Current Draft: September, 2002
First Draft: April, 2002
Comments welcome
To Steal or Not to Steal:
Firm Attributes, Legal Environment, and Valuation
Art Durnev* and E. Han Kim**
GEL Classification: G32 (Financial Policy; Capital and Ownership Structure), K23 (Corporation and
Securities Law)
Keywords: Corporate Governance, Investment Opportunities, External Financing, Ownership, Legal
Environment
*
Doctoral Student, Department of Finance, University of Michigan Business School, Ann Arbor, Michigan
48109-1234. Tel: (734) 358-2427. E-mail: [email protected].
**
Fred M. Taylor Professor of Business Administration, University of Michigan Business School, Ann
Arbor, MI 48109-1234. Tel: (734) 764-5222 & (734) 764-2282. Fax: (734) 763-3117. Email:
[email protected].
We are grateful for helpful comments and suggestions by Sugato Bhattacharyya, Serdar Dinç, Mara Faccio,
Michael Fuerst, Kathleen Fuller, Charles Hadlock, Woochan Kim, Florencio Lopez-de-Silanes, Vojislav
Maksimovic, Todd Mitton, M. P. Narayanan, Andrei Shleifer, David Smith, Daniel Wolfenzon, Bernard
Yeung, and participants of the Conference on International Corporate Governance at the Tuck School of
Business, 2nd Asian Corporate Governance Conference in Seoul, Korea, Estes Park Conference; and
seminars at the University of Michigan and KAIST where earlier versions of the paper were presented
under different titles. We would like to thank Nick Bradley, Ian Byrne, George Dallas, and Laurie Kizik for
providing us with S&P Transparency Rankings and CLSA Corporate Governance Scores, and Vlad
Cherniavsky for excellent research assistance.
ABSTRACT
A simple model is presented in which the controlling shareholder chooses the quality of
corporate governance under different legal regimes. The model identifies three firm-specific
attributes that affect governance: investment opportunities, reliance on external financing, and
ownership structure. Using firm-level corporate governance and transparency data on 859 firms
in 27 countries, which reveal a wide variation in corporate governance and disclosure practices
across firms within countries, we find results that are consistent with the hypotheses derived
from the model. Firms with profitable investment opportunities, more reliance on external
financing, and more concentrated ownership have higher-quality governance and disclose more.
And firms with higher governance and transparency ratings are valued higher and invest more.
Moreover, these relations are stronger in countries that are less investor-friendly, demonstrating
that individual firms do adapt to poor legal environments to achieve efficient governance
practices.
This paper examines the relation between firm-specific factors and corporate governance
practices and how governance practices are in turn related to firm valuation. To this end we
develop a simple model that shows firms with good investment opportunities, greater reliance on
external financing, and higher ownership concentration practice better governance. The model
also predicts that the relations are stronger in legal environments that are less investor friendly
and that firms with better governance are valued higher. We test these predictions with newly
released data on governance and disclosure practices for 859 firms in 27 countries. The results
are consistent with the predictions of the model.
Previous studies have examined the effects of legal environment on corporate governance
and the relation between corporate governance and firm valuation. Specifically, it has been
shown that better legal protection for investors is associated with higher value of stock market
(La Porta et al. (1997)), higher valuation of listed firms relative to their assets or changes in
investments (Claessens et al. (2002), La Porta et al. (2002), Wurgler (2000)), and larger listed
firms in terms of their sales and assets (Kumar et al. (1999)). Furthermore, industries and firms in
better legal regimes rely more on external financing to fund their growth (Rajan and Zingales
(1998) and Demirgüç-Kunt and Maksimovic (1998)).
Although these studies provide valuable insights into the effects of regulatory environment,
they do not address firm-level issues such as what drives different governance practices across
firms within a given legal regime and how corporate governance affects individual firm
valuation. Current studies attempting to address these issues include Black (2001) and Black et
al. (2002), who demonstrate a strong relation between corporate governance and firm valuation
in Russia and Korea, and Doidge et al. (2001), who show that foreign firms listed on U.S. stock
markets are valued higher. For a more complete survey on international corporate governance see
Denis and McConnell (2002).
1
The relation between corporate governance and firm valuation has also been studied for U.S.
firms (e.g., Bhagat and Brickley (1984), Bhagat and Jefferis (1991), Denis et al. (1997), Karpoff
(1998), Demsetz and Lehn (1998), Denis (2001), Gompers et al. (2002)). Most of these studies
show mixed results with the exception of Gompers et al., who find that corporate governance
provisions related to takeover defenses are significantly related to firm valuation.
The contribution of this paper is three-fold. First, we identify three firm attributes that may
influence individual firms’ choice of the quality of corporate governance within a given legal
environment and empirically examine the hypothesized relations. Second, we extend previous
studies on the relation between corporate governance and firm valuation using a large sample of
firms for 27 countries. Finally, we document that the relations between firm attributes,
governance practices, and firm valuation are stronger in less investor friendly countries,
suggesting individual firms adapt to poor legal environments to effect efficient governance
practices.
To identify the relevant firm attributes, we provide a simple model in which the controlling
shareholder faces a trade-off between diverting resources for private benefits and sharing gains
with minority shareholders by investing in profitable projects. Diversion of resources results in
rejection of profitable projects, which lowers the value of the controlling stockholder’s share of
the firm value. The diversion also imposes on the controlling shareholders private costs that
depend on the quality of legal environment.
The model predicts that the quality of corporate governance in a given legal environment is
positively related to the availability of profitable investment opportunities, concentration of
ownership, and the need for external financing. It also shows firm valuation is positively related
to the quality of governance practices. Equally important, it predicts that the above relations are
stronger in weaker legal regimes.
2
The basic intuitions underlying the model are simple. One is less likely to commit crime if
one has something valuable to lose–profitable investment opportunities; one does not spit into
the well that one drinks from–external financing; one does not steal from oneself–ownership
concentration; good rules and effective enforcement reduce thievery–legal environment. As for
the interplay between firm attributes and legal environment, good corporate governance driven
by private incentives becomes a more important complement to regulation when regulation is
less effective. And of course, good corporate governance is valued higher where it is scarce–the
stronger relation between firm valuation and corporate governance in weaker legal regimes.
To examine these issues we use data on corporate governance and transparency practices on
859 firms in 27 countries. The data show a wide variation in governance and disclosure practices.
The variation appears to be greater in weaker legal regimes, dispelling the stereotype that all
firms in weak legal regimes suffer from poor corporate governance and all firms in strong legal
regimes practice uniformly high-quality corporate governance.
The data are consistent with the predictions of the model. We find that firms with better
investment opportunities, higher concentration of ownership, and more reliance on external
financing practice better governance. Furthermore, these relations are stronger in legal regimes
that are less investor-friendly. Thus, in Coase’s sense (Coase (1960)) firms located in countries
with poor legal protection appear to show the adaptability to achieve efficient governance
practices.
The data also reveal that firms with better governance invest more and enjoy higher
valuation. This is consistent with the contemporaneous findings of Klapper and Love (2002) who
examine the relation between firm valuation and corporate governance using a set of data that
partially overlaps with our data.
3
Section I presents the simple model, followed by hypotheses and empirical design in Section
II. Section III describes data, Section IV reports empirical results, with Section V providing
robustness checks. The concluding section contains summary and implications.
I. A Simple Model
This model assumes an environment that is similar to Johnson et al. (2000) and Shleifer and
Wolfenzon (2002). The primary purpose of the model is to provide motivation for empirical tests
and as such it is devoid of many interesting real world complexities like determinants of initial
ownership and capital structure in different legal regimes 1. Nor does it allow for a simultaneous
determination of investment and financing decisions. Following the Modigliani and Miller
(1958) tradition of examining investment and financing decisions separately, we first assume
financing is given and then allow for external financing by assuming investment is given. The
model does not consider the reputation-type issues discussed in Diamond (1991), Maksimovic
and Titman (1991), and Gomes (2000).
In defining corporate governance practice, we take Shleifer and Vishny’s view, “Corporate
governance deals with the ways in which suppliers of finance to corporations assure themselves
of getting a return on their investment.” (Journal of Finance, 1997, p. 737). Thus, the quality of
governance practice is defined as the degree to which non-controlling, outside shareholders get
their fair share of return on investment, or more specifically, as (1-d) where d is the proportion of
cash flows that the controlling shareholder diverts for private benefits at the expense of other
shareholders.
Defined as such, a good governance system would make it difficult for the controlling
shareholders to steal d, which depends on attributes such as independence and accountability of
the board of directors, financial incentives and managerial disciplines for value creation,
enforcement of managerial responsibility and accountability, timely and accurate disclosure of
1
See Johnson (2002) for the effects of legal regimes on corporate capital structure.
4
relevant information, assurance to maintain auditors’ independence, and protection of minority
shareholders2. Our definition of the quality of corporate governance captures these attributes and,
hence, focuses on the overall practice of corporate governance instead of the standard notion of
legally binding contracts that are designed to shape and constrain management actions.
We consider a single period model in which the profit per unit of physical capital invested in
project j is equal to 1   ( j ) , where j  0 and the cost of a unit of capital is standardized at 1.
We assume that  ( j ) is linear and decreasing in j for all firms with each firm having a
maximum of   0 , which varies across firms3. Thus,  is the measure that distinguishes the
level of profitable investment opportunities across firms. With this definition, the gross return for
the jth unit of capital invested can be written as 1   ( j )  1    j . For brevity and without
loss of generality, we assume that the interest rate is zero and investors are risk-neutral. Thus, if a
firm takes all non-negative NPV projects, it will invest until 1    j  1 , and the last unit of
capital invested will be j   ; that is, the firm’s total investment will be  , and its market
value will be    2 / 2 . Figure 1 summarizes the investment opportunity, as well as the firm’s
optimal investment and market value when d is suppressed to be zero.
Finally, we assume that the firm will liquidate at the end of the period when the controlling
shareholder, who holds  fraction of the firm, collects her share of liquidating dividends. The
ownership fraction  must be large enough to give her the controlling interest.
A. Internal Financing
To illustrate the effect of profitable investment opportunities on governance practice, we
start with a firm that has internal funds, F    e  0 , where e is a constant that indicates
2
These are the variables used by Credit Lyonnais Securities Asia (CLSA) in constructing their corporate
governance rankings, which is one of our empirical proxies for the quality of corporate governance. The
other proxy is Standard and Poor’s transparency rankings, which is based on the number of items a firm
chooses to disclose concerning ownership structure and investor relation, accounting and financial policies,
and board and management structure and process (see Appendix B).
3
That is, firm i has its own  i , but we do not put subscript i to  for notational simplicity.
5
whether the firm has sufficient funds to invest in all non-negative NPV projects (e  0) or not (e
< 0). The controlling shareholder makes the investment decision subject to the budget constraint
F 4. She is also free to divert any portion of F for private benefits but there are associated costs.
The costs of diversion are of three types. The first is the (expected) penalties imposed on the
controlling shareholder when the diversion is illegal and she gets caught. Such penalties take the
form of fines, jail terms, and loss of reputation that may hurt her future business or employment
opportunities. The second is the cost of diverting corporate resources, requiring such transaction
costs as bribes to internal and external agents. Additionally, private consumption or conversion
of the diverted resources into cash equivalents incurs deadweight loss because the consumption
value is often less than the replacement costs.
We assume the sum of all these costs is a fixed fraction, c, of the amount diverted5. These
costs vary across countries due to differences in de jure and de facto regulation, and across
industries within a legal regime due to differences in the nature of assets (e.g., tangible vs.
intangible) and business models involved. For instance, the costs of diversion may be higher for
tangible assets that are more visible and for firms in regulated industries 6.
4
This assumption of no external financing is relaxed later.
We assume that diversion occurs before the investment decision is made (as in Johnson et al. (2000)) and
the cost of diversion is linear in the amount of funds diverted. Johnson et al. (2000), La Porta et al. (2002),
and Shleifer and Wolfenzon (2002) assume that the cost of diversion is convex. We do not need the
convexity assumption, and the results do not change with a convex cost function.
6
A potential weakness of our approach is that we assume c, the strength of legal regimes, is exogenous to
the observed level of malfeasance, d. As witnessed during the recent corporate scandals following the
Enron debacle, governments react to revelations of wide spread corporate misdeeds, with those more
responsive to public opinions being more inclined to undertake legal reforms. Reforming legal systems,
however, is a slow process because it inevitably becomes a political issue, with the controlling
shareholders—and those who benefit by association—insisting on the status quo (see Bebchuk and Roe
(1999) and Rajan and Zingales (2001)). Moreover, if the number of firms is sufficiently large, the
controlling shareholder may behave as if her actions will have no effect on the cost of diversion.
Alternatively, c can be interpreted as the controlling shareholder’s perceived cost of diversion that takes
into account the effects of her action on future legal reforms.
5
6
In this setup, the controlling shareholder may not invest in all positive NPV projects; instead,
she will invest in project j only if her share of the liquidating dividends from the project is
greater than the after-cost diversion,
 (1   ( j ))  max[ , (1  c)]
[1]
The right-hand side of [1] incorporates the possibility that if the rate of return from the project,
 ( j ) , is negative, the controlling shareholder may keep the money rather than invest into a
negative return project. For example, if  (1   ( j ))  1  c but  ( j )  0 she will neither divert
nor invest; instead, she will retain the funds within the firm, of which she has claim to  fraction.
Thus, the controlling shareholder will invest up to the point where7:
 (1    j )  max[ , (1  c)]
[2]
Hence, the optimal level of investment for the controlling shareholder is 8
if c  1  


1 c

j *  1   
if 1  (1   )  c  1  


if c  1  (1   )
0
[3]
The funds remaining after the investment, F  j * , will be diverted if the after-cost diversion is
greater than the controlling stockholder’s share of liquidating dividends from it: (F-j*)(1-c)>a(Fj*) or 1  c    Thus, the optimal amount of diversion D* is equal to F  j if c  1    and 0
otherwise. Since F    e , it follows from Eq.[3] that:
The budget constraint, j    e , may become binding if e is negative, i.e. the endowment is insufficient
to fund all non-negative NPV projects. Although the derivations in the text assume non-negative e all the
results continue to hold with negative e.
8
If e < 0,
if c  1  (1  e)
  e

1 c

j *  1   
if 1  (1   )  c  1  (1  e)


if c  1  (1   )

[3A]
0
The maximum investment is constrained by   e and one of the conditions in Eq.[3] changes from 1  
to 1  (1  e) .
7
7
0
1  c  

D*  
+e


  e
if c  1  
if 1  (1   )  c  1  
if c  1  (1   )
[4]
Equation [4] shows that if the cost of diversion is high relative to non-controlling
shareholders’ ownership such that c  1   , there will be no diversion and all the leftover funds
will be kept within the firm. This is because for each dollar of diversion, the cost c is greater than
the wealth transfer from the minority shareholders who own 1   fraction of cashflow rights.
Diversion takes place only if the cost, c, is less than the wealth transfer per dollar of diversion,
1   9.
Thus, our measure of corporate governance, d, the proportion of cash flows that are diverted
as a percentage of the firm’s endowment,   e , is:
0

 1  c    e
d*  
  (  e)
1
if c  1  
if 1  (1   )  c  1  
if c  1  (1   )
[5]
It follows from [5] that d* is negatively related to the cost of diversion c. Therefore, in more
investor-friendly countries (high-c countries), firms will divert less and have better corporate
governance10.
9
When the cost of diversion is so small that c  1   (1   ) the optimal diversion will be the entire
endowment   e and all positive NPV projects will be rejected. This is because when c  1   (1   ) the
controlling stockholder’s share of the liquidating dividend from the highest return project,  (1   ) , is less
than the after-cost diversion, 1  c .
10
If e < 0,
0

1  c    e
d*  
  (  e)
1
if c  1  (1  e)
if 1  (1   )  c  1  (1  e)
if c  1  (1   )
and all subsequent discussions and derivatives of d* in the text remain valid.
8
[5A]
A.1 Investment Opportunities
Proposition 1: Controlling shareholders of firms with more profitable investment opportunities
divert less for private gains.
Proof: Since  represents the profitability of investment opportunities, we take the partial
derivative of d* with respect to  :
   c  1  e
< 0 if 1  (1   )  c  1  
d * 
   (  e) 2
 
otherwise
0
[6]
This derivative is non-positive because diversion takes place only when c  1   for reasons
explained earlier.
Q.E.D.
The intuition behind Proposition 1 is simple. When investment opportunities become more
profitable, the controlling shareholder’s share of return from investment increases relative to the
benefits from diversion. Thus, firms with profitable investment opportunities will practice low d,
i.e. high quality governance.
Conversely, when a firm suffers a substantial drop in profitable investment opportunities, the
controlling shareholders will divert more corporate resources. Johnson et al. (2000) document
such behavior before the Asian financial crisis and the media alleges similar actions by the top
management of Enron and other firms with subsequent scandals prior to their bankruptcy
filings11.
Eq. [6] also shows that the partial derivative is zero when c  1   (1   ) , which means that when
investor protection is so weak (i.e., c is so small) that the firm’s entire resources are diverted, profitability of
projects makes no difference. However, even in such a legal regime, a sufficient increase in profitability
(  ) will lead some firms to switch into the condition of 1   (1   )  c  1   , where firms make positive
investments. Thus, even in a virtual absence of investor protection, some firms will invest if profits are
sufficiently large.
11
9
A.2 Legal Regimes
Proposition 2: A given increase in profitable investment opportunities has a lesser impact on
diversion in a country with high costs of diversion than in a country with low costs of diversion.
Proof: This result follows because the derivative of Eq. [6] with respect to c is non-negative,

1
if 1  (1   )  c  1  
>0
d * 
   (  e) 2
c 
0
otherwise
[7]
Q.E.D.
Proposition 2 implies that the sensitivity of diversion to investment opportunities falls as the
cost of diversion rises. That is, the positive relation between investment opportunities and the
quality of corporate governance is stronger in weaker legal regimes 12. To illustrate, consider two
countries, say the U.S., which has relatively low tolerance for diversion and imposes high cost,
and Russia, which is more tolerant and imposes lower costs. Proposition 2 implies that the same
increase in profitable investment opportunities will have a smaller negative impact on d in the
U.S. than in Russia.
To demonstrate numerically, assume the cost of diversion, c, is equal to 0.6 in the U.S. and
0.3 in Russia and the excess cash, e, is equal to 0 in all cases. Also consider a firm that has low
investment opportunity  L  2.0 , and the controlling shareholder owns 30%. The payoff to the
controlling shareholder can be defined as MV  (1  c) D where the market value, MV, is the
present
Since
value
MV 

