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The Effect of Block Ownership on Investment and Financing Decisions
around the World
Li Jiang
School of Accounting and Finance
The Hong Kong Polytechnic University
(852) 2766-4672
Lei Pang*
Department of Finance & Decision Sciences
Hong Kong Baptist University
(852) 3411-5230
ABSTRACT
We investigate the effect of block ownership on investment and financing decisions using a large
sample of non-U.S. firms from 46 countries. We find that block ownership is negatively related
to investment sensitivity to price but is positively related to investment sensitivity to cash flow.
We further find that the adverse effect of block ownership is mitigated by industry-level product
market competition; block ownership reduces external equity finance and lowers ex post
investment efficiency and future growth. The findings are consistent with the notion that private
benefit extraction by blockholders outweighs the shared benefits of investments in the
international context. We conclude that block ownership weakens the role played by the stock
market in resource allocation.
Keywords: Block ownership, Private control benefit, Corporate investment, Managerial learning,
Financial constraints
JEL Classification: G14, G31
This Version: February 28, 2015
*Corresponding author
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1. Introduction
Block ownership is a prevalent phenomenon in the global markets and it often emerges as
a response to weak country-level investor protection. In these countries, owners who are lack of
protection are motivated to become block owners as ownership concentration ensures large
shareholders to get a return on their investment (La Porta et al. (1998); La Porta et al. (2000)).
Given that country-level characteristics matter so much for corporate governance (Doidge et al.
(2007)) and the evidence that country-level investor protection plays an important role in
resource allocation (e.g., McLean et al. (2012)), whether firm-level block ownership affects
investment and financing decisions beyond the country-level investor protection is an important
but unexplored question.
Our focus on the firm-level block ownership is motivated by studies showing firm-level
corporate governance and country-level investor protection may not be perfect substitutes.
Klapper and Love (2004) show that there is a great variation in investor protection at the firm
level and they find that firm-level corporate governance mechanism actually matters more in
countries with weak investor protection. Holderness (2009) criticizes the studies linking country-
level average ownership concentration to investor protection to reach their conclusion. Burkart
and Panunzi (2006) demonstrate that the relation between investor protection and ownership
concentration can be non-monotone, casting doubt on the commonly accepted argument that
investor protection and ownership concentration are substitutes.
Large block ownership can be driven by two opposing factors: private benefits versus
shared benefits (Holderness 2003). On the one hand, Private Benefit Hypothesis argues that
concentrated ownership provides incentives to blockholders to extract private benefits of control.
Although blockholders resolve the conflict between shareholders and managers, blockholders
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exacerbate the conflict between controlling and minority shareholders. Blockholders may pursue
objectives other than firm value maximization at the expense of minority shareholders’ interests.
Prior studies find that block trades are priced at a premium, suggesting a positive private benefit
of control (Barclay and Holderness (1989); Albuquerque and Schroth (2010)).1 On the other
hand, Shared Benefit Hypothesis argues that blockholders can monitor managers more effectively
by overcoming the free-rider problem (Grossman and Hart, 1980). Edmans et al. (2013) show
that blockholders can improve monitoring through either direct intervention or threat of exit by
trading. The incentives for blockholders to invest in positive NPV projects increase with their
ownership stake. Lins (2003) finds that outside block ownership is positively related to firm
value in 18 emerging markets. Furthermore, as Foley and Greenwood (2010) examine the
evolution of corporate ownership after IPO and show that ownership concentration is the result
of the interaction between investor protection and growth opportunities. To the extent that firms
depend on external finance to capitalize on growth opportunities, blockholders may suppress
their private benefit extraction in favor of shared benefits with minority shareholders.
To investigate the effect of block ownership on investment and financing decisions, we
follow and extend McLean et al. (2012) by examining two measures: investment sensitivity to
stock price (q) and investment sensitivity to cash flow (CF). 2 The first measure captures
managerial learning from the stock market (Chen et al. 2007) or the ability of stock price in
1 Prior research confirms that block ownership in the international context is often associated with agency problems
(LaFond et al. 2007). Claessens et al. (2002) find the wedge between control and ownership is negatively related to
firm value when the largest blockholder is a family or the state using eight East Asian economies. Block holders
have incentives to hide their expropriation activities from outside investors (Leuz et al. 2003), and ownership
concentration is positively associated with earnings management (Leuz 2006). Concentrated ownership by family or
management group discourages analyst following, thus stock prices are less informative and insiders are subject to
less scrutiny by analysts (Lang et al. 2004). 2 McLean et al. (2012) study the impact of country-level investor protection on corporate investment and financing
decisions. They find that investor protection is positively associated with investment-q sensitivity while negatively
associated with investment-cash flow sensitivity. They argue that investor protection improves the ability of stock
price in reflecting firm fundamentals and relaxing financial constraints.
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reflecting firm fundamentals (McLean et al. 2012). To the extent that blockholders pursue shared
(private) benefits of control, they would improve (deteriorate) a firm’s resource allocation by
listening to the stock market, resulting in an increase (decrease) in investment-q sensitivity. The
second measure is linked to the degree of financial constraints (Fazzari et al. 1988; Fazzari et al.
2000). To the extent that blockholders pursue the shared (private) benefits of control, firms
would experience lower (greater) financial constraints and rely less (more) on internal cash flow,
resulting in lower (higher) investment-CF sensitivity.
Non-U.S. countries provide an excellent context to study the effect of block ownership as
a number of international literature draw the conclusions that ownership is more concentrated in
the non-U.S. countries than in the U.S. (e.g., La Porta et al. (1998); La Porta et al. (2000);
Claessens, Djankov, Fan and Lang (2002)). In this study, we use non-U.S. firms from 46
countries to investigate the effect of block ownership on investment and financing decisions. We
make two main predictions: First, we predict that block ownership exerts a negative effect on
managerial learning (as measured by investment sensitivity to stock price). Second, we predict
that block ownership exerts a positive effect on financing constraints (as measured by investment
sensitivity to cash flow).
Our first prediction is based on the hypothesis that private benefit extraction by
blockholders causes distortions in corporate investments and therefore managers do not listen to
the stock market as much as they should. Prior research shows that stock markets have a
significant impact on corporate investment. Dow and Gorton (1997) and Subrahmanyam and
Titman (1999) argue that stock market has its cost advantage in the production and aggregation
of serendipitous information from outside investors. Chen et al. (2007) find that managers can
make better investment decisions through learning from stock price. Maug (2001) suggests that
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industry- and country-level information is increasingly important to corporate decision makers
especially when firms are at a later stage of life cycle. Empirical studies (Durnev et al. 2004;
Giammarino et al. 2004; Luo 2005; Chen et al. 2007) support the notion that stock prices serve
as a useful signal for guiding and facilitating the efficient allocation of resources, especially
when outside investors have an information advantage over insiders.
Evidence on the effect of block ownership on resource allocation is mixed. For example,
Faccio et al. (2011) show that undiversified large shareholders are relatively conservative in
pursuing investment projects with lower volatility. Jiang et al. (2011) find a negative relation
between the wedge between control and ownership and investment sensitivity to stock price. In
contrast, Kau et al. (2008) find that block ownership in the U.S. has a positive impact on
managers’ propensity to listen to the stock market. Moreover, Bolton and Von Thadden (1998)
show that block ownership reduces market liquidity, which in turn makes it less attractive for
informed risk arbitrageurs to produce firm-specific information (Morck et al. 2000). As Chen et
al. (2007) show that stock price informativeness is positively linked to managerial learning,
reduced market liquidity adversely affects the allocational role of stock price. 3 Put all these
aspects together, we expect that block ownership in the international context adversely affects
managerial learning from the stock market. Consistent with our prediction, we find that corporate
investments are less sensitive to stock prices for firms with greater block ownership.
Our second prediction suggests that block ownership positively affects financial
constraint. We follow Fazzari et al. (1988) in using investment-CF sensitivity as a proxy for
financial constraints. There are different views on whether investment-CF sensitivity can capture
financial constraints. Fazzari et al. (1988) argue that investment sensitivity to cash flow captures
3 Market liquidity is positively linked to the willingness for large shareholders to engage in monitoring activities
(Maug, 1998).
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financial constraints. However, Kaplan and Zingales (1997) show that there is some ambiguity in
the relation between financial constraints and investment sensitivity to cash flow. Fazzari et al.
(2000) disagree with Kaplan and Zingales (1997)’s critique and argue that their empirical
classifications cannot identify financially constrained firms. Erickson and Whited (2000) also
challenge Fazzari et al. (1988) interpretation of investment sensitivity to cash flow based on the
measurement error problem. In particular, Tobin’s q is an imperfect proxy for true investment
opportunities or marginal q, and cash flow is highly positively correlated with Tobin’s q. They
show that the coefficient on cash flow is biased upward. While the controversies are around
investment sensitivity to cash flow, McLean et al. (2012) argue that the criticism by Kaplan and
Zingales (1997) does not hold for the relation between external finance and cash flow. We follow
McLean et al. (2012) in examining the sensitivity of equity or debt issuance to cash flow. Based
on their findings that investment-CF sensitivity declines with investor protection, McLean et al.
