The Determinants of Corporate Governance and the Link
between Corporate Governance and Performance: Evidence
from the U.K. Using a Corporate Governance Scorecard
Thesis Proposal by
Luo Lei
School of Business
National University of Singapore
1. Introduction
1.1 Introduction
Corporate governance practices in the U.K. have received increasing attention since
the 1990s, with influential reports issued by the Cadbury Committee (1992), Greenbury
Committee (1995), Hampel Committee (1998), and Turnbull Committee (2003) and Sir
Derek Higgs (2003). These reports resulted in various corporate governance codes and
recommendations, the most recent being the Combined Code on Corporate Governance,
July 2003 (hereafter U.K. Code).
In this study, we use a scorecard developed by Standard & Poor’s to assess the
corporate governance of U.K. listed companies. It provides a comprehensive measure of
the extent to which a company has adopted international best practices in corporate
governance, as disclosed in their corporate governance disclosures.
The evidence on whether there is a link between governance structure and
performance remains weak. We argue that one possible reason could be due to the
research methodology. Earlier research has examined subsets of governance mechanisms,
usually one or two governance variables only. As the firms can choose and modify the
structure of their governance system to suit their circumstances, we argue that we should
examine a number of governance variables and over a longer time period.
1.2 Motivation of Study
Some of recent studies have used a broader measure of corporate governance through
a composite corporate governance rating, including Gompers et al. (2003) for the U.S.,
Klapper and Love (2004) for fourteen emerging markets, Durnev and Kim (2002) for
twenty seven countries, Bauer et al. (2003) for the EMU and the U.K.. These studies
generally find a positive relationship between governance standards and firm value.
Baure et al. (2003) and other studies are based on ratings of one or two years only,
assuming that governance ratings should remain constant for a number of years. However
our data shows otherwise — there is a significant upward trend for the corporate
governance scores over the time. Without time series data, researchers cannot study how
firms adjust their governance structure over time, or analyze the causality between
1
governance and firm performance found in Black et al. (2002). A recent study by Leora,
Klapper and Love (2004) find that differences in firm-level contracting environment
would affect a firm’s choice of governance mechanisms, in line with arguments put forth
in Himmelberg et al. (1999). However with only one year data, they are not able to
control the fixed effects and to test the causality.
Our study can make a potential contribution in this area by analyzing a number of
corporate governance mechanisms based on time-varying firm-specific data. Using the
methodology in Agrawal and Knoeber (1996), we examine the four mechanisms used in
controlling agency problems — insider shareholdings, blockholdings, institutional
shareholdings and leverage status of the firm. In addition, we also include a
comprehensive measure of governance using a corporate governance scorecard and
measuring governance over a longer time period.
Our findings reveal an interesting relationship between governance and performance.
It is the change of governance that determines performance rather than the governance
level. We find that an investment strategy that buys firms with greatest improvement in
governance and sells firms with largest deterioration in governance yields 36.7 percent
excess returns over the sample period. Contrary to the findings in Bauer et al. (2003), we
find that investors will lose money if they buy firms ranking highest and sell firms
ranking lowest.
1.3 Objective of Study
This is an empirical study on whether better corporate governance leads to higher
valuation through lower expected rate of return. We investigate the interdependence of
various governance practices, the change of governance structure and the impact on the
firm value.
1.4 Potential Contributions of Study
This study includes a more complete set of governance mechanisms including the
composite governance scorecard as well as ownership and firm leverage. We control the
firm heterogeneity with a fixed effects estimator and firm endogeneity with the
simultaneous equation system. The margin effect of governance improvement is also a
2
new finding in research on corporate governance.
1.5 Organization of Study
The remainder of this study is organized as follows: Chapter Two reviews the
literature concerning to firm level corporate governance. Chapter Three describes the
sample and data. In Chapter Four, Five, Six and Seven, respectively, we present empirical
evidence on the determinants of corporate governance, and on the relationship between
corporate governance and firm performance, and on its relationship with stock returns.
2. Literature Review
2.1 The Interaction of Different Governance Mechanisms
Corporate governance comprises many dimensions. Based on the U.K. Code, it can
be divided broadly into the role of directors, directors’ remuneration, the role of
shareholders, and accountability and audit.
Some of the structures are complements while others are substitutes to certain extent.
The previous research has found different governance patterns. For example, Peasnell et
al. (2001) find evidence of a convex association between the proportion of outside board
members and the level of insider ownership in the U.K. corporate control process.
Shivdasani and Yermack (1999) observe, using U.S. data, that when the CEO serves on
the nominating committee or no nominating committee exists, firms usually appoint
fewer independent outside directors and more grey outsiders. Similarly, Vafeas (1999)
discover that the likelihood of engaging a nominating committee is related to board
characteristics such as inside ownership, number and quality of outsider directors for U.S.
firms.
Board structure is an important governance mechanism. Kenneth et al. (1995) note
the substitution effects between outside directors, blockholders, and incentives to insiders
using eighty one U.S. bank-holding companies in his study. Both Dedman and Elisabeth
(2002) and Young (2000) investigate the board structure determinants before and after
Cadbury Report. They either find managerial entrenchment is reduced or non executive
directors are increased following the imposition of new standards of “best practice”
3
regarding board structure.
2.2 The Relationship between Governance Mechanism and Firm Performance
Our study builds on Himmelberg et al. (1999) who use panel data to show that
managerial ownership is explained by key variables in the contracting environment. A
large fraction of the cross-sectional variation in managerial ownership is explained by
unobserved firm heterogeneity. Moreover, after controlling for both observed firm
characteristics and firm fixed effects, changes in managerial ownership do not affect firm
performance econometrically.
In literature, many other researchers have examined the relationship between variety
of governance mechanisms and firm performance. The results, however, are mixed. Some
examine only the impact of one governance mechanism on performance as Himmelberg
et al. did, while others investigate the influence of several mechanisms together on
performance. None of them covers a complete set of governance mechanisms. Below, we
will briefly review some of previous studies on the governance-performance relationship.
(1) Board Composition
It is suggested that higher proportion of non-executive directors in the board helps to
reduce the agency cost. Kee et al. (2003) and Hutchinson and Gul (2003) support this
view by showing that that higher levels of non-executive directors on the board weaken
the negative relationship between the firm’s investment opportunities and firm’s
performance. However, de Jong et al. (2002), Coles et al. (2001), and Weir et al. (2002)
dispute it by stating that there is no significant relationship between non-executive
directors’ representation and performance. In contrast, in the U.K., Weir and Laing (2000)
find a negative relationship between non-executive director representation and
performance. In addition, Yermack (1996) present that small board has a higher market
valuation.
Stronger support for the positive impact of non-executive directors comes from event
study analysis. The studies by Rosenstei and Wyatt (1990 and 1997) and Shivdasani and
Yermack (1999) show that the appointment of non-executive directors increases company
value.
(2) Leadership Structure
4
Although U.K. Code regards separation of the role of CEO and chairman as a sign of
good governance, previous empirical analyses do not support it. For example, Coles et al.
(2001), Weir et al. (2002), and Weir and Laing (2000) do not find any significant
relationship between CEO duality and performance. Brickley et al. (1997) observe that
costs of separation are larger than benefits for most large U.S. firms.
(3) Board Ownership
The findings of the primarily U.S. based literature suggest that management is
aligned at low or possibly high levels of ownership but is entrenched at intermediate
ownership levels (e.g., Morck et al., 1988; McConnell and Servaes, 1990). U.K. evidence
confirms that U.K. management becomes entrenched at higher levels of ownership than
their U.S. counterparts (e.g. Faccio et al., 1999; Short and Keasey, 1999). Hutchinson and
Gul (2003) report that management share ownership and managers’ remuneration weaken
the negative relationship between the firm’s investment opportunities and firm’s
performance. In contrast, Coles et al. (2001) do not find any contribution to performance
by managerial ownership.
(4) Institutional Holdings
As the U.K. Code encourages institutions to take an active role in governance, we
may expect a positive relationship between institutional holdings and firm performance.
Unfortunately, empirical evidence is not supportive of this recommendation. Both Faccio
and Lasfer (1999, 2000) fail to find such a significant relationship for U.K. firms. Besides,
de Jong et al. (2002) find that major outside and industrial shareholders negatively
influence the firm value.
(5) Committee Composition
For U.K. companies, Conyon (1997) provides a thorough review of the workings of
remuneration committees and shows that firms with remuneration committees pay
directors less remuneration. Conyon & Mallin (1997) observe that U.K. firms have been
slow in adopting nominating committees, a symptom of failure of the corporate
governance system. By contrast, audit committee use in the U.K. has been widespread
(e.g. Conyon, 1994; Collier, 1993). The results in Forker’s (1992) study suggest that the
quality of disclosure is only weakly related with audit committees and non-executive
directors.
5
(6) Managers’ Remuneration
The empirical work shows that the role of managers’ remuneration in coordinating
managers’ and investors’ interests is limited. Hutchinson and Gul (2003) find a positive
role for managers’ remuneration, while Coles et al. (2001) do not.
2.3 Endogeneity of Corporate Governance Mechanisms in Firm Valuation
Bhagat and Black (2002) find evidence that firms suffering from low profitability
respond by increasing the independence of their board of directors, but no evidence that
firms with more independent boards achieve improved profitability. Vafeas (1999)
observes that the annual number of board meetings increases following share price
declines. He further finds that operating performance improves following years of
abnormal board activity.
Some other studies are in the ownership area. None of them provides support to the
governance-performance relationship. Oyvind and Bernt (2001) discover that qualitative
conclusions are sensitive to choice of instruments. Demsetz and Villalonga (2001) fail to
find significant relationship between ownership and performance. What is more, Cho
(1998) concludes that investment affects corporate value and in turn corporate value
affects ownership but not vice versa.
Agrawal and Knoeber (1996) examine the use of seven mechanisms to control
agency problems between managers and shareholders. These mechanisms are:
shareholdings of insiders, institutions, and large blockholders; use of outside directors;
debt policy; the managerial labor market; and the market for corporate control. The
findings are consistent with optimal use of each control mechanism except outside
directors. Closely following their approach, we construct a simultaneous equation system
to investigate the influence of corporate governance scorecard on firm performance.
Barnhart and Rosenstein (1998) investigate the combined effect of ownership structure
and board composition on corporate performance. The results indicate that managerial
ownership, board composition, and Tobin’s Q are jointly determined. Vafeas and
Theodorou (1998) examine a broad group of board structure variables for U.K. firms.