(1 d * ) 
0
of
gross
returns
from
(1    j )dj   (1  d * ) 
all
projects
that
are
undertaken.
 2 (1  d *2 )
12
2
, the optimal diversion is d L*  0.67
The intuition for this proposition requires a two-step explanation: When the cost of diversion is high, the
net benefit of diversion is low and the optimal tradeoff point at which the marginal benefit of diversion is
equal to the marginal benefit of investment occurs at a relatively low level of d. It would take much greater
increases in profitability to bring the trade-off point to an even lower level of d in a high c country than in a
low c country where the trade-off point occurs at a relatively high level of d.
10
if the firm is located in Russia, but d L*  017
. if in the U.S. Now, if the firm’s investment
opportunity improves to  H  2.2 , the optimal diversion will decrease from 0.67 to 0.61 for the
firm in Russia, whereas for the U.S. firm the decrease will be smaller, from 0.17 to 0.15 (see
Figure 2).
This example can also be used to illustrate the relation between variation in the quality of
corporate governance and legal regime13.
Corollary 1: If the mean and variation in profitable investment opportunities across firms are
held constant, then variation in diversion is greater in weaker legal regimes 14.
A.3 Ownership
Proposition 3: Controlling shareholders of firms with higher ownership divert less corporate
resources for private gains.
Proof: Differentiating Eq. [5] with respect to controlling shareholder’s ownership we obtain

1 c
<0
d *   2
   (  e)
 
0
if 1  (1   )  c  1  
otherwise
[8]
Q.E.D.
Proposition 3 is a restatement of the well-known Jensen and Meckling (1976) agency
argument that entrepreneurs with higher ownership divert less.
13
Assume two-firm economies in which both firms L and H exist in the U.S. and in Russia such that the
deviation in  between the two firms in both countries is the same and equal to 0.2. But the deviation in
d* is 0.06 in Russia and 0.02 in the U.S. Thus, if the variation in profitable investment opportunities across
firms were the same, firms in Russia would show greater variation in corporate governance than those in the
U.S. The variation in corporate governance also depends on the level of  itself because d* is negatively
related to  . To illustrate, consider again a two-firm economy in which firm L’s  is 3 and firm H’s  is
3.3, the same 10% difference as above. In this case, however, d*L = 0.44 and d*H = 0.40 in Russia and, d*L
= 0.11 and d*H = 0.10 in the U.S., reducing the deviation in d* to 0.04 in Russia and 0.01 in the U.S.
14
An alternative scenario that provides the same prediction is that the law and country-level institutions set
a minimum standard on the quality of governance that firms cannot go below. The minimum standard
truncates the distribution of governance qualities in which the truncation point–the minimum standard–
affects the observed variation. However, this explanation requires an assumption that the law is effective in
enforcing and maintaining the minimum standard, which appears to have been broken rather blatantly in the
recent corporate scandals in the U.S. In contrast, our approach requires no limit on d–the minimum
standard, as we proxy the strength of regulation by the private cost c.
11
Proposition 4: A given increase in controlling shareholder’s ownership has a lesser impact on
diversion in a country with high costs of diversion than in a country with low costs of diversion.
Proof: Differentiating [8] with respect to the cost of diversion c,

1
>0
d *  2
   (  e)
c 
0
if 1  (1   )  c  1  
otherwise
[9]
Q.E.D.
Proposition 4 implies that the sensitivity of diversion to ownership concentration falls as the
cost of diversion rises. That is, the positive relation between ownership and the quality of
corporate governance is stronger in weaker legal regimes. This is because with weaker legal
protection for investors, concentrated ownership becomes a more important tool to resolve the
agency conflict between controlling and minority shareholders15.
A.4 Investment and Valuation
Proposition 5: Firms with low diversion invest more and are valued higher.
Proof: The relation between the level of corporate investments and governance is already
established in the preceding analysis. By combining equations [3] and [5] the optimal investment
can be written as:

j*  
*
(  e)(1  d )
if c  1  
otherwise
[10]
Since the market value of the firm, MV, is the present value of gross returns from projects,
MV 

(1 d * )(   e )
0
(1    j )dj  (1   )(1  d * )(  e) 
(  e) 2 (1  d * ) 2
2
[11]
Equations [10] and [11] show that both the optimal investment and the market value of the firm
either increase or remains unchanged as d* decreases. Q.E.D.
15
Shleifer and Wolfenzon (2002) obtain a similar result under a more restrictive set of assumptions.
12
B. External Financing
There are several reasons for external financing to be related to corporate governance. The
first follows from what we have already shown: Firms with profitable investment opportunities
will have better corporate governance. If profitable firms invest more and more investment leads
to greater external financing, firms with greater external financing are likely to have better
corporate governance.
According to Demirgüç-Kunt and Maksimovic (1998), however, the prediction would be the
opposite. They argue that profitable firms have more internally generated funds and hence rely
less on external financing. Thus, to isolate the impact of external financing from that of
profitability of investment opportunities, we assume investment is given and analyze how d is
related to external financing. In this setting, firms relying more on external financing have
greater incentives to convince investors that they will be protected through good corporate
governance, because new investors rationally anticipate diversion by the controlling
shareholders.
Proposition 6: For a given level of profitable investment opportunities, controlling shareholders
of firms with greater dependence on external financing will divert less corporate resources for
private benefits.
Proof: See Appendix A.
II. Empirical Hypotheses and Design
A. Hypotheses
The preceding theoretical discussions are summarized in the following hypotheses:
Everything else being equal,
1. The average quality of corporate governance is lower and the variation is greater in less
investor-friendly legal regimes.
13
2. Firms with more profitable investment opportunities practice higher-quality corporate
governance.
3. Firms with greater reliance on external financing practice higher-quality corporate governance.
4. Firms with more concentrated ownership practice higher-quality corporate governance.
5. Firms with higher-quality corporate governance will invest more and will be valued higher.
6. The above relations are stronger in less investor-friendly legal regimes16.
B. Empirical Design
To test hypotheses 2, 3, 4, and 6 we regress individual firms’ corporate governance and
transparency scores on measures of profitable investment opportunities, reliance on external
financing, ownership concentration, and countries’ legal regime, while controlling for industry
and other firm characteristics. Specifically, we estimate the following cross-sectional regression
using OLS,
CG cj    1  INV _ OPPjc   2  EXT _ FIN cj   3  OWNERSHIPjc   1  LEGALc 
 2  EXT _ FIN cj  LEGALc   3  INV _ OPPjc  LEGALc   4  OWNERSHIPjc  LEGALc 
I 1
K

k 1
k
* Z kc, j 
d
i 1
i
  jc
[S1]
where c stands for country; i, industry; j, firm; k, control variables; I, the number of industries;
and K, the number of control variables. CG is corporate governance or transparency scores;
INV_OPP, investment opportunities; EXT_FIN, reliance on external financing; OWNERSHIP,
concentration of ownership; Z, control variables; and d, industry dummy. The strength of a
country’s legal regime is denoted by LEGAL, and INV_OPP*LEGAL, EXT_FIN*LEGAL, and
OWNERSHIP*LEGAL are interaction terms of legal regime with investment opportunities,
external financing, and ownership concentration, respectively.
16
It does not directly follow from our model that the relation of corporate governance with external
financing, valuation, and investment is greater in less investor-friendly regimes.
14
We expect firms with more profitable investment opportunities, greater demand for external
financing, and higher ownership concentration to have better corporate governance (Hypotheses
2, 3, 4). We also expect that the relation of profitability, external financing, and ownership to
corporate governance is stronger in countries that belong to weaker legal regimes (Hypothesis 6).
That is, we expect coefficients  1,  2,  3, and 1 to be positive and 2, 3, and 4 to be negative.
To examine the relation between individual firm valuation or investment with corporate
governance, we again control for countries’ legal regime, industry, and firm characteristics and
estimate the following cross-sectional regression using OLS:
Valuationcj or Investment cj    1 CG cj   1 LEGALc 
 2  CG cj  LEGALc 
K