(2012) conclude that country-level investor protection reduces the cost of external finance. We
expect that private benefit extraction by blockholders makes it more costly for obtaining external
financing. Therefore, firms with greater block ownership would rely more on internal cash flow
for their investments. Consistent with our prediction, we find that corporate investments are
more sensitive to cash flow for firms with greater block ownership.
Next, we investigate how industry- and country-level governance characteristics interact
with block ownership in affecting investment and financing decisions. First, we examine whether
industry-level product market competition mitigates the adverse effect of block ownership. As
Giroud and Mueller (2011) show that product market competition substitutes corporate
governance, we predict and find that product market competition mitigates the adverse effect of
block ownership on both investment-q sensitivity and investment-CF sensitivity. Second, we
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examine whether country-level investor protection mitigates the adverse effect of block
ownership on both investment-q sensitivity and investment-CF sensitivity. We find that the effect
of block ownership on investment and financing remains intact after we control for country-level
investor protection. Our finding suggests that block ownership substitutes for country-level
investor protection as a country’s legal regime may shape firms’ ownership formation (La Porta
et al. 1998).
A primary concern for studying firm-level block ownership is the potential endogeneity
issue. There are possible omitted variables that affect both investment and block ownership. Also,
a reverse causality is possible since blockholders may prefer firms with better managerial
learning or lower financial constraints. First, we examine whether the residual block ownership
with country-level investor protection removed is still negatively related to investment-q
sensitivity and positively related to investment-CF sensitivity. We find that the adverse effect of
block ownership remains intact. We then follow Lemmon and Lins (2003) by using financial
crisis as an exogenous event to identify the causal relation. Specifically, market-wide financial
crisis can be considered as an exogenous (relative to individual firms) shock to firms’ investment
opportunities. As blockholders keep a balance between private benefits of control and shared
benefits based on firms’ investment opportunity, a negative shock to investment opportunity
leads to greater incentives for private benefit extraction. This allows us to identify a causal
relation from block ownership to managerial learning and financial constraints.
Finally, we examine whether block ownership affects external financing and ex post
investment efficiency and firms’ growth. McLean et al. (2012) find that better country-level
investor protection improves a firm’s access to external finance. In the international context, the
expropriation by blockholders makes it costly for firms to obtain external finance, therefore,
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investments by firms with greater block ownership are more likely to depend on internal cash
flow. We therefore expect and find that the relation between external financing and q declines
with block ownership, and external financing are more sensitive to cash flow for firms with
greater block ownership. As Edmans (2014) points out that block ownership is chosen by
blockholders rather than the firm, it is likely that the block ownership is not optimal for the firm
(at least not for minority shareholders) and therefore block ownership matters for firm
performance. We find that block ownership reduces ex post investment efficiency and firms’
growth. This provides further support for the private benefit hypothesis.
We contribute to a growing strand of research that links corporate governance to financial
markets. First, our findings shed lights on the effect of block ownership on the allocational role
of the stock market. We show that block ownership adversely affects the informational feedback
from stock price to corporate investments, confirming that the real effect of block ownership in
the international context is negative. Second, we provide firm-level evidence that block
ownership increases the dependence of investments on internal cash flow, suggesting that block
ownership leads to greater financial constraints. Our findings on investment-q sensitivity and
investment-CF sensitivity lend support to private benefit extraction by blockholders in the
international context. Third, extending previous studies using the country-level investor
protection, we show that firm-specific attributes such as block ownership plays an important role
in corporate governance beyond country-level factors. Our findings suggest that ownership
concentration contributes to a diminishing role of stock markets around the world.
The remainder of the paper is organized as follows. In the next section, we describe data
and descriptive statistics. Section 3 presents the main empirical results. Section 4 addresses
endogeneity issue. Section 5 performs additional tests. Section 6 concludes.
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2. Data and Descriptive Statistics
2.1. Data
We use the percentage of closely held shares from the Worldscope database as a proxy for
block ownership (BLK) for the period 1991 to 2011. The percentage of closely held shares
reports the fraction of equity owned by corporate officers, directors and their immediate families,
trusts, individuals with more than 5% holdings, other corporations, and the firm’s own pension
funds and trusts. Thus, the percentage of closely held shares represents inside block ownership.
Ammer et al. (2012) and Kho et al. (2009) discuss both strength and weakness of Worldscope
data on the percentage of closely held shares. The coverage of closely held shares varies from
country to country. Following Leuz (2006), we adjust the closely held shares by excluding the
value of depositary institution holdings, which are often mistakenly counted as closely held
shares.
We obtain financial data from the Datastream and Worldscope databases. We first
exclude financial firms (SIC 6000–6999). We then exclude observations with missing values for
block ownership capital expenditure, total asset, market capitalization, revenue, leverage, quick
ratio, and data required to compute Tobin’s q and cash flow. R&D expenditure is set to be zero if
missing. We require the total assets for each firm to be greater than $100 million. We obtain a
final sample of 20,738 firms which comprise a total of 143,337 firm-years from 46 counties over
the sample period of 1991–2011. To mitigate the effect of extreme values in the regression
analyses, we winsorize all financial variables at the 1% and 99% levels.
2.2. Descriptive statistics
Table 1 presents the summary statistics. Panel A reports the summary statistics for each
country, including number of observations, mean and standard deviation of block ownership,
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mean of market capitalization, and country-level measures of investor protection. The panel
shows that the total number of firm-year observations varies from a minimum of 117 in
Colombia to a maximum of 23,495 in Japan. The countries, other than the U.S., that have highest
average block ownership are Czech, Russian, Peru, Philippine and Indonesia, while countries
with lowest average block ownership are Canada, Ireland and the U.K. We also find that among
the countries, other than the U.S., the standard deviation of block ownership varies from a
minimum of 16.36% in Japan to a maximum of 27.53% in Colombia.
Panel B reports the sample distribution and descriptive statistics of main variables by year.
The panel shows that the total number of firm-year observations increases gradually from 1991
to 2011. The average block ownership increases from 0.3983 in 1991 to 0.4923 in 2011,
suggesting a systematic increase in ownership concentration during our sample period. Panel C
reports the correlation matrix among country-level legal variables and country average block
ownership. There is no significant correlation between the average block ownership in a country
and its legal origins or investor protection.
Table 2 presents descriptive statistics for the variables used in our regression analysis.
Our primary variable of interest is block ownership (BLK). We use the percentage of adjusted
closely-held shares from Worldscope as our measure of block ownership. Panel A shows that the
mean and median of block ownership are 46.9% and 48.4%, respectively. They are qualitatively
consistent with Kho et al. (2009) that average block ownership is 49.0% in 1994 and 50.9% in
2004, respectively. The standard deviation of BLK is 0.2311, suggesting that our sample firms
exhibit a relatively large variation in ownership concentration. Panel B reports the descriptive
statistics for investments and control variables.
[INSERT TABLES 1 AND 2 HERE]
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3. Empirical Results
In this section, we examine the effect of block ownership on investment-q sensitivity and
investment-CF sensitivity, respectively. We then investigate whether industry-level product
market competition or country-level investor protection mitigates the effect of block ownership
on investment and financing decisions.
3.1. Block ownership and investment sensitivities to price and cash flow
To test our prediction of the effect of block ownership on investment sensitivities to both
price and cash flow, we use the following firm-level cross-sectional regression:
Ii,t = i + t + c + l + qi,t-1 + CFi,t + BLKi,t-1 + qi,t-1*BLKi,t-1
+ CFi,t*BLKi,t-1+ MKTCAPi,t-1 + LEVi,t-1 + RETURNi,t-1
+ 1/ASSETi,t +i,t (1)
where Ii,t denotes firm i’ investment in year t. Two measures for corporate investment are used: (i)
INV is the combination of the yearly growth in property, plant and equipment, plus growth in
inventory, plus R&D spending, all scaled by beginning-of-year total assets; (ii) CAPX is the
capital expenditure scaled by beginning-of-year total assets. And the variables i and t are firm
and year fixed effects. The variables c and l are country and industry fixed effects. In all of our
tests, Panel A reports estimates of pooled OLS regressions with year, country and industry
dummies and standard errors corrected for country-level clustering, while Panel B reports
estimates of firm fixed effect regressions with year dummies and standard errors corrected for
firm-level clustering.
q is normalized price at the beginning of the year (t-1) and is estimated as the market
value of equity, minus book value of equity, plus book value of assets, all scaled by book value
of total assets. CF, cash flow from operation in the same year (t), is measured as net income
before extraordinary item, minus change in current asset, plus change in current liability, plus
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change in cash and equivalents, minus change in debt in current liability, plus depreciation and
amortization expense, all scaled by beginning-of-year total assets. The focus of this research is
BLK, which represents block ownership. Our two hypotheses can be tested with the interaction
terms, q*BLK and CF*BLK, which capture the effect of block ownership on investment-q
sensitivity and investment-CF sensitivity.