Contrary to expectations, the results reveal an insignificant relationship between board
6
structure (percentage of non-executive directors, leadership structure, board ownership
and committee composition) and firm performance.
2.4 Corporate Governance Scorecard in Examining Stock return, Firm value and
Performance
Other than focusing on one or two separate variables for corporate control, recently
there has been increasing number of studies that employ corporate governance scorecard
as a comprehensive measure to examine the agency problem. It has the advantage to
implicitly incorporate either the substitutive or complementary effect of variety of
governance practices into one study. The empirical literature on the relationship between
firm value and corporate governance scorecard usually analyzes either inter-country
difference or inter-firm variation within a country. The most prominent example of
studies on inter-country difference is LaPorta et al. (2002), who investigate differences in
governance standards among twenty seven countries. Their evidence shows that firms
incorporated in countries with better governance standards tend to have higher valuations.
Examples of studies investigating inter-firm variation within one country are Drobetz et
al. (2003) for Germany, Gompers et al. (2003) and Marry and Stangeland (2003) for the
U.S., Klapper and Love (2004) for fourteen emerging markets, Durnev and Kim (2002)
for twenty seven countries, Bauer et al. (2003) for the EMU and the U.K., Black et al.
(2002) for Korea, Black (2001) for Russia, and Callahan et al. (2003) for Fortune 1000
firms. The results appear to confirm a positive relationship between governance standards
and firm value. More importantly, the relationship seems to be stronger in countries with
less developed standards.
To the best of our knowledge, Klapper and Love (2004) and Durnev and Kim (2002)
are the only two research that investigate the determinants of corporate governance
scorecard. Overall Klapper and Love (2004) find that firm-level governance is correlated
with firm size, sales growth and assets composition. Moreover, they report that good
governance is positively correlated with market valuation and operating performance.
Simliarly, Durnev and Kim (2002) report higher disclosure level and in turn higher
market valuation for firms with greater growth opportunities, greater needs for external
financing, and more concentrated cash flow rights. Our study differs from theirs in the
7
following ways: Firstly, we use time-varying governance scorecard to control unobserved
firm heterogeneity with fixed effects. Secondly, we broaden governance measurements
with governance scorecard as well as shareholding variables. Finally, we explicitly put
governance mechanisms into a simultaneous equation system to address the endogeneity
problem.
Among the inter-firm variation studies, Gompers et al. (2003), Marry and Stangeland
(2003), Klapper and Love (2004), and Bauer et al. (2003) examine the impact of the
governance standards on firm performance approximated by profitability ratios as well.
All of them document a positive relationship between governance scorecard and
performance except for Bauer et al. (2003) who surprisingly detect a significant negative
relationship.
When set up a zero investment portfolio, investors can earn abnormal returns by
buying firms from higher level corporate governance group and short-selling those from
lower level corporate governance group (Gompers et al., 2003; Drobetz et al., 2003;
Bauer et al., 2003).
Drobetz et al. (2003) and Chen et al. (2003) investigate the influence of governance
scorecard on cost of equity capital for Germany and nine Asia markets respectively. Their
findings show that good corporate governance helps to reduce such cost.
Creamers et al. (2003) find that external and internal governance mechanisms are
strong complements in association with long term abnormal returns and accounting
measures of profitability. Besides, Q’s of firms with both high takeover vulnerability and
high public pension fund ownership are high, but lower than the Q’s of firms where only
one of the two governance mechanisms is high.
3. The Data
The starting point of our sample is the set of firms listed in Index Constituent
Rankings FTSE 100 and Index Constituent Rankings FTSE 250 from FTSE European
Monthly Review, January 2001 issue. It will be denoted FTSE 350 hereafter. We exclude
financial firms as they have different financial reporting formats and many of the key
variables needed in our study are not available in COMPUSTAT database. For each of
the remaining industrial and commercial firms, with at least three years of annual reports
8
containing the relevant corporate governance information over the time-period 1999 till
2003, we calculate its corporate governance score using the corporate governance
disclosure scorecard provided by the S&P. In this study, scorecard will be used
interchangeably with score, rating, ranking and/or index.
S&P’s corporate governance disclosure scorecard is a methodology based on a
synthesis of governance codes and guidelines of global best practices, as well as its own
experience in reviewing individual companies. With the information extracted from
company annual report, we answered 119 questions on governance practices for each
company each year. It enabled us to construct a time-varying corporate governance
scorecard for our study.
Ninety three of the questions are binary questions and one point is given for each
best practice complied with and zero otherwise or if the company did not disclose
whether it had or not complied with such best practice. The remaining questions were
answered with specific integers such as “number of members in the remuneration
committee”. Since for a few questions, the answer corresponded to more than one score
item, there are altogether 136 best practice items for the scorecard. Four out of the 136
items can score up to a maximum two points instead of one. Therefore, the maximum
possible score for one company with the 136 score items is 140. The calculation of the
score is from the MS Excel build-in formula. After filling in the answers to these 119
questions, the formula automatically computes the total score for the company of
interests. Generally such measure assigns an equal weightage to each disclosed item with
the exception of the four items which can attain a maximum score of two. These four
items carry slightly higher weightage.
Scores on the 119 questions are grouped into five categories of corporate governance:
Board Matters, Nomination Matters, Remuneration Matters, Audit Matters and
Communication. We compute the composite governance scorecard by summing up the
scores for each group.
The financial data employed in our analysis are obtained from COMPUTSTAT
Global Industrial/Commercial file from 1999 to 2003 (as for previous growth, profit
measures, we use the corresponding earlier period data). We obtain the following data
from DataStream: total stock return index for individual firm, FTSE all share total return
9
index, U.K. Treasury Bill interest rates and market capitalization.
The 2-digit SIC codes are from COMPUSTAT (global) database. Average number of
institutional shareholders for 2-digit SIC industry of a firm is collected from Shareworld.
The dummy variable indicating if a firm trades American Depositary Receipts (ADRs) on
a major exchange (NYSE, AMEX, or NASDAQ) in the U.S. is identified using the JP
Morgan website: http://www.adr.com. The ownership data are collected manually from
annual reports.
Our final sample includes 206 firms with 3 to 5 years of data. The scores cover
between 116 and 206 firms over the time-period 1999 till 2003. When the scores are
supplemented with ownership and accounting data to perform analysis, the firm-year
observations vary in the range of 837 to 962 with respect to different model
specifications.
Table 1: Summary statistics of composite governance scorecard
Mean
Median
St. Dev.
Min
Max
No. of firms
1999
57.72
57.25
8.96
35.00
85.00
116
2000
58.40
58.00
8.62
33.00
85.00
164
2001 2002
61.32 63.94
61.00 64.00
8.18
8.63
38.00 41.00
86.00 105.00
199
205
2003 1999-20002000-20012001-2002 2002-2003
72.13
1.74
2.84
2.46
8.31
70.60
1.00
2.53
1.00
5.50
11.76
3.53
4.12
4.66
8.66
45.50
-6.00
-10.00
-11.00
-4.00
116.00 13.00
21.00
26.00
41.55
206
113
166
202
205
Table 1 provides summary statistics by year for corporate governance scorecard.
This table reports the mean, median, standard deviation, minimum, and maximum of
corporate governance scores for the sample year 1999-2003. In addition, we present the
statistics for the changes of scores each year. The last row of Table 1 shows that the
number of firms increases from 116 in 1999 to 206 in 2003. Also, the score is increasing
from 1999 to 2003, changing from a mean (median) of 57.72 (57.25) in 1999 to a mean
(median) of 72.13 (70.60) in 2003. Overall, there exists an upward trend for the
governance scorecard, as can be detected from the average score change that is positive
for all the sample years. However some firms’ corporate governance disclosure
deteriorates during the sample period. For example, the firm with the most score decrease
loses 11 points in 2002. From both the range between minimum and maximum scores
10
and the standard deviation of scores, we can see a certain degree of dispersion for our
sample across firms and years. The sample is not skewed as the mean and median is very
close.
Table 2 shows the Pearson correlations among the variables employed in our study.
All variables are measured over the sample period from 1999 to 2003. The second and
third columns of the table show the correlations between each of these variables with our
absolute governance scorecard and change of the scorecard respectively. The scorecard is
negatively correlated with both Tobin’s Q and return on assets. However, a simple
correlation between corporate governance and performance may be masking a more
complex functional form for this relationship, a possibility that we later examine in
multivariate tests. The negative relationships between score and shareholding variable
suggest a substitution effect among difference governance mechanisms. This correlation
analysis shows that without controlling for other variables, the larger firms with higher
debt ratio, capable outsiders serving the board, and issuing ADR in major U.S. stock
exchanges disclose more.
While the level of governance is significantly correlated with many other variables, it
is not the case for the change of governance variable. Only firm size, outsiders’ quality
and the availability of internal funds denoted by cash flow return are positively correlated
with the change of governance score.
Q is positively correlated with ROA, R&D intensity and firm risk. Q is also
negatively correlated with size and proportion of fixed assets, consistent with growth
firms generally being smaller firms and intangibles being evaluated higher. The impact of
corporate governance variables as measured by score and ownership on Q is mixed.
Further investigation will be carried out in multiple regressions later.
ROA is positively correlated with market power and negatively correlated with debt
ratio and R&D intensity. The correlation between ROA and market power and the
correlation between ROA and R&D intensity are expected, as the activity of investing
and expensing of R&D will reduce current profitability.
The multicollinearity is not a serious problem here. Most of the correlations are less
than 0.5. The only correlation of concern is between institutional shareholdings and
blockholdings.