k 1
I 1
c
c
k * Z k , j   di   j
i 1
[S2]
Because we expect firms with better corporate governance to be valued higher and invest
more and this relation to be stronger in weaker legal regimes (Hypotheses 5 and 6), we expect  1
and 1 to be positive and 2 to be negative.
We first run these regressions using OLS and report the results. However, the inferences that
can be drawn from OLS regression results are limited because of potential econometric
problems. We conduct various robustness checks on endogeneity, regression model specification,
and alternative definition of main variables and describe the results in Section V.
III. Data
A. Corporate Governance and Disclosure Scores
To measure the quality of corporate governance and disclosure practices for individual
companies, CG, we rely on scores provided by two sources: Credit Lyonnais Securities Asia
(CLSA) Corporate Governance Scores and Standard & Poor’s Transparency Ranking.
15
A.1 CLSA Corporate Governance Scores
CLSA issued a report on the corporate governance of 494 companies in 24 countries in
March 200117. Firms are selected based on size (large) and investor interest (high). The corporate
governance scores are based on answers from financial analysts to 57 questions, which are used
to construct scores on a 1-100 scale, where a higher number indicates better corporate
governance. All questions have binary answers (yes/no) to reduce analysts’ subjectivity. In
tabulating the scores, CLSA attempts to differentiate corporate governance attributes from
country-level legal environment. Their report states, “Our scores do not mark down a company
simply for being in a country that might be perceived to have a weak regulatory or legal
framework." (CLSA Emerging Markets, 2002, p. 9).
Scores on the 57 questions are grouped into seven categories: discipline (managerial
incentives and discipline towards value maximizing actions), transparency (timely and accurate
disclosure), independence (board independence), accountability (board accountability),
responsibility (enforcement and management accountability), fairness (minority shareholder
protection), and social awareness. These seven attributes are then averaged by CLSA to
construct the composite governance index, where each of the first six attributes is weighted 15%
and social awareness is weighted 10%.
To check the validity of CLSA scores, Khanna et al. (2002) manually construct a corporate
governance “scandal index" for a group of Indian firms covered by CLSA. The "scandal index" is
based on the number of media reports revealing shareholder expropriation, tax evasion, and price
fixing. They report that companies with low CLSA corporate governance scores are more likely
to have scandals reported in media.
17
Although issued in March 2001, we treat CLSA scores as 2000 data because the companies were rated in
2000.
16
A.2 Standard & Poor’s Transparency Ranking
Standard & Poor’s recently released its survey of corporate disclosure practices for 568
companies in 16 emerging markets and 3 developed countries. It reports whether or not a
particular firm discloses relevant information on 98 possible items that would be of interest to
investors: 28 items on ownership structure and investor relations (ownership), 35 items on
accounting and financial policies (disclosure), and 35 items on board and management structure
and process (board). We calculate the number of items disclosed in each category in 2000 and
assign scores from 0 to 28 for ownership, from 0 to 35 for disclosure, and from 0 to 35 for board.
We also compute an aggregate transparency score, aggregate, ranging from 0 to 98, by totaling
the scores of the three categories18.
If a firm has more disclosures on ownership related items and fewer disclosures on items
concerning accounting and financial policies, for example, the implication is that the firm has
less to hide and relatively sound practices on issues concerning ownership. Conversely,
reluctance to reveal practices concerning accounting and financial policies implies relatively
unsound practices in that category.
The disadvantage of the S&P ranking is that it depends only on the number of disclosures
and does not reflect the content. It is best viewed as a measure of transparency and not a
comprehensive measure of corporate governance. The advantage is that the S&P rankings in each
category are objective, whereas the CLSA rankings are partially based on opinions of financial
analysts. Appendix B contains a detailed description of both.
A.3 Consistency across CLSA and S&P Scores
To determine whether companies scored high on corporate governance by CLSA are also
scored high on disclosure by S&P, we identify 203 companies that are ranked by both agencies.
Table I reports correlation coefficients between attributes of CLSA and S&P scores. Most of the
18
This is equivalent to assigning an equal weight to each disclosed item.
17
attributes of CLSA scores are highly correlated with each other except for social. Different
categories of S&P scores are also significantly correlated with each other, indicating that firms
that disclose more in one category tend to disclose more in other categories.
The correlation across CLSA and S&P rankings reveal that CLSA’s composite index is
significantly correlated with S&P’s aggregate score. To check whether the correlation is driven
by country and industry differences, we regress CLSA’s composite index on S&P’s aggregate
score with country and industry dummies. The relation remains significant, confirming the
consistency between the two rankings.19
Although many CLSA individual attributes are not correlated with S&P’s, the correlations
are positive and significant when they are measured on overlapping characteristics. For instance,
S&P’s measure of disclosure on accounting and financial policies (disclosure) is significantly
correlated with CLSA’s measure of transparency (transparency); S&P’s disclosure on board and
management structure and process (board) is significantly correlated with CLSA’s ranking on
board
accountability
(accountability),
enforcement
and
management
accountability
(responsibility); and S&P’s disclosure on ownership structure and investor relations (ownership)
is significantly correlated with CLSA’s transparency (transparency) and investor protection
(fairness). These correlations suggest that CLSA scores are largely consistent with the objective
measure reported by S&P20.
19
The regression is:
COMP = 0.17AGGR + i di + c dc
R2 = 0.45,
[0.06]
where di and dc are industry and country dummies (coefficients not reported), respectively, and the number
in parentheses is the probability level at which zero coefficient can be rejected.
20
It is possible that the firms in CLSA and S&P rankings may suffer from selection and reporting bias;
namely, only firms with good governance practices may cooperate with CLSA’s survey and the ranking
agencies may choose firms that are easier to assign scores. However, companies have incentives to
cooperate because exclusion from the ranking may create ill reputation, and the ranking agencies have
commercial interest to have a well-balanced portfolio of companies on their lists.
18
B. Other Firm-specific Variables
Because much of the firm-level data originate from financial statements and accounting
practices that vary across countries, it is difficult to directly compare the data in our sample.
Consequently, all the regression specifications contain legal regimes and industry dummies as
independent variables. Because one of the key distinguishing characteristics in legal regimes is
accounting standards, the legal regime variable controls for their differences to some extent.
Additionally, industry dummies help control for different accounting practices across industries.
Any remaining noise would weaken the power of our tests.
Most of the firm-level data are obtained from the Worldscope. All variables are measured in
U.S. dollars.
B.1 Investment Profitability, Reliance on External Financing and Ownership
To measure the opportunity for profitable investments, INV_OPP, we use return on invested
capital (ROIC) 21 . Reliance on external financing, EXT_FIN, is measured as the sum of new
equity and changes in long-term debt over capital expenditures, where new equity issue is
defined as changes in book value of equity less changes in retained earnings. Ownership
concentration, OWNERSHIP, is defined as the percentage of shares held by major shareholders,
either individuals or corporations, that hold more than 5% of outstanding shares.
For all these variables we use two-year averages for 1999 and 2000 to reduce the impact of
outliers. The 1998 data are not used because of the 1997-1998 financial crises.
B.2 Valuation and Investment
To run regression specification S[2] we use the two-year average (2000-2001) Tobin’s Q as
the measure of firm valuation. As in Doidge et al. (2001) and La Porta et al. (2002), we define
Tobin’s Q as the sum of total assets plus market value of equity less book value of equity over
21
Worldscope defines return on invested capital as (Net Income before Preferred Dividends + ((Interest
Expense on Debt - Interest Capitalized) * (1-Tax Rate)) / (Last Year's Total Capital + Last Year's Short
Term Debt & Current Portion of Long Term Debt).
19
total assets. The market value of equity is the number of common shares outstanding times the
year-end price. We measure investment, INVEST, as the two-year average (2000-2001) of capital
expenditures over total assets.
Since Tobin’s Qs and the level of investment may not be directly comparable across
countries, it would be natural to control for country fixed effects. However, it is not feasible to
estimate country fixed effects and LEGAL at the same time as there is no within country variation
in LEGAL. We deal with this problem in the robustness section by repeating our estimation
without LEGAL but with country fixed effects.
We separate time periods during which dependent and independent variables are measured in
order to reduce endogeneity. Specifically, we use 2000-2001 two-year average for Q and
INVEST; 2000 for CG; and 1999-2000 two-year average for INV_OPP, EXT_FIN, and
OWNERSHIP.
C. Legal Regime Variables
To measure investor protection, INVESTOR, we use the antidirector rights (shareholder
rights) index defined in La Porta et al. (1998), Claessens et al. (1999) and Pistor et al. (2000),
which ranges from 0 to 622. This investor-protection measure reflects only de jure regulation, and
countries like India and Pakistan that may not have the best de facto investor protection score
INVESTOR values of 5, the highest in our sample. To measure the strength of de facto regulation
we use the rule of law index, ENFORCE, from La Porta et al. (1998), Claessens et al. (1999), and
22
An index is formed by adding 1 when (1) the country allows shareholders to mail their proxy vote to the
firm; (2) shareholders are not required to deposit their shares prior to the general shareholders’ meeting; (3)
cumulative voting or proportional representation of minorities mechanism is in place; (4) an oppressed
minorities mechanism is in place; (5) the minimum percentage of share capital that entitles a shareholder to
call for an extraordinary shareholders’ meeting is less than or equal to 10 percent; or (6) shareholders have
preemptive rights that can be waived only by a shareholders’ vote.
20
Pistor et al. (2000) as a proxy for law enforcement. The rule of law is the assessment of the law
and order tradition of a country and ranges from 0 to 1023.
There is little correlation between de jure and de facto measures of regulation. The
correlation coefficient between INVESTOR and ENFORCE is only 0.05 with p-value = 0.82.
Thus, we multiply INVESTOR by ENFORCE to construct a measure that reflects both aspects of
regulation and define it LEGAL 24. Finally, the legal origin of company law or commercial code
of each country (English Common Law, French Civil Law, German Civil Law, or Scandinavian
Civil Law), ORIGIN, is also taken from La Porta et al. (1998) and Claessens et al. (1999)25.
D. Control Variables
Several control variables are used in S[1] and S[2]. One-digit SIC industry dummies (di)
are included in both regression specifications to account for the differences in assets structure,
accounting practices, government regulation, and competitiveness, all of which may affect
corporate governance and firm valuation. We use two-digit dummies as a robustness check.
We also control for firm size, SIZE, defined as logarithm of total assets. Because larger firms
tend to attract more attention and may be under greater scrutiny by the public, size may affect
governance structure. Size also proxies for firm age, and older and larger firms tend to have
higher book-to-market value ratio. As a robustness check, we use the log of sales as a proxy for
23
ENFORCE is calculated as 1982-1995 average for all countries except Russia. For Russia, it is calculated
in 1998. La Porta et al. (1997) also use the rule of law as a measure of law enforcement. An alternative is to
use the efficiency of the judicial system index reported in La Porta et al. (1998), which is defined as
assessment of the efficiency and integrity of the legal environment as it affects business. We decided to use
the rule of law for two reasons. First, using the efficiency of the judicial system as a proxy for law
enforcement would reduce our sample size because this variable is not defined for China, Hungary, Poland,
and Russia in La Porta et al. (1998). Second, the two variables are highly correlated. The correlation
between the rule of law and the efficiency of the judicial system is 0.64 (p-val = 0.00). As a robustness
check we drop firms in countries for which this measure is not defined and use the efficiency of the judicial
system as a measure of law enforcement.
24
Our main results do not change if INVESTOR and ENFORCE enter [S1] and [S2] separately rather than
as a product.
25
INVESTOR, ENFORCE, and ORIGIN are defined in La Porta et al. (1998) for each country in our sample
except China, Hungary, Russia and Poland. For these countries we rely on the variables reported in
Claessens et al. (1999) and Pistor et al. (2000). ORIGIN is not defined for Russia.
21
firm size because sales data are less sensitive to differences in the accounting standards across
countries.
Research and development expenditure scaled by total assets, R&D, is used to control for
differences in intangibility of corporate resources, which may be related to cost of diversion.
Moreover, companies with high R&D expenditures tend to be high-growth firms and may enjoy
high valuation.
Finally, we use export intensity, EXPORT, defined as the revenue generated from shipping
merchandise to another country for sale, scaled by total sales. This measure is used to control for
differences in exposure to globalization pressures in the product market. Companies that conduct
more business globally may feel more pressure to conform their corporate governance to global
standards (see Khanna et al. (2002)). All control variables are two-year averages, 1999-2000.
Table II summarizes definitions of variables and their sources.
E. Sample Construction
The original CLSA and S&P samples contain 494 firms in 24 countries and 568 firms in 19
countries, respectively. Most of the financial information for these companies is obtained from
Worldscope. When the identity of companies is ambiguous, we check it in the ISI (Internet
Securities, Inc.) Emerging Markets database. If the ambiguity cannot be resolved, the companies
are dropped from the sample, resulting in 475 firms in the CLSA sample and 557 firms in the
S&P sample26.
Sample sizes are reduced further when relevant variables for each regression are unavailable
from Worldscope. When INV_OPP and EXT_FIN enter as independent variables in S[1], 15 and
16 companies, respectively, are dropped from CLSA and S&P samples due to missing data.
26
CLSA and S&P samples contain 102 and 109 financial institutions, respectively. It is customary to
exclude them (SICs 6,000 through 6,999) because their financial and accounting statements are not
comparable to those of non-financial firms. Although including industry dummies partially mitigates this
problem, we repeat all regressions excluding banks and financial firms as a robustness check.
22
When the regression is run with OWNERSHIP as the independent variable, 95 and 99 companies,
respectively, are dropped from the CLSA and S&P samples because ownership data are missing.
When regression specification S[2] is run for Tobin’s Q as the dependent variable, 5 and 7
companies, respectively, are dropped from the CLSA and S&P samples because of missing data.
When we use INVEST as the dependent variable 7 and 8 companies, respectively, are dropped
from the CLSA and S&P samples.
If a firm has all major financial variables except R&D and export, we assume zero for those
two variables. In other words, we assume that when a company does not report these variables it
is because R&D spending or sales generated through export are negligible. Dropping companies
with missing data for R&D and export would reduce our sample size considerably and may bias
our sample towards technology-oriented firms. As a robustness check and as suggested by
Himmelberg (1999), we also use two dummy variables which take values of 1 when a firm does
not report R&D or export. These dummies control for the possibility that non-reporting firms are
different from reporting firms.
IV. Empirical Results
In this section we first provide summary statistics, compare the mean and variance of
governance and transparency rankings across legal regimes and show correlation among main
variables. We then conduct regression analysis on how corporate governance is related to
investment profitability, reliance on external financing, ownership concentration, and legal
regimes. Finally, we examine the relation of corporate governance to firm valuation and
investment.
A. Data Analysis
A.1 Relation between Variation in Corporate Governance and Legal Regime
Table III provides summary statistics by country for legal regime variables, CLSA corporate
governance rankings, and S&P transparency rankings. To examine the relation between legal
23
regime and governance and transparency rankings (Hypothesis 1), the average CLSA composite
governance scores are regressed on LEGAL while controlling for average investment profitability
(ROIC). The same regression is run for the variance of corporate governance rankings with an
addition of the variance of investment profitability and the number of firms as the control
variables. Countries with fewer than two firms are deleted in both regressions.
Figure 3A presents a partial regression plot, which shows a significant positive relation
between the strength of legal regime and the average quality of corporate governance at the
country level. This is not surprising given the previous findings on corporate governance at the
country level.
Figure 4A shows a partial regression plot on the variance of corporate governance
scores. It shows a negative relation between the variance of corporate governance and the
strength of legal regime but the relation is not statistically significant. One of the reasons
for the weak relation could be that the composite score includes many aspects of
corporate governance, while LEGAL mostly measures one aspect--investor protection and
its enforcement. For instance, when we use the measure of investor protection in CLSA
(fairness) and relate its variance to LEGAL the relation becomes highly significant (Figure
4B).
An alternative measure for LEGAL is the minimum governance score observed in a
country, which can be viewed as the minimum quality of governance practice that the
country’s legal system tolerates. Figure 4D plots the partial regression results that show a
significant relation between this alternative measure of legal environment and variation of
governance rankings.
24
The same regressions were repeated for S&P transparency scores and the results are
displayed in Figures 3C, 3E and 4C and 4E 27 . The results are qualitatively the same,
except for 4E28.
These results clearly dispel the stereotypical notion that all firms in weak legal regimes
suffer from poor corporate governance while firms in strong legal regimes practice uniformly
high-quality corporate governance. To the contrary, there seems to be as much–perhaps more–
variation in the quality of corporate governance in weaker legal regimes.
A.2 Correlation between Main Variables
Table IV, Panel A, shows correlation coefficients among main variables. The overall
measure of CLSA corporate governance scores, the composite index (COMP), is highly
correlated with investment opportunities, external financing, and Tobin’s Q. COMP is also
positively correlated with all legal regime variables and the correlation is the largest for the
combined measure, LEGAL. However, COMP is uncorrelated with investment, ownership, firm
size, R&D, and export intensity.
The S&P aggregate score shows stronger relations. It is not only correlated with all the
variables with which CLSA scores are correlated but also with ownership concentration, firm
size, R&D, and export intensity.
Panel B of the same table shows correlation coefficients between legal and firm-specific
variables. Consistent with the earlier findings (La Porta et al. (1997), Rajan and Zingales (1998),
and Demirgüç-Kunt and Maksimovic (1998)), firms in countries with better investor protection
27
For consistency we also report how the country average CLSA measure of investor protection (fairness)
is related to LEGAL and how country average CLSA composite score is related to LEGAL defined as the
country minimum governance score. Figure 3B and 3D indicates that the relations between the two are
positive and significant.
28
The results are also qualitatively the same if we use spread, defined as the difference between country
maximum and minimum scores, instead of the variance of the scores.
25
and law enforcement are more reliant on external financing and enjoy higher valuation29. Firm
size, R&D, and exports are also correlated with Tobin’s Q, investment, S&P rankings, and with
each other, confirming our reasons to control for these variables in regression analyses.
B. Regression Results
For regression specification [S1] separate regressions are run for INV_OPP and EXT_FIN,
and for OWNERSHIP. These variables are separated for two reasons. First, using all three as
independent variables in addition to their interaction terms with LEGAL creates a severe
multicollinearity problem. Second, using all three variables in a single regression substantially
reduces the sample size because the ownership data is unavailable for 95 and 99 firms in the
CLSA and S&P samples, respectively. As a robustness check, we estimate S[1] with INV_OPP,
EXT_FIN, OWNERSHIP, and their interaction terms altogether. For this regression, we control
for multicollinearity by centering these variables (subtracting the sample mean from each
observation). All p-values reported in tables are based on robust standard errors.
B.1 Investment Opportunities, External Financing, and Ownership
Table V reports the results of regression [S1] with INV_OPP and EXT_FIN for CLSA scores.
The results are supportive of our hypotheses. Both investment opportunities and external
financing are significantly, positively related to the composite index. The strength of legal
regimes is also positively related to the index.
The interaction terms of legal regime with investment opportunities and external financing
are also supportive of the hypothesis that positive relations for investment opportunities and
external financing are stronger (weaker) in weaker (stronger) legal environment. Although the
interaction term INV_OPP*LEGAL is not significant for the composite index, perhaps due to
29
The correlation between LEGAL and the level of investment is negative, which implies firms in poor legal
regimes invest more. Although this may be due to over-investment, it is difficult to determine the cause
because the investment data do not distinguish between profitable and unprofitable investments.
26
multicollinearity, the test of joint significance indicates that INV_OPP and INV_OPP*LEGAL
are jointly significant.
The table also displays the results for seven governance attributes as the dependent variable.
Investment opportunities are related to discipline, responsibility, and fairness, while external
financing is related to transparency, independence, responsibility, and fairness. It appears that
profitable firms tend to stress financial incentives and discipline for managers, enforcement and
managerial accountability, and minority shareholder protection in their governance structure.
Firms that are more reliant on external financing appear to gravitate toward governance
structures
stressing
transparency,
board
independence,
enforcement
and
managerial
accountability, and minority investor protection – important concerns to new investors.
When social awareness is used as the dependent variable, however, none of the firm-specific
independent variables of interest are significant. Although social awareness attracts a great deal
of public attention, corporate profitability and external financing are not related to it. Apparently,
firms do not become more socially responsible when they become more profitable or more reliant
on external financing.
The regression results of [S1] with ownership concentration are reported in Table VI. The
regressions also contain the OWNERSHIP2 term to account for possible non-linearity between
ownership concentration and corporate governance as in Himmelberg et al. (1999) and
McConnell and Servaes (1999). The significant positive coefficient for OWNERSHIP and the
significant negative coefficient for the OWNERSHIP2 term imply that corporate governance
improves with the concentration of ownership but at a decreasing rate. This is consistent with
earlier findings of Morck et al. (1988) and McConnell and Servaes (1990) who argue that greater
ownership concentration by insiders may align their interests with those of minority shareholders
but it also may result in a greater degree of managerial entrenchment for greater consumption of
private benefits.
27
The interaction term with LEGAL indicates that the positive relation between ownership and
corporate governance is stronger in weaker legal regimes. This is consistent with Proposition 4
and with the intuition that in weaker legal regimes concentrated ownership becomes a more
important tool to resolve agency conflict between controlling and minority shareholders.
Table VII reports results from the same exercise with S&P scores as the dependent variable.
The results are generally consistent with our findings from the CLSA scores. The interaction
terms exhibit the expected signs but the significance is weaker; however, they are significant in
joint tests. Thus, the results seem to be robust whether the quality of corporate governance is
measured by a partially subjective, intuitively appealing method employed by CSLA or an
objective but limited method employed by S&P30.
Taken together, these results suggest that it is not only the difference in legal environment
that matters, but also firm-level differences in profitability, external financing, and ownership
concentration make a difference in a firm’s choice of corporate governance. Furthermore, these
firm-specific attributes matter more as the legal environment becomes less investor friendly.
B.2. Valuation and Investment
To test the hypothesis that corporate governance and valuation are positively related, we run
regression specification [S2] with Tobin’s Q as the dependent variable. Independent variables are
CLSA scores or S&P rankings, legal regime, an interaction term of legal regime with corporate
governance or disclosure scores, return on invested capital (ROIC), firm size, R&D expenditures,
and export. ROIC is added to control for a possible spurious relation between corporate
governance and valuation because profitability is likely to influence both valuation and corporate
governance.
30
In contrast to the CLSA scores, S&P rankings are strongly related to the control variables; namely, larger
firms and firms with more R&D expenditures tend to disclose more, while firms that are more exportoriented tend to disclose less.
28
Table VIII reports results based on CLSA scores. Consistent with the hypothesis, firms with
higher-quality corporate governance are valued higher. The composite governance score is
positively related with firm valuation, and of the seven attributes, four show significant relations
with firm valuation. The social awareness score again shows no relation to valuation, indicating
that socially responsible firms do not necessarily enjoy higher valuation.
The LEGAL variable also has the expected positive sign, which is consistent with the
findings of La Porta et al. (2002). However, it is not significant for the composite index and five
of the seven attributes. The last column in Table VIII shows that the coefficient on LEGAL
becomes significant when the same regression is run without the governance scores. This is
perhaps due to the high correlation between CLSA scores and legal regimes (see Table IV).
Apparently, the corporate governance rankings provided by CLSA contain more information on
firm valuation than the legal environment in which a firm is located.
The interaction term has the expected negative sign and is significant for three of the
attributes. In joint significance tests the interaction term is significant for the composite index as
well as for four attributes, suggesting the positive relation between corporate governance and
valuation is weaker in stronger legal regimes. This explains why previous studies based on U.S.
data found mixed results.
All control variables are of expected sign and highly significant. Firms with high ROICs
enjoy higher valuation as do firms of smaller size, greater R&D expenditures, and more export
orientation.
The same regressions are run with S&P scores and the results are reported in Panel A of
Table IX. They are qualitatively the same. Firms that disclose more are valued higher and their
disclosure practices seem to have a stronger link to firm valuation than does the legal
environment in which a firm is located.
29
Finally, to investigate the relation between corporate governance and the level of investment
activities, the same regression is run using investment as a dependent variable. Table X and
Panel B of Table IX report regression results for CLSA and S&P scores. The results are similar
and consistent with the predictions of the model. Firms that have higher-quality corporate
governance, or those that disclose more, tend to invest more, and this relation is stronger in
weaker legal regimes.
V. Robustness
Our results remain robust to a battery of checks addressing endogeneity, the possibility of
firm-level observations not being independent, heteroscedasticity, multicollinearity, alternative
definitions of main variables, and the problem of outliers.
A. Endogeneity
There is an endogeneity problem in our regression analyses. For example, firms that had
good governance in the past may have better investment opportunities and raise more external
capital, which results in correlation between the regression error term and explanatory variables
making the estimates inconsistent. As stated earlier, we lag all independent variables to lessen
this problem. To address this issue further, we estimate regression specification [S2] using the
Instrumental Variables (IV) approach with the legal origin (English, French, German, or
Scandinavian) as instruments for corporate governance scores. La Porta et al. (1998) argue that
investor protection is better in common law countries, implying firms located in countries with
English legal origin have better corporate governance 31 . The legal origin is used as the
instrumental variable because it is reasonable to assume that the legal origin is to some extent
31
The average CLSA composite corporate governance scores in common law (English) countries and civil
law (German and French) countries are 59.28 and 51.46, respectively, and they are significantly different
from each other at 0.001% level. The average S&P aggregate transparency scores are 45.5 and 39.04 in
common law and civil law countries, and the difference is again highly significant.
30
exogenous to individual firm valuation and investment32. For IV regression we use the following
specification,
Valuation    1  CG 
c
j
c
j
C 1
I 1
K
 d   d  
c 1
c
i 1
i
k 1
k
 Zkc, j   jc
[S3]
where CG is either CLSA or S&P scores, dc are country dummies, di industry dummies, and Z are
control variables that include size, R&D expenditures, and export intensity. Legal origin
dummies, ORIGIN, are used as instruments for firm-level corporate governance, CG33.
Table XI reports and compares IV regression coefficients to those of the OLS regression for
CLSA scores when the dependent variable is valuation; Table XII reports results for investment
as the dependent variable 34 . At the bottom of these tables we report the 2 statistics of the
Hausman specification test (Hausman (1978)) of null hypothesis that the OLS estimator is a
consistent and efficient estimator of the true parameter 35. The IV regression results show that the
majority of CLSA scores are positively and significantly related to valuation and investment.
Moreover, the Hausman test indicates that coefficients for corporate governance in OLS
regressions are not significantly different from those in the IV regressions for most cases.
The results using S&P scores are reported in Tables XIII and XIV and they are consistent
with those using CLSA scores. In the IV regression, all scores are positively related to valuation
Average values of Tobin’s Q are 1.96 and 1.48 in common law and civil law countries, respectively, and
the averages are significantly different at 10% but not at 5%. The average level of investment is 0.088 in
common law countries, which is not significantly different from an average level of investment of 0.115 in
civil law countries at 10%.
33
Unlike specification S[1] and S[2], S[3] does not control for the quality of legal regime, LEGAL, or past
investment opportunities, ROIC, because both of them are endogenous. For identification purposes the
number of instrumented variables, corporate governance scores in our case, cannot exceed the number of
instruments. For this reason S[3] includes country dummies to control for country-specific characteristics.
34
Since legal origin is not defined for Russia, we excluded Russia from Instrumental Variables estimation.
35
Hausman specification test (Hausman (1978)) compares the IV estimates with OLS estimates. The null
hypothesis is that the OLS estimator is a consistent and efficient estimator of the true parameter. It is
calculated as H  (  IV   OLS ) ' (V IV  VOLS )(  IV   OLS ) , where  IV is the coefficient vector from the IV
32
regression,  OLS is the coefficient vector from the OLS regression, V IV is the covariance matrix of the
coefficients from the IV regression, and VOLS is the covariance matrix of the coefficients from the OLS
regression.
31
and investment, and coefficients estimated in the IV regressions are not significantly different
from those estimated using simple OLS regressions36.
B. Regression Specifications
Each company-specific observation is treated as independent in all regressions. However, it
is natural to expect observations within the same country to be correlated due to similar
institutional, legal, and socio-economic factors. Ignoring the inter-firm correlations could make
our estimates inefficient. We deal with this problem by adjusting coefficients standard errors
assuming that firm-specific observations are correlated within each country but are independent
across countries and that there is heteroscedasticity of the error term within and across
countries 37 . Correlation between the independent variables and their interaction terms with
LEGAL can cause multicollinearity problem making the estimates inefficient. As suggested by
Jaccard et al. (1990), this correlation can be reduced by centering the original variables 38. Thus
we re-estimate S[1] and S[2] after centering main independent variables and adjusting
coefficients standard errors to account for within-countries correlations.
Tables XV and XVI report the estimation results for the CLSA composite index and the S&P
aggregate index, respectively. Although both tables show slight changes in standard errors, the
magnitude and overall significance for most of the results remain unchanged. As an additional
36
Unfortunately, we could not use the IV approach for S[1] when investment profitability, reliance on
external financing, and ownership concentration enter as independent variables because we are unable to
identify suitable instruments that would be closely related to those variables but unrelated to firm corporate
governance structure.
37
The estimator of coefficient’s variance when observations are correlated within groups (countries or
 M /(M  1)  ˆ
M
ˆ , were
Q k 1 u (kG )' u (kG ) Q
industries in our case) but uncorrelated between groups is Vˆ  
(
N

1
)
/(
N

k
)