Following Chen et al. (2007), we include the following control variables in the baseline
regression specification. Market capitalization (MKTCAP) is included to control for the firm size
effect. Financial leverage (LEV) is included to control for potential ‘debt overhang’ problem. To
account for market timing tendency of investing more when their stock is overpriced, we follow
Roll et al. (2009) by including one-year leading annual return (RETURN). To isolate the
correlation between investment and both Tobin’s q and cash flow induced by common scaling
variable, we include 1/ASSET. The Appendix provides detailed definitions of all the variables
included in Eq. (1).
Table 3 reports our main findings. Panel A presents the estimation results of the pooled
OLS regressions in Eq. (1). In Columns 1 and 2, which do not include any interaction terms, the
coefficients on both q and cash flow are positive and statistically significant, consistent with
previous studies (e.g., Chen et al., 2007). The economic impact of q is substantial. In Column 1,
one standard deviation increase in q yield a 0.0153 increase in investment (INV), which is
109.49% of median of investment. This shows our sample firms exhibit high investment–q
sensitivity. Similarly, one standard deviation increase in CF creates a 0.0105 increase in
investment (INV), which translates to 75.00% of median of investment. This indicates our sample
firms are on average financially constrained. The coefficient on block ownership is positive and
statistically significant, suggesting that block ownership, on average, increases corporate
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investment. With one standard deviation increase in block ownership, investment increases by
about 0.0030, which is about 21.29% of median of investment. The effect is economically large.
Our test on the impact of block ownership on investment sensitivities centers on the
interaction terms of block ownership with q and cash flow, respectively. As shown in Columns 3
and 4, the coefficient on the interaction term, q*BLK is negative and statistically significant (-
0.0115 with t = -3.91) with INV as the dependent variable, and negative but insignificant (-
0.0071 with t = -0.90) with CAPX as the dependent variable. This confirms our hypothesis that
the investment-q sensitivity declines with block ownership. In other words, block ownership
appears to weaken the allocational role of stock price.
The economic impact of block ownership on investment-q sensitivity is substantial. The
overall investment sensitivity to stock price is captured by the sum of the q coefficient and the
interaction coefficient multiplied by the mean value of block ownership (+BLK). For
example, the coefficient for q in Column 3 is 0.0246 and the interaction coefficient is –0.0115.
Given that the mean of block ownership (BLK) is 46.89%, the average investment-q sensitivity is
positive for our sample firms, (0.0246 - 0.0115ⅹ0.0620 = 0.0192). Even when block ownership
(BLK) reaches its 95th percentile at 84.73%, investment-q sensitivity remains positive, (0.0246 -
0.0115ⅹ0.8473 = 0.0149). This shows that the overall investment-q sensitivity is positive for
our sample firms. With one standard deviation increase in block ownership, investment-q
sensitivity decreases by about 0.0027. This effect is economically significant of about 14.21% of
the median of investment-q sensitivity.
As shown in Columns 5 and 6, the coefficient on the interaction term, CF*BLK is
positive and marginally significant (0.0317 with t = 1.83) with INV as the dependent variable,
and positive and statistically significant (0.0835 with t = 5.89) with CAPX as the dependent
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variable. The results are consistent with our hypothesis that investment is more sensitive to cash
flow for firms with greater block ownership. This suggests that the private benefit extraction by
blockholders makes it more costly for firms with greater block ownership to obtain external
financing. As such, investments of these firms rely more on internal cash flow.
The effect of block ownership on investment-CF sensitivity is economically significant
as well. The overall investment-CF sensitivity is captured by the sum of the CF coefficient and
the interaction coefficient multiplied by the mean value of block ownership (+BLK). Take
Column 5 as an example, the CF coefficient is 0.0817 and the interaction coefficient is 0.0317.
Given that block ownership (BLK) at its median is 48.40%, the median investment-CF sensitivity
for our sample firms are 0.0970. With one standard deviation increase in block ownership,
investment-CF sensitivity increases by about 0.0073. This effect is economically significant of
about 7.55% of median level of investment-CF sensitivity.
Panel B of Table 3 reports the firm fixed-effect regressions and the results are largely
consistent with those from Panel A. The coefficient on block ownership is positive and
significant at the 1% level. More importantly, the coefficients of interaction terms, q*BLK and
CF*BLK, are more significant in the firm fixed-effect regressions than those in the pooled OLS
regressions. The results confirm that block ownership increases corporate investment, decreases
investment-q sensitivity but increases investment-CF sensitivity.
Overall, our findings suggest that block ownership is an important firm-level determinant
for investment and financing decisions. Our results are consistent with the private benefit
hypothesis that the expropriation incentives of blockholders lead to both a reduction in
managerial learning from the stock market and an increasing dependence on internal cash flow.
[INSERT TABLE 3 HERE]
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3.2. Effect of product market competition
Researchers have long argued that product market competition disciplines managers to
maximize long-term firm value (Machlup 1967). Giroud and Mueller (2011) predict and find that
incentives provided by firm-level corporate governance mechanism matters only if the firm
operates in a non-competitive industry and firms make value-destroying acquisitions only in non-
competitive industries. Thus, product market competition may substitute for firm-level corporate
governance. Drawn on this literature, we extend our analysis by investigating whether product
market competition at the industry level can mitigate the adverse impact of block ownership on
investment-q sensitivity and investment-CF sensitivity.
Following Giroud and Mueller (2011), we measure product market competition using
Herfindahl-Hirschman index (HHI) of industry-level concentration, which is calculated as the
sum of squared market shares of each firm in an industry. HHI ranges from close to zero to
10,000. Specially, HHI is estimated as
jN
i
tjitj SHHI1
2
,,, (2)
where Si,j,t is the sales of firm i in industry j in year t. We exclude firms with missing or negative
value for sales. We estimate HHI for each 3-digit SIC industry classification. The inverse of HHI
(RHHI) is used in the extended regression specification. Given the fact that a higher HHI
indicates a lower product market competition, a higher RHHI indicates higher product market
competition and greater pressure. To test the impact of industry-level product market competition,
we run the following regression specification:
Ii,t = i + t + c + l + qi,t-1 + CFi,t + BLKi,t-1 + qi,t-1*BLKi,t-1
+ CFi,t*BLKi,t-1 + RHHIi,t-1 + BLKi,t-1*RHHIi,t-1 + qi,t-1*RHHIi,t-1
+ CFi,t*RHHIi,t-1 + qi,t-1*BLKi,t-1 *RHHIi,t-1
+ CFi,t*BLKi,t-1*RHHIi,t-1 + MKTCAPi,t-1 + LEVi,t-1
+ RETURNi,t-1 + 1/ASSETi,t +i,t (3)
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In Eq. (3), we include RHHI and its interaction with the testing variables in Eq. (1). The
dependent variable is INV4. The variables i and t are firm and year fixed effects. The variables
c and l are country and industry fixed effects. We expect that product market competition at
the industry level mitigates the negative effect of block ownership on managerial learning and
financial constraints. Thus, we predict that the coefficient on the three-way interaction term,
q*BLK*RHHI, is positive, while the coefficient on the three-way interaction term,
CF*BLK*RHHI, is negative.
Panel A of Table 4 reports the estimation results of the pooled OLS regressions. In
Column 1, the coefficient on q, CF, BLK remains positive and statistically significant with the
inclusion of RHHI and interaction term, BLK*RHHI, while the coefficient on BLK*RHHI is
insignificant. In Column 2, the coefficient on two-way interaction term, q*BLK, remains
significantly negative and the coefficient on q*RHHI is significantly negative. We focus on the
three-way interaction term among q, block ownership and product market competition,
q*BLK*RHHI. Its coefficient is positive and significant (0.0004 with t = 1.98), suggesting that
product market competition at the industry level mitigates the negative impact of block
ownership on investment-q sensitivity.
In Column 3, the coefficient on the two-way interaction term, CF*BLK, remains
significantly positive and the coefficient on CF*RHHI is significantly positive. This suggests that
product market competition itself increases the correlation between investment and internal cash
flow. With intensive product market competition in the industry, it is likely that firms depend
more on internal cash flow. The coefficient of the three-way interaction among CF, block
ownership and product market competition, CF*BLK*RHHI, is negative and significant (t = -
4 We repeat the analysis using CAPX as dependent variable. We do not tabulate the results since the results are
similar to those in Table 4.
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2.65). In other words, the positive association between block ownership and investment-CF
sensitivity declines with the intensity of product market competition. This confirms that product
market competition reduces the difficulty to raise external capital for firms with high block
ownership. Panel B of Table 4 reports the estimation results of firm fixed-effect regressions. The
findings are qualitatively similar to those reported in Panel A.
Overall, our findings are consistent with Giroud and Mueller (2011) that product market
competition serves as an alternative external corporate governance mechanism. In our specific
setting, product market competition appears to play a role of disciplining blockholders and
therefore mitigate the adverse effect of block ownership on investment and financing decisions.
Our findings further support the notion that block ownership in the international context is linked
to private benefit extraction.
[INSERT TABLE 4 HERE]
3.3. Effect of country-level investor protection
Next, we examine the role of country-level investor protection in mitigating the adverse
effect of block ownership on investment-q sensitivity and investment-CF sensitivity. On the one
hand, a country’s legal protection of minority investors influences the implementation of firms’
governance (Doidge et al. 2007). Under a strong country-level corporate governance
environment, block holders are less likely to extract private benefits of control (Doidge et al.