We may need
to
cautiously explain
it
when examining them
11
Table 2: Pearson correlations among study variables
SCORE
SCORE_C
Q
SCORE_C
0.589**
Q
-0.109**
-0.047
ROA
-0.097**
-0.035
0.108**
ROA
INSIDE
BLOCK
INST
FSIZE
K/S
INSIDE
-0.2**
0
0.069*
0.017
BLOCK
-0.125**
0.02
-0.035
-0.067*
0.287**
INST
-0.009
0.002
-0.121**
-0.014
-0.051
0.555**
FSIZE
0.246**
0.106**
-0.206**
0.011
-0.307**
-0.183**
-0.178**
K/S
0.079*
-0.007
-0.093**
-0.094**
-0.117**
-0.074*
-0.152**
-0.058
Y/S
-0.057
0.024
0.068*
0.448**
0.007
-0.146**
-0.076*
-0.035
0.152**
R&D/K
Y/S
R&D/K
DTV
REG
ADR
OUTDIRE TENURE
NOD
-0.014
0.019
0.233**
-0.137**
0.007
-0.087*
-0.094**
-0.181**
-0.085**
-0.186**
DTV
0.178**
0.009
-0.185**
-0.121**
-0.153**
-0.068*
-0.125**
0.139**
0.475**
0.054
-0.196**
REG
0.056
0.028
-0.019
-0.118**
-0.127**
-0.059
-0.12**
0.005
0.519**
0.021
-0.08*
ADR
0.082*
0.043
0.095**
-0.059
-0.166**
-0.153**
-0.21**
0.33**
0.076*
-0.006
0.134**
0.018
0.151**
OUTDIRE
0.2**
0.109**
-0.036
-0.027
-0.22**
-0.126**
-0.169**
0.545**
0.125**
0.046
-0.052
0.093**
0.156**
0.448**
TENURE
-0.2**
-0.016
0.015
0.1**
0.37**
0.053
0.054
-0.219**
-0.15**
0.051
-0.032
-0.125**
-0.184**
-0.25**
NOD
0.007
-0.002
-0.065
0.022
-0.176**
-0.055
-0.154**
0.522**
0.054
-0.028
-0.046
0.055
0.089*
0.232**
0.419**
-0.106**
RISK
-0.034
-0.068
0.142**
-0.303**
0.035
0.054
0.093**
-0.092**
-0.045
-0.142**
0.176**
-0.088**
-0.007
-0.002
-0.092**
-0.08*
-0.148**
RDAI
-0.08*
0.014
0.189**
-0.005
0.076*
0.011
0.02
-0.176**
-0.188**
-0.116**
0.263**
-0.292**
-0.168**
0.042
-0.086*
0.018
-0.073*
NINSTI
CR
RISK
RDAI
NINSTI
0.26**
-0.134**
0.084**
-0.112**
0.054
0.06
-0.006
0.068*
-0.011
-0.155**
0.17**
0.013
0.166**
0.129**
-0.075*
0.096**
0.186**
0.168**
0.037
0.208**
0.012
0.115**
0.046
0.105**
-0.125**
-0.007
0.076*
-0.006
-0.003
0.197**
-0.042
-0.036
-0.088**
0.068*
-0.057
0.039
0.084*
0.031
0.058
-0.012
-0.083*
0.028
Definitions of each variable are given in the Appendix. * Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).
12
simultaneously.
4. Determinants of Corporate Governance: Empirical Evidence
Following Himmelberg et al. (1999), we explore the determinants of firm-level
governance controlling for firm-specific contracting environment. Himmelberg et al.
argue that contracting environment influences the optimal level of managerial
shareholdings. This argument can be easily transferred to our corporate governance
scorecard as a comprehensive measure of control.
Similar to their setup, we make a comparison among the pooled data without
fixed effects, panel with industry fixed effects and panel with firm fixed effects
empirically. Briefly, estimates from pooled data depend only on the variation in
average corporate governance scorecard and performance levels across firms and will
not utilize the variation over time in these variables for individual firm. It is achieved
by pooling all data and implicitly forcing equal intercepts. In contrast, estimates from
panel with firm fixed effects will depend only on the variation over time in corporate
governance scorecard and performance for each individual firm, and will not utilize
the variation in variables across firms. Similarly, panel with industry fixed effects
assumes unobserved heterogeneity is constant for the same industry instead of same
firm. Such measure is noisier than the measure with firm fixed effects. Including
models of panel with industry fixed effects is for the purpose of completion, but our
main focus is on pooled data and panel with firm fixed effects.
Besides for the purpose of controlling for unobserved firm heterogeneity, we use
fixed effects to detect the different impact of within- and between- variation on
governance choices and firm performance. Specifications using pooled data serve as a
base line to examine cross-section differences; whereas, we use fixed effects to
investigate the changes of governance in specific firms over time. It is interesting to
discover whether it is the absolute ranking of the corporate governance or the
improvement of corporate governance over time that has more influence on
performance.
Our research is distinct from Himmelberg et al., in that we substitute corporate
governance scorecard as indicated by SCORE for managerial ownership as dependent
variable. We use the same set of x variables chosen by Himmelberg et al. to proxy
firm attributes except that we exclude capital expenditure to Property, Plant and
13
Equipment and the ratio of advertising expenditures to the stock of Property, Plant
and Equipment, for COMPUSTAT does not report data of capital expenditure and
advertising expenditure for U.K. companies. We also exclude the managerial risk
aversion measure since it is not relevant to corporate governance scorecard here.
As previously described, the corporate governance scorecard synthesizes many
governance devices, including board structures, managerial compensation, nomination
matters, audit matters and communication with shareholders. Although it is a rather
comprehensive measure, it does not comprise corporate ownership as one of
important mechanisms. To complement this governance scorecard, we add ownership
to one set of our models to explicitly capture the interdependence among various
governance mechanisms as well as their impact on performance.
This study proceeds in two parts. The first part deals with determinants of
corporate governance. In the second part, we explore the determinants of firm value.
In the first part, we regress SCORE on a vector of x variables with and without
ownership variables. In the second part, we test for a correlation between SCORE and
performance measures indicated either by Tobin’s Q or by Return On Assets (ROA).
We add the variables found in part one to be associated with higher governance
rankings as controls to filter out their effects on firm performance. Following what we
have done in part one, we run regressions including and excluding ownership
variables in part two. Each set of specifications comprises all the three setups, i.e.,
pooled data, panel with industry fixed effects and panel with firm fixed effects, which
will be noted as pooled, SIC effects and firm effects for short hereafter.
To summarize, in part one, the model is specified as:
SCORE it   0  1 INSIDE it   2 BLOCK it   3 INSTit   it
(1)
SCORE it   0   4 FSIZEit   5 FSIZEit2   6 ( K / S ) it   7 ( K / S ) it2   8 (Y / S ) it
 9 RDUM it  10 ( R & D / K ) it   it
(2)
SCORE it   0  1 INSIDE it   2 BLOCK it   3 INSTit   4 FSIZEit   5 FSIZEit2
  6 ( K / S ) it   7 ( K / S ) it2   8 (Y / S ) it   9 RDUM it  10 ( R & D / K ) it
  it
(3)
where i and t represent the firm and time respectively
 it is regarded as white-noise error term under the pooled setup
 it  ui   it ; where  it is a white-noise error term and;
14
under the SIC effects setup, u i is the one digit SIC-specific effect; whereas, under
the firm effects setup, u i is the firm-specific effect
Table 3: Determinants of corporate governance: OLS regressions of SCORE on
ownership and/or firm characteristics variables
Variable
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
（1）
（2）
（3）
（4）
（5）
（6）
（7）
（8）
（9）
55.96147***
59.40037***
33.87671***
64.25206***
40.96122***
41.11096***
71.44729***
(75.9784)
(70.75563)
(177.0958)
(6.95664)
(8.974009)
(18.57954)
(8.042877)
(9.436761)
INSIDE
-16.06609***
-15.79054***
-4.272417
-10.5107***
-6.611079***
-3.382244
(-18.4896)
(-7.08289)
(-1.586956)
(-7.462303)
(-2.763165)
(-1.511576)
BLOCK
-6.938328***
-4.773461**
-6.445102***
-7.158255***
-5.697996***
-6.925284***
(-9.032477)
(-2.273427)
(-2.846321)
(-7.328492)
(-2.66959)
(-2.863008)
Constant
INST
58.05115*** 28.88907***
(27.9939)
0.939323**
-1.985681
2.147458
2.503498***
2.110175
4.022158**
(2.165657)
(-0.799738)
(0.807763)
(2.594623)
(0.80902)
(2.17402)
-3.739628*
2.223979***
3.150366**
-5.301039**
FSIZE
4.550914***
4.589387***
(8.746813)
(3.401587)
(-1.682148)
(3.949855)
(2.293114)
(-2.356665)
-0.186168***
-0.196495**
0.349015**
-0.05481
-0.114163
0.437058***
(-3.866729)
(-2.103435)
(2.152406)
(-1.183328)
(-1.200681)
(2.78033)
1.340481***
1.103453***
1.109216
1.139145***
1.032287***
1.275402
(9.879849)
(3.22452)
(1.159435)
(6.138696)
(2.860012)
(1.509986)
(K/S)
-0.058498***
-0.05009**
-0.096103
-0.053986***
-0.049954**
-0.126848
(-4.945446)
(-2.285439)
(-0.966653)
(-3.722203)
(-2.235631)
(-1.387066)
Y/S
3.971113***
-1.379066
2.049784***
1.911605***
-2.111866
1.708782***
(4.292142)
(-0.587055)
(3.502352)
(3.849142)
(-0.859553)
(2.614216)
1.364066***
1.322713*
1.011787**
1.152648***
0.922667
0.716456
(3.403433)
(1.949953)
(2.038836)
(3.038125)
(1.340745)
(1.460918)
2
FSIZE
K/S
2
RDUM
R&D/K
no. of obs
Adj. R
1.73979*
1.061954
-2.66807*
0.471562
0.583304
-2.077036
(1.89676)
(0.974518)
(-1.81549)
(0.472598)
(0.527791)
(-1.440353)
853
853
853
845
845
845
845
845
845
0.9784
0.2868
0.9947
0.9774
0.3187
0.9942
0.9798
0.3306
0.9947
2
Numbers in parentheses are t statistics. Definitions of each variable are given in the Appendix. Year
dummies are included for all regressions, but not reported. Fixed effects at the industry or firm level are
included where indicated, but not reported. * Means significant at 0.10 level (two-tailed). ** Means
significant at 0.05 level (two-tailed). *** Means significant at 0.01 level (two-tailed).
Our part one empirical analysis of determinants of corporate governance is
summarized in Table 3. We begin our exploration of corporate governance
determinants with ownership variables only. The results are presented in the first three
columns. The first column reports results from a baseline specification using pooled
data for all firm years. It shows that insider shareholdings and blockholdings are
negative and significant, whereas institutional shareholdings are positive and
significant. It implies a substitutive effect between insider/block shareholdings and
15
other corporate governance mechanisms as synthesized by the corporate governance
scorecard. It is not surprising to see a positive relationship between institutional
shareholdings and SCORE, as institutions may require firms to disclose more or they
simply follow firms with a transparent disclosure history.
The results in second and third columns control for SIC effects and firm effects,
respectively. The patterns we identified in the first column carry over to the estimates
in Column two and three, with the exception that institutions’ following is
insignificant. Besides, board shareholdings seem to have no influence on SCORE
when controls for firm effects.
Next, we replace ownership with firm characteristics as explanatory variables.
The estimated coefficients are in Columns four to six for the pooled, SIC effects and
firm effects, respectively.