M is the number of groups, N the number of observations, k the number of estimated parameters,
ˆ  ( ln L /  ) 1 the conventional estimator of variance, L the likelihood function,  the vector of
Q
)
parameters estimated and u (G
the contribution of the kth group to the scores  ln L /  (see Huber (1967)).
k
38
For example, in CLSA sample, the correlation between the original EXT_FIN and EXT_FIN*LEGAL is
0.90 which drops to 0.23 after we center EXT_FIN and LEGAL.
32
robustness check we assume firm-specific observations are correlated across industries and the
results are qualitatively the same.
As mentioned earlier, Tobin’s Q and the level of investment are not comparable across
countries and we cannot use country fixed effects along with LEGAL. This problem is partially
addressed when we compare IV and OLS regressions in Tables XI-XIV, where all OLS
regressions are run without LEGAL but with country fixed effects. Tables XI-XIV show that
almost all attributes of CLSA and S&P scores are significantly related to valuation and
investment39.
Finally, several CLSA scores contain a few companies that score either 0 (minimum
possible) or 100 (maximum possible). For those scores, a more appropriate estimation of
specification S[1] would be to use a limited dependent variable approach (Tobit regression). The
results in Tables V and VI do not change if we use Tobit regression instead of simple OLS.
C. Alternative Variables
Our results are also robust to alternative definitions of independent variables or to addition of
more control variables. It is possible, for instance, that corporate governance and transparency
scores are more related to other proxies of legal environment than to investor protection and rule
of law. Several alternative variables are used to measure de jure protection of capital suppliers
and de facto enforcement. Specifically, we substitute investor protection with creditor protection
and rule of law with measures of efficiency of the judicial system, risk of expropriation, risk of
contract repudiation, and absence of corruption40. In addition, following the principal component
analysis outlined in Berkowitz et al. (2002) we combine investor and creditor protection to
39
To conserve space we do not report the estimation results for S[1] with country fixed effects but without
LEGAL and interaction terms. The results are similar to those reported in Tables V-VII. Specifically,
INV_OPP, EXT_FIN, and OWNERSHIP are positively and significantly related to most of the CLSA and
S&P attributes. Furthermore, regressions with country fixed effects in Tables XI-XIV do not include
interaction terms. When we estimate S[1] and S[2] with country fixed effects along with interaction terms
but without LEGAL itself, the results are similar to those reported in Tables V-X.
40
Creditor protection, efficiency of judicial system, risk of expropriation, risk of contract repudiation, and
corruption index are defined in La Porta et al. (1998).
33
construct a single capital providers’ protection index; for enforcement, we combine efficiency of
the judicial system, rule of law, absence of corruption, risk of expropriation, and risk of contract
repudiation to derive a single index. Finally, we redefine LEGAL as a country minimum CLSA
or S&P scores. The results remain virtually unchanged with alternative definitions for LEGAL.
Our external financing measure is defined as a proportion of investment funded by new
equity issues and changes in long-term debt. Using only new equity issues over investment as a
measure of the reliance on external financing does not change our results; neither does including
reliance on short-term debt in addition to long term debt.
As mentioned earlier, we instrument current values of INV_OPP and EXT_FIN by their
lagged values to reduce the endogeneity problem. Using contemporaneous measures does not
change our findings nor does using two-digit industry dummies instead of one-digit ones. The
findings also remain valid when we define firm size using sales instead of assets and when we
include dummies when R&D or export data is missing.
We also use OWNERSHIP and OWNERSHIP2 as additional control variables in specification
S[2] because ownership concentration is related to valuation (Morck et al. (1988) and La Porta et
al. (2002)), investment, and corporate governance scores. Finally, we include INV_OPP,
EXT_FIN, and OWNERSHIP, as well as their interaction terms with LEGAL all together in S[1].
Our results are robust to all of the above.
D. Outliers
Because outliers could affect our results, all but corporate governance and legal regime
variables are calculated as two-year averages. As a further check, we apply different
methodologies to trim outliers: Hadi’s (1992, 1994) multivariate method (with a percent cut-off)
and Cook’s D statistics. We also drop the top and bottom 1% observations of the main variables
and Winsorize the main variables at 1%. None of these procedures change the results.
34
Several countries contain data for only a few companies. As an additional robustness check
we rerun all regressions dropping countries with fewer than 3 firms 41. The qualitative results
remain the same.
In sum, our results do not appear to be driven by the endogeneity problem (in S[2]) or
outliers, and they are robust to alternative estimation techniques, variables definitions, and more
control parameters.
V. Summary and Implications
This paper analyzes how firm-specific attributes interact with legal environment in firms’
choices of governance practice. With a simple model we illustrate that profitable investment
opportunities, reliance on external financing, and more concentrated ownership lead to better
governance practice and that the effects are stronger in weaker legal environments. We also show
that firms with better governance are valued higher and invest more.
To determine the empirical validity of these implications we use two newly-constructed sets
of data on the quality of corporate governance and transparency. One data set relies on an
intuitively appealing, yet partially subjective method, while the other relies on a restrictive but
objective method. Consequently, all tests are conducted for both sets of data. All regressions are
run with control variables that include industry dummies, size, R&D expenditures, and export.
The results show that aforementioned firm attributes are positively related to the quality of
governance practice.
These positive relations are stronger in countries with weaker legal regimes. Apparently,
firms in weak legal regimes structure their own governance to take better advantage of profitable
investment opportunities, as do firms with greater reliance on external financing to overcome the
deleterious effects of poor legal protection on their ability to raise external capital. Firms in
41
In the CLSA sample these countries are Argentina, Columbia, Czech Republic, Greece, Hungary, Peru,
Poland, and Russia. In the S&P sample only New Zealand falls in this category.
35
weaker legal regimes also seem more reliant on ownership concentration to resolve agency
conflict between controlling and outside shareholders as a response to a lack of investor
protection.
These results have implications for the debate concerning the Coase argument (Coase
(1960)). Our results on legal regimes confirm the La Porta et al. (1998) basic thesis that law
matters in determining corporate governance. But our results on firm-specific attributes also
illustrate that firms located in countries with poor legal protection do adapt to achieve efficient
governance practice.42
We find that companies with better corporate governance and greater disclosure enjoy higher
valuation (Tobin’s Q) and invest more. For instance, a 10-point increase (out of 100) in CLSA
corporate governance scores increases a firm’s market value by 13.3%, while a 10-point increase
(out of 98) in S&P transparency scores increases a firm’s market value by 16.3% 43. These results
are consistent with practitioners’ views of the worth of good corporate governance in emerging
economies.44
Our results also indicate that the positive relation between governance and valuation is
weaker in stronger legal regimes. This explains why previous studies based on U.S. data show
mixed results on the relation between corporate governance and firm valuation.
One governance attribute that consistently shows no relation to firm-specific variables or to
firm valuation is social awareness. Firms do not seem to become more socially responsible when
they become more profitable or more reliant on external financing, perhaps because they believe
42
See Johnson and Shleifer (2000) for an excellent literature review on the debate concerning the Coasian
argument in corporate governance.
43
These numbers are based on the magnitude of OLS coefficients in Tables X and XIII. The percentage is
calculated against the median of Tobin’s Q.
44
According to a study by a Russian investment bank, Troika Dialog, “poor image of corporate governance
is currently downgrading the value of quoted shares in Russia by approximately $50bln on a simple
valuation model. That means a two-fold downgrading of the Russian market, as the total value of the
country’s capital market is $47bln…” (see Vardanian (2001)). In a recent survey conducted by McKinsey,
two-thirds of 119 large institutional investors said they would pay more for the stock of a company with
good corporate governance (see the McKinsey Quarterly (1998)).
36
it is not important to investors. Indeed, our evidence indicates that while investors value other
governance attributes, they attach little value to the criteria CLSA uses to define social
awareness, which are child labor, political legitimacy, environmental responsibility, equal
employment policy, and ethical behavior. Child labor, for example, is a controversial issue to
economists. They debate whether child labor in developing economies is damaging to those
societies, as the alternatives could be starvation, prostitution, or drug peddling.
Our results imply that economic policies affect corporate governance. To the extent that progrowth policies generate more profitable investment opportunities to corporations, the
controlling shareholders will have greater incentives to improve governance practices. Strong
legal protection of investors can also be beneficial. If strengthening disclosure rules, corporate
transparency, and managerial accountability improve the functioning of new issues markets and
enhance investor confidence, firms are more likely to rely on external financing, which our
results imply will provide an impetus for firms to improve governance.
Our results also have implications for the debate on whether globalization leads to
convergence in corporate governance (see Coffee (1999), Bebchuk and Roe (1999), Berglöf and
von Thadden (1999), Khanna et al. (2002)). With increasing globalization of trade and capital
flows, national boundaries and legal jurisdictions are becoming less effective in defining
corporate behavior in governance and finance. Therefore, any debate over convergence must
consider firm-specific factors and their interplay with legal systems in determining corporate
governance.
Finally, caveats are in order. Although we have attempted to address endogeneity, a full
treatment requires time-series analyses of changes in corporate governance practices, a task we
plan to pursue upon sufficient accumulation of data over time. On the theoretical level, we are
able to identify only three firm-specific attributes related to corporate governance; however,
other variables of greater importance may exist, which only further research can reveal.
37
Appendix A
Proof of Proposition 6
Consider a firm that has decided to invest I but does not have internal funds to finance it. The
lack of internal funds necessitates the firm to finance the investment by selling 1-fraction of the
firm. The amount the firm must raise is I/(1-d) such that when the controlling shareholder diverts
dI/(1-d) the firm will be left with I for investment. Under these assumptions the controlling
shareholder’s payoff is:
P   (MV )  (1  c)
dI
1 d .
[A1]
Since new investors earn only the equilibrium rate of return on their investment, which is
zero by assumption,
1  
I / (1  d )
MV
[A2]
Substituting Eq.[A2] to Eq. [A1],
P   ( MV 
I
d
)  (1  c)
I
1 d
1 d
[A3]
Differentiating [A3] with respect to d45,
P  1  c   

I
d  (1  d ) 2 
[A4]
If c > 1 - Eq.[A4] is negative and the optimal d = 0. When the cost of diversion, c, is greater
than the wealth transfer that d can generate from the existing shareholders, 1 – , the controlling
shareholder will find it optimal not to divert any corporate resources.
One special case of such a situation is when a firm is 100% owned prior to the external
financing, i.e., = 1. Here there are no minority shareholders, only the cost of diversion. Hence
45
We obtain Eq. [A4] because MV   (1    j )dj  (1   ) I  I 2 / 2 , and hence, MV / d  0 .
I
0
38
the obvious solution is d = 0. New shareholders will be protected ex-ante because they will price
the expected diversion at the time of purchasing the new shares. The controlling shareholder will
find it in her self-interest to convince new investors that there will be no diversion.
If  < 1 and c < 1 – , Eq.[A4] is positive and there is an incentive to maximize d. As can be
seen from Eq.[A2], however, increasing d means the controlling shareholder must sell a greater
fraction of the firm but when 
Therefore the maximum fraction of the firm she sells to new investors is bounded by a minimum
 min, below which the controlling shareholder loses the control of the firm46. Substituting  min
into Eq.[A2], we obtain:
 1  I
d*  1 

 1  min  MV
[A5]
Since I determines the amount of external financing, taking partial derivative of d* with respect
to I,
 1 
d *
1/ 2
 
