profitability has no impact on the disclosure level (Lang and Lundholm 1993).
Neither does empirical research provide conclusive evidence on the direction of the relationship
between firm profitability and voluntary disclosure level (Lang and Lundholm 1993). We do hence not
predict the sign on ROA.
Industry. Operations of financial institutions (such as banks, real estate businesses and holding
companies) are significantly different from those of manufacturing and commercial companies.
Disclosure by financial institutions may therefore differ from disclosure by non-financial companies.
We include a dummy (FIN) that indicates whether the company is operating in the financial industry
(FIN=1) or not (FIN=0) to control for this effect.
5. Empirical results
5.1 Sample and data
We examine disclosure by Belgian companies listed on the Brussels Stock Exchange (now Euronext
Brussels). Those companies are recommended, but not required, to disclose information on CG in their
annual reports. To avoid any selection bias caused by non-disclosure of CG information, we collected
6 The costs of information production are assumed to decrease in firm size (see for example Lang and Lundholm 1993), whereas political costs are argued to increase in firm size (Watts and Zimmerman 1986, Wallace and Naser 1995). There is
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this information by sending questionnaires to the chairman of the board of directors of all Belgian
listed companies for which we find company addresses on the Belfirst7, 145 companies in total. We
received 40 completed questionnaires from a first mailing in the fall of 2001 and 32 additional
questionnaires from a follow-up mailing in January 2002, i.e. a total response rate of 49.66%. We
collected the annual reports of the responding companies and read them back-to-cover to determine the
degree of disclosure on financial and non-financial performance measures (cf. infra). Consolidated
financial statement data were manually collected from the companies’ annual reports. Two companies
had no consolidated financial statements. We therefore use only 70 companies in our analyses. Table 2
reports a breakdown of our sample by industry. Our observations are split over 21 industries without
significant clustering in particular industries.
[Insert Table 2 about here]
5.2 Descriptive statistics
Table 3 reports the descriptive statistics on our dependent and independent variables. The results on
DISCL show that companies report on average only on 31.14% of the 25 disclosure items of our
disclosure index (see DISCL). The results on DISCL also indicate that the best firm only reports on
68% of the 25 disclosure items. More detailed analysis (Table 3, Panel C) shows that all companies
report some financial performance information, while only 78.58% of the companies report some non-
financial performance information. The type of non-financial information that is most disclosed is
customer-related information (95.54%), followed by information on innovation (80%), production
(89%) and learning and growth (76%). All companies that disclose non-financial performance
information disclose historic information, 70.90% disclose forward-looking non-financial information
no consensus on the relationship between audit firm size and proprietary costs.
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and 20% of them disclose some segment information. Historic non-financial performance information
is mostly qualitative in nature (98% of the companies). Only 87% of the companies that disclose non-
financial performance information release quantitative historic information. If forward-looking non-
financial performance information is released, then this information is mostly qualitative in nature
(94.87% of the companies). Only about 25 percent of the forward-looking information is quantitative in
nature. These results suggest that the level of reporting on non-financial performance measures (and
especially quantitative forward-looking information) is low and that there is still much room for
improving non-financial performance reporting. All companies that disclose financial performance
information disclose historic financial information, 62.85% disclose forward-looking information and
85.45% disclose some segment information. Historic financial information is mostly quantitative in
nature, while forward-looking information is mostly qualitative in nature.
The results on CGIND (Table 3, Panel A) show that at least 75 percent of the companies in our sample
have at least one of the four CG components, i.e. an independent board, an audit committee, a high
quality (i.e. Big5) auditor and an internal audit department. Results on PBIND, AC, BIG5 and
INTAUD in Table 3, Panels A and B indicate that, on average, less than half of the members of the
board of directors are independent from management and the majority shareholders, about 57 percent
of the sample companies have an audit committee, 71 percent are audited by a Big5 auditor and about
43 percent have an internal audit department. We further find (PBINDD) that only about 22.85 percent
of the companies have an independent board (i.e. a board which contains for over 50% of independent
directors).