Nonlinearity exists between firm size and corporate governance scorecard. The
inclusion of fixed effects changes the estimated coefficients significantly in some
cases. For example, both the estimated coefficient of the firm size and the coefficient
of the R&D expense to fixed assets changed signs when controls for firm fixed effects.
Consistent with the results of Himmelberg et al. (1999), the ratios of operating income
to sales are significantly positive in pooled and firm effects models, supportive of the
argument that the higher a firm’s free cash flow, ceteris paribus, the higher the desired
level of governance. Our estimates for the K/S are contradictory to the hypothesis that
firms with a greater concentration of “hard” capital in their inputs will generally have
a lower optimal level of governance level. Once we control for firm fixed effects, the
coefficients become insignificant. To fine tune the proxies for the scope for
discretionary spending, following Himmelberg et al., we use the ratio of R&D
spending to capital as a measure of “soft capital”. We use dummy variable (RDUM)
to indicate the availability of R&D. Generally, firms report R&D expenses have
higher corporate governance disclosure level. Among our three setups, R&D intensity
appears to have a positive effect on corporate governance scorecard for pooled data,
but have a negative effect on corporate governance scorecard if controls for firm
effects. This result is concurrent with the finding of Himmelberg et al. (1999) but is
against the hypothesis.
Our model specifications in the last three columns of Table 3 include ownership
in combination with firm characteristics (the combined model) to address the issue in
a contracting environment network. Broadly speaking, previously observed separate
16
correlations of governance scorecard with ownership and firm characteristics still hold
when combined together. Save for the risk measure becoming insignificant, the results
are qualitatively the same as those in separate regression settings.
Taking together, this research reveals the differences existing in cross-section and
within-variation in determining governance scorecard. One proposition is that the
unobserved firm characteristics are correlated with the observed characteristics, thus
bias the estimated coefficients in the cross-sectional or pooled regression. A second
possibility is that the factors driving a firm to improve governance over time are not
necessarily the ones shaping the cross-sectional governance ranking. Due to
availability of our longitude data, we are able to control the unobserved firm specific
characteristics. As Klapper and Love (2004) study the determinants of corporate
governance using only cross-sectional regression, they fail to control the firm fixed
effects. Another difference from their study is the inclusion of ownership variables
and discovered significant substitutive and complementary effects among different
control mechanisms.
5. Corporate Governance and Firm Performance
A common approach to testing whether established good governance is reflected
in the firm’s performance and market valuation is to regress Tobin’s Q (and later ROA)
on corporate governance scorecard controlling for those factors affecting firm value.
The following models are for part two analysis.
Qit   0  1 SCORE it   3 INSIDE it   4 BLOCK it   5 INSTit   it
(4)
Qit   0  1 SCORE it   2 SCORE it2   3 INSIDE it   4 BLOCK it   5 INSTit   it
Qit   0  1 SCORE it   6 FSIZEit   7 FSIZE   8 ( K / S ) it   9 ( K / S )
2
it
(5)
2
it
 10 (Y / S ) it  11 RDUM it  12 ( R & D / K ) it   it
(6)
Qit   0  1 SCORE it   2 SCORE it2   6 FSIZEit   7 FSIZEit2   8 ( K / S ) it
  9 ( K / S ) it2  10 (Y / S ) it  11 RDUM it  12 ( R & D / K ) it   it
Qit   0  1 SCORE it   3 INSIDE it   4 BLOCK it   5 INSTit   6 FSIZEit
(7)
  7 FSIZEit2   8 ( K / S ) it   9 ( K / S ) it2  10 (Y / S ) it  11 RDUM it
 12 ( R & D / K ) it   it
(8)
Qit   0  1 SCORE it   2 SCORE it2   3 INSIDE it   4 BLOCK it   5 INSTit
  6 FSIZEit   7 FSIZEit2   8 ( K / S ) it   9 ( K / S ) it2  10 (Y / S ) it
 11 RDUM it  12 ( R & D / K ) it   it
(9)
17
The three setups namely pooled, SIC effects and firm effects are studied for all
the equations as did in part one. SCORE2 is added to control for nonlinearity if any.
We also use return on total assets (ROA) as an alternative measure of firm
performance, which is calculated as Net Income/Total Assets. The model structures
are the same as in equations (4) to (9).
Table 4 reports the estimated coefficients with Q as dependent variable. The first
three columns report results from Equation (4). Equation (5) with SCORE2 term does
not change the ownership pattern, whose estimates are shown in Columns four to six.
Columns seven to twelve are for regressors of firm characteristics as given in
Equation (6) and (7). Finally, the last six columns present results from Equation (8)
and (9).
The prominent finding of performance analysis is that SCORE has the predicted
sign and is significant only after controlling for firm fixed effects. Under pooled
settings, results are mixed for corporate governance scorecard with different set of
regressors. Based on the combined model, it is at best positive but insignificant. We
interpret this inconsistency as information content difference in cross-section and
time-series disclosure.
As our findings suggest that firms improving their corporate governance over
time perform better, changes in governance are of more importance than level of
governance in determining market valuation. This conclusion has intuitive appeal to
investors because they may find that information obtained from longitude corporate
governance disclosure in annual report matters more than information gained from
cross-sectional absolute governance ranking in performance evaluation.
The reason that higher absolute governance ranking may not lead to better
performance can be explained as follows. Some firms especially FTSE100 firms
autonomously build their control mechanisms in line with the regulator’s
recommendations and are likely to disclose more on their governance status. There is
not necessarily a strong link between governance level and performance. On the other
hand, as we do not explicitly control endogeneity in pooled model, the inverse
relation may go from poorer performance to more voluntary disclosure. This is in
spirit consistent with Skinner’s (1994) findings, in that large negative earnings
surprises are more often preempted by voluntary corporate disclosures. In contrast,
firms improving their governance over time are more likely to be those really in need
of suitable control
and get
rewarded
when
they improve their
governance.
18
Table 4: Determinants of firm value: OLS regressions of Q on SCORE, ownership and/or firm characteristics variables
Variable
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
Pooled
SIC effects
Firm effects
（1）
（2）
（3）
（4）
（5）
（6）
（7）
（8）
（9）
（10）
（11）
（12）
（13）
（14）
（15）
（16）
（17）
（18）
31.78157***
Constant 2.759674***
SCORE
4.493743***
2.249247***
4.094326***
(17.42437)
(5.266383)
(23.43348)
(11.22082)
(3.361911)
(22.41674)
(6.079696)
(3.098614)
-0.01505***
-0.023161**
0.011593***
-0.059091***
-0.055915
-0.02025***
0.00702*
0.009407
(-5.546556)
(-2.235835)
(9.993812)
(1.76358)
(1.542072)
SCORE2
INSIDE
BLOCK
INST
5.558441*** 3.405946*** 7.928633*** 18.06996*** 23.02716*** 6.668153*** 15.95895*** 23.7445***
(16.32905)
(5.567803)
0.007372*** 0.058354***
(-5.589061)
(-1.394785)
(-6.539344)
0.000339***
0.000244
0.000205***
(4.721459)
-0.000378***
(6.33683)
(-6.285277)
8.046232***
17.67705***
(3.097695)
(14.60573)
8.448892***
(5.66655)
20.09381*** 32.02045***
(2.904444)
(8.030322)
(5.446788)
(2.791095)
(8.225666)
0.083361*
0.028046***
0.001492
0.007938
0.020162***
0.020266
0.089761*
0.030739***
(1.779851)
(4.677233)
(0.588329)
(1.257599)
(4.610561)
-0.00055* -0.000146***
(5.464599)
-0.0000721
(-1.204835)
(4.860281)
(0.994123)
(11.09699)
(-1.655879)
(-1.807005)
1.651935
0.295882*
0.993226***
1.623644
0.116591
-0.940306***
-2.478577
0.085406
-0.879827***
-2.445326
0.081809
(2.738532)
(1.625118)
(1.949508)
(2.790624)
(1.600105)
(0.631133)
(-4.426394)
(-1.386137)
(0.224876)
(-4.056722)
(-1.374397)
(0.205507)
-0.029765
0.264562
-1.251913***
-0.079112
0.223979
-1.259964***
0.08413
1.179029*
-3.506412***
0.08048
1.286319*
-3.446506***
(-0.103947)
(0.390769)
(-4.411869)
(-0.267574)
(0.3284)
(-4.035511)
(0.787339)
(1.684589)
(-5.29069)
(0.706549)
(1.789211)
(-5.388761)
-0.499667
-1.787671***
-5.090279***
-0.54881
(-0.743669)
(-7.953435)
(-3.160981)
(-0.755913)
-4.297917**
-7.016808***
(-6.332588)
(-2.507102)
-1.177655**
(-2.566741)
(-3.463759)
(1.894149)
-0.000607*
0.931126***
-2.108797*** -3.344322**
(-1.692426)
(1.643103)
-0.000135*
-1.986151*** -3.316135** -1.177129***
(-6.131664)
(-2.500458)
-1.829341*** -5.017831***
(-2.683586)
FSIZE
(-8.306281)
(-3.15844)
-1.833521*** -4.078812** -4.806644*** -1.936639*** -4.159003** -5.182238*** -1.738816*** -4.226105** -6.985821*** -1.796149***
(-4.760182)
(-12.04592)
(-4.325895)
(-2.459564)
(-7.658723)
(-4.275481)
(-2.4699)
(-7.784987)
FSIZE
0.112651*** 0.245607** 0.262345*** 0.119168*** 0.250951** 0.286727***
0.10391***
0.246058**
0.388457***
0.107636***
0.250902**
0.390221***
(4.852069) (2.448865) (16.40142)
(5.425976) (2.448371) (11.28123)
(4.21277)
(2.36064)
(7.428214)
(4.150309)
-0.430899*** 0.528027*** -0.82756*** -0.432437*** -0.53148*** -0.866926*** -0.468586*** -0.660806*** -0.78402*** -0.465287***
(2.373208)
(7.56001)
K/S
(-6.701769)
(-7.097566)
(-5.138291)
(-11.94536)
(-7.183776)
(-5.122539)
(-13.08488)
(K/S)2
0.018088*** 0.023584*** 0.045263*** 0.017965*** 0.023419*** 0.047809***
0.019101***
0.027158***
0.03074***
0.018918***
0.027048***
0.030254***
(5.065266)
(4.758425)
(5.584297)
(5.073997)
(5.117677)
(6.161239)
(5.146378)
(3.504461)
(6.27135)
(5.167821)
(3.86924)
Y/S
2.316546*** 2.564064**
0.752678**
2.30485***
2.630133**
0.806431**
2.371925***
2.420318**
0.259351
2.299589***
2.50555**
0.257997
(2.047519)
(23.45225)
(2.241754)
(2.144942)
(18.12079)
(2.32418)
(1.386297)
(17.44321)
(2.352721)
(1.421213)
2
(-6.357395)
(5.57978)
(19.60453)
RDUM
R&D/K
no. of obs
Adj. R2
(-2.548349)
(-5.676426)
(2.236502)
(-15.69847)
(-6.638321)
(-5.282313)
(-6.432556)
(-2.546418)
(-5.644637)
-0.665535*** -0.781639***
0.085781***
0.33165
-0.049388
0.092542***
0.32009
0.009317
0.150674***
0.414027*
0.11428
0.154004***
0.406732*
0.106431
(3.765397)
(1.449976)
(-0.918623)
(4.413436)
(1.421528)
(0.154946)
(4.164634)
(1.850849)
(1.279151)
(4.370691)
(1.832202)
(1.302241)
3.413439***
2.603778*
-0.800851*
3.423775***
2.608852*
-0.939299*
2.981034***
2.168062
2.177431
-2.573496***
(6.560545)
(1.737764)
(-1.75424)
(5.811305)
(1.74209)
(-1.823642)
(10.52544)
(1.578216)
(-5.312895)
(9.955066)
(1.586252)
(-5.411619)
-2.558149*** 2.946802***
848
848
848
848
848
848
843
843
843
843
843
843
843
843
843
843
843
843
0.8561
0.0356
0.9039
0.8279
0.0356
0.8896
0.7239
0.1495
0.9285
0.7572
0.1502
0.9315
0.7373
0.1700
0.9742
0.7263
0.1707
0.9747
Numbers in parentheses are t statistics. Definitions of each variable are given in the Appendix. Year dummies are included for all regressions, but not reported. Fixed effects
at the industry or firm level are included where indicated, but not reported. * Means significant at 0.10 level (two-tailed);** Means significant at 0.05 level (two-tailed);***
Means significant at 0.01 level (two-tailed)
19
Furthermore, not examined here but elsewhere in this study, we find that with the same
degree of increase in governance, firms having lower initial governance rankings show
larger performance increase than their counterparts.