0

I
 1  min  (1   )  I / 22
Q.E.D.
46
We thank Daniel Wolfenzon for pointing this out.
39
[A6]
Appendix B
CLSA Corporate Governance Scores and S&P Transparency Rankings
1. CLSA Corporate Governance Scores
The CLSA corporate governance scores are based on how analysts rate a company on 57 elements under seven major aspects of corporate governance in 2000.
Below is the summary of those attributes.
1. Discipline
1.1 Explicit public statement placing priority on corporate governance
1.2 Management incentives toward a higher share price
1.3 Sticking to clearly defined core business
1.4 Having an appropriate estimate of cost of equity
1.5 Having an appropriate estimate of cost of capital
1.6 Conservatism in issuance of equity or dilutive instruments
1.7 Ensuring debt is manageable, used only for projects with adequate returns
1.8 Returning excess cash to shareholders
1.9 Discussion in annual report on corporate governance
2. Transparency
2.1 Disclosure of financial targets, e.g., three- and five-year ROA/ROE
2.2 Timely release of Annual Report
2.3 Timely release of semi-annual financial announcements
2.4 Timely release of quarterly results
2.5 Prompt disclosure of results with no leakage ahead of announcement
2.6 Clear and informative results disclosure
2.7 Accounts presented according to IGAAP
2.8 Prompt disclosure of market-sensitive information
2.9 Accessibility of investors to senior management
2.10 Website where announcements are updated promptly
3. Independence
3.1 Board and senior management treatment of shareholders
3.2 Chairman who is independent from management
3.3 Executive management committee comprised differently from the board
3.4 Audit committee chaired by independent director
3.5 Remuneration committee chaired by independent director
3.6 Nominating committee chaired by independent director
3.7 External auditors unrelated to the company
3.8 No representatives of banks or other large creditors on the board
4. Accountability
4.1 Board plays a supervisory rather than executive role
4.2 Non-executive directors demonstrably independent
4.3 Independent, non-executive directors at least half of the board
4.4 Foreign nationals presence on the board
4.5 Full board meeting at least every quarter
4.6 Board members able to exercise effective scrutiny
4.7 Audit committee that nominates and reviews work of external auditors
4.8 Audit committee that supervises internal audit and accounting procedures
5. Responsibility
5.1 Acting effectively against individuals who have transgressed
5.2 Record on taking measures in cases of mismanagement
5.3 Measures to protect minority interests
5.4 Mechanisms to allow punishment of executive/management committee
5.5 Share trading by board members fair and fully transparent
5.6 Board small enough to be efficient and effective
6. Fairness
6.1 Majority shareholders treatment of minority shareholders
6.2 All equity holders right to call general meetings
6.3 Voting methods easily accessible (e.g., through proxy voting)
6.4 Quality of information provided for general meetings
6.5 Guiding market expectation on fundamentals
6.6 Issuance of ADRs or placement of shares fair to all shareholders
40
6.7 Controlling shareholder group owning less than 40% of company
6.8 Portfolio investors owning at least 20% of voting share
6.9 Priority given to investor relations
6.10 Total board remuneration rising no faster than net profit
7. Social awareness
4.1 Explicit policy emphasizing strict ethical behavior
4.2 Not employing the under-aged
4.3 Explicit equal employment policy
4.4 Adherence to specified industry guidelines on sourcing of materials
4.5 Explicit policy on environmental responsibility
4.6 Abstaining from countries where leaders lack legitimacy
41
2. S&P Transparency Ranking
S&P scores are based on transparency and disclosure, which is evaluated by searching for the inclusion of 98 possible information items (‘attributes’). These 98 attributes were selected after examination of
the annual report and accounts of leading companies around the world and the identification of the most common disclosure items in 2000. These attributes are then grouped into three subcategories: (i)
Ownership structure and investor relations (28 attributes), (ii) Financial transparency and information disclosure (35 attributes), (iii) Board and management structure and process (35 attributes).
I
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
Ownership Structure and Investor Relations
Does the company disclose:
number of issued and outstanding ordinary shares disclosed?
number of issued and outstanding other shares disclosed (preferred, non-voting)?
par value of each ordinary share disclosed?
par value of each other shares disclosed (preferred, non-voting)?
number of authorized but unissued & outstanding ordinary shares disclosed?
number of authorized but unissued & outstanding other shares disclosed?
par value of authorized but unissued & outstanding ordinary shares disclosed?
par value of authorized but unissued & outstanding other shares disclosed?
top 1 shareholder?
top 3 shareholders?
top 5 shareholders?
top 10 shareholders?
description of share classes provided?
review of shareholders by type?
number and identity of shareholders holding more than 3%?
number and identity of shareholders holding more than 5%?
number and identity of shareholders holding more than 10%?
percentage of cross-ownership?
existence of a Corporate Governance Charter or Code of Best Practice?
Corporate Governance Charter / Code of Best Practice itself?
details about its Articles of Association. (e.g., changes)?
voting rights for each voting or non-voting share?
way that shareholders nominate directors to board?
way shareholders convene an EGM?
procedure for putting inquiry rights to the board?
procedure for putting proposals at shareholders meetings?
review of last shareholders meeting? (e.g., minutes)
calendar of important shareholders dates?
II
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
42
Financial Transparency & Information Disclosure
Does the company disclose:
its accounting policy?
the accounting standards it uses for its accounts?
accounts according to the local accounting standards?
accounts according to an internationally recognized accounting standard (IAS/US GAAP)?
its balance sheet according to international accounting standard (IAS/US GAAP)?
its income statement according to international accounting standard (IAS/US GAAP)?
its cash flow statement according to international accounting standard (IAS/US GAAP)?
a basic earnings forecast of any kind?
a detailed earnings forecast?
financial information on a quarterly basis?
a segment analysis (broken down by business line)?
the name of its auditing firm?
a reproduction of the auditors' report?
how much it pays in audit fees to the auditor?
any non-audit fees paid to auditor?
consolidated financial statements (or only the parent/holding co)?
methods of asset valuation?
information on method of fixed assets depreciation?
a list of affiliates in which it holds a minority stake?
a reconciliation of its domestic accounting standards to IAS/US GAAP?
the ownership structure of affiliates?
details of the kind of business it is in?
details of the products or services produced/provided?
output in physical terms? (number of users etc.)
characteristics of assets employed?
efficiency indicators (ROA ROE etc.)
any industry-specific ratios?
a discussion of corporate strategy?
any plans for investment in the coming year(s)?
detailed information about investment plans in the coming year(s)?
an output forecast of any kind?
an overview of trends in its industry?
its market share for any or all of its businesses?
a list/register of related party transactions?
a list/register of group transactions?
2. S&P Transparency Ranking (continued)
III
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
Board and Management Structure and Process
Does the company disclose:
a list of board members (names)?
details about directors (other than name/title)?
details about current employment/position of directors provided?
details about previous employment/positions provided?
when each of the directors joined the board?
classification of directors as an executive or an outside director?
a chairman's name?
detail about the chairman (other than name/title)?
details about role of the board of directors at the company?
a list of matters reserved for the board?
a list of board committees?
the existence of an audit committee?
the names on the audit committee?
the existence of a remuneration/compensation committee?
the names on the remuneration/compensation committee)?
existence of a nomination committee?
the names on the nomination committee?
the existence of other internal audit functions besides the Audit Committee?
the existence of a strategy/investment/finance committee?
the number of shares in the company held by directors?
a review of the last board meeting? (e.g., minutes)
whether they provide director training?
the decision-making process of directors' pay?
the specifics of directors' pay (e.g., the salary levels etc.)?
the form of directors' salaries (e.g., cash, shares, etc.)?
the specifics on performance-related pay for directors?
the decision-making of managers' (not board) pay?
the specifics of managers' (not on board) pay (e.g., salary levels etc.)?
the form of managers' (not on board) pay?
the specifics on performance-related pay for managers?
the list of the senior managers (not on the board of directors)?
the backgrounds of senior managers?
the details of the CEO's contract?
the number of shares held by the senior managers?
the number of shares held in other affiliated companies by managers?
43
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47
Figure 1. Investment opportunity for a firm with a maximum profit of
zero
 and its optimal investment and market value when diversion is suppressed to
This figure presents a firm’s investment opportunity. The area 0ABC is equal to the firm’s market value,
MV   
2
2
, if the firm invests in all non-negative NPV projects.
1  j  1   j
A
1
B
1
C
0
j 
j
Figure 2. Controlling shareholder’s payoff as a function of diversion d with different investment profitability and legal regimes
This graph plots the payoff to controlling shareholders as a function of diversion d for the same ownership = 0.3, excess cash e = 0, and different cost of diversion c and
profitability  .
US, LOW PROFITABILITY: cUS=0.6;  L  2.0 (black dotted line)
US, HIGH PROFITABILITY: cUS=0.6;  H  2.2 (black solid line)
RUSSIA, LOW PROFITABILITY: cRUS=0.3;  L  2.0 (gray dotted line)
RUSSIA, HIGH PROFITABILITY: cRUS=0.3;  H  2.2 (gray solid line)
The corresponding optimal levels of diversion are dHUS = 0.15; dLUS = 0.17; dHRUS = 0.61; and dLRUS = 0.67.
L
H
As profitability changes from  L  2.0 to  H  2.2 , the optimal level of diversion drops by dUS  dUSL  dUSH  0.02 in the U.S. and by d RUS  d RUS
 d RUS
 0.06 in Russia.
1.7
RUSSIA, HIGH PROF.
RUSSIA, LOW PROF.
1.5
US, HIGH PROF.
1.4
dRUS=d
H
L
RUS-d RUS
1.3
US, LOW PROF
1.2
dUS=d
H
L
US-d US
1.1
d
d
H
US
d
H
RUS
d
L
RUS
L
US
diversion d
1.
00
0.
96
0.
92
0.
88
0.
84
0.
80
0.
76
0.
72
0.
68
0.
64
0.
60
0.
56
0.
52
0.
48
0.
44
0.
40
0.
36
0.
32
0.
28
0.
24
0.
20
0.
16
0.
12
0.
08
0.
04
1
0.
00
the controlling shareholder's payoff
1.6
Table I
Correlation Coefficients between Attributes of CLSA Corporate Governance Scores and S&P Transparency Ranking
This table reports simple correlation coefficients between main attributes of CLSA and S&P samples. CLSA sample size is 494 companies. S&P sample size is 568 companies. The correlation
coefficients between S&P and CLSA attributes are based on Spearman rank order correlation coefficients and they are based on 203 companies. Numbers in parentheses are probability levels at which
the hypothesis of zero correlation can be rejected. Coefficients significant at 10% (based on two-tail test) are in bold face. Refer to Table II for variables definitions.
TRANS
0.19
(0.00)
INDEP
0.27
(0.00)
0.18
(0.00)
CLSA Corporate Governance Scores
ACCOUNT
RESP
FAIR
SOCIAL
0.28
(0.00)
0.23
(0.00)
0.21
(0.00)
0.36
(0.00)
0.34
(0.00)
0.36
(0.00)
0.28
(0.00)
0.23
(0.00)
0.13
(0.01)
0.29
(0.00)
0.06
(0.19)
0.40
(0.00)
0.21
(0.00)
0.05
(0.23)
0.05
(0.22)
0.24
(0.00)
0.02
(0.69)
0.02
(0.60)
COMP
0.61
(0.00)
0.51
(0.00)
0.65
(0.00)
0.55
(0.00)
0.71
(0.00)
0.60
(0.00)
0.311
(0.00)
OWN
-0.03
(0.63)
0.12
(0.08)
0.05
(0.44)
0.04
(0.56)
0.06
(0.37)
0.15
(0.03)
-0.18
(0.01)
0.09
(0.22)
S&P Transparency Ranking
DISCL
BOARD
AGGR
0.03
(0.64)
0.16
(0.03)
-0.12
(0.09)
0.01
(0.89)
0.26
(0.00)
0.08
(0.23)
-0.21
(0.00)
0.06
(0.40)
-0.01
(0.92)
0.08
(0.23)
0.04
(0.56)
0.14
(0.05)
0.17
(0.01)
0.21
(0.00)
-0.03
(0.67)
0.15
(0.04)
0.00
(0.99)
0.14
(0.05)
0.01
(0.87)
0.08
(0.23)
0.19
(0.00)
0.18
(0.01)
-0.14
(0.05)
0.12
(0.08)
0.60
(0.00)
0.62
(0.00)
0.67
(0.00)
0.85
(0.00)
0.81
(0.00)
0.89
(0.00)
CLSA Corporate Governance
Scores
DISC
TRANS
INDEP
ACCOUNT
RESP
FAIR
SOCIAL
COMP
S&P Transparency Ranking
OWN
DISCL
BOARD
Table II
Variables, Definitions, and Source
Variable
CLSA Corporate Governance Scores
Discipline
Transparency
Independence
Accountability
Responsibility
Fairness
Social Awareness
Composite
S&P Transparency Ranking
Ownership
Disclosure
Board
Aggregate
Firm-level Variables
Firm valuation
Notation
Definition and Source
DISC
Measure of managerial incentives and discipline towards value maximizing actions. Source: 2000 CLSA Corporate Governance
Scores. Range: 0-100.
Measure of timeliness and accuracy of financial information disclosure. Source: 2000 CLSA Corporate Governance Scores.
Range: 0-100.
Measure of board independence. Source: 2000 CLSA Corporate Governance Scores. Range: 0-100.
Measure of board accountability. Source: 2000 CLSA Corporate Governance Scores. Range: 0-100.
Measure of enforcement and management accountability. Source: 2000 CLSA Corporate Governance Scores. Range: 0-100.
Measure of minority shareholder protection. Source: 2000 CLSA Corporate Governance Scores. Range: 0-100.
Measure of social awareness. Source: 2000 CLSA Corporate Governance Scores. Range: 0-100.
Composite corporate governance score. It is equal to
0.15*DISC+0.15*TRANS+0.15*INDEP+0.15*ACCOUNT+0.15*RESP+0.15*FAIR+0.1*SOCIAL. Source: 2000 CLSA
Corporate Governance Scores. Range: 0-100.
TRANS
INDEP
ACCOUNT
RESP
FAIR
SOCIAL
COMP
OWN
DISCL
BOARD
AGGR
Q
Firm level of investment
Firm reliance on external financing
INVEST
EXT_FIN
Firm investment profitability
INV_OPP
Ownership concentration
OWNERSHIP
Firm size
Firm research and development expenditures
Firm export intensity
SIZE
R&D
EXPORT
Legal Regime Variables
Legal origin
ORIGIN
Investor protection
INVESTOR
Rule of law
ENFORCE
Quality of legal regime
LEGAL
Measure of transparency of ownership structure and investor relations. Source: 2000 S&P Transparency Ranking. Range: 0-28.
Measure of financial transparency and information disclosure. Source: 2000 S&P Transparency Ranking. Range: 0-35.
Measure of transparency of board and management structure and processes. Source: 2000 S&P Transparency Ranking. Range:
0-35.
Aggregate S&P Transparency score. It is equal to OWN+DISC+BOARD. Range: 0-98.
2000-2001 average of Tobin’s Q. Tobin's Q is the sum of total assets plus market value of common stock less book value of
equity over total assets. The market value of equity is the number of common shares outstanding times year-end price. Source:
Worldscope.
Capital expenditures over total assets, 2000-2001 average. Source: Worldscope.
Proportion of investment financed externally, 1999-2000 average. It is defined as the sum of new equity issues and changes in
long-term debt over capital expenditures. New equity issue is change in book equity minus change in retained earnings. Source:
Worldscope.
Return on invested capital, 1999-2000 average. Return on invested capital = (Net Income before Preferred Dividends +
((Interest Expense on Debt - Interest Capitalized) * (1-Tax Rate))) / (Last Year's Total Capital + Last Year's Short Term Debt &
Current Portion of Long Term Debt). Source: Worldscope.
Ratio of shares held by major shareholders, either individuals or corporations, that hold more than 5% of outstanding shares,
1999-2000 average. Source: Worldscope.
Logarithm of total assets, 1999-2000 average. Source: Worldscope.
Research and development expenditures over total assets*100, 1999-2000 average. Source: Worldscope.
Exports over total sales*100, 1999-2000 average. Exports is defined as the revenues generated from the shipment of
merchandise to another country for sale. Source: Worldscope.
The legal origin of the company law or commercial code of each country (English common law, French civil law, German civil
law, and Scandinavian civil law). Source: La Porta et al. (1998) and Claessens et al. (1999).
Anti-director index. Range 0-6. An index is formed by adding 1 when (1) the country allows shareholders to mail their proxy
vote to the firm, (2) shareholders are not required to deposit their shares prior to the general shareholders’ meeting, (3)
cumulative voting or proportional representation of minorities mechanism is in place, (4) an oppressed minorities mechanism is
in place, (5) the minimum percentage of share capital that entitles a shareholder to call for an extraordinary shareholders’
meeting is less than or equal to 10 percent, or (6) shareholders have preemptive rights that can be waived only by a
shareholders’ vote. Range: 0-6. Source: La Porta et al. (1998), Claessens et al. (1999), and Pistor et al. (2000).
Assessment of the law and order tradition of the country. Average between 1982 and 1995 for all countries except Russia for
which it is measured in 1998. Range: 0-10. Source: La Porta et al. (1998), Claessens et al. (1999), and Pistor et al. (2000).
Defined as INVESTOR*ENFORCE.
Table III
Summary Statistics of Legal Regime Variables and Corporate Governance and Transparency Scores by Country
ORIGIN is legal origin; INVESTOR is anti-director index; ENFORCE is rule of law; LEGAL is INVESTOR*ENFORCE. Spread is the difference between maximum and minimum. of the scores. N is the number of
firms in the country.
Legal Regime Variables
Country
ORIGIN
Argentina
Civil (French)
Australia
Common
Brazil
Civil (French)
Chile
Civil (French)
China
Civil (German)
Columbia
Civil (French)
Czech Rep. Civil (German)
Greece
Civil (French)
Hong Kong Common
Hungary
Civil (German)
India
Common
Indonesia
Civil (French)
Japan
Civil (German)
Korea
Civil (German)
Malaysia
Common
Mexico
Civil (French)
New Zealand Common
Pakistan
Common
Peru
Civil (French)
Philippines Civil (French)
Poland
Civil (German)
Russia
na
Singapore Common
South Africa Common
Taiwan
Civil (German)
Thailand
Common
Turkey
Civil (French)
Average
INVESTOR ENFORCE
LEGAL
CLSA Composite Corporate Governance Score, COMP
Mean St Dev
Min
Max
Spread
N
Mean
4
4
3
5
1
3
2
2
5
3
5
2
4
2
4
1
4
5
3
3
3
5
4
5
3
2
2
5.35
10.00
6.32
7.02
5.83
2.08
9.03
6.18
8.22
8.15
4.17
3.98
8.98
5.35
6.78
5.35
10.00
3.03
2.50
2.73
7.52
3.70
8.57
4.42
8.52
6.25
5.18
21.40
40.00
18.96
35.10
5.83
6.24
18.06
12.36
41.10
24.45
20.85
7.96
35.92
10.70
27.12
5.35
40.00
15.15
7.50
8.19
22.56
18.50
34.28
22.10
25.56
12.50
10.36
66.70
.
61.86
62.30
48.97
57.90
51.40
57.15
62.96
52.85
55.88
37.28
.
45.24
56.61
64.44
.
33.97
67.45
43.87
36.20
15.40
64.48
69.34
54.64
55.08
42.98
.
.
8.18
5.90
11.11
.
.
5.16
12.25
10.68
10.54
12.05
.
5.44
13.61
7.97
.
13.50
11.38
12.67
3.11
.
9.06
8.54
9.18
12.33
9.79
66.70
.
45.40
46.70
29.90
57.90
51.40
53.50
40.60
45.30
39.10
13.90
.
38.00
24.60
51.90
.
18.90
59.40
25.80
34.00
15.40
45.80
45.00
34.10
33.80
29.40
66.70
.
76.50
71.30
71.10
57.90
51.40
60.80
93.50
60.40
93.30
64.90
.
57.90
77.80
74.00
.
65.60
75.50
67.90
38.40
15.40
85.70
82.60
77.10
77.80
59.40
.
.
31.10
24.60
41.20
.
.
7.30
52.90
15.10
54.20
51.00
.
19.90
53.20
22.10
.
46.70
16.10
42.10
4.40
.
39.90
37.60
43.00
44.00
30.00
1
.
30
15
25
1
1
2
38
2
79
18
.
24
47
8
.
11
2
20
2
1
43
40
47
20
17
24.78
58.35
31.05
29.95
43.81
.
.
.
44.57
.
35.73
37.69
48.55
44.16
42.62
24.28
52.00
34.22
21.29
28.00
.
.
55.13
.
20.77
46.04
.
3.30
6.12
20.3
55.81
.
.
.
.
Total: 494
38.05
S&P Aggregate Transparency Ranking, AGGR
St Dev
Min
Max
Spread
8.04
7.68
11.42
11.11
10.45
.
.
.
4.19
.
6.36
10.20
5.41
10.26
6.86
8.55
.
6.74
4.23
7.55
.
.
9.21
.
6.87
11.56
.
12.00
38.00
18.00
14.00
27.00
.
.
.
36.00
.
25.00
19.00