7 Belfirst is a database of Bureau van Dijck which contains financial statement data of all Belgian companies that submit their financial statements with the Belgian Central Bank.
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Further, average leverage (LEV) of the sample companies is about 18 percent and average profitability
(ROA) is 5 percent. Companies are on average about 27.26 years listed on the Brussels Stock
Exchange. 19 companies operate in the financial industry (see also Table 1).
[Insert Table 3 about here]
5.3 Regression results
Table 4 reports the results of the OLS estimation of our two multiple regression models. The F-values
are significant and the adjusted R-squared of the models is 57,06 percent and 55,73 percent
respectively. The variance inflation factors (VIF's) in table 4 and the Pearson correlation coefficients in
Table 5 do not indicate any multicollinearity problems.
[Insert Table 4 about here]
[Insert Table 5 about here]
We report in Table 4, Panel A a significantly negative coefficient on CGIND (p<0.001) which supports our hypothesis that a stronger corporate governance system enhances the level of corporate reporting on non-financial performance measures. We further report in Table 4, Panel B that three of the five individual corporate governance characteristics have a positive impact on disclosure and an incremental impact over one another. The presence of a Big5 auditor has the highest impact (coefficient = 9.28, p<0.05) on disclosure followed by board independence (coefficient=7.99, p<0.05) and the existence of an internal audit department (coefficient=5.74, p<0.10). Panels A and B further show a significantly positive coefficient on LEV (p<0.001) and a significantly negative coefficient YQUOTED (p<0.05) which is consistent with more disclosure by firms with higher agency costs of debt and more information asymmetry problems. Finally, the significantly negative coefficient on FIN (p<0.001) confirms our conjecture that companies operating in the financial industry report significantly different levels of non-financial performance measures as compared to non-financial companies. Board size, company size and profitability have no significant impact on the extent of performance measure disclosures (all p>0.10). The insignificant result on profitability is consistent with the inconclusive theory on the relation between profitability and disclosure
6. Conclusion
This paper examined whether companies with stronger corporate governance systems disclose more
financial and non-financial performance information in their corporate annual reports. We proxied the
extent of disclosure by the extent to which the companies report qualitative and quantitative historic,
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forward-looking, as well as segment information with respect to the perspectives of the balanced
scorecard, i.e. the customers, internal business processes and learning and growth perspectives. We first
proxied the strength of a corporate governance system by the extent to which the following
characteristics of a "good" corporate governance system are present: an independent board of directors,
an audit committee, a high quality auditor and an internal audit department. We next examined the
individual impact of each one of the characteristics of "good" corporate governance. We found that
companies with stronger corporate governance systems disclose more financial and non-financial
performance information. We also found that the presence of a Big5 auditor has the highest impact on
disclosure, followed by the presence of an independent board and an internal audit department. Our
results suggests that there is still much room for improving the value and usefulness of corporate
reporting through more disclosure of financial and non-financial performance information and that
corporate governance may play a significant role in attaining this objective.