As for control variables, the inclusion of firm characteristics changes the signs of
estimated coefficients of insider shareholdings and blockholdings for the pooled data.
Estimates are not affected in firm fixed effects panel. The coefficient of the institutional
shareholdings is dominantly negative and statistically significantly different from zero,
suggesting that instead of increasing monitoring of managers’ behavior institutions seem
to exploit minority shareholders. The firm characteristics variables are robust to the
inclusion of additional ownership controls. For all Q regression, Q is negatively related to
firm size and “hard” capital but positively related to operating income. R&D intensity is
positively correlated with Q in cross-section setup. However, it becomes puzzlingly
negative when controls for firm effects.
We then repeat the same exercise using ROA as an alternative performance measure.
This time SCORE is positively and significantly related to operating performance not
only for the within-variation but also for the between-variation. Although evidence from
our regressions implies blockholders consistently do a bad job in control, institutions and
insiders seem to play different roles for between- and within- variations. The relationship
between ROA and firm characteristics is similar to the relationship between Q and firm
characteristics. To be specific, the larger firms, firms holding more “hard” capitals and
firms with less operating income to sales have lower return on assets. R&D intensity is
negatively correlated with ROA, which is not surprising, since the expensing of R&D
expenditure will reduce current profitability.
6. Further Evidence on the Link between Firm Performance and
Corporate Governance from Simultaneous Equation System
Two-stage least squares regressions are employed to examine the interaction of
different governance devices and their impact on firm performance. Agrawal and
Knoeber (1996) set up a good model for us to study the interlocked variables together.
They simultaneously examine the use of seven mechanisms to control agency problems.
Variables included in their study are: shareholdings of insiders, institutions, and large
20
blockholders, use of outside directors, debt policy, the managerial labor market, and the
market for corporate control.
Our research setup differs from Agrawal and Knoeber’s (1996) in a number of
respects: First, we include the comprehensive governance scorecard measure. Second, we
control for fixed effects in the simultaneous analysis. On one hand, we double control for
the endogenous problem. On the other hand, as argued previously, we believe that the
changes of governance have more impact on performance than the level of governance
itself. Third, since use of outsiders in the board has already been captured in our
governance scorecard, we exclude it from the equations. The managerial labor market is
also excluded because it is less related with governance ratings. Our focus is on the
internal control. Therefore, the market for corporate control falls outside this study and is
excluded.
Altogether, there are five equations relating to governance mechanisms in our
investigation. The governance mechanisms are corporate governance scorecard,
shareholdings of insiders, institutions, and blockholders, and debt policy. All of them are
treated as endogenously determined. In estimating the system of equations, we use the
following variables as instruments: FSIZE, ADR, OUTDIRE, TENURE, NOD, RISK_D,
RISK, RDAI_D, RDAI, NINSTI, and CR. In order to satisfy the order condition to
ensure that the equations in the system are identified, each equation must exclude at least
four of the exogenous variables since each equation includes four endogenous variables
as regressors. The specification of Equations (10) to (14) below is partially driven by the
need to satisfy this order condition. Nevertheless, as far as possible, we rely on theory or
prior research to determine the exogenous variables to be included or excluded in each of
the equations.
We estimate the following simultaneous equation systems:
SCORE   0    i M i   5 REG   6 FSIZE   7 ADR   8 OUTDIRE
(10)
i j
INSIDE   0    i M i   5 REG   6 FSIZE   7TENURE   8 NOD   9 RISK _ D
i j
 10 RISK
(11)
BLOCK   0    i M i   5 REG   6 FSIZE   7 RISK _ D   8 RISK   9 RDAI _ D
i j
21
 10 RDAI
INST   0    i M i   5 REG   6 NINSTI   7 ADR
(12)
(13)
i j
DTV   0    i M i   5 REG   6 FSIZE   7UNIQ   8 CR
(14)
i j
M j denotes the mechanism on the left-hand side and M i  j denotes the other four
mechanisms. Variable definitions are in the Appendix.
Besides interacting with other mechanisms, SCORE is assumed to be positively
correlated with firm size, ADR dummy and outside directors’ quality measured by
average number of directorships held by non-executive directors in unaffiliated firms. As
larger firms and firms issuing ADR may have more channels for disclosure. Outside
directors may encourage firms to comply with higher standard of governance. Regulated
firms may have different disclosure requirement. Such difference will be captured by
REG dummy.
Equation for insider shareholdings is similar to that in Agrawal and Knoeber (1996).
We expect insider shareholdings to be positively correlated with average years of service
as directors and number of directors but negatively correlated with firm risk, regulation
and firm size. We exclude FOUNDER dummy, as the majority of firms in our sample are
mature large firms and do not have founders holding key positions.
Equation (12) relates to blockholdings. We include the similar set of exogenous
variables as inside shareholdings, except for replacing TENURE and NOD with RDAI.
The directions of our predictions are similar to those for INSIDE. Additionally, as argued
by Agrawal and Knoeber that as the industry average R&D to asset ratio rises,
technology becomes more firm-specific, making outside monitoring less effective, we
expect a negative coefficient for RDAI.
The choice of explanatory variables for INST closely follows Agrawal and Knoeber
(1996). Following the same line of argument for NYSE listing, we expect larger firms
and those issuing ADR in the U.S. to be more attractive to institutions. NINSTI is used as
an additional measure of attractiveness to institutions by Agrawal and Knoeber (1996).
However, we do not expect a positive relation between INST and NINSTI, because our
institutional shareholdings are calculated by summing up the shareholdings larger than
22
3%. As the number of institutions in the industry increases, the proportional
shareholdings of each institution may decrease. Some may be less than 3% and not
included in our calculation of INST. This may spuriously lead to a negative relationship
between INST and NINSTI.
The last endogenous variable is use of debt. As asserted in Agrawal and Knoeber
(1996), DTV should depend positively upon firm size, negatively on REG and CR.
Table 5: Coefficient estimates from 2SLS regressions of control mechanisms (pooled)
Independent Variables
CONSTANT
SCORE
51.97518***
(10.10664)
SCORE
INSIDE
BLOCK
INST
DTV
REG
FSIZE
ADR
OUTDIRE
-57.71976***
(-6.063851)
-27.45351***
(-5.771991)
34.21344**
(2.465674)
11.39047***
(4.112163)
-0.321513
(-0.467379)
-0.422026
(-1.00521)
2.711416***
(7.824606)
1.546051***
(3.264105)
INSIDE
0.21751**
(2.413612)
-0.002321***
(-4.790076)
0.208538***
(3.472738)
-0.135519**
(-2.168495)
-0.210274***
(-6.222729)
-0.019462***
(-5.653298)
-0.009799**
(-2.161044)
0.010459***
(17.66012)
-0.002252**
(-2.14963)
0.011894
(1.118989)
-0.27273
(-1.554202)
TENURE
NOD
RISK_D
RISK
RDAI_D
RDAI
Dependent Variable
BLOCK
INST
0.354756***
0.29437***
(3.408335)
(4.006225)
-0.004554***
0.000264
(-5.670119)
(0.242618)
-0.179574
-0.065229
(-1.066364)
(-1.399228)
0.108409***
(2.706624)
0.769814***
(4.190407)
0.112851
-0.273009***
(1.312055)
(-7.768218)
0.007333
-0.039858**
(0.422633)
(-2.492005)
-0.003298
-0.010601***
(-0.357078)
(-4.324699)
-0.041579***
(-5.025436)
-0.031932***
(-3.41251)
-0.014727***
(-4.985219)
-0.062193***
(-5.884713)
0.621076
(1.121057)
-0.045951***
(-5.020283)
0.173023**
(2.278546)
-0.000106***
(-4.826986)
NINSTI
CR
Adjusted R2
no. of obs.
DTV
0.411281***
(4.686151)
-0.00216*
(-1.744386)
-1.317086***
(-4.516105)
0.575976***
(2.589891)
-0.761683***
(-3.608497)
0.9565
837
0.2671
837
0.9249
837
0.7667
837
0.039527***
(15.82375)
0.4134
837
Numbers in parentheses are t statistics. Variables are defined in the Appendix. * Means significant at 0.10
23
level (two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
Results of the 2SLS estimation for the pooled data without fixed effects are presented
in Table 5. The coefficients on the exogenous variables generally have the predicted sign
and most of them are significant at normal level. However, the coefficient on NOD for
Equation (11) is negative and significant, contradictory to our assumption. We suspect
that it is due to the positive relationship between firm size and board size. As INSIDE
decreases when FSIZE increases, it decreases when NOD increases if FSIZE affects
INSIDE through NOD. This explanation may be incomplete since the coefficient on
FSIZE is already negatively significant. Less use of debt by larger firms is also
unexpected. If managers set debt levels in terms of book value rather than market value
ratios, DTV may not be a good measure for firm’s long-term capital structure.