34.00
13.00
33.00
14.00
52.00
23.00
17.00
12.00
.
.
31.00
.
14.00
19.00
.
35.00
70.00
58.00
51.00
59.00
.
.
.
57.00
.
46.00
58.00
62.00
61.00
59.00
49.00
52.00
46.00
29.00
37.00
.
.
75.00
.
37.00
64.00
.
23.00
32.00
40.00
37.00
32.00
.
.
.
21.00
.
21.00
39.00
28.00
48.00
26.00
35.00
.
23.00
12.00
25.00
.
.
44.00
.
23.00
45.00
.
N
9
26
30
19
16
.
.
.
34
.
11
42
150
48
50
16
1
9
7
9
.
.
26
.
39
26
.
Total: 568
Figure 3. Country Average CLSA Corporate Governance and S&P Transparency Scores vs. Quality of Legal Regime
This figure presents partial regression plots of the following cross-country regressions (p-values based on robust standard errors are in parentheses and coefficients
significant at 10% are in boldface):
COMP = 46.8 + 0.54  LEGAL - 0.12  INV_OPP
Number of countries = 16
R2 = 0.34 (Panel A)
[0.00] [0.01]
[0.67]
FAIR = 43.10 + 0.826  LEGAL - 0.01  INV_OPP
[0.00] [0.00]
[0.99]
Number of countries = 16
R2 = 0.29 (Panel B)
AGGR = 34.0 + 0.41  LEGAL - 0.52  INV_OPP
[0.00] [0.07]
[0.42]
Number of countries = 18
R2 = 0.25 (Panel C)
COMP = 22.85 + 0.826  CG_MIN - 0.04  INV_OPP
[0.01] [0.00]
[0.80]
Number of countries = 16
R2 = 0.74 (Panel D)
AGGR = 23.1 + 1.00  CG_MIN - 0.81  INV_OPP
[0.00] [0.00]
[0.05]
Number of countries = 18
R2 = 0.68 (Panel E)
where COMP is country average CLSA composite corporate governance score (Panels A and E); FAIR is country average CLSA investor protection score (Panle B);
AGGR is country average S&P aggregate transparency ranking (Panels C and E); LEGAL is INVESTOR*ENFORCE; CG_MIN is CLSA country minimum composite
score (Panel D) or S&P country minimum aggregate transparency ranking (Panel E); INV_OPP is country average return on invested capital. We drop countries
containing fewer than 3 companies (Argentina, Columbia, Czech Rep, Greece, Hungary, Peru, Poland, Russia in Panels A and B, and New Zealand in Panel C). Refer
to Table II for definition of variables.
Panel A: CLSA Composite Score vs. Legal
20
Panel B: CLSA Investor Protection Score (Fairness) vs. Legal
South Africa
20
South Africa
10
Mexico
Chile
Brazil
Thailand
China
Singapore
Hong Kong
Singapore
Chile
India
Malaysia
Hong Kong
0
India
Thailand
0
Indonesia
Mexico
Malaysia
Taiwan
Taiwan
Korea
Philippines
Turkey
China
-20
Turkey
Korea
Philippines
-10
Indonesia
Pakistan
Pakistan
-20
-20
-40
-10
0
10
20
-20
LEGAL
Australia
20
Singapore
10
Malaysia
Korea
Thailand
Indonesia
0
Pakistan
Japan
Hong Kong
India
Brazil
Philippines
Mexico
Peru
-10
Chile
Argentine
Taiwan
-20
-20
-10
0
LEGAL
-10
0
LEGAL
Panel C: S&P Aggregate Score vs. Legal
China
Brazil
10
20
10
20
Panel D: CLSA Composite Score vs. CG_MIN
Panel E: S&P Aggregate Score vs. CG_MIN
Australia
20
20
South Africa
Singapore
Singapore
10
Hong Kong
Mexico
10
Chile
Brazil
Malaysia
Korea
India
Malaysia
0
Indonesia
Taiwan
Thailand
Pakistan India
0
China
Brazil
Turkey
-10
Hong Kong
Japan
China
Thailan
d
Mexico
Korea
-10
Philippines
Philippines
Taiwan
Indonesia
Chile
Peru
Argentina
Pakistan
-20
-20
-20
-10
0
CG_MIN
10
20
-10
0
CG_MIN
10
20
Figure 4. Standard Deviation of CLSA Corporate Governance and S&P Transparency Scores vs. Quality of Legal
Regime
This figure presents partial regression plots of the following cross-country regressions (p-values based on robust standard errors are in parentheses and
coefficients significant at 10% are in boldface):
SDCOMP = 8.56 - 0.07  LEGAL - 0.16  INV_OPP + 0.25  SDINV_OPP + 0.04  N
Number of countries = 16
R2 = 0.09 (Panel A)
[0.02] [0.40]
[0.46]
[0.39]
[0.53]
SDFAIR = 25.65 - 0.54  LEGAL - 1.12  INV_OPP + 1.28  SDINV_OPP + 0.25  N
[0.00] [0.00]
[0.04]
[0.06]
[0.07]
Number of countries = 16
R2 = 0.63 (Panle B)
SDAGGR = 10.39 - 0.06  LEGAL - 0.31  INV_OPP + 0.29  SDINV_OPP - 0.01  N
[0.02] [0.29]
[0.02]
[0.21]
[0.49]
Number of countries = 18
R2 = 0.25 (Panel C)
SDCOMP = 15.3 - 0.18  CG_MIN - 0.104  INV_OPP + 0.125  SDINV_OPP - 0.04  N
[0.00] [0.00]
[0.47]
[0.57]
[0.16]
Number of countries = 16
R2 = 0.57 (Panel D)
SDAGGR = 10.81 - 0.07  CG_MIN - 0.25  INV_OPP + 0.23  SDINV_OPP - 0.01  N
[0.00] [0.25]
[0.15]
[0.42]
[0.55]
Number of countries = 18
R2 = 0.24 (Panel E)
where SDCOMP is country standard deviation of CLSA composite corporate governance score (Panels A and D); SDFAIR is country average of CLSA
investor protection score (fairness) (Panel B); SDAGGR is country standard deviation of S&P aggregate transparency ranking (Panels C and E); LEGAL is
INVESTOR*LAW; CG_MIN is CLSA country minimum composite score (Panel D) or S&P country minimum aggregate transparency ranking (Panel E);
INV_OPP is country average return on invested capital; SDIMV_OPP is standard deviation of return on invested capital; N is the number of firms. We drop
countries containing fewer than 3 companies (Argentina, Columbia, Czech Rep, Greece, Hungary, Peru, Poland, and Russia in Pan els A and B, and New
Zealand in Panel C). Refer to Table II for definition of variables.
Panel A: Standard Deviation of CLSA Composite Score vs. Legal
4
Malaysia
10
Pakistan
Indonesia
Philippines
2
Panel B: Standard Deviation of CLSA Investor Protection Score (Fairness)
vs. Legal
Thailand
Turkey
China
India
Indonesia
China
Hong Kong
0
Mexico
Philippines
Taiwan
Korea
Malaysia
Thailand
South Africa
Brazil
0
Singapore
India
Turkey
Chile
Taiwan
Mexico
Brazil
Singapore
South Africa
Hong Kong
-10
-2
Pakistan
-4
Korea
Chile
-10
0
-20
10
20
LEGAL
Brazil
Indonesia
China
2
Chile
Korea
Thailand
Mexico
0
Singapore
Australia
Pakistan Taiwan
Malaysia
Japan
Philippines
Argentine
India
-2
Peru
Hong Kong
-4
-20
-10
0
LEGAL
0
LEGAL
Panel C: Standard Deviation of S&P Aggregate Score vs. Legal
4
-10
10
20
10
20
Panel D: Standard Deviation of CLSA Composite Score vs. CG_MIN
4
Panel E: Standard Deviation of S&P Aggregate Score vs. CG_MIN
4
Pakistan
Malaysia
Philippines
Brazil
Indonesia
Thailand
2
Indonesia
China
China
Chile
2
Korea
Hong Kong
Thailand
Mexico
Singapore
India
0
0
Turkey
Taiwan
Pakistan
Australia
Singapore
Brazil
Taiwan
-2
Malaysia
Philippines
South Africa
Mexico
India
-2
-4
-10
0
CG_MIN
Argentina
Peru
Kore
a
-20
Japan
10
Hong Kong
-4
Chil
e
20
-10
0
10
CG_MIN
20
Table IV
Correlation Coefficients between Main Variables
This table reports simple correlation coefficients between main variables. Numbers in parentheses are probability levels at which the hypothesis of zero correlation can be rejected. All correlation
coefficients are based on CLSA sample firm level data. The sample size ranges from 380 to 450 firms depending on a particular correlation coefficient. Coefficients significant at 10% (based on twotail test) are in bold face. Refer to Table II for definition of variables.
Panel A. Correlation Coefficients between Attributes of CLSA Composite Corporate Governance Scores, S&P Aggregate Transparency Ranking, Legal Regime,
and Firm-level Variables
COMP
AGGR
0.18
(0.00)
0.02
(0.63)
0.21
(0.00)
0.13
(0.00)
0.07
(0.14)
0.02
(0.60)
0.00
(0.95)
-0.06
(0.17)
0.07
(0.09)
-0.05
(0.18)
0.09
(0.00)
0.21
(0.00)
0.10
(0.04)
0.28
(0.00)
0.12
(0.00)
-0.14
(0.00)
0.30
(0.00)
0.26
(0.00)
0.40
(0.00)
0.17
(0.00)
0.39
(0.00)
0.38
(0.00)
Firm-level variables
Q
INVEST
EXT_FIN
INV_OPP
OWNERSHIP
SIZE
R&D
EXPORT
Legal Regime variables
INVESTOR
ENFORCE
LEGAL
Panel B. Correlation Coefficients between Legal Regime Variables and Firm-level Variables
Q
0.185
(0.00)
-0.040
(0.39)
0.081
(0.08)
INVEST
-0.113
(0.02)
-0.002
(0.96)
-0.098
(0.03)
EXT_FIN
0.138
(0.00)
0.184
(0.00)
0.221
(0.00)
INV_OPP
0.168
(0.00)
-0.144
(0.00)
-0.005
(0.91)
OWNER
-0.031
(0.54)
0.015
(0.77)
0.003
(0.95)
SIZE
-0.129
(0.00)
0.167
(0.00)
0.045
(0.33)
R&D
0.036
(0.43)
0.108
(0.02)
0.058
(0.21)
EXPORT
-0.062
(0.18)
0.043
(0.36)
-0.048
(0.30)
0.21
(0.00)
0.15
(0.00)
-0.27
(0.00)
0.57
(0.00)
0.20
(0.00)
0.10
(0.00)
0.080
(0.12)
0.114
(0.02)
-0.105
(0.04)
0.114
(0.03)
-0.44
(0.00)
-0.19
(0.00)
0.01
(0.87)
-0.36
(0.00)
-0.13
(0.01)
0.26
(0.00)
0.17
(0.00)
0.03
(0.49)
0.23
(0.00)
-0.02
(0.65)
-0.21
(0.00)
0.22
(0.00)
0.13
(0.00)
0.04
(0.36)
0.18
(0.00)
-0.078
(0.13)
-0.17
(0.00)
0.41
(0.00)
Legal Regime variables
INVESTOR
ENFORCE
LEGAL
Firm-level variables
Q
INVEST
EXT_FIN
INV_OPP
OWNER
SIZE
R&D
Table V
OLS Regression of CLSA Corporate Governance Scores on Investment Opportunities, Reliance on External Financing, and Control
Variables
This table reports the results of cross-section regression:
I 1
K
CG cj    1 INV _ OPPjc  2  EXT _ FIN cj   1 LEGALc   2  INV _ OPPjc  LEGALc   3  EXT _ FIN cj  LEGALc 
  Z   d  
k
k 1
c
k,j
i
c
j
i 1
where c indexes country; i indexes industry; j indexes firm; and t indexes time. CG is one of CLSA Corporate Governance Scores (COMP, DISC, TRANS, INDEP, ACCOUNT, RESP, FAIR, SOCIAL) in 2000. d are
industry dummies (coefficients are not reported). INV_OPP (investment opportunities) is defined as return on invested capital, 1999-2000 average; EXT_FIN (external financing) is 1999-2000 average ratio of the sum of
new equity issues and changes in long-term debt over capital expenditures; LEGAL is defined as INVESTOR*ENFORCE where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule of law. Z
are control variables: SIZE is the log of total assets,1999-2000 average; R&D is research and development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times
100, 1999-2000 average. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. All pvalues are based on robust (heteroscedasticity consistent) standard errors. Sample size is 460 firms. We drop companies that do not have one of the following items: total assets, sales, ROIC, long-term debt, book equity,
or retained earnings. If all variables are available except R&D expenditures and export, we set those two to zero.
Dep Variable: CLSA Corporate Gov
Scores
INV_OPP
EXT_FIN
LEGAL
INV_OPP*LEGAL
EXT_FIN*LEGAL
SIZE
R&D
EXPORT
F test statistics of joint significance
INV_OPP=0 and INV_OPP*LEGAL=0
EXT_FIN=0 and EXT_FIN*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
COMP
DISC
TRAN
INDEP
ACCOUNT
RESP
FAIR
SOCIAL
10.625
(0.07)
2.590
(0.01)
0.609
(0.00)
-0.152
(0.55)
-0.064
(0.07)
0.418
(0.35)
-0.081
(0.94)
-0.044
(0.29)
4.510
(0.01)
24.777
(0.01)
1.044
(0.47)
0.411
(0.01)
-0.498
(0.10)
-0.061
(0.07)
1.032
(0.13)
-1.475
(0.39)
0.017
(0.78)
13.290
(0.00)
6.454
(0.38)
6.033
(0.00)
0.488
(0.00)
-0.284
(0.48)
-0.095
(0.10)
1.662
(0.03)
-1.394
(0.50)
-0.085
(0.23)
2.070
(0.18)
3.843
(0.71)
4.100
(0.02)
1.065
(0.00)
-0.419
(0.10)
-0.133
(0.07)
-0.660
(0.48)
2.956
(0.22)
-0.028
(0.74)
4.230
(0.01)
1.947
(0.86)
0.006
(0.90)
0.312
(0.05)
0.128
(0.79)
-0.007
(0.90)
3.392
(0.00)
1.213
(0.56)
-0.065
(0.40)
0.500
(0.60)
18.898
(0.02)
3.862
(0.04)
1.028
(0.00)
-0.402
(0.24)
-0.081
(0.03)
-1.002
(0.16)
-1.372
(0.43)
-0.128
(0.06)
4.270
(0.02)
21.320
(0.06)
2.536
(0.10)
0.803
(0.00)
-0.054
(0.40)
-0.067
(0.31)
-1.876
(0.05)
-0.971
(0.70)
-0.046
(0.60)
2.440
(0.09)
5.594
(0.62)
0.248
(0.87)
-0.080
(0.62)
0.186
(0.68)
-0.008
(0.87)
0.425
(0.61)
-4.862
(0.02)
0.074
(0.38)
0.080
(0.92)
5.170
(0.01)
5.800
(0.00)
0.216
2.500
(0.03)
4.540
(0.00)
0.097
2.090
(0.10)
18.820
(0.00)
0.109
7.870
(0.00)
34.430
(0.00)
0.195
0.020
(0.98)
2.680
(0.00)
0.075
2.800
(0.06)
20.120
(0.00)
0.239
4.790
(0.01)
6.430
(0.00)
0.143
1.320
(0.27)
1.740
(0.04)
0.058
460
Table VI
OLS Regression of CLSA Corporate Governance Scores on Ownership Concentration and Control Variables
This table reports the results of cross-section regression:
I 1
K
CG cj    1 OWNERSHIPjc  2  (OWNERSHIPjc ) 2   1 LEGALc  OWNERSHIPjc  LEGALc 
  Z   d  
k
k 1
c
k,j
i
c
j
i 1
where c indexes country; i indexes industry; j indexes firm; and t indexes time. CG is one of CLSA Corporate Governance Scores (COMP, DISC, TRANS, INDEP, ACCOUNT, RESP, FAIR, SOCIAL) in 2000. d are
industry dummies (coefficients are not reported). OWNERSHIP is the ratio of shares held by major shareholders, either individuals or corporations, that hold more than 5% of outstanding shares, 1999-2000 average;
OWNERSHIP^2 is the squared term of ownership; LEGAL is INVESTOR*ENFORCE where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule of law. Z are control variables: SIZE is the
log of total assets, 1999-2000 average; R&D is research and development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers
in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on robust
(heteroscedasticity consistent) standard errors. Sample size is 380 firms. We drop companies that do not have one of the following items: total assets, sales, or ownership. If all variables are available except R&D
expenditures and export, we set those two to zero.
Dep Variable: CLSA Corporate Gov Scores
OWNERSHIP
OWNERSHIP^2
LEGAL
OWNERSHIP*LEGAL
SIZE
R&D
EXPORT
F test statistics of joint significance
OWNERSHIP=0 and OWNERSHIP*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
COMP
DISC
TRAN
INDEP
ACCOUNT
37.456
(0.01)
-24.818
(0.05)
0.822
(0.00)
-0.536
(0.06)
0.293
(0.49)
-0.177
(0.75)
-0.052
(0.14)
4.020
(0.02)
12.780
(0.00)
0.229
31.493
(0.07)
-9.622
(0.58)
0.989
(0.00)
-1.130
(0.00)
0.470
(0.49)
-0.319
(0.71)
0.000
(0.99)
4.520
(0.01)
42.4220
(0.00)
0.069
72.108
(0.00)
-50.543
(0.01)
0.669
(0.03)
-1.477
(0.00)
0.300
(0.71)
0.347
(0.01)
0.091
(0.23)
6.950
(0.00)
48.650
(0.00)
0.085
20.432
(0.37)
-5.742
(0.79)
1.205
(0.00)
-0.420
(0.42)
-1.261
(0.17)
0565
(0.69)
-0.144
(0.03)
0.440
(0.64)
47.560
(0.00)
0.190
47.449
(0.03)
-54.166
(0.01)
0.099
(0.73)
0.168
(0.73)
3.023
(0.00)
-0.952
(0.26)
-0.020
(0.79)
3.140
(0.04)
15.130
(0.00)
0.092
380
RESP
FAIR
SOCIAL
28.243
(0.10)
-10.917
(0.52)
1.212
(0.00)
-0.432
(0.22)
-1.325
(0.05)
-0.173
(0.85)
-0.011
(0.11)
1.360
(0.26)
98.500
(0.00)
0.289
56.422
(0.02)
-40.152
(0.09)
0.981
(0.00)
-0.522
(0.32)
-0.606
(0.52)
-0.446
(0.75)
-0.075
(0.41)
2.800
(0.06)
16.170
(0.00)
0.130
17.801
(0.36)
-11.500
(0.51)
0.548
(0.01)
-0.254
(0.47)
1.454
(0.06)
-2.171
(0.05)
-0.063
(0.29)
0.480
(0.61)
73.040
(0.00)
0.115
Table VII
OLS Regression of S&P Transparency Ranking on Investment Opportunities, Reliance on External Financing, Ownership Concentration
and Control Variables
This table reports the results of cross-section regressions:
I 1
K
CG cj    1 INV _ OPPjc  2  EXT _ FIN cj   1 LEGALc   2  INV _ OPPjc  LEGALc   3  EXT _ FIN cj  LEGALc 
  Z   d  
k
c
k,j
i
k 1
i 1
  Z   d  
k
k 1
(Panel A)
I 1
K
CG cj    1 OWNERSHIPjc  2  (OWNERSHIPjc ) 2   1 LEGALc  OWNERSHIPjc  LEGALc 
c
j
c
k,j
i
c
j
i 1
(Panel B)
where c indexes country; i indexes industry; j indexes firm; and t indexes time. CG is one of S&P Transparency Rankings (AGGR, OWN, DISCL, BOARD) in 2000. d are industry dummies (coefficients are not reported).
INV_OPP (investment opportunities) is defined as return on invested capital, 1999-2000 average; EXT_FIN (external financing) is 1999-2000 average ratio of the sum of new equity issues and changes in long-term debt
over capital expenditures; OWNERSHIP is the ratio of shares held by major shareholders, either individual or corporations, that hold more than 5% of outstanding shares, 1999-2000 average; OWNERSHIP^2 is the
squared term of ownership; LEGAL is INVESTOR*ENFORCE where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule of law. Z are control variables: SIZE is the log of total assets, 19992000 average; R&D is research and development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers in parentheses are
probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on robust (heteroscedasticity
consistent) standard errors. Sample size is 541 (Panel A) and 458 (Panel B) firms. In Panel A we drop companies that do not have one of the following items: total assets, sales, ROIC, long-term debt, book equity, or
retained earnings. In panel B we drop companies that do not have one of the following items: total assets, sales, or ownership. If all variables are available except R&D expenditures and export, we set those two to zero.
Dep Variable: S&P Transparency Ranking
INV_OPP
EXT_FIN
AGGR
15.397
(0.09)
9.899
(0.03)
OWN
8.944
(0.04)
2.966
(0.03)
Panel A
DISCL
3.428
(0.17)
1.421
(0.37)
Panel B
BOARD
3.025
(0.44)
4.986
(0.03)
OWNERSHIP
OWNERSHIP^2
LEGAL
INV_OPP*LEGAL
EXT_FIN*LEGAL
0.313
(0.00)
-0.033
(0.86)
-0.110
(0.49)
0.106
(0.00)
-0.008
(0.94)
-0.043
(0.44)
0.076
(0.00)
-0.048
(0.26)
-0.026
(0.67)
0.130
(0.00)
-0.073
(0.16)
-0.039
(0.72)
OWNERSHIP*LEGAL
SIZE
R&D
EXPORT
F test statistics of joint significance
INV_OPP=0 and INV_OPP*LEGAL=0
EXT_FIN=0 and EXT_FIN*LEGAL=0
1.282
(0.00)
0.029
(0.03)
-0.050
(0.10)
3.050
(0.05)
0.307
(0.01)
0.141
(0.00)
-0.022
(0.00)
7.770
(0.00)
0.658
(0.00)
0.112
(0.02)
-0.012
(0.04)
5.750
(0.00)
0.317
(0.07)
0.041
(0.63)
-0.016
(0.07)
1.010
(0.37)
4.930
(0.01)
4.960
(0.01)
0.600
(0.55)
7.110
(0.00)
OWNERSHIP=0 and OWNERSHIP*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
13.280
(0.00)
0.236
14.780
(0.00)
0.232
14.490
(0.00)
0.250
541
6.080
(0.00)
0.144
AGGR
OWN
DISCL
BOARD
33.616
(0.01)
-28.962
(0.01)
0.231
(0.04)
16.547
(0.00)
-16.985
(0.00)
0.059
(0.16)
9.036
(0.03)
-6.080
(0.07)
0.070
(0.07)
8.033
(0.17)
-5.897
(0.26)
0.102
(0.10)
-0.081
(0.70)
1.261
(0.00)
0.179
(0.11)
-0.002
(0.89)
-0.002
(0.97)
0.340
(0.01)
0.088
(0.02)
-0.008
(0.23)
-0.044
(0.54)
0.692
(0.00)
0.084
(0.06)
-0.002
(0.60)
-0.003
(0.76)
0.229
(0.17)
0.007
(0.93)
0.008
(0.24)
5.220
(0.01)
7.800
(0.00)
0.177
10.680
(0.00)
8.41
(0.00)
0.179
2.840
(0.06)
9.860
(0.00)
0.240
1.060
(0.35)
3.030
(0.00)
0.083
458
Table VIII
OLS Regression of Valuation on CLSA Corporate Governance Scores and Control Variables
This table reports the results of regression:
I 1
K
Valuation cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
  *Z   d  
k
k 1
c
k,j
i
c
j
i 1
where c indexes country; i indexes industry; j indexes firm; and t indexes time. Valuation is 2000-2001 average Tobin’s Q defined as total assets plus market value
of equity less book value of equity over total assets and market value of equity is the number of common shares outstanding times year-end share price. CG is one of
CLSA Corporate Governance Scores (COMP, DISC, TRANS, INDEP, ACCOUNT, RESP, FAIR, SOCIAL) in 2000. LEGAL is INVESTOR*ENFORCE where
INVESTOR measures investor protection and ENFORCE (enforcement) is the rule of law. Z are control variables: ROIC (return on invested capital), 1999-2000
average; SIZE is the log of total assets, 1999-2000 average; R&D is research and development expenditures scaled by total assets times 100, 1999-2000 average; and
EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can
be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on robust (heteroscedasticity consistent) standard
errors. Sample size is 470 firms. We drop companies that do not have one of the following items: total assets, sales, ROIC, book equity, number of shares
outstanding, or year-end price. If all variables are available except R&D expenditures and export, we set those two to zero. Coefficients on CG are multiplied by
100.
Tobin’s Q
Dependent variable
COMPOSITE
1.383
(0.03)
DISCIPLINE
0.200
(0.43)
1.319
(0.00)
TRANSPARENCY
0.465
(0.10)
INDEPENDENCE
ACCOUNTABILITY
0.372
(0.43)
0.476
(0.07)
RESPONSIBILITY
0.642
(0.00)
FAIRNESS
SOCIAL AWARENESS
LEGAL
CG*LEGAL
ROIC
SIZE
R&D
EXPORT
F test statistics of joint
significance
CG=0 and CG*LEGAL=0
F test statistics of overall
significance
Regression R2
Number of Companies
0.019
(0.24)
-0.002
(0.42)
1.759
(0.00)
-0.157
(0.00)
0.184
(0.04)
0.008
(0.02)
0.013
(0.24)
-0.004
(0.31)
1.910
(0.00)
-0.155
(0.00)
0.191
(0.04)
0.008
(0.04)
0.024
(0.01)
-0.032
(0.09)
1.742
(0.00)
-0.160
(0.00)
0.187
(0.04)
0.007
(0.03)
0.015
(0.14)
-0.012
(0.49)
1.825
(0.00)
-0.154
(0.00)
0.180
(0.05)
0.007
(0.03)
0.014
(0.23)
-0.007
(0.76)
1.836
(0.00)
-0.161
(0.00)
0.191
(0.04)
0.007
(0.03)
0.021
(0.02)
-0.018
(0.08)
1.836
(0.00)
-0.153
(0.00)
0.185
(0.05)
0.007
(0.03)
0.004
(0.56)
-0.010
(0.10)
1.763
(0.00)
-0.152
(0.00)
0.165
(0.06)
0.007
(0.04)
0.242
(0.42)
0.008
(0.31)
0.001
(0.94)
1.814
(0.00)
-0.149
(0.00)
0.194
(0.04)
0.007
(0.03)
4.880
(0.01)
0.230
(0.79)
7.790
(0.00)
3.300
(0.07)
0.880
(0.41)
4.630
(0.03)
5.000
(0.00)
1.850
(0.16)
46.370
(0.00)
0.454
36.170
(0.00)
0.443
43.190
(0.00)
0.458
30.350
(0.00)
0.446
44.680
(0.00)
0.445
470
44.890
(0.00)
0.444
38.300
(0.00)
0.462
38.410
(0.00)
0.447
0.011
(0.01)
1.847
(0.00)
-0.153
(0.00)
0.190
(0.04)
0.007
(0.04)
39.970
(0.00)
0.443
Table IX
OLS Regression of Valuation and Investment on S&P Transparency Rankings and Control
Variables
This table reports the results of regression:
I 1
K
Valuation cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 