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24
Table 1: Summary of our empirical models
Construct Proxy Predicted sign
Dependent variable DISCL Disclosure index (cf. infra) Independent variables Test variables CGIND Corporate governance index = a
percentage that indicates how many of the following corporate governance components/ characteristics are present: more than half of the directors on the board are independent from management and the majority shareholders, existence of an audit committee, high quality (Big5) auditor, presence of an internal audit department
+
PBINDD Dummy, = 1 if more than half of the board members are independent from management and the majority shareholder, =0 otherwise
+
AC Dummy, =1 if the company has an audit committee, =0 otherwise
+
B5 Dummy, =1 if the company has a Big5 auditor, =0 otherwise
+
INTAUD Dummy, =1 if the company has an internal audit department, =0 otherwise
+
Control variables LNBS Board size = natural logarithm of
the number of directors on the board
+/-
LEV Leverage = Long term debt/ total assets
+
YQUOTED
Years quoted on the Brussels Stock Exchange
-
LNTA Firm size = natural logarithm of total assets
+/-
ROA Profitability = Return on assets= profit before taxes and financial charges/ total assets
+/0/-
FIN Dummy, =1 if the company is a holding company, a real estate business or bank, =0 otherwise
-
25
Table2: Breakdown of our sample companies by two-digit FTSE* industry code Two-digit FTSE industry code FTSE description Number of companies 11 Chemicals 4 13 Construction and building
material 8
15 Forestry and paper 1 24 Diversified industrials 1 25 Electronic and electrical
equipment 2
26 Engineering and machinery 5 34 Household goods and textiles 2 41 Beverages 5 43 Food producers and processors 5 44 Health 2 48 Pharmaceuticals 4 53 Leisure, entertainment and
hotels 2
54 Media and photography 2 58 Support services 1 63 Food and drug retailers 2 72 Electricity 1 73 Gas distribution 1 81 Banks 2 85 Investment companies 10 86 Real estate 7 97 Software and computer services 3 Total 70 * The FTSETM Global Classification System is used as the default industry classification system on Euronext.
26 Table 3: Descriptive statistics*
Panel A: Continuous variables
Variable N Mean St. dev. Min. 1st quartile
Median 2nd quartile
Max.
DISCL 70 35.14% 17.87 4% 20% 36% 48% 68% CGIND 70 55% 29.36 0% 25% 50% 75% 100% BS 70 9 4.85 2 6 8 11 27 LNBS 70 2.06 0.53 0.69 1.79 2.07 2.39 3.29 LEV 70 0.18 0.15 0 0.08 0.15 0.27 0.68 YQUOTED 70 27.26 30.77 1 3 12 45 99 TA (in thousand Euros) 70 12246915 64960707.9 993 127697 329972 1110951 482970000 LNTA 70 12.98 2.13 6.90 11.75 12.70 13.92 19.99 ROA 70 0.05 0.08 -0.1398 0.02 0.05 0.09 0.45 PBIND 70 0.42 0.25 0 0.29 0.43 0.50 1
Panel B: Discrete variables
Variable N Proportion of dummy=1 PBINDD 70 22.85% INTAUD 70 42.86% BIG5 70 71.42% AC 70 57.14% FIN 70 27.14%
Panel C: Disclosure index Financial performance measures
70 62.85%a
Historic 70 100%b
Quantitative 70 100%
Qualitative 65 92.86%
Forward-looking
44 62.85%b
Quantitative 19 43.18%d
Qualitative 41 93.18%d
Segment 47 85.45%b
Non-financial performance measures
55 78.58%a
Historic 55 100%c
Quantitative 48 87.27%e
Customer related perspective
52 95.54%c
Qualitative 54 98.18%e
Internal business processes: Innovation
44 80%c
Forward-looking
39 70.90%c
Quantitative 10 25.64%f
Internal business processes: Production
49 89%c
Qualitative 37 94.87%f
Learning and growth perspective
42 76%c
Segment 14 20%c
* For variable definitions see Table 1. PBIND= proportion of independent board members, measured as the number of independent directors on the board divided by the total number of directors on the board; TA= Total assets; BS= board size measured by the total number of directors on the board. a As a percentage of the total number of sample companies. b As a percentage of the total number of companies that report financial performance measures. c As a percentage of the total number of companies that report non-financial performance measures. d As a percentage of the companies that report financial forward-looking information. e As a percentage of companies that report historic non-financial performance measures. f As a percentage of companies that report forward-looking non-financial performance measures.