Consequently, the hypothesized relationship between debt use and firm size may not
hold.
Similar as in previous analysis of corporate governance determinants, score and
insider shareholdings and score and blockholdings are substitutes pairs, whereas higher
institutions following causes firms to disclose more but not the other way round. From
Equation (10) we find that use of debt is positively correlated with governance disclosure,
whereas from Equation (14) the relation between SCORE and DTV is negative. Although
it seems contradictory at surface, it may make sense. On the one hand pressure from debt
holders may force firms to disclose more. On the other hand, firms already having a
higher level of corporate governance as measured by SCORE have less incentive to use
debts as a signal of monitoring by lenders.
Shareholdings by institutions and by blockholders appear to be complementary
avenues for outsider activism since each increase along with the other. For the managerial
ownership, we find that it increases with increasing blockholdings but decreases with
increasing institutional shareholdings. This reveals that the correspondence among these
ownership variables is not necessarily a simple one.
Table 6 reports the results of the 2SLS estimation for the fixed effects specification.
The direction of the relationship for the governance variables is generally consistent with
that in Table 5. However, many of them lose significance. For instance, the direction that
24
ownership variables affecting SCORE is the same as in pooled data but not statistically
significant. Except that insider ownership increases when governance scorecard increases,
the corporate governance scorecard has no effect on other governance devices. The same
as in pooled model, debt holders help to monitor firms by encouraging more disclosure.
After controlling for unobserved heterogeneity, firms with improved governance ratings
use more debt. This contrasts with the findings from pooled data, where firms with higher
ratings use less debt.
Table 6: Coefficient estimates from 2SLS regressions of control mechanisms (firm
effects)
Independent Variables
CONSTANT
SCORE
65.2161***
(23.9849)
SCORE
INSIDE
BLOCK
INST
DTV
FSIZE
ADR
OUTDIRE
-34.22183
(-1.437403)
-33.47095
(-1.616105)
1.529976
(0.05616)
28.45252**
(2.104068)
-1.105833*
(-1.953737)
7.430205***
(2.827952)
1.555363***
(3.801831)
INSIDE
-0.113946
(-1.014247)
0.002654**
(2.235584)
0.495296*
(1.935605)
-0.423529**
(-2.096267)
0.111632
(1.104175)
-0.020484
(-1.565991)
0.004156
(1.619382)
0.002907*
(1.859992)
0.096327
(1.157198)
-0.446157***
(-2.729671)
TENURE
NOD
RISK_D
RISK
RDAI_D
RDAI
Dependent Variable
BLOCK
INST
0.150255
0.001737
(1.194111)
(0.009267)
-0.000298
0.000577
(-0.111998)
(0.169737)
0.516066*
-0.004882
(1.669124)
(-0.016221)
0.693809***
(3.883323)
0.335432
(0.761882)
0.006505
0.150129
(0.040141)
(0.922396)
-0.003014
-0.000251
(-0.369485)
(-0.02536)
0.027731
(0.994238)
0.045322***
(3.776087)
-0.043671
(-0.727797)
1.224965**
(2.140507)
-0.016305*
(-1.93352)
0.180563
(1.062426)
0.0000168
(0.131514)
NINSTI
CR
Adjusted R2
no. of obs.
DTV
-0.409345**
(-2.570506)
0.000864
(0.293578)
0.41992
(1.443012)
-0.187119
(-0.298541)
0.818321
(0.992231)
0.6494
837
0.8040
837
0.8800
837
0.8475
837
-0.026515***
(-5.147966)
0.6484
837
Numbers in parentheses are t statistics. Variables are defined in the Appendix. * Means significant at 0.10
25
level (two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
Result in Table 6 shows that as firm size increases, debt financing increases as well,
consistent with the hypothesis that the expected bankruptcy costs of debt should be
smaller for larger firms. But the coefficient for CR carries the wrong sign and is
significant, implying that the availability of internal funds does not replace the debt
financing.
For Equation (11) relating to managerial ownership, the relationship between NOD
and INSIDE is significantly positive, suggesting that when the number of directors
increases, directors’ shareholdings increase accordingly. Insider shareholdings decrease
as firm risk increase, consistent with insiders’ diversification requirement. However, firm
risk seems to have the opposite effect on blockholders.
In addition to the interdependence among the control mechanisms, we also
investigate the relationship between the mechanisms and firm performance, measured by
Tobin’s Q. As in Agrawal and Knoeber (1996), we control for R&D expenditure and firm
size. We expect R&D expenditure to indicate growth opportunities and to be positively
related to Q. Since Q should be lower for larger firms, we control for firm size in our
regression. Equation (15) will be added to the previous simultaneous equations (10) to
(14). Q is added as an endogenous variable along with all the control mechanisms
together. Other variables are treated as exogenous and used as instruments.
Q   0    i M i   5 RDUM   6 RDA   7 FSIZE
(15)
i j
In addition to treating Q and all the governance mechanisms as endogenous and
doing estimation with 2SLS, we also estimate Q with OLS, treating all the other variables
as exogenous for comparison purpose. For either OLS or 2SLS, we run regressions with
and without fixed effects as what we did previously.
For the pooled specification, the OLS and 2SLS results of the estimates for Equation
(15) are shown in Table 7. The evidence from OLS regression is consistent with lower
corporate governance ratings, greater shareholdings by blockholders, fewer shareholdings
by institutions and less corporate debt all leading to improved firm performance. The
negative relationship between governance scorecard and performance contradicts with
our hypothesis that higher level of governance helps to improve performance. But these
26
results only suggest correlation rather than causality. The 2SLS estimates in column two
permit us to address the issue of which way the relation runs.
Table 7: Coefficients estimates from OLS and 2SLS regressions of Tobin’s Q on control
mechanisms (pooled)
Independent Variables
CONSTANT
SCORE
INSIDE
BLOCK
INST
DTV
RDUM
RDA
FSIZE
Adjusted R2
no. of obs.
OLS Estimates
(1)
3.612755***
(13.13777)
-0.005809***
(-4.026671)
0.065667
(0.235783)
0.349697**
(2.22556)
-2.282972***
(-9.193773)
2SLS Estimates
(2)
11.13153***
(6.283291)
-0.016372
(-1.019165)
-9.385708***
(-6.292297)
0.026568
(0.01482)
-12.39682***
(-8.764227)
-2.394069***
-12.44773***
(-6.154964)
-0.186827**
(-1.990409)
19.64734***
(7.967797)
-0.1855***
(-5.728549)
0.7256
843
(-10.93111)
-0.472478***
(-5.768452)
6.451534***
(3.020857)
-0.666452***
(-7.118031)
0.3435
837
Numbers in parentheses are t statistics. Variables are defined in the Appendix. * Means significant at 0.10
level (two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
In comparison of the 2SLS estimates with the OLS estimates in Table 7, the negative
coefficient on corporate governance scorecard is slightly larger in magnitude in the 2SLS
estimate but no longer statistically significant. Similarly, the coefficient on blockholdings
loses statistical significance. The negative coefficients on INST and DTV remain
significant. Finally, unlike in OLS, the coefficient on INSIDE becomes surprisingly
negative and significant.
The evidence in Table 7 is not consistent with optimal choice of the control
mechanisms as asserted by Agrawal and Knoeber (1996). They document that except for
board composition, all of the control mechanisms are optimally chosen. However, our
results differ considerably from theirs. Insider shareholdings, institutional shareholdings
and corporate debt all reduce firm performance rather than being chosen to maximize
27
firm value.
To further control the unobserved heterogeneity and to explore the impact of changes
of governance scorecard on firm performance, we estimate Equation (15) controlling for
firm fixed effects. The results are reported in Table 8.
Table 8: Coefficients estimates from OLS and 2SLS regressions of Tobin’s Q on control
mechanisms (firm effects)
Independent Variables
CONSTANT
SCORE
INSIDE
BLOCK
INST
DTV
RDUM
RDA
FSIZE
Adjusted R2
no. of obs.
OLS Estimates
(1)
7.361743***
(15.05201)
0.009604***
(3.472724)
-0.303205
(-0.738345)
-0.96302***
(-4.897517)
-1.292731***
(-2.955394)
2SLS Estimates
(2)
21.54325***
(3.341931)
-0.029884
(-0.223506)
-11.41653
(-1.380169)
-10.68916
(-0.68975)
-3.125125
(-0.268161)
0.173951
9.763631***
(0.663806)
-0.260004***
(-5.139865)
4.916769***
(4.042345)
-0.703885***
(-9.452368)
0.8605
843
(4.07339)
-0.873003
(-1.292993)
2.572016
(0.396355)
-2.088306***
(-3.402679)
0.3429
837
Numbers in parentheses are t statistics. Variables are defined in the Appendix. * Means significant at 0.10
level (two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
For the fixed effects specification, the results for either OLS or 2SLS are quite
different from those for pooled specification. In much the same way as the findings in
Chapter 5 with panel data, the coefficient for SCORE in Table 8 for OLS suggests a
positive but small effect of governance disclosure on performance. This at least partially
implies that improving in governance disclosure will be reflected in firm value.
While the coefficient on INST remains negative and significant, the coefficients on
INSIDE, BLOCK and DTV change their signs. To further control for endogenous
problem, we now turn to the 2SLS estimates as shown in Column 2, Table 8.
When controls for fixed effects, the evidence here is consistent with optimal choice
28
of all the control mechanisms except for use of debt. As our focus is on changes of
control mechanisms in the fixed effects specification, this may imply that increase in debt
borrowing increases firm valuation in the short-run but does not optimally maximize the
long-run firm value.
7. Corporate Governance and Stock Return
Bauer et al. (2003) construct value weighted corporate governance factor portfolios to
analyze the impact of corporate governance on equity returns. Their approach is as
follows: First, rank firms on the basis of their corporate governance ratings. Second,
assign the 20% (25%) of companies with the highest ranking to the “good governance
portfolio” and the bottom 20% (25%) to the “bad governance portfolio” for the EMU
(U.K.) sample. Third, compute the equal weighted returns to a zero investment strategy,
which is holding a long position in the “good governance portfolio” and a short position
in the “bad governance portfolio”. They report an annual return of 2.1% for the EMU
portfolio and 7.1% for the U.K. portfolio from January 1997 till July 2002. When
controls for risk factors with Carhart’s (1997) four factor model, Bauer et al. find a
positive, though statistically insignificant abnormal return for U.K. firms.