 k * Zkc, j 
k 1
i
c
j
i 1
  *Z   d  
k
k 1
(Panel A)
I 1
K
Investment cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
 d 
c
k,j
i
i 1
c
j
(Panel B)
where c indexes country; i indexes industry; j indexes firm; and t indexes time. Valuation is 2000-2001 average Tobin’s Q defined as total assets plus market value
of equity less book value of equity over total assets and market value of equity is the number of common shares outstanding times year-end share price. Investment
is 2000-2001 average ratio of capital expenditures to total assets. CG is one of S&P Transparency Rankings (AGGR, OWN, DISCL, BOARD) in 2000. d are industry
dummies (coefficients are not reported). LEGAL is INVESTOR*ENFORCE where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule
of law. Z are control variables: ROIC (return on invested capital), 1999-2000 average; SIZE is the log of total assets, 1999-2000 average; R&D is research and
development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers in
parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in
boldface. All p-values are based on robust (heteroscedasticity consistent) standard errors. Sample size is 550 firms (Panel A) and 549 (Panel B). In Panel A we drop
companies that do not have one of the following items: total assets, sales, ROIC, book equity, number of shares outstanding, or year-end price. In Panel B we drop
companies that do not have one of the following items: total assets, sales, ROIC, or capital expenditures. If all variables are available except R&D expenditures and
export, we set those two to zero. Coefficients on CG are multiplied by 100.
Panel A
Tobin’s Q
Dependent variable
AGGREGATE
1.353
(0.02)
OWN
0.162
(0.07)
2.002
(0.19)
0.321
(0.18)
4.197
(0.07)
DISCLOSURE
CG*LEGAL
ROIC
SIZE
R&D
EXPORT
F test statistics of joint
significance
CG=0 and CG*LEGAL=0
F test statistics of overall
significance
Regression R2
Number of Companies
0.534
(0.06)
0.007
(0.41)
-0.029
(0.50)
2.500
(0.03)
-0.257
(0.00)
0.270
(0.05)
0.006
(0.15)
0.009
(0.20)
-0.024
(0.44)
2.479
(0.03)
-0.248
(0.00)
0.270
(0.05)
0.006
(0.15)
0.009
(0.32)
-0.009
(0.53)
2.510
(0.03)
-0.269
(0.00)
0.269
(0.04)
0.006
(0.15)
2.571
(0.06)
0.008
(0.39)
-0.005
(0.54)
2.449
(0.03)
-0.246
(0.00)
0.272
(0.04)
0.006
(0.16)
6.020
(0.00)
0.670
(0.51)
5.440
(0.03)
3.180
(0.10)
36.160
(0.00)
0.392
36.460
(0.00)
0.380
36.360
(0.00)
0.392
550
36.020
(0.00)
0.391
BOARD
LEGAL
Panel B
INVESTMENT
0.012
(0.09)
0.024
(0.03)
-0.241
(0.00)
0.273
(0.05)
0.006
(0.15)
32.990
(0.00)
0.387
0.002
(0.08)
-0.007
(0.02)
0.046
(0.03)
-0.002
(0.51)
0.003
(0.03)
0.000
(0.90)
0.002
(0.22)
-0.002
(0.02)
0.041
(0.04)
-0.001
(0.64)
0.003
(0.01)
0.000
(0.84)
0.004
(0.08)
-0.002
(0.03)
0.043
(0.03)
-0.002
(0.42)
0.003
(0.03)
0.000
(0.99)
0.245
(0.17)
0.000
(0.92)
-0.001
(0.11)
0.047
(0.03)
-0.003
(0.28)
0.003
(0.05)
0.000
(0.99)
3.310
(0.03)
4.040
(0.02)
2.390
(0.10)
1.320
(0.26)
38.400
(0.00)
0.257
39.250
37.880
(0.00)
(0.00)
0.259
0.256
549
37.430
(0.00)
0.252
Table X
OLS Regression of Investment on CLSA Corporate Governance Scores and Control Variables
This table reports the results of regression:
I 1
K
Investment cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
  *Z   d  
k
k 1
c
k,j
i
c
j
i 1
where c indexes country; i indexes industry; j indexes firm and t indexes time. d are industry dummies (coefficients are not reported). Investment is 20002001 average ratio of capital expenditures to total assets. CG is one of CLSA Corporate Governance Scores (COMP, DISC, TRANS, INDEP, ACCOUNT,
RESP, FAIR, SOCIAL) in 2000. LEGAL is INVESTOR*ENFORCE where INVESTOR measures investor protection and ENFORCE (enforcement) is the
rule of law. Z are control variables: ROIC (return on invested capital), 1999-2000 average; SIZE is the log of total assets, 1999-2000 average; R&D is
research and development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 19992000 average. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at
10% level (based on 2-tailed test) are in boldface. All p-values are based on robust (heteroscedasticity consistent) standard errors. Sample size is 468
firms. We drop companies that do not have one of the following items: total assets, sales, ROIC, or capital expenditures If all variables are available
except R&D expenditures and export, we set those two to zero. Coefficients on CG are multiplied by 100.
Dependent variable
COMPOSITE
INVESTMENT
0.127
(0.00)
DISCIPLINE
0.052
(0.13)
0.060
(0.04)
TRANSPARENCY
0.038
(0.05)
INDEPENDENCE
ACCOUNTABILITY
0.021
(0.42)
0.034
(0.10)
RESPONSIBILITY
FAIRNESS
-0.009
(0.66)
SOCIAL AWARENESS
LEGAL
CG*LEGAL
ROIC
SIZE
R&D
EXPORT
F test statistics of joint significance
CG=0 and CG*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
0.002
(0.00)
-0.003
(0.00)
0.007
(0.48)
0.000
(0.91)
0.009
(0.08)
0.000
(0.46)
15.480
(0.00)
32.260
(0.00)
0.364
0.001
(0.44)
-0.001
(0.23)
0.015
(0.16)
0.000
(0.90)
0.010
(0.07)
0.000
(0.55)
1.180
(0.31)
37.090
(0.00)
0.330
0.000
(0.65)
-0.001
(0.34)
0.017
(0.10)
0.000
(0.84)
0.010
(0.04)
0.000
(0.49)
4.390
(0.01)
38.000
(0.00)
0.343
0.000
(0.83)
-0.001
(0.18)
0.022
(0.03)
0.000
(0.89)
0.010
(0.05)
0.000
(0.67)
7.810
(0.00)
38.230
(0.00)
0.351
0.000
(0.72)
0.000
(0.93)
0.016
(0.13)
0.000
(0.85)
0.010
(0.04)
0.000
(0.56)
1.490
(0.23)
36.350
(0.00)
0.333
468
0.001
(0.37)
-0.001
(0.10)
0.016
(0.13)
0.000
(0.83)
0.010
(0.06)
0.000
(0.56)
2.190
(0.10)
36.800
(0.00)
0.330
0.000
(0.52)
0.000
(0.75)
0.018
(0.10)
0.000
(0.84)
0.010
(0.04)
0.000
(0.62)
0.120
(0.89)
36.360
(0.00)
0.328
0.040
(0.09)
0.001
(0.24)
-0.001
(0.10)
0.018
(0.10)
0.000
(0.78)
0.010
(0.05)
0.000
(0.57)
5.450
(0.00)
35.580
(0.00)
0.332
Table XI
Comparison of Coefficients Estimated Using Instrumental Variables Approach vs. OLS:
Valuation on CLSA Corporate Governance Scores and Control Variables
This table reports and compares the results of regression:
C 1
Valuation cj    1 CG cj 
I 1
K
    Z
dc 
c1
di 
i 1
k
c
k,j
 cj
k 1
using OLS and IV estimation approaches, where c indexes country, i indexes industry; j indexes firm; and t indexes time. dc and di are country and
industry dummies, respectively (coefficients are not reported). We use legal origin (English, French, or German), ORIGIN , as instruments for the
corporate governance scores in IV estimation. Valuation is defined as total assets plus market value of equity less book value of equity over total assets
where market value of equity is the number of common shares outstanding times year-end share price. CG is one of CLSA Corporate Governance Scores
(COMP, DISC, TRANS, INDEP, ACCOUNT, RESP, FAIR, SOCIAL) in 2000. Z are control variables: SIZE is the log of total assets, 1999-2000 average;
R&D is research and development expenditures scaled by total assets, 1999-2000 average times 100; and EXPORT is export scaled by sales times 100,
1999-2000 average. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients
significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on robust (heteroscedasticity consistent) standard errors. Sample
size is 470 and 469 firms in OLS and IV regression, respectively. ORIGIN is not defined for Russia, thus, we drop it from IV estimation. Hausman
(1978) specification test compares the IV estimates with OLS estimates. The null hypothesis is that the OLS estimator is a consistent and efficient
estimator of the true parameter. P-val is the probability level at which the null hypothesis can be rejected. The Hausman statistic is calculated as
H  ( IV  OLS )' (VIV  VOLS )( IV  OLS ) where  IV is the coefficient vector from the IV regression,  OLS is the coefficient vector from the OLS
regression, VIV is the covariance matrix of the coefficients from the IV regression, and VOLS is the covariance matrix of the coefficients from the OLS
regression. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10%
level (based on 2-tailed test) are in boldface. Coefficients on CG are multiplied by 100.
Dependent variable
COMPOSITE
OLS
1.622
(0.00)
IV
1.019
(0.00)
DISCIPLINE
Tobin’s Q
OLS
OLS
IV
1.008
(0.00)
0.496
(0.42)
0.569
(0.02)
TRANSPARENCY
IV
R&D
EXPORT
F test statistics of overall significance
Regression R2
Number of Companies
Hausman test statistics, 2
-0.230
(0.00)
0.195
(0.07)
0.008
(0.07)
20.120
(0.00)
0.384
470
0.570
(0.74)
-0.228
(0.00)
0.193
(0.07)
0.008
(0.06)
24.240
(0.00)
0.382
469
-0.241
(0.00)
0.218
(0.04)
0.007
(0.09)
43.350
(0.00)
0.389
470
2.270
(0.00)
-0.197
(0.07)
0.188
(0.00)
0.014
(0.25)
21.760
(0.00)
0.272
469
-0.240
(0.00)
0.197
(0.07)
0.009
(0.06)
24.950
(0.00)
0.368
470
IV
0.481
(0.03)
-0.232
(0.00)
0.199
(0.07)
0.008
(0.07)
8.310
(0.00)
0.372
470
0.902
(0.00)
-0.200
(0.00)
0.197
(0.10)
0.010
(0.05)
14.210
(0.00)
0.339
469
2.220
(0.19)
OLS
IV
0.151
(0.34)
-0.239
(0.00)
0.183
(0.09)
0.008
(0.09)
0.164
(0.21)
-0.260
(0.00)
0.119
(0.00)
0.006
(0.48)
16.440
(0.00)
0.371
470
3.170
(0.05)
43.110
(0.00)
0.315
469
1.664
(0.00)
INDEPENDENCE
SIZE
OLS
-0.244
(0.00)
0.191
(0.09)
0.010
(0.03)
42.530
(0.00)
0.344
469
1.050
(0.31)
(Continued)
Dependent variable
ACCOUNTABILITY
OLS
0.115
(0.66)
IV
0.189
(0.53)
OLS
0.407
(0.10)
RESPONSIBILITY
Tobin’s Q
IV
OLS
IV
0.642
(0.04)
0.271
(0.10)
FAIRNESS
0.235
(0.00)
SOCIAL AWARENESS
SIZE
R&D
EXPORT
F test statistics of overall
significance
Regression R2
Number of Companies
Hausman test statistics, 2
-0.240
(0.00)
0.200
(0.07)
0.008
(0.08)
-0.279
(0.00)
0.206
(0.09)
0.009
(0.05)
-0.226
(0.00)
0.193
(0.08)
0.008
(0.08)
0.096
(0.79)
0.191
(0.00)
0.010
(0.22)
-0.231
(0.00)
0.198
(0.07)
0.008
(0.09)
-0.192
(0.00)
0.190
(0.07)
0.006
(0.19)
7.670
(0.00)
0.362
470
0.330
(0.88)
24.310
(0.00)
0.276
469
21.290
(0.00)
0.366
470
1.140
(0.53)
3.950
(0.00)
0.333
469
14.440
(0.00)
0.365
470
1.050
(0.31)
26.360
(0.00)
0.236
469
Table XII
Comparison of Coefficients Estimated Using Instrumental Variables Approach vs. OLS:
Investment on CLSA Corporate Governance Scores and Control Variables
This table reports and compares the results of regression:
C 1
Investement cj    1 CG cj 
I 1
K
    Z
dc 
c1
di 
i 1
k
c
k,j
 cj
k 1
using OLS and IV estimation approaches, where c indexes country, i indexes industry; j indexes firm; and t indexes time. dc and di are country and
industry dummies, respectively. We use legal origin (English, French, or German), ORIGIN , as instruments for corporate governance scores in IV
estimation. Investment is defined as capital expenditures over total assets, 2000-20001 average. CG is one of CLSA Corporate Governance Scores
(COMP, DISC, TRANS, INDEP, ACCOUNT, RESP, FAIR, SOCIAL) in 2000. Z are control variables: SIZE is the log of total assets, 1999-2000 average;
R&D is research and development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100,
1999-2000 average. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients
significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on robust (heteroscedasticity consistent) standard errors. Sample
size is 468 firms. ORIGIN is not defined for Russia, thus, we drop it from IV estimation. Hausman (1978) specification test compares the IV estimates
with OLS estimates. The null hypothesis is that the OLS estimator is a consistent and efficient estimator of the true parameter. P-val is the probability
level at which the null hypothesis can be rejected. The Hausman statistic is calculated as H  ( IV  OLS )' (VIV  VOLS )( IV  OLS ) where  IV is the
coefficient vector from the IV regression,  OLS is the coefficient vector from the OLS regression, VIV is the covariance matrix of the coefficients from the
IV regression, and VOLS is the covariance matrix of the coefficients from the OLS regression. Numbers in parentheses are probability levels at which the
null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. Coefficients on CG are
multiplied by 100.
Dependent variable
COMPOSITE
OLS
0.043
(0.00)
IV
0.059
(0.02)
OLS
DISCIPLINE
0.020
(0.31)
INVESTMENT
IV
OLS
IV
0.045
(0.00)
EXPORT
F test statistics of overall
significance
Regression R2
Number of Companies
Hausman test statistics, 2
0.028
(0.04)
-0.004
(0.06)
0.008
(0.13)
0.000
(0.92)
0.025
(0.01)
-0.004
(0.04)
0.008
(0.14)
0.000
(0.91)
0.050
(0.09)
INDEPENDENCE
R&D
IV
-0.002
(0.87)
TRANSPARENCY
SIZE
OLS
-0.003
(0.05)
0.008
(0.14)
0.000
(0.79)
-0.002
(0.12)
0.008
(0.15)
0.000
(0.85)
-0.003
(0.08)
0.008
(0.15)
0.000
(0.97)
-0.003
(0.08)
0.008
(0.15)
0.000
(0.99)
-0.003
(0.05)
0.009
(0.12)
0.000
(0.93)
-0.003
(0.07)
0.008
(0.15)
0.000
(0.99)
41.780
(0.00)
0.407
468
1.660
(0.99)
24.050
(0.00)
0.401
468
20.440
(0.00)
0.388
468
0.980
(0.99)
19.520
(0.00)
0.386
468
29.760
(0.00)
0.324
468
0.270
(0.10)
22.000
(0.00)
0.387
468
45.070
(0.00)
0.352
468
1.100
(0.85)
35.740
(0.00)
0.397
468
IV
OLS
IV
-0.012
(0.40)
-0.003
(0.08)
0.008
(0.13)
0.000
(0.97)
0.289
(0.28)
-0.004
(0.12)
0.008
(0.00)
0.000
(0.77)
23.150
(0.00)
0.388
468
1.180
(0.71)
8.390
(0.00)
0.223
468
(Continued)
Dependent variable
Regression specification
ACCOUNTABILITY
OLS
0.017
(0.17)
IV
-0.455
(0.38)
RESPONSIBILITY
OLS
INVESTMENT
IV
OLS
0.033
(0.07)
0.031
(0.04)
FAIRNESS
0.003
(0.81)
0.018
(0.46)
SOCIAL AWARENESS
SIZE
R&D
EXPORT
F test statistics of overall
significance
Regression R2
Number of Companies
Hausman test statistics, 2
-0.003
(0.05)
0.008
(0.10)
0.000
(0.94)
-0.001
(0.81)
0.008
(0.00)
0.000
(0.58)
0.008
(0.15)
0.000
(0.98)
0.000
(0.81)
0.008
(0.10)
0.000
(0.99)
0.000
(0.88)
-0.003
(0.09)
0.008
(0.15)
0.000
(0.98)
-0.003
(0.07)
0.008
(0.00)
0.000
(0.80)
21.720
(0.00)
0.390
468
3.990
(0.99)
12.450
(0.00)
0.299
468
18.700
(0.00)
0.387
468
2.198
(0.85)
44.710
(0.00)
0.377
468
14.200
(0.00)
0.387
468
0.518
(0.46)
6.930
(0.00)
0.271
468
Table XIII
Comparison of Coefficients Estimated Using Instrumental Variables Approach vs. OLS:
Valuation on S&P Transparency Rankings
This table reports and compares the results of regression:
C 1
Valuation cj    1 CG cj 
I 1
K
    Z
dc 
c1
di 
i 1
k
c
k,j
 cj
k 1
using OLS and IV estimation approaches, where c indexes country, i indexes industry; j indexes firm; and t indexes time. dc and di are country and
industry dummies, respectively. We use legal origin (English, French, or German), ORIGIN , as instruments for transparency scores in IV estimation.
Valuation is defined as total assets plus market value of equity less book value of equity over total assets and market value of equity is the number of
common shares outstanding times year-end share price. CG is one of S&P Transparency Rankings (AGGR, OWN, DISCL, BOARD) in 2000. Z are
control variables: SIZE is the log of total assets, 1999-2000 average; R&D is research and development expenditures scaled by total assets times 100,
1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers in parentheses are probability levels at which the
null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. All p-values are based on
robust (heteroscedasticity consistent) standard errors. Sample size is 550 firms. ORIGIN is not defined for Russia, thus, we drop it from IV estimation.
Hausman (1978) specification test compares the IV estimates with OLS estimates. The null hypothesis is that the OLS estimator is a consistent and
efficient estimator of the true parameter. P-val is the probability level at which the null hypothesis can be rejected. The Hausman statistic is calculated as
H  ( IV  OLS )' (VIV  VOLS )( IV  OLS ) where  IV is the coefficient vector from the IV regression,  OLS is the coefficient vector from the OLS
regression, VIV is the covariance matrix of the coefficients from the IV regression, and VOLS is the covariance matrix of the coefficients from the OLS
regression. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10%
level (based on 2-tailed test) are in boldface. Coefficients on CG are multiplied by 100.
Tobin’s Q
Dependent variable
AGGREGATE
OLS
IV
1.886
(0.09)
4.406
(0.00)
OWNER
OLS
IV
3.469
(0.09)
16.158
(0.00)
DISCLOSURE
OLS
IV
5.360
(0.10)
13.500
(0.00)
BOARD
SIZE
R&D
EXPORT
F test statistics of overall
significance
Regression R2
Number of Companies
Hausman test statistics, 2
-0.169
(0.00)
0.268
(0.07)
0.006
(0.16)
-0.191
(0.00)
0.263
(0.07)
0.006
(0.18)
-0.158
(0.00)
0.271
(0.07)
0.006
(0.15)
-0.179
(0.00)
0.268
(0.07)
0.006
(0.16)
-0.177
(0.00)
0.269
(0.06)
0.006
(0.15)
-0.214
(0.00)
0.264
(0.06)
0.006
(0.16)
10.720
(0.00)
0.444
550
0.310
(0.66)
15.440
(0.00)
0.435
550
12.330
(0.00)
0.441
550
5.260
(0.05)
11.800
(0.00)
0.414
550
11.330
(0.00)
0.445
550
0.170
(0.84)
12.150
(0.00)
0.431
550
OLS
IV
2.635
(0.21)
-0.159
(0.00)
0.268
(0.07)
0.006
(0.17)
10.045
(0.00)
-0.177
(0.00)
0.260
(0.07)
0.005
(0.22)
9.830
(0.00)
0.442
550
3.130
(0.00)
17.670
(0.00)
0.424
550
Table XIV
Comparison of Coefficients Estimated Using Instrumental Variables Approach vs. OLS:
Investment on S&P Transparency Rankings
This table reports and compares the results of regression:
C 1
Investement cj    1 CG cj 
I 1
K
    Z
dc 
c1
di 
i 1
k
c
k,j
 cj
k 1
using OLS and IV estimation approaches, where c indexes country, i indexes industry; j indexes firm; and t indexes time. dc and di are country and
industry dummies, respectively. We use legal origin (English, French, or German), ORIGIN , as instruments for transparency scores in IV estimation.
Investment is defined as capital expenditures over total assets, 2000-20001 average. CG is one of S&P Transparency Rankings (AGGR, OWN, DISCL,
BOARD) in 2000. Z are control variables: SIZE is the log of total assets, 1999-2000 average; R&D is research and development expenditures scaled by
total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. Numbers in parentheses are
probability levels at which the null hypothesis of zero coefficient can be rejected. Coefficients significant at 10% level (based on 2-tailed test) are in
boldface. All p-values are based on robust (heteroscedasticity consistent) standard errors. Sample size is 549 firms. ORIGIN is not defined for Russia,
thus, we drop it from IV estimation. Hausman (1978) specification test compares the IV estimates with OLS estimates. The null hypothesis is that the
OLS estimator is a consistent and efficient estimator of the true parameter. P-val is the probability level at which the null hypothesis can be rejected.
The Hausman statistic is calculated as H  ( IV  OLS )' (VIV  VOLS )( IV  OLS ) where  IV is the coefficient vector from the IV regression,  OLS is the
coefficient vector from the OLS regression, VIV is the covariance matrix of the coefficients from the IV regression, and VOLS is the covariance matrix of
the coefficients from the OLS regression. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be
rejected. Coefficients significant at 10% level (based on 2-tailed test) are in boldface. Coefficients on CG are multiplied by 100.
Dependent variable
AGGR
INVESTMENT
OLS
IV
0.096
(0.02)
0.183
(0.01)
OWN
OLS
IV
0.145
(0.18)
0.685
(0.00)
DISCL
OLS
IV
0.263
(0.02)
0.415
(0.03)
BOARD
SIZE
R&D
EXPORT
F test statistics of overall significance
Regression R2
Number of Companies
Hausman test statistics, 2
-0.003
(0.28)
0.002
(0.00)
0.000
(0.11)
18.360
(0.00)
0.379
549
0.920
(0.43)
-0.004
(0.22)
0.002
(0.00)
0.000
(0.08)
21.070
(0.00)
0.373
549
-0.002
(0.39)
0.003
(0.00)
0.000
(0.14)
17.760
(0.00)
0.374
549
3.280
(0.00)
-0.003
(0.28)
0.003
(0.00)
0.000
(0.14)
18.310
(0.00)
0.344
549
-0.003
(0.23)
0.003
(0.00)
0.000
(0.11)
18.030
(0.00)
0.380
549
0.414
(0.38)
-0.004
(0.22)
0.002
(0.00)
0.000
(0.10)
18.610
(0.00)
0.378
549
OLS
IV
0.155
(0.05)
-0.002
(0.36)
0.002
(0.00)
0.000
(0.11)
18.000
(0.00)
0.376
549
0.960
(0.20)
0.418
(0.01)
-0.003
(0.28)
0.002
(0.01)
0.000
(0.07)
23.570
(0.00)
0.362
549
Table XV
CLSA Sample
Regressions with Centered Independent Variables and the Presence of Within-Country Correlations
This table reports the results of regressions:
I 1
K
CG cj    1 INV _ OPPjc  2  EXT _ FIN cj   1 LEGALc   2  INV _ OPPjc  LEGALc   3  EXT _ FIN cj  LEGALc 
  Z   d  
c
k,j
k
i
k 1
i 1
  Z   d  
k
k 1
  *Z   d  
k
c
k,j
k 1
i
i 1
c
j
(Panel B)
(Panel C)
I 1
 * Z   d  
k
k 1
i
c
j
i 1
K
Investment cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
c
k,j
I 1
K
Valuation cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
(Panel A)
I 1
K
CG cj    1 OWNERSHIPjc  2  (OWNERSHIPjc ) 2   1 LEGALc  OWNERSHIPjc  LEGALc 
c
j
c
k,j
i
i 1
c
j
(Panel D)
where c indexes country; i indexes industry; j indexes firm; and t indexes time. CG is CLSA Corporate Governance Composite Score in 2000 minus the sample mean (in Panels
C and D). Valuation is 2000-2001 average Tobin’s Q defined as total assets plus market value of equity less book value of equity over total assets and market value of equity is
the number of common shares outstanding times year-end share price. Investment is 2000-2001 average ratio of capital expenditures to total assets. INV_OPP (investment
opportunities) is 1999-2000 average of return on invested capital minus the sample mean; EXT_FIN (external financing) is 1999-2000 average ratio of the sum of new equity
issues and changes in long-term debt over capital expenditures minus the sample mean. OWNERSHIP is 1999-2000 average percentage of shares held by major shareholders,
either individuals or corporations, that hold more than 5% of outstanding shares minus the sample mean; OWNERSHIP^2 is the squared term of ownership; LEGAL is
INVESTOR*ENFORCE minus the sample mean where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule of law. Z are control variables: ROIC
(return on invested capital), 1999-2000 average; SIZE is the log of total assets, 1999-2000 average; R&D is research and development expenditures scaled by total assets times
100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. d are industry dummies (coefficients are not reported). P-values are based on
standard errors that allow for within countries correlations. Numbers in parentheses are probability levels at which the null hypothesis of zero coefficient can be rejected. The
estimator of coefficient’s variance when observations are correlated within groups (countries or industries in our case) but uncorrelated between groups is
 M /( M  1)  ˆ 
Q
Vˆ  