2004) and more firm-specific information is incorporated into stock price (Morck et al. 2000).
On the other hand, the formation of block ownership is primarily driven by a country’s legal
investor protection at the country level (La Porta et al. 1998) and therefore it is less clear whether
country-level investor protection can curb the extraction of private control benefits by
blockholders. Thus, whether country-level investor protection mitigates the adverse effect of
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block ownership on investment-q sensitivity and investment-CF sensitivity is an empirical
question.
We extend our main analysis to examine whether the effect of block ownership on
investment-q sensitivity and investment-CF sensitivity varies with country-level investor
protection. We run the following regression specification:
Ii,t = i + t + c + l + qi,t-1 + CFi,t + BLKi,t-1 + qi,t-1*BLKi,t-1
+ CFi,t*BLKi,t-1+ BLKi,t-1*CLc+ qi,t-1*CLc
+ CFi,t*CLc + qi,t-1*BLKi,t-1*CLc
+ CFi,t*BLKi,t-1*CLc + MKTCAPi,t-1 + LEVi,t-1
+ RETURNi,t-1 + 1/ASSETi,t +i,t (4)
In Eq. (4), we include the interaction terms between country-level proxies for legal
regimes and investor protection (CLc) and our testing variables in Eq. Because CLc does not vary
over time and our regression include either firm or country fixed effect, we do not include CLc in
our regressions. We use two proxies for investor protection at the country level, including legal
origins (COMMON) from La Porta et al. (1998) and the anti-self-dealing index (ANTISELF)
from Djankov et al. (2008) 5. The variables i and t are firm and year fixed effects. The
variables c and l are country and industry fixed effects. To the extent that country-level
investor protection mitigates the adverse effect of block ownership, we expect the coefficient on
the three-way interaction term, q*BLK*CL, to be positive, while the coefficient on the three-way
interaction term, CF*BLK*CL, to be negative.
Table 5 reports our results. Panel A reports the estimation results of the pooled OLS
regressions. In Columns 1 and 2, the coefficient on q and CF remains positive and statistically
significant with the inclusion of CL and interaction term, BLK*CL. The coefficient on BLK*CL
5 We repeat the analysis using two other proxies for investor protection at the country level, including good
governance index (GoodGov) calculated the sum of Corruption, Expropriation, and Repudiation from La Porta et al.
(1998) and La Porta et al. (1999), and investor protection index (InvPro) calculated as the arithmetic mean of
percentage rank of AntiDir, EffJud and LawRule from La Porta et al. (1998) and Djankov et al. (2008). We do not
tabulate the results since the results are similar to those in Table 5.
19
is insignificant. In Columns 3 and 4, the coefficient on two-way interaction term, q*BLK,
remains significantly negative while the coefficient on q*CL is insignificant. In Columns 5 and 6,
the coefficients on the two-way interaction term, CF*BLK, are insignificant while the
coefficients on CF*CL are significantly negative. This suggests that in countries with strong
investor protection, firms in general face less financial constraints and depend less on internal
cash flow.
Our main results focus on the three-way interaction terms. In Panel A, the coefficients of
interaction among q, block ownership and country-level investor protection, q*BLK*CL, and
interaction among CF, block ownership and country-level investor protection, CF*BLK*CL, are
insignificant, respectively. In Panel B with firm fixed-effects regressions, the results are similar
except that the coefficient of the interaction, q*BLK*CL is positive and marginally significant
(when country-level investor protection is ANTISELF). Overall, our findings suggest that
country-level investor protection cannot mitigate the observed adverse impact of block
ownership on investment-q sensitivity and investment-CF sensitivity.
[INSERT TABLE 5 HERE]
4. Endogeneity Issue
In this section, we conduct a variety of tests to address potential endogeneity concerns.
So far we have established a negative association between block ownership and investment-q
sensitivity and a positive association between block ownership and investment-CF sensitivity.
Our finding is consistent with the notion that block ownership adversely affects managerial
learning from the stock market and increases firms’ financial constraints. However, there is a
possibility for a reverse causality from investment-q sensitivity or investment-CF sensitivity to
block ownership. For example, blockholders may respond to a change in a firm’s investment
20
opportunities or the availability of internal cash flow. Thus, our evidence does not necessarily
imply block ownership causes the stock market to play a diminished role. Moreover, there may
be omitted variables that determine both block ownership and managerial learning. For example,
Maug (2001) shows that a firm chooses to go public when the demand for public information
exceeds the incremental cost of getting listed. Thus, the firm’s technology, product development,
and the scope of its market may jointly determine its block ownership and investment-q
sensitivity.
4.1. Residual block ownership and investment sensitivities to price and cash flow
A country’s legal regime and investor protection may shape firms’ ownership formation
process and determine block ownership. Block ownership substitutes for investor protection,
suggesting an inverse relation between block ownership and country-level investor protection
(La Porta et al. 1998). To the extent that the country-level determinants of block ownership affect
investment sensitivities to price and cash flow, they may introduce a spurious relation between
block ownership and investment sensitivities.
Following the approach used in Lafond and Roychowdhury (2008) and Ramalingegowda
and Yu (2012), we use residual block ownership in place of the raw block ownership in Eq. (1) to
remove the country-level determines of block ownership. Specifically, we estimate the residual
block ownership by using a yearly regression of block ownership on various country-level
characteristics as specified below:
BLKi,t-1 = l + GDPPCAPi,t-1 + CLc +i,t (5)
where block ownership (BLK) is the dependent variable. The independent variables are chosen at
country-level to address the concern that our main findings are driven by the variation in
country-level legal regimes and investor protection. Block ownership is regressed on GDP per
capita, industry dummies and two proxies for legal regime and investor protection at the country
21
level used in Eq. (4), including legal COMMON and ANTISELF6. For brevity, we do not report
the results of regression in Eq. (5). We re-estimate the regression Eq. (1) using residual block
ownership. We expect that the regression results using residual block ownership remain intact
even after we remove country-level determents of block ownership.
Table 6 presents the regression analysis using the residual block ownership. Note that the
results in Table 6 correspond to the main results in Table 3. Panel A reports the estimation results
of the pooled OLS regressions. We find that the coefficient on residual block ownership, RBLK,
is positive and significant at the 10% level or better. The coefficient on interaction term of
Tobin’s q and residual block ownership, q*RBLK, is negative and significant at the 5% level or
better. However, the coefficient on interaction term of cash flow and residual block ownership,
CF*RBLK, is insignificant. Panel B reports the estimation results of firm fixed-effects
regressions. We find that the coefficient on residual block ownership, RBLK, is positive and
significant at the 10% level only in Column 1. The coefficient on interaction term of Tobin’s q
and residual block ownership, q*RBLK, is negative and significant at the 1% level. The
coefficient on interaction term of cash flow and residual block ownership, CF*RBLK, is positive
and significant at the 1% level.
In short, the regression results using residual block ownership in Table 6 are in general
consistent with our earlier results and lend further support to our main results presented in Table
3. The findings are consistent with our view that firm-level block ownership exerts an adverse
impact on investment and financing decisions beyond the country-level investor protection.
[INSERT TABLE 6 HERE]
4.2. Financial crisis as an exogenous shock
6 We also estimate residual block ownership using two other proxies for investor protection at the country level,
including good governance index (GoodGov) and investor protection index (InvPro). We do not tabulate the results
since the results are similar to those in Table 6.
22
To further address the endogeneity concern, we search for an exogenous event that may
trigger changes in blockholders’ expropriation incentives. Prior studies examine the link between
firm corporate governance and firm value during the financial crisis. In particular, Lemmon and
Lins (2003) argue that financial crisis significantly lowers the expected return on firms’
investment, which in turn reduces the marginal costs to controlling shareholders for diverting
resources from firms’ profitable investments. They find that, as controlling shareholders’
expropriation incentives increase, firms with weak corporate governance experience greater
declines in value during the 1997 Asian financial crisis.
We use the 2008 financial crisis as an exogenous shock to blockholders’ incentives to
extract private control benefits and examine whether firms with different block ownership
exhibit different changes in investment-q sensitivity and investment-CF sensitivity during the
crisis. Since our sample cover firms from 46 countries around the world, a global financial crisis
is more suitable. As the economic condition around the world become deteriorate quickly during
the 2008 financial crisis, firms with blockholders experience declines in expected return from
their investments during the crisis. This in turn increases their incentives for expropriation. We
therefore predict that blockholders are more likely to adversely affect resource allocation during
the 2008 financial crisis.
We examine how the associations between block ownership and investment sensitivities
to both stock price and cash flow shift around the crisis period. Our identification strategy
employs crisis dummy on the basis of Eq. (1) over the period from January 2006 to August 2009.