As in Bauer et al. (2003), we build a zero investment portfolio by buying the 20%
highest ranked firms and selling the 20% lowest ranked firms. The portfolios are reset in
each January from year 1999 to year 2003, assuming the corporate governance scorecard
is available in January for each firm. Since such assumption is not valid for every firm,
we reset the portfolios in April and July respectively to do the robust check. As the
results are qualitatively similar, we only report the January results. Our sample period is
from January 1999 to December 2003.
Even though the governance level is important to equity returns, a more interesting
question is whether the change of governance has any association with stock returns. We
employ the similar methodology to construct another zero investment strategy.
Specifically, all firms are ranked on the basis of the one year change of their corporate
governance ratings. We assign the 20% of companies with the greatest improvement in
the ratings to the “improvement portfolio”. The 20% of companies with the largest
deterioration in the ratings are allocated to the “deterioration portfolio”. These portfolios
29
are reset in January of each year from 1999 to 2003.
For each of the strategies, we compute both the value weighted and the equal
weighted cumulative returns. Table 9 presents the annual returns for the “good
governance portfolio” and “bad governance portfolio”. And Table 10 demonstrates the
annual returns for the “improvement portfolio” and “deterioration portfolio”.
Table 9: Annual returns for the “good governance portfolio” and “bad governance
portfolio”
buy and hold
lowest 20%
panel A: value weighted
1999
0.3312
2000
0.2406
2001
-0.0954
2002
-0.2254
2003
0.2254
1999-2003 zero investment portfolio
panel B: equal weighted
1999
0.8766
2000
0.1996
2001
-0.0618
2002
-0.2171
2003
0.5539
1999-2003 zero investment portfolio
highest 20%
0.3488
-0.1817
-0.0578
-0.2295
0.2488
-0.3924
0.3920
0.0125
0.0336
-0.1702
0.3001
-0.5196
Panel A is for value weighted returns. Panel B is for equal weighted returns. Lowest 20% means “bad
governance portfolio” and highest 20% means “good governance portfolio”. Zero investment portfolio
return is the return from buying “good governance portfolio” and selling “bad governance portfolio”. It
is rebalanced in January 1999, January 2000, January 2001, January 2002 and January 2003, held till
December 2003.
Reported returns in Table 9 contradict our expectations. The 5-year cumulative
returns for the zero investment strategy are negative and big in magnitude. Our results are
in clear contrast with those of Bauer et al., who find an annual return of 7.1% for U.K.
firms from January 1997 till July 2002. Besides the difference in sample period, our
strategy differs with theirs in the following ways: First, our portfolios are rebalanced
whereas theirs are not. Since they assign the 2001 ratings backwards, their portfolio
construction is kind of ex post. In contrast, our portfolios are formed ex ante each year
when the corporate governance ratings are available. Second, since our sample is about
twice as much in size as theirs, we choose 20% instead of their 25% as cutoff. Therefore,
30
our portfolios are more representative. If it goes as hypothesized, extreme values should
bring about better results. But our findings fail to support it.
When we look into the returns for individual year, we cannot detect a clear pattern for
the value weighted portfolios. As for the equal weighted portfolios, it seems that for the
“good years” when stock returns are positive (1999, 2000 and 2003), “bad governance
portfolio” performs better, whereas for “bad years” when stock returns are negative (2001
and 2002), “good governance portfolio” outperforms “bad governance portfolio”. This
may indicate that governance matters more in bad years, especially for small firms
(indicated by equal weighted portfolio), or perhaps this pattern is just coincidental.
Table 10: Annual returns for the “improvement portfolio” and “deterioration governance
portfolio”
buy and hold
lowest 20%
panel A: value weighted
1999-2000
-0.0989
2000-2001
-0.1019
2001-2002
-0.2532
2002-2003
0.2654
1999-2003 zero investment portfolio
panel B: equal weighted
1999-2000
0.0353
2000-2001
-0.1389
2001-2002
-0.2534
2002-2003
0.5094
1999-2003 zero investment portfolio
highest 20%
0.0681
0.0224
-0.2019
0.2562
0.3667
0.0256
0.0994
-0.1734
0.3973
0.1760
Panel A is for value weighted returns. Panel B is for equal weighted returns. Lowest 20% means
“deterioration portfolio” and highest 20% means “improvement portfolio”. Zero investment portfolio return
is the return from buying “improvement portfolio” and selling “deterioration portfolio”. It is rebalanced in
January 2000, January 2001, January 2002 and January 2003, held till December 2003.
Next, let us move on to the findings from buying “improvement portfolio” and
selling “deterioration portfolio”. They are given in Table 10. Both the value weighted and
the equal weighted portfolios support our hypothesis that firms with improved
governance disclosure outperform those with deteriorated governance disclosure
worsened in terms of equity returns. Overall, the zero-investment strategy leads to a
5-year cumulative return of 36.7 percent for the value weighted portfolio and 17.6
percent for the equal weighted portfolio. Combining with the previous findings in firm
31
valuation measured with Tobin’s Q, our results suggest that current improvement in
governance disclosure is valued but only partly incorporated in the present stock price.
These findings have some implications for investors. When they invest in “improvement
portfolio”, they may earn excess returns because good governance will be translated in
higher valuation in the future. But we should remember that these findings are not
adjusted for any risk style yet.
When investigating the yearly portfolio returns, similarly as before, it seems that
governance is more important in valuation for “bad years” than for “good years”. This
pattern shows up in both the value weighted and the equal weighted portfolios. So far, we
cannot remove the possibility of coincidence.
To account for the risk factors documented in previous stock return studies, we
employ the Carhart’s (1997) four-factor model, which is estimated by:
RLSt    1 ( Rmt  R ft )   2 SMBt   3 HML t   4 MOM t   t
(16)
where RLSt is the excess monthly return of the zero-investment portfolio, Rmt is the
monthly return on the market portfolio and Rft is the monthly risk-free interest rate. SMB
(Small Minus Big) is the monthly return on a size factor portfolio. HML (High Minus
Low) is the monthly return on a book-to-price factor-mimicking portfolio based on the
book-to-market ratio. MOM (Momentum) is the monthly return on a momentum factor
portfolio. The constant, α, represents the excess return an investor could have earned
pursuing this investment strategy.
Table 11: Four-factor model (“good governance portfolio” minus “bad governance
portfolio”)
Independent Variables
Value Weighted
(1)
α
(
R
M
R
F
-
0
.
0
0
3
3
5
4
-
0
.
5
2
7
6
1
1
0
.
(
S
M
M
(
M
O
.
-
L
(
Adjusted R
no. of obs.
2
2
1
0
4
3
.
8
8
0
6
2
9
9
9
9
*
*
3
)
0
.
8
2
8
8
3
6
-
0
.
0
4
4
7
5
2
0
.
5
5
0
(
7
5
.
0
0
4
0
6
8
0
.
0
5
0
3
9
8
0.0224
60
)
)
0
.
0
0
3
2
8
8
0
.
7
6
9
6
5
3
(
)
7
0
-
-
0
4
-
-
M
9
2
B
(
H
2
)
Equal Weighted
(2)
(
(
(
.
1
1
.
4
6
3
1
5
5
2
5
)
1
9
6
)
-
0
.
0
0
6
9
4
9
-
0
.
0
8
0
5
3
1
-
0
.
0
7
6
2
4
1
-
1
.
0
8
5
2
1
2
-
0
.
0
4
6
5
4
6
-
0
.
9
3
8
4
4
3
)
)
)
0.0405
60
Numbers in parentheses are t statistics. Dependent variable is zero investment portfolio returns from buying
32
“good governance portfolio” and selling “bad governance portfolio”. It is rebalanced in January 2000,
January 2001, January 2002 and January 2003, held till December 2003. * Means significant at 0.10 level
(two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
The results of estimating Equation (16) for the “good minus bad” are shown in Table
11. As indicated by the alpha, the performance differential between the “good governance
portfolio” and the “bad governance portfolio is negative but not statistically significant.
With respect to size, value versus growth, and momentum the portfolios do not seem to
differ substantially. For value weighted portfolio, the coefficient on RMRF is
significantly positive, revealing that the “good governance portfolio” has a higher
exposure to market risk than the “bad governance portfolio”. However, it is not the case
for equal weighted portfolio.
Table 12: Four-factor model (“improvement portfolio” minus “deterioration portfolio”)
Independent Variables
Value Weighted
(1)
α
(
R
M
R
F
(
S
M
B
0
.
0
0
9
8
9
2
1
.
4
3
9
6
1
6
M
L
(
M
O
M
(
Adjusted R
no. of obs.
2
)
-
0
.
0
8
5
8
9
5
-
0
.
5
8
0
3
6
4
-
0
.
1
6
0
6
2
3
(
H
-
1
.
0
2
3
4
8
.
1
4
8
9
0
8
1
.
4
1
5
0
7
5
0
.
0
7
0
8
5
9
0
.
7
5
3
2
8
5
0.0275
60
(
)
)
0
Equal Weighted
(2)
(
(
)
(
0
.
0
0
5
6
9
6
1
.
0
9
8
9
3
3
0
.
0
8
1
6
9
2
-
0
.
7
2
0
9
8
4
-
0
.
0
1
6
2
8
6
-
0
.
1
3
1
9
6
8
-
0
.
0
6
4
3
8
2
-
1
.
0
)
)
-
(
.
0
1
4
0
.
1
4
7
6
9
9
6
4
4
)
2
8
5
)
)
4
3
)
0.0391
60
Numbers in parentheses are t statistics. Dependent variable is zero investment portfolio returns from buying
“improvement portfolio” and selling “deterioration portfolio”. It is rebalanced in January 2000, January
2001, January 2002 and January 2003, held till December 2003. * Means significant at 0.10 level
(two-tailed). ** Means significant at 0.05 level (two-tailed). *** Means significant at 0.01 level
(two-tailed).