 ( N  1) /( N  k )  

M
k 1
ˆ , were M is number of groups, N number of observations, k number of estimated parameters, Q
ˆ  ( ln L /  ) 1 conventional
u (kG )'u (kG ) Q

estimator of variance, L likelihood function,  is the vector of parameters estimated and
)
u (G
the contribution of the kth group to the scores  ln L /  . Coefficients
k
significant at 10% level (based on 2-tailed test) are in boldface. Coefficients on CG are multiplied by 100.
Panel
A
B
Dependent variable
COMPOSITE
COMPOSITE
7.292
INV_OPP
OWNERSHIP
(0.03)
1.179
EXT_FIN
OWNERSHIP^2
(0.03)
0.484
LEGAL
LEGAL
(0.00)
INV_OPP*LEGAL
-0.152
OWNERSHIP*LEGAL
(0.59)
EXT_FIN*LEGAL
-0.064
(0.11)
SIZE
0.418
SIZE
(0.54)
R&D
-0.081
R&D
(0.93)
EXPORT
-0.044
EXPORT
(0.22)
F test statistics of joint significance
INV_OPP=0 and INV_OPP*LEGAL=0
EXT_FIN=0 and EXT_FIN*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
2.86
(0.08)
3.38
(0.05)
75.50
(0.00)
0.216
460
F test statistics of joint significance
OWNERSHIP=0 and
OWNERSHIP*LEGAL=0
F test statistics of overall significance
Regression R2
Number of Companies
25.509
(0.05)
-24.818
(0.05)
0.538
(0.00)
-0.536
(0.07)
COMPOSITE
LEGAL
CG*LEGAL
C
D
Tobin’s Q INVESTMENT
0.912
0.042
(0.00)
(0.00)
0.006
0.000
(0.25)
(0.13)
-0.003
-0.002
(0.40)
(0.00)
1.759
(0.00)
-0.157
(0.01)
0.184
(0.06)
0.008
(0.04)
0.007
(0.41)
0.000
(0.94)
0.009
(0.11)
0.000
(0.23)
14.96
(0.00)
8.99
(0.00)
F test statistics of overall
94.88 significance
662.75
(0.00)
(0.00)
0.229 Regression R2
0.454
380 Number of Companies
470
868.36
(0.00)
0.364
468
ROIC
0.293
(0.55)
-0.177
(0.71)
-0.052
(0.28)
SIZE
R&D
EXPORT
F test statistics of joint
significance
4.02 CG=0 and CG*LEGAL=0
(0.02)
Table XVI
S&P Sample:
Regressions with Centered Independent Variables and the Presence of Within-Country Correlations
This table reports the results of regressions:
I 1
K
CG cj    1 INV _ OPPjc  2  EXT _ FIN cj   1 LEGALc   2  INV _ OPPjc  LEGALc   3  EXT _ FIN cj  LEGALc 
  Z   d  
k
c
k,j
i
k 1
i 1
  Z   d  
k
k 1
  *Z   d  
k
c
k,j
k 1
i
i
i 1
c
j
(Panel B)
c
j
(Panel C)
i 1
I 1
K
Investment cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
c
k,j
I 1
K
Valuation cj    1 CG cj   1 LEGALc   2  CG cj  LEGALc 
(Panel A)
I 1
K
CG cj    1 OWNERSHIPjc  2  (OWNERSHIPjc ) 2   1 LEGALc  OWNERSHIPjc  LEGALc 
c
j
 * Z   d  
k
c
k,j
k 1
i
i 1
c
j
(Panel D)
where c indexes country; i indexes industry; j indexes firm; and t indexes time. CG is S&P Corporate Governance Aggregate Score in 2000 minus the sample mean (in
Panels C and D). Valuation is 2000-2001 average Tobin’s Q defined as total assets plus market value of equity less book value of equity over total assets and market
value of equity is the number of common shares outstanding times year-end share price. Investment is 2000-2001 average ratio of capital expenditures to total assets.
INV_OPP (investment opportunities) is 1999-2000 average of return on invested capital minus the sample mean; EXT_FIN (external financing) is 1999-2000 average
ratio of the sum of new equity issues and changes in long-term debt over capital expenditures minus the sample mean. OWNERSHIP is 1999-2000 average percentage of
shares held by major shareholders, either individual or corporations, that hold more than 5% of outstanding shares minus the sample mean; OWNERSHIP^2 is the squared
term of ownership; LEGAL is INVESTOR*ENFORCE minus the sample mean where INVESTOR measures investor protection and ENFORCE (enforcement) is the rule
of law. Z are control variables: ROIC (return on invested capital), 1999-2000 average; SIZE is the log of total assets, 1999-2000 average; R&D is research and
development expenditures scaled by total assets times 100, 1999-2000 average; and EXPORT is export scaled by sales times 100, 1999-2000 average. d are industry
dummies (coefficients are not reported). P-values are based on standard errors that allow for within countries correlations. Numbers in parentheses are probability levels at
which the null hypothesis of zero coefficient can be rejected. The estimator of coefficient’s variance when observations are correlated within groups (countries or
M
 M /( M  1)  ˆ 
( G )' ( G )  ˆ
Q
industries in our case) but uncorrelated between groups is Vˆ  
  k 1 u k u k Q , were M is number of groups, N number of observations, k number
(
N

1
)
/(
N

k
)


ˆ  ( ln L /  ) 1 conventional estimator of variance, L likelihood function,  is the vector of parameters estimated and u (G ) the contribution
of estimated parameters, Q

k
of the kth group to the scores
Panel
Dependent variable
INV_OPP
EXT_FIN
LEGAL
INV_OPP*LEGAL
EXT_FIN*LEGAL
SIZE
R&D
EXPORT
F test statistics of joint
significance
INV_OPP=0 and
INV_OPP*LEGAL=0
EXT_FIN=0 and
EXT_FIN*LEGAL=0
F test statistics of overall
significance
Regression R2
Number of Companies
 ln L /  . Coefficients significant at 10% level (based on 2-tailed test) are in boldface. Coefficients on CG are multiplied by 100.
A
AGGREGATE
9.881
(0.10)
5.332
(0.03)
0.414
(0.00)
-0.033
(0.88)
-0.110
(0.36)
1.282
(0.00)
0.029
(0.05)
-0.050
(0.17)
3.18
(0.08)
B
AGGREGATE
OWNERSHIP
OWNERSHIP^2
LEGAL
OWNERSHIP*LEGAL
7.000
(0.05)
-28.962
(0.01)
0.197
(0.00)
-0.081
(0.76)
AGGREGATE
LEGAL
CG*LEGAL
ROIC
SIZE
R&D
EXPORT
F test statistics of joint
significance
OWNERSHIP=0 and
OWNERSHIP*LEGAL=0
1.261
(0.00)
0.179
(0.17)
-0.002
(0.93)
SIZE
R&D
EXPORT
C
D
Tobin’s Q INVESTMENT
0.881
0.031
(0.05)
(0.15)
0.000
0.006
(0.23)
(0.08)
-0.029
-0.007
(0.58)
(0.13)
2.500
(0.07)
-0.257
(0.12)
0.270
(0.12)
0.006
(0.10)
0.046
(0.03)
-0.002
(0.42)
0.003
(0.11)
0.000
(0.91)
F test statistics of joint significance
3.74
(0.02)
CG=0 and CG*LEGAL=0
7.08
(0.00)
1.59
(0.23)
32.42
(0.00)
0.177
458
F test statistics of overall significance
157.43
(0.00)
0.392
550
128.24
(0.00)
0.257
549
4.18
(0.04)
103.08
(0.00)
0.236
541
F test statistics of overall
significance
Regression R2
Number of Companies
Regression R2
Number of Companies