In Particular, CRISIS takes the value of one for fiscal year ends between September 2008 to
August 2009, and zero otherwise. Note that in all our specifications, q is lagged by one period
therefore the definition of crisis period guarantees that q is pre-crisis price and unaffected by
23
market fluctuation during the financial crisis. We run the following regression specification:
Ii,t = i + t + c + l + qi,t-1 + CFi,t + BLKi,t-1 + qi,t-1*BLKi,t-1
+ CFi,t*BLKi,t-1+ BLKi,t-1*CRISIS + qi,t-1*CRISIS
+ CFi,t*CRISIS + qi,t-1*BLKi,t-1*CRISIS
+ CFi,t*BLKi,t-1*CRISIS + MKTCAPi,t-1 + LEVi,t-1
+ RETURNi,t-1 + 1/ASSETi,t +i,t (6)
where we include the interaction terms between CRISIS and our testing variables in Eq.(1). The
dependent variable is INV7. We predict that the coefficient on the three-way interaction term,
q*BLK*CRISIS, is negative and significant, and the coefficient on the three-way interaction term,
CF*BLK*CRISIS, is positive and significant. We expect that the negative effects of block
ownership on managerial learning and financial constraints become more pronounced during the
financial crisis.
Panel A of Table 7 reports the estimation results of pooled OLS regressions. It is
noteworthy that in Column 1 the coefficient on the interaction term BLK*CRISIS is positive but
insignificant. In Column 2 when we interact CRISIS with BLK and q, the coefficients on the two-
way interaction terms, q*BLK and q*CRISIS, are insignificant, and the coefficient on the three-
way interaction term q*BLK*CRISIS is negative and significant at the 10% level. In Column 3
when we interact CRISIS with BLK and CF, the coefficients on the two-way interaction terms,
CF*BLK and CF*CRISIS are positive and significant, showing that the association between
investment and cash flow become stronger during the financial crisis. The coefficient on the
interaction term CF*BLK*CRISIS is insignificant.
Panel B of Table 7 reports the estimation results of firm fixed-effects regressions. The
coefficient on the interaction term BLK*CRISIS is positive and significant at the 1% level,
suggesting that block ownership is associated with more investment during the financial crisis.
7 We repeat the analysis using CAPX as dependent variable. We do not tabulate the results since the results are
similar to those in Table 7.
24
The coefficient on the three-way interaction term, q*BLK*CRISIS, is negative and significant at
the 1% level. The result suggests that the negative effects of block ownership on managerial
learning from the stock market in making investment decisions deteriorate during financial crisis.
The coefficient on the interaction term CF*BLK*CRISIS is insignificant. Overall, these results
are consistent with the conjecture that block ownership has a casual impact on investment-q
sensitivity and investment-CF sensitivity.
[INSERT TABLE 7 HERE]
5. Additional Tests
5.1. Block ownership and external financing
So far, our findings suggest that block ownership adversely affects managerial learning
from the stock market and increases firms’ financial constraints because of blockholders’
extraction of private benefit of control. If this interpretation is valid, we conjecture that block
ownership makes it more difficult for firms to obtain external finance. Thus, we predict that the
relation between external financing and cash flow is weakened with block ownership. To test this
prediction we use the following regression specification:
ISSi,t = i + t + c + l + qi,t-1 + CFi,t + BLKi,t-1 + qi,t-1*BLKi,t-1
+ CFi,t*BLKi,t-1 + MKTCAPi,t-1 + LEVi,t-1 +i,t (7)
where the dependent variable is external financing, either equity issuance or debt issuance.
EQISS is the change in book equity, plus the change in deferred taxes, minus the change in
retained earnings, all scaled by lagged assets. DEISS is the annual percentage change in total
debt. The variables i and t are firm and year fixed effects. The variables c and l are country
and industry fixed effects.
Table 8 reports the estimation analysis of the impact of block ownership on external
financing. Panel A reports the estimation results of the pooled OLS regressions with equity and
25
debt issuance as the dependent variables. The dependent variable is equity issuance in Columns 1
to 3. In Column 1, the coefficient on q is positive and statistically significant (0.0581 with t =
5.81), showing that high Tobin’s q firms have greater growth opportunity and engage in more
equity issuance. In Column 2, the coefficient on the interaction term, q*BLK, is negative and
significant (-0.395 with t = -2.88), suggesting that private benefit extraction by blockholders
makes it less likely to rely on equity financing.
The coefficient on cash flow is negative and statistically significant (-0.67 with t = -30.17)
in Column 1, suggesting that low cash-flow firms have greater needs for equity issuance. In
Column 3, the coefficient on the interaction term, CF*BLK, is positive and statistically
significant (0.4650 with t = 4.31), suggesting that firms with greater block ownership are less
likely to issue equity shares. This corroborates our findings in Table 3 that investment depends
more on internal cash flow for firms with greater block ownership.
Columns 4 to 6 use debt financing as dependent variable. Debt financing is measured as
the percentage of change in total debt. The findings in Column 4 show that higher Tobin’s q leads
to more debt issuance and higher cash flow leads to more debt issuance. The coefficient on cash
flow suggests that low cash-flow firms use less debt issuance. However as shown in Columns 5
and 6, the interaction terms are insignificant.
Panel B of Table 8 reports the estimation results of firm fixed-effects regressions. The
findings in Columns 1 to 3 using equity financing as dependent variable are consistent with those
from Panel A. The findings in Columns 4 to 6 using debt financing as dependent variable support
our prediction that block ownership makes it costly for low cash-flow firms to fund investment
with debt. The coefficient on the interaction term, CF*BLK, is positive and significant,
suggesting that low cash-flow firms are less likely to borrow for firms with greater block
26
ownership. The coefficient on the interaction term, q*BLK, is insignificant.
Collectively, the findings in Table 8 confirm that external equity financing is positively
related to q and negatively related to cash flow. External debt financing is positively related to
both q and cash flow. The private benefit extraction by blockholders makes it costly for firms to
use external equity and debt financing. This is consistent with our findings in Table 3 that
investment is less sensitive to Tobin’s q but more sensitive to internal cash flow for firms with
greater block ownership.
[INSERT TABLE 8 HERE]
5.2. Block ownership and ex post efficiency
Finally, we examine whether block ownership affects ex post investment efficiency and
firms’ growth through its impact on managerial learning and financial constraints. So far we find
that blockholders are likely to expropriate firm resources and lower investment efficiency.
Meanwhile, private benefit extraction by blockholders makes it costly to obtain external finance
and these firms face more financial constraints. The findings indicate that the investment made
by firms with high block ownership deviates from its optimal level and therefore block
ownership matters for firm performance8 . We expect that block ownership reduces ex post
investment efficiency and firms’ growth and this provides further support for the private benefit
hypothesis. To examine our prediction we employ the following framework used by Masulis,
Wang and Xie (2009).
qi,t / GROWTHi,t = i + t + c + l + Ii,t + BLKi,t-1 + Ii,t *BLKi,t-1
+ CFi,t+ MKTCAPi,t-1 + LEVi,t-1 +i,t (8)
where the dependent variable is either market value or five-year revenue growth. The
8 Roll et al. (2009) examine the effect of option trading on investment sensitivity to stock price as well as on firm
performance. They find that option trading improves the learning from the stock market and has a positive impact on
firm valuation.
27
independent variables include investment, interaction between investment and block ownership,
cash flow, firm size, leverage. The variables i and t are firm and year fixed effects. The
variables c and l are country and industry fixed effects. We predict that the coefficient on the
interaction term, I*BLK, is significantly negative. This suggests that block ownership lowers ex
post investment efficiency and firms’ growth, and provides further support for the private benefit
hypothesis. The regression results are reported in Table 9.
Panel A of Table 9 reports the estimation results of the pooled OLS regressions with q
and GROWTH as dependent variables and investment as independent variables. In Columns 1
and 2 when q is the dependent variable, the coefficients on investment (both CAPX and INV) are
positive and significant (0.567 with t = 1.75 and 0.383 with t = 2.80, respectively), showing that
investment increases firm value. The coefficient on the interaction term, INV*BLK, is negative
and significant (-0.250 with t = -1.91), suggesting that block ownership decreases the market
valuation of corporate investment. However, the coefficient on the interaction term CAPX*BLK
is negative but insignificant. In Columns 3 and 4 when GROWTH is dependent variable, the
coefficients on investment (both CAPX and INV) are positive and statistically significant (1.106
with t = 13.88 and 0.890 with t = 7.91, respectively), showing that investment increases firms’
growth. The coefficient on the interaction terms, CAPX*BLK and INV*BLK, are negative and
significant (-0.392 with t = -2.16 and -0.332 with t = -2.18, respectively). The results indicate
that block ownership reduces the contribution of corporate investment to firm growth. Panel B of
Table 9 reports the estimation results of firm fixed-effects regressions and the findings are
qualitatively consistent with Panel A.
[INSERT TABLE 9 HERE]
28
6. Conclusion
In this study, we investigate the effect of block ownership on investment and financing
decisions in the international setting where block ownership is a prevalent phenomenon. We
argue that private benefit extraction by blockholders not only causes distortions in corporate
investments so that managers learn less from the stock market, but also increases the cost of
external financing so that investments rely more on internal cash flow. Using a large sample of
non-U.S. firms from 46 countries, we find supporting evidence that block ownership exerts a
negative impact on investment-q sensitivity and a positive impact on investment-CF sensitivity.