Table 12 presents the alphas for the “improvement minus deterioration” portfolio. It
is the abnormal return on a zero investment strategy that buys the top 20% firms with
greatest improvement in governance ratings and sells the bottom 20% firms with largest
deterioration in governance ratings. For the value weighted (equal weighted) setup, the
alpha is 98.9 (57.0) basis points per month, or about 11.9 (6.8) percent per year. Although
the abnormal returns are economically large, they are not statically significant at normal
level. Comparing our results for the portfolios based on change of governance to those of
33
Bauer et al. (2003) for portfolios based on level of governance, we observe that both of
our findings are positive but with insignificant returns. Following their argument that the
insignificance is to some extent due to lack of observations, we may expect to find
significant results when more years of data are available. None of the coefficients on the
four factors is significant, implying that the loadings for the different governance
portfolios are not tied to any of the factors.
34
References
Agrawal, A. and Knoeber, C.R., 1996. Firm performance and mechanisms to control
agency problems between managers and shareholders. The Journal of Financial and
Quantitative Analysis, vol. 31, No. 3, 377-397
Barnhart, S. W. and Rosenstein, S., 1998. Board composition, managerial ownership, and
firm performance: an empirical analysis. The Financial Review, vol. 33, 1-16
Bauer, R., Gunster, N. and Otten, R., 2003. Empirical evidence on corporate governance
in Europe. Journal of Asset Management, forthcoming
Bhagat, S. and Black, B., 2002. The non-correlation between board independence and
long-term firm performance. Journal of Corporation Law, vol. 27, No. 2, 231-273
Black, B., 2001. The corporate governance behavior and market value of Russian firms.
Emerging Markets Review 2, 89-108
Black, B., Jang, H. and Kim, W., 2002. Does corporate governance affect firm value?
Evidence from Korea. Working paper, Stanford Law School, Korea University Business
School, and KDI School of Public Policy and Management.
Brickley, J.A. and Coles, J.L. and Gregg Jarrell, 1997. Leadership structure: separating
the CEO and chairman of the board. Journal of Corporate Finance 3, 189-210
Callahan, W., Millar, J.A. and Schulman, C., 2003. An analysis of the effect of
management participation in director selection on the long-term performance of the firm.
Journal of Corporate Finance 9, 169-181
Chen, K.W., Chen, Z. and Wei, K.C. John, 2003. Disclosure, corporate governance, and
the cost of equity capital: evidence from Asia’s emerging markets. Working Paper, Hong
Kong University of Science and Technology
Cho, M.H., 1998. Ownership structure, investment, and the corporate value: an empirical
analysis. Journal of Financial Economics 47, 103-121
Coles, J.W., McWilliams, V.B., and Sen, N., 2001. An examination of the relationship of
governance mechanisms to performance. Journal of Management 27, 23-50
Collier, P., 1993. Factors affecting the formation of audit committees in major UK listed
companies. Accounting and Business Research, vol. 23, No. 91A, 421-430
Conyon, M. and Mallin, C., 1997. A review of compliance with Cadbury. Journal of
General Management 2, 24-37
Conyon, M., 1994. Corporate governance changes in UK companies between 1988-1993.
Corporate Governance: An International Review 2, 87-99
35
Conyon, M., 1997. Institutional arrangements for setting directors’ compensation. In
Keasey, K., Thompson, S. and Wright, M. (eds), Corporate Governance: Economic,
Management, and Financial Issues, New York, Oxford University Press
Cremers, K.J. Martijn and Nair, V.B., 2003. Governance mechanisms and equity prices.
Working Paper, Yale International Center for Finance, and New York University
de Jong A., Douglas D. V., Mertens, G. and Wasley, C., 2002. The role of self-regulation
in corporate governance: evidence from the Netherlands. Working paper, Erasmus
University Rotterdam, University of Iowa, Nyenrode University, and University of
Rochester
Dedman and Elisabeth, 2002. An investigation into the determinants of UK boards
structure before and after Cadbury. Corporate Governance: An International Review 8,
133-153
Demsetz, H., and Villalonga, B., 2001. Ownership structure and corporate performance.
Journal of Corporate Finance 7, 209-233
Drobetz, W., Schillhofer, A. and Zimmermann, H., 2003. Corporate governance and
expected stock returns: evidence from Germany. Working Paper, University of Basel
Durnev, A., and Kim E.H., 2002. To steal or not to steal: firm attributes, legal
environment, and valuation. Journal of Finance, forthcoming
Faccio, M. and M. Lasfer, 1999. Managerial ownership, board structure and firm value:
the UK evidence. Mimeo, Universita Cattolica del sa cro cuore and City University
Business School
Faccio, M. and M. Lasfer, 2000. Do occupational pension funds monitor companies in
which they hold large stakes? Journal of Corporate Finance 6, 71-110
Forker, J., 1992. Corporate governance and disclosure quality, Accounting and Business
Research 22, 111-124
Gompers, P., Ishii, J. and Metrick, A., 2003. Corporate governance and equity prices. The
Quarterly Journal of Economics, vol. 118, 107-155
Himmelberg, C.P. Hubbard, R.G., and Palia, D., 1999. Understanding the determinants of
managerial ownership and the link between ownership and performance. Journal of
Financial Economics 53, 353-384
Hutchinson, M. and Gul, F.A., 2003. Investment opportunity set, corporate governance
practices and firm performance. Journal of Corporate Finance 182, 1-20
Kee H. Chung, Peter Wright, and Ben Kedia, 2003. Corporate governance and market
valuation of capital and R&D investments. Review of Financial Economics 12, 161–172
36
Kenneth, J. Rediker and Anju Seth, 1995. Boards of directors and substitution effects of
alternative governance mechanisms. Strategic Management Journal, vol. 16, No. 2, 85-99
Klapper, L.F. and Love, I., 2004. Corporate governance, investor protection, and
performance in emerging markets. Journal of Corporate Finance, vol. 10, no. 5, 703-728
LaPorta, R., de-Silanes, F.L., Shleifer, A., and Vishny, R., 2002. Investor protection and
corporate valuation. Journal of Finance, vol. 57, No. 3, 1147-1170
Marry, D. and Stangeland, D., 2003. The United Shareholders Association Shareholder
1000 and firm performance. Journal of Corporate Finance 9, 353-375
McConnell, J.J. and Servaes, H., 1990. Additional evidence on equity ownership and
corporate value. Journal of Financial Economics 27, 595-612
Morck, R., Shleifer, A. and Vishny R.W., 1988. Managerial Ownership and Market
Valuation: an empirical analysis. Journal of Financial Economics 20, 293-315
Oyvind Bohren and Bernt Arne Odegaard, 2001. Corporate governance and economic
performance in Norwegian listed firms. Working Paper, The Norwegian School of
Management
Peasnell, K.V., Pope, P.F. and Young, S., 2001. Managerial equity ownership and the
demand for outside directors. Working Paper, Lancaster University
Shivdasani, A. and D. Yermack, 1999. CEO involvement in the selection of new board
members: an empirical analysis. Journal of Finance, vol. 54, No. 99, 1829-1853
Short, H. and Keasey, K., 1999. Managerial ownership and the performance of firms:
evidence from the UK. Journal of Corporate Finance 5, 79-101
Vafeas, N. and Theodorou, E., 1998. The relationship between board structure and firm
performance in the UK. British Accounting Review 30, 383–407
Vafeas, N., 1999, The nature of board nominating committees and their role in corporate
governance, Journal of Business Finance and Accounting 26, 199-225
Vafeas, N., 1999. Board meeting frequency and firm performance. Journal of Financial
Economics 53, 113-142
Weir, C., Laing, D., and McKnight, P.J., 2002. Internal and external governance
mechanisms: their impact on the performance of large UK public companies. Journal of
Business Finance and Accounting 29, No. 5 & No. 6, 579-611
Weir, C., Laing, D., 2000. The performance-governance relationship: the effects of
Cadbury compliance on UK quoted companies. Journal of Management and Governance
37
4, 265-281
Yermack, D., 1996. Higher market valuation of companies with a small board of directors.
Journal of Financial Economics 40, 185-211
Young, S., 2000. The increasing use of non-executive directors: its impact on UK board
structure and governance arrangements. Journal of Business Finance and Accounting 27,
No. 9 & No. 10, 1311-1342
38
Appendix: Variable Definitions
SCORE: corporate governance index obtained from the corporate governance disclosure
scorecard
SCORE_C: one year change of SCORE = SCORE t  SCORE t 1
INSIDE: percentage of beneficial shares held by executive directors and non-executive
directors
INST: percentage of shares held by institutions
BLOCK: percentage of shares held by shareholders who have more than 5% of shares of
the firm
FSIZE: ln(sales)
K/S: Property, Plant and Equipment to sales
Y/S: operating income/sales
RDUM: a dummy variable equal to unity if R&D data are available, and zero otherwise
R&D/K: R&D expenditure to Property, Plant and Equipment
Q: [(No. of common shares ×Price of shares at calendar year end) + Book value of
Preferred Capital + Book value of total liabilities]/ Book value of total assets
ROA: Net Income/Total Assets
LTD  STD  PFD  CASHS
V
DTV: D/V=
where
V = liabilities-total+ preferred capital+ market value of equity
LTD = Book value of long-term debt
STD = Book value of short-term debt
PFD = preferred capital
CASHS = cash and short-term investments
REG: 1 for a firm in a regulated industry, viz. public utility, railroad (i.e., primary 3-digit
SIC code=40, 48, 49, 60, 61 or 63), 0 otherwise.
ADR: a dummy variable equal to unity if a firm trade ADRs on a major US exchange
(NYSE, AMEX, or NASDAQ), zero otherwise. The data is obtained from JP Morgan
website. Since it is only available for current year, we assigned the current number to
39
previous years as well, namely, there is no variation for the ADR dummy for each firm
over time.
OUTDIRE: average number of directorships held by non-executive directors in
unaffiliated firms
TENURE: average years of directors stayed in board
NOD: total number of directors
RISK_D: a dummy variable equal to unity if RISK data are available, and zero otherwise
RISK: the standard deviation of idiosyncratic stock price risk, calculated as the stand
error of the residuals from a CAPM model estimated using daily data for the period
covered by the annual sample
RDAI_D: a dummy variable equal to unity if data required to estimated RDAI is
available, and otherwise equal to zero
RDAI: average RDA for the 2-digit SIC industry of the firm
NINSTI: average number of institutional owners for the 2-digit SIC industry of the firm.
The data is obtained from SHEREWORLD. Since it is only available for current year, we
assigned the current number to previous years as well, namely, there is no variation for
the NINSTI for each firm over time.
CR: operating cash flow return on firm value =
OCF
V
where
V = liabilities-total+ preferred capital+ market value of equity
OCF = sales – cost of goods sold – selling, general and administrative expense
+ depreciation
RDA: research and development expenditures/Total assets
40