27
Table 4: OLS results of the multiple regression model of disclosure (DISCL) on corporate governance and control variables*
Panel A: Corporate governance index (CGIND)
Parameter Standard Variance
Variable Estimate Error t Value Pr > |t| Inflation
Intercept 25.13274 9.01687 2.79 0.0070 0 CGIND 0.23841 0.05231 4.56 <.0001 1.18677 LNBS -2.18597 3.12624 -0.70 0.4870 1.39061 LEV 45.12617 9.84172 4.59 <.0001 1.11849 YQUOTED -0.12817 0.04866 -2.63 0.0106 1.12768 LNTA 0.01918 0.78112 0.02 0.9805 1.39714 ROA 21.62521 18.56538 1.16 0.2486 1.16077 FIN -18.12769 3.46979 -5.22 <.0001 1.21488
F-value = 10.36 Pr > F < 0.0001 R-Square 0.6142 Adj R-Sq 0.5706
Panel B: Individual corporate governance characteristics (i.e. board independence, audit committee existence, audit quality, internal audit department existence and board size) Parameter Standard Variance
Variable Estimate Error t Value Pr > |t| Inflation
Intercept 26.46905 9.21093 2.87 0.0056 0 PBINDD 7.99041 3.82550 2.09 0.0411 1.27717 AC 0.77153 3.18485 0.24 0.8094 1.22947 B5 9.28821 3.59849 2.58 0.0124 1.30797 INTAUD 5.74364 3.13358 1.83 0.0719 1.19020 LNBS -0.61625 3.34401 -0.18 0.8544 1.54330 LEV 40.57798 9.89973 4.10 0.0001 1.09772 YQUOTED -0.11915 0.05027 -2.37 0.0211 1.16738 LNTA -0.09483 0.80221 -0.12 0.9063 1.42934 ROA 13.80966 19.26556 0.72 0.4763 1.21243 FIN -19.29994 3.84961 -5.01 <.0001 1.45049
F-value = 9.69 Pr > F < 0.0001 R-Square 0.6215 Adj R-Sq 0.5573
* For variable definitions see Table 1.
28
Table 5: Pearson correlation coefficients between independent variables*
Pearson Correlation Coefficients, N = 70 Prob > |r| under H0: Rho=0
CGIND PBINDD AC B5 INTAUD LNBS LEV YQUOTED ROA LNTA FIN CGIND 1.00000 CGIND PBINDD N/A 1.00000 PBINDD N/A AC N/A 0.26517 1.00000 AC N/A 0.0265 B5 N/A 0.11834 0.28299 1.00000 B5 N/A 0.3292 0.0176 INTAUD N/A 0.00982 0.16667 0.22822 1.00000 INTAUD N/A 0.9357 0.1679 0.0574 LNBS 0.25185 -0.13468 0.14552 0.26772 0.18872 1.00000 LNBS 0.0354 0.2663 0.2293 0.0251 0.1177 LEV -0.12488 -0.03694 -0.00576 -0.07937 0.04926 0.18129 1.00000 LEV 0.3030 0.7614 0.9623 0.5137 0.6855 0.1331 YQUOTED -0.12093 -0.06360 -0.01255 -0.19237 0.02767 0.15598 0.14436 1.00000 YQUOTED 0.3187 0.6009 0.9179 0.1106 0.8201 0.1973 0.2331 ROA 0.02334 0.08924 0.08159 0.00166 0.07469 0.18864 0.10573 0.24245 1.00000 ROA 0.8479 0.4625 0.5019 0.9891 0.5389 0.1178 0.3837 0.0431 LNTA 0.15431 0.07236 0.14988 0.12183 0.13301 0.44777 0.15657 0.15822 0.07535 1.00000 LNTA 0.2022 0.5517 0.2156 0.3150 0.2723 0.0001 0.1955 0.1908 0.5353 FIN -0.13222 0.20328 -0.12056 -0.11175 -0.26893 0.06301 -0.09647 0.01484 -0.22009 0.25898 1.00000 FIN 0.2752 0.0914 0.3202 0.3571 0.0244 0.6043 0.4270 0.9029 0.0671 0.0304
* For variable definitions see Table 1.
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