We further investigate the role of product market competition and country-level investor
protection as alternative governance mechanism in mitigating the adverse effect of block
ownership. We find that the effect of block ownership on investments is weakened for firms with
greater product market competition but remains intact even after we control for country-level
investor protection. We address the potential endogeneity issue by using the 2008 financial crisis
as an exogenous shock to blockholders’ incentives for private benefit extraction and confirm the
causal effect of block ownership on resource allocation. Furthermore, block ownership increases
the difficulty to obtain external financing, lower ex post investment efficiency and firms’ growth.
Overall, block ownership weakens the role played by the stock market in resource allocation,
suggesting that private benefit extraction by blockholders outweighs their shared benefits of
investments in the international context.
29
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31
Appendix
Variable Definitions
Firm-Level Variables
BLK is the percentage of closely held shares by blockholders. Closely held shares reports the fraction of
equity owned by corporate officers, directors and their immediate families, trusts, individuals with more
than 5% holdings, other corporations, and pension funds. Following Leuz (2006), we adjust closely held
shares by excluding the value of depositary institution holdings, which are often mistakenly counted as
closely held shares.
INV is the combination of the yearly growth in property, plant and equipment, plus growth in inventory,
plus R&D spending, all scaled by beginning-of-year total assets.
CAPX is capital expenditure, scaled by beginning-of-year total assets.
Tobin’ q is estimated as the market value of equity, minus book value of equity, plus book value of assets,
all scaled by book value of total assets.
CF is net income before extraordinary item, minus change in current asset, plus change in current liability,
plus change in cash and equivalents, minus change in debt in current liability, plus depreciation and
amortization expense, all scaled by beginning-of-year total assets.
MKTCAP is the natural log of market capitalization at the end of the year ($million).
LEV is the total debt scaled by the sum of market value of equity and book value of long-term debt at the
end of the year.
RETURN is annual stock return for the next one year.
1/ASSET is the inverse of beginning-of-year assets.
RHHI is the reverse of Herfindahl-Hirschman index (HHI). HHI of industry-level concentration is
calculated as the sum of squared market shares of each firm competing in the industry.
EQISS is the change in book equity, plus the change in deferred taxes, minus the change in retained
earnings, all scaled by lagged assets.
DEISS is the annual percentage change in total debt.
GROWTH is the natural log of growth in sales over the past five years.
Industry dummies are industry indicators based on two-digit SIC code.
Country-Level Proxies for Governance
Law origin: The legal origin of the company law or commercial code of each country. Equals one if the
origin is common law, and zero otherwise. Source: La Porta et al. (1998).
Anti-self-dealing index: Average of ex ante and ex post private control of self-dealing. Index of ex ante
control of self-dealing transactions is based on the average of approval by disinterested shareholders and
ex ante disclosure. Index of ex post control over self-dealing transactions is based on the average of
disclosure in periodic filings and ease of proving wrongdoing. First principal component of: (1) approval
by disinterested shareholders; (2) disclosures by Buyer; (3) disclosures by the insider self-dealer; (4)
independent review; (5) each of the elements in the index of disclosure in periodic filings; (6) standing to
sue; (7) rescission; (8) ease of holding the insider self-dealer liable; (9) ease of holding the approving
body liable; and (10) access to evidence. The index ranges from zero (weak private enforcement) to one
(strong private enforcement). Source: Djankov et al. (2008).
32
Table 1 Sample Distribution
This table shows the number of observations, the average of block ownership, firm size for each country and by year,
and the country-level proxies for investor protection.
Panel A: Country distribution
Country # Obs. BLK Mean BLK
Std.Dev. MKTCAP
Country-level proxies(CL)
COMMON ANTISELF
ARGENTINA 255 0.6382 0.1832 5.4351 0 0.34
AUSTRALIA 9,390 0.4021 0.2458 4.0838 1 0.76
AUSTRIA 660 0.5593 0.2153 5.5271 0 0.21
BELGIUM 1,137 0.5356 0.1984 5.5545 0 0.54
BERMUDA 113 0.4567 0.2019 4.6590 - -
BRAZIL 1,763 0.5857 0.2351 6.4606 0 0.27
CANADA 4,514 0.2644 0.2327 5.5639 1 0.64
CHILE 1,357 0.6362 0.1944 5.9622 0 0.63
CHINA 5,259 0.5584 0.1887 6.3229 0 -
COLOMBIA 117 0.5330 0.2753 6.1313 0 0.57
CZECH REPUBLIC 256 0.7281 0.1650 5.0297 1 -
DENMARK 1,339 0.3575 0.2510 5.0783 0 0.46
FINLAND 1,334 0.3596 0.2253 5.5773 0 0.46
FRANCE 7,067 0.5742 0.2209 5.2261 0 0.38
GERMANY 6,578 0.5771 0.2420 5.2447 0 0.28
GREECE 622 0.5685 0.1916 4.8662 0 0.22
HONG KONG 7,157 0.5760 0.1758 4.7633 1 0.96
HUNGARY 249 0.6120 0.2231 4.7837 0 -
INDIA 5,847 0.5213 0.1805 4.0711 1 0.58
INDONESIA 2,372 0.6624 0.1764 3.9857 0 0.65
IRELAND 852 0.3299 0.2128 4.9329 1 0.79
ISRAEL 649 0.5720 0.2160 5.6944 1 0.73
ITALY 2,487 0.5231 0.2021 5.8520 0 0.42
JAPAN 23,495 0.4386 0.1636 5.9058 0 0.5
KOREA (SOUTH) 5,669 0.3804 0.1863 4.8843 0 0.47
LUXEMBOURG 164 0.5222 0.2213 6.8238 0 -
MALAYSIA 7,601 0.4926 0.2004 4.0377 1 0.95
MEXICO 297 0.5892 0.2291 6.3653 0 0.17
NETHERLANDS 1,835 0.4293 0.2555 5.5838 0 0.2
NEW ZEALAND 795 0.5440 0.2354 5.2150 1 0.95
NORWAY 1,365 0.4731 0.2271 5.4994 0 0.42
PAKISTAN 566 0.5963 0.2170 4.7826 1 0.41
PERU 255 0.6877 0.2128 5.9443 0 0.45
PHILIPPINES 1,123 0.6778 0.2106 3.9869 0 0.22
POLAND 914 0.5577 0.2013 5.1097 0 -
PORTUGAL 569 0.5927 0.2134 5.4191 0 0.44
RUSSIAN
FEDERATION 569 0.7180 0.1668 7.1000 0 -
33
SINGAPORE 4,801 0.5585 0.1897 4.3251 1 1
SOUTH AFRICA 2,282 0.5040 0.2429 5.6018 1 0.81
SPAIN 1,492 0.4836 0.2307 6.4367 0 0.37
SRI LANKA 202 0.4835 0.2570 3.7742 1 0.39
SWEDEN 2,173 0.3592 0.2081 5.8355 0 0.33
SWITZERLAND 2,395 0.4415 0.2481 5.9277 0 0.27
THAILAND 3,571 0.5509 0.2136 4.0950 1 0.81
UNITED KINGDOM 19,830 0.3344 0.2278 4.4855 1 0.95
Total /Mean 143,337 0.5233 0.2136 5.2877
Panel B: Yearly distribution
Year #. Obs. BLK Mean BLK Std.Dev. MKTCAP
1991 1,463 0.3983 0.2657 4.5063
1992 1,835 0.4306 0.2645 4.4547
1993 2,377 0.4337 0.2547 4.9640
1994 3,407 0.4391 0.2447 5.3339
1995 3,867 0.4365 0.2355 5.3415
1996 4,695 0.4478 0.2202 5.4913
1997 4,993 0.4560 0.2216 5.3107
1998 5,356 0.4574 0.2223 5.0325
1999 5,723 0.4664 0.2274 5.0011
2000 6,609 0.4712 0.2238 4.9682
2001 7,234 0.4731 0.2326 4.6733
2002 8,024 0.4786 0.2317 4.4080
2003 9,178 0.4810 0.2287 4.6315
2004 9,223 0.4799 0.2269 4.9220
2005 9,677 0.4680 0.2308 5.0522
2006 10,174 0.4614 0.2313 5.2782
2007 9,159 0.4695 0.2237 5.5346
2008 9,686 0.4676 0.2262 5.0595
2009 11,441 0.4834 0.2333 4.9624
2010 12,015 0.4805 0.2283 5.3394
2011 7,201 0.4923 0.2394 5.5922
Total /Mean 143,337 0.4606 0.2340 5.0408
Panel C: Block Ownership and Country-Level proxies Correlation Matrix
BLK Mean MKTCAP COMMON ANTISELF
MKTCAP 0.0869 1.0000
(0.57)
COMMON -0.1846 -0.5371 1.0000
(-0.23) (-0.00)
ANTISELF -0.1670 -0.4140 0.7563 1.0000
(-0.32) (-0.01) (<.00)
34
Table 2 Descriptive Statistics
This table presents summary statistics for test and control variables. To be included in sample, a firm must have
capital expenditure, assets and other financial data in the Worldscope database from 1991 to 2011. All variables are
defined in the Appendix.
Variables # Obs. Mean Std. Dev. 5th Pctl 25th Pctl 50th Pctl 75th Pctl 95th Pctl
BLK 143,337 0.4689 0.2311 0.0620 0.2937 0.4840 0.6501 0.8473
INV 143,337 0.0378 0.1057 -0.1113 -0.0202 0.0140 0.0742 0.2759
CAPX 143,337 0.0603 0.0602 0.0025 0.0170 0.0405 0.0810 0.2025
Q 143,337 1.4443 0.7942 0.6885 0.9348 1.1756 1.6557 3.2683
CF 143,337 0.1185 0.1103 -0.0122 0.0569 0.0993 0.1608 0.3133
MKTCAP 143,337 5.0696 1.9182 1.9879 3.6333 5.0094 6.4280 8.4851
LEV 143,337 0.1183 0.1252 0.0000 0.0041 0.0784 0.1965 0.3935
RETURN 143,337 0.0948 0.4821 -0.5773 -0.2360 0.0283 0.3371 1.0604
1/ASSET 143,337 16.8110 31.5117 0.1634 1.1354 4.3097 15.9890 85.5505
RHHI 143,287 0.2564 0.2307 0.0337 0.0883 0.1868 0.3409 0.8027
EQISS 143,337 0.0415 0.2448 -0.1634 -0.0088 0.0042 0.0488 0.2880
DEISS 143,337 0.0449 0.1450 -0.1564 -0.0390 0.0159 0.0977 0.3446
GROWTH 89,947 0.2660 0.4989 -0.6443 -0.0358 0.2410 0.5597 1.1754
35
Table 3 The Effect of Block Ownership on Investment-q Sensitivity and Investment-CF Sensitivity
This table reports estimates of equation (1) from 1991 to 2011. The dependent variable is investment (INV, CAPX)
in year t. The main independent variable is BLK in year t-1, the percentage of shares held by blockholders. Tobin’s q
is defined as the market value of equity plus assets minus book value of equity over assets in year t-1. CF is
operating cash flow over beginning-of-year assets in year t. Other variables are defined in the Appendix. Panel A
reports estimates of pooled OLS regressions with country, industry and year dummies and standard errors corrected
for country-level clustering. Panel B report estimates of firm fixed-effects regressions with year dummies and
standard errors corrected for firm-level clustering. Robusted t-values are reported in parentheses. ***, **, and *
indicate a two-tailed test significance level of 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5) (6)
Dependent variables INV CAPX INV CAPX INV CAPX
Panel A: Pooled OLS
q 0.0193*** 0.0090*** 0.0246*** 0.0102*** 0.0192*** 0.0089***
(10.20) (6.80) (9.82) (4.27) (10.13) (6.89)
CF 0.0952*** 0.0981*** 0.0953*** 0.0981*** 0.0817*** 0.0625***
(4.16) (5.65) (4.19) (5.67) (3.54) (4.21)
BLK 0.0129*** 0.0084*** 0.0302*** 0.0125*** 0.0090* -0.0021
(2.73) (3.09) (6.87) (3.64) (1.72) (-0.71)
q*BLK -0.0115*** -0.0028
(-3.91) (-0.90)
CF*BLK 0.0317* 0.0835***
(1.83) (5.89)
MKTCAP 0.0055*** 0.0020*** 0.0056*** 0.0020*** 0.0055*** 0.0020***
(5.28) (4.89) (5.35) (4.92) (5.27) (4.73)
LEV 0.0749*** 0.0608*** 0.0753*** 0.0609*** 0.0750*** 0.0610***
(5.69) (6.69) (5.75) (6.71) (5.68) (6.65)
RETURN -0.0004 0.0003 -0.0004 0.0003 -0.0004 0.0002
(-0.30) (0.35) (-0.30) (0.35) (-0.33) (0.23)
1/ASSET 0.0001** -0.0001*** 0.0001** -0.0001*** 0.0001** -0.0001***
(2.28) (-2.95) (2.31) (-2.93) (2.27) (-2.76)
Intercept -0.0731*** -0.0170 -0.0816*** -0.0191 -0.0709*** -0.0112
(-3.91) (-1.22) (-4.37) (-1.32) (-3.79) (-0.80)
# Obs. 143,337 143,337 143,337 143,337 143,337 143,337
Adjusted R2 0.128 0.221 0.128 0.221 0.128 0.222
Panel B: Firm fixed effect
q 0.0268*** 0.0137*** 0.0327*** 0.0151*** 0.0267*** 0.0137***
(35.67) (36.16) (23.49) (22.18) (35.59) (36.06)
CF 0.0897*** 0.0474*** 0.0902*** 0.0475*** 0.0504*** 0.0259***
(16.87) (18.96) (17.00) (19.04) (5.27) (5.84)
BLK 0.0063** 0.0025* 0.0256*** 0.0071*** -0.0054* -0.0038**
(2.44) (1.94) (6.05) (3.42) (-1.65) (-2.34)
q*BLK -0.0131*** -0.0031**
(-5.32) (-2.51)
CF*BLK 0.0933*** 0.0509***
(5.08) (5.90)
MKTCAP 0.0238*** 0.0079*** 0.0239*** 0.0079*** 0.0238*** 0.0079***
(37.59) (24.05) (37.59) (24.04) (37.49) (23.96)
LEV 0.0730*** 0.0373*** 0.0730*** 0.0373*** 0.0733*** 0.0374***
(15.35) (15.33) (15.36) (15.33) (15.39) (15.38)
RETURN 0.0020*** 0.0005 0.0020*** 0.0005 0.0019*** 0.0004
(2.74) (1.39) (2.71) (1.38) (2.67) (1.31)
1/ASSET 0.0005*** 0.0001*** 0.0005*** 0.0001*** 0.0005*** 0.0001***
36
(16.39) (7.03) (16.51) (7.08) (16.57) (7.19)
Intercept -0.162*** -0.0247*** -0.171*** -0.0269*** -0.157*** -0.0218***
(-43.40) (-12.61) (-41.90) (-12.73) (-40.59) (-10.84)
# Obs. 143,337 143,337 143,337 143,337 143,337 143,337
# Firms 20,738 20,738 20,738 20,738 20,738 20,738
Adjusted R2 0.108 0.109 0.109 0.109 0.109 0.110
37
Table 4 Production Market Competition and the Effect of Block Ownership on Investment-q
Sensitivity and Investment-CF Sensitivity
This table reports estimates of equation (3) from 1991 to 2011. The dependent variable is investment (INV) in year t.
The main independent variable is BLK in year t-1, the percentage of shares held by blockholders. Tobin’s q is
defined as the market value of equity plus assets minus book value of equity over assets in year t-1. CF is operating
cash flow over beginning-of-year assets in year t. RHHI is the reverse of HHI, calculated as the sum of squared
market shares of each firm competing in the industry. Other variables are defined in the Appendix. Panel A reports
estimates of pooled OLS regressions with country, industry and year dummies and standard errors corrected for
country-level clustering. Panel B report estimates of firm fixed-effects regressions with year dummies and standard
errors corrected for firm-level clustering. Robusted t-values are reported in parentheses. ***, **, and * indicate a
two-tailed test significance level of 1%, 5%, and 10%, respectively.
(1) (2) (3)
Dependent variables INV INV INV
Panel A: Pooled OLS
q 0.0193*** 0.0291*** 0.0192***
(10.12) (13.03) (9.99)
CF 0.0955*** 0.0952*** 0.0499*
(4.15) (4.21) (1.88)
BLK 0.0122** 0.0328*** 0.0025
(2.20) (5.05) (0.38)
RHHI -0.0000 0.0008* -0.0005
(-0.06) (1.94) (-1.66)
BLK* RHHI 0.0001 -0.0004 0.0008**
(0.19) (-0.66) (2.18)
q*BLK -0.0139***
(-3.61)
q* RHHI -0.0006***
(-2.90)
q*BLK* RHHI 0.0004**
(1.98)
CF*BLK 0.0783***
(3.57)
CF* RHHI 0.0045**
(2.25)
CF*BLK*RHHI -0.0066**
(-2.65)
MKTCAP 0.0055*** 0.0056*** 0.0055***
(5.30) (5.46) (5.25)
LEV 0.0750*** 0.0754*** 0.0753***
(5.70) (5.86) (5.65)
RETURN -0.0004 -0.0003 -0.0004
(-0.30) (-0.26) (-0.31)
1/ASSET 0.0001** 0.0001** 0.0001**
(2.29) (2.21) (2.35)
Intercept -0.0273* -0.0409*** -0.0216
(-1.98) (-2.84) (-1.54)
# Obs. 143,287 143,287 143,287
Adjusted R2 0.128 0.129 0.128
Panel B: Firm fixed effect
q 0.0268*** 0.0392*** 0.0266***
(35.59) (21.08) (35.47)
CF 0.0902*** 0.0905*** 0.0312**
38
(16.99) (17.10) (2.30)
BLK 0.0067** 0.0333*** -0.0088**
(2.08) (6.04) (-2.03)
RHHI -0.0000 0.0012*** -0.0004*
(-0.11) (5.05) (-1.84)
BLK* RHHI -0.0001 -0.0011*** 0.0005
(-0.26) (-2.64) (1.33)
q*BLK -0.0182***
(-5.62)
q* RHHI -0.0008***
(-5.94)
q*BLK* RHHI 0.0007***
(2.99)
CF*BLK 0.1232***
(4.81)
CF* RHHI 0.0029**
(2.27)
CF*BLK*RHHI -0.0045*
(-1.88)
MKTCAP 0.0239*** 0.0238*** 0.0238***
(37.57) (37.52) (37.45)
LEV 0.0731*** 0.0734*** 0.0736***
(15.38) (15.46) (15.45)
RETURN 0.0020*** 0.0020*** 0.0020***
(2.75) (2.76) (2.70)
1/ASSET 0.0005*** 0.0005*** 0.0005***
(16.22) (16.18) (16.50)
Intercept -0.1621*** -0.1801*** -0.1540***
(-41.36) (-39.55) (-37.03)
# Obs. 143,287 143,287 143,287
# Firms 20,734 20,734 20,734
Adjusted R2 0.108 0.109 0.109
39
Table 5 Country-Level Investor Protection and the Effect of Block Ownership on Investment-q
Sensitivity and Investment-CF Sensitivity
This table reports estimates of equation (4) from 1991 to 2011. The dependent variable is investment (INV) in year t.
The main independent variable is BLK in year t-1, the percentage of shares held by blockholders. Tobin’s q is
defined as the market value of equity plus assets minus book value of equity over assets in year t-1. CF is operating
cash flow over beginning-of-year assets in year t. CL is the country-level proxies for legal regimes and investor
protection, including law origins (COMMON) and anti-self dealing index (ANTISELF). Other variables are defined
in the Appendix. Panel A reports estimates of pooled OLS regressions with country, industry and year dummies and
standard errors corrected for country-level clustering. Panel B report estimates of firm fixed-effects regressions with
year dummies and standard errors corrected for firm-level clustering. Robusted t-values are reported in parentheses.
***, **, and * indicate a two-tailed test significance level of 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5) (6)
CL = COMMON ANTISELF COMMON ANTISELF COMMON ANTISELF
Panel A: Pooled OLS
q 0.0192*** 0.0203*** 0.0200*** 0.0254*** 0.0189*** 0.0201***
(10.19) (13.01) (5.75) (5.36) (10.15) (12.65)
CF 0.0952*** 0.0868*** 0.0955*** 0.0869*** 0.1402*** 0.1831***
(4.15) (4.18) (4.22) (4.24) (5.77) (4.13)
BLK 0.0164** 0.0002 0.0257*** 0.0265** 0.0150** 0.0074
(2.55) (0.02) (4.09) (2.64) (2.52) (0.54)
BLK*CL -0.0070 0.0179 0.0035 0.0058 -0.0070 0.0052
(-0.68) (1.15) (0.37) (0.43) (-0.62) (0.23)
q*BLK -0.0065 -0.0186**
(-1.60) (-2.67)
q*CL 0.0064 0.0006
(1.66) (0.11)
q*BLK*CL -0.0059 0.0098
(-1.05) (1.14)
CF*BLK 0.0115 -0.0516
(0.28) (-0.65)
CF*CL -0.0731** -0.1440**
(-2.35) (-2.18)
CF*BLK* CL 0.0004 0.0915
(0.01) (0.70)
MKTCAP 0.0056*** 0.0052*** 0.0056*** 0.0053*** 0.0056*** 0.0052***
(5.48) (5.14) (5.53) (5.19) (5.41) (5.14)
LEV 0.0749*** 0.0691*** 0.0750*** 0.0690*** 0.0758*** 0.0701***
(5.73) (5.67) (5.72) (5.72) (5.89) (5.69)
RETURN -0.0004 0.0003 -0.0004 0.0003 -0.0006 0.0002
(-0.316) (0.30) (-0.34) (0.31) (-0.45) (0.16)
1/ASSET 0.0001** 0.0001** 0.0001** 0.0001 0.0001*** 0.0001**
(2.42) (2.05) (2.32) (1.98) (2.72) (2.35)
Intercept -0.0503*** -0.0452** -0.0527*** -0.0548*** -0.0533*** -0.0458**
(-2.73) (-2.43) (-2.94) (-2.89) (-2.75) (-2.37)
# Obs. 143,224 135,813 143,224 135,813 143,224 135,813
Adjusted R2 0.128 0.125 0.128 0.126 0.129 0.126
Panel B: Firm fixed effect
q 0.0268*** 0.0272*** 0.0246*** 0.0326*** 0.0264*** 0.0271***
(35.65) (34.59) (12.45) (8.37) (35.23) (34.46)
CF 0.0894*** 0.0840*** 0.0903*** 0.0845*** 0.135*** 0.1830***
(16.80) (15.60) (17.02) (15.73) (7.95) (6.26)
BLK 0.0057* 0.0003 0.0195*** 0.0357*** -0.0021 -0.0065
40
(1.67) (0.04) (3.36) (3.20) (-0.46) (-0.76)
BLK*CL 0.0009 0.0073 0.0041 -0.0158 0.0013 0.0039
(0.19) (0.76) (0.49) (-0.98) (0.20) (0.31)
q*BLK -0.0093*** -0.0249***
(-2.69) (-3.65)
q*CL 0.0114*** 0.0008
(4.32) (0.14)
q*BLK*CL -0.0018 0.0169*
(-0.37) (1.75)
CF*BLK 0.0689** 0.0627
(2.20) (1.14)
CF*CL -0.1050*** -0.1820***
(-5.18) (-4.65)
CF*BLK* CL -0.0081 0.0153
(-0.21) (0.20)
MKTCAP 0.0238*** 0.0244*** 0.0240*** 0.0245*** 0.0236*** 0.0242***
(37.55) (37.52) (37.78) (37.63) (37.12) (37.15)
LEV 0.0729*** 0.0705*** 0.0724*** 0.0702*** 0.0747*** 0.0717***
(15.32) (14.58) (15.23) (14.54) (15.64) (14.80)
RETURN 0.0020*** 0.0025*** 0.0019*** 0.0024*** 0.0017** 0.0023***
(2.72) (3.30) (2.58) (3.25) (2.40) (3.01)
1/ASSET 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005***
(16.29) (16.74) (16.16) (16.76) (18.01) (17.78)
Intercept -0.1620*** -0.1640*** -0.1681*** -0.1732*** -0.1593*** -0.1602***
(-43.37) (-42.96) (-41.50) (-41.57) (-40.98) (-40.44)
# Obs. 143,224 135,813 143,224 135,813 143,224 135,813
# Firms 20,713 19,418 20,713 19,418 20,713 19,418
Adjusted R2 0.108 0.110 0.109 0.111 0.110 0.112
41
Table 6 The Effect of Residual Block Ownership on Investment-q Sensitivity and Investment-CF
Sensitivity
This table reports estimates of equation (6) from 1991 to 2011. The dependent variable is investment (INV) in year t.
The main independent variable is RBLK in year t-1, estimated as a residual from a regression of ownership on its
country-level proxies for investor protection. Tobin’s q is defined as the market value of equity plus assets minus
book value of equity over assets in year t-1. CF is operating cash flow over beginning-of-year assets in year t. CL is
the country-level proxies for legal regimes and investor protection, including law origins (COMMON) and anti-self
dealing index (ANTISELF). Other variables are defined in the Appendix. Panel A reports estimates of pooled OLS
regressions with country, industry and year dummies and standard errors corrected for country-level clustering.
Panel B report estimates of firm fixed-effects regressions with year dummies and standard errors corrected for firm-
level clustering. Robusted t-values are reported in parentheses. ***, **, and * indicate a two-tailed test significance
level of 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5) (6)
Remove CL from BLK COMMON ANTISELF COMMON ANTISELF COMMON ANTISELF
Panel A: Pooled OLS
q 0.0175*** 0.0185*** 0.0176*** 0.0186*** 0.0174*** 0.0185***
(9.02) (12.04) (9.20) (12.82) (8.97) (11.99)
CF 0.0926*** 0.0851*** 0.0927*** 0.0852*** 0.0929*** 0.0855***
(4.35) (4.47) (4.36) (4.50) (4.35) (4.48)
RBLK 0.0090** 0.0076* 0.0231*** 0.0241*** 0.0073* 0.0059
(2.05) (1.84) (5.27) (6.68) (1.82) (1.51)
q*RBLK -0.0096** -0.0113***
(-2.57) (-4.04)
CF*RBLK 0.0145 0.0137
(0.65) (0.62)
MKTCAP 0.0054*** 0.0050*** 0.0054*** 0.0051*** 0.0054*** 0.0050***
(5.45) (5.41) (5.45) (5.41) (5.45) (5.40)
LEV 0.0700*** 0.0643*** 0.0702*** 0.0646*** 0.0701*** 0.0643***
(5.54) (5.63) (5.58) (5.68) (5.53) (5.63)
RETURN -0.0011 -0.0004 -0.0011 -0.0004 -0.0011 -0.0004
(-0.99) (-0.43) (-0.98) (-0.43) (-1.00) (-0.44)
1/ASSET 0.0001 0.0001 0.0001 0.0001 0.0000 0.0001
(1.59) (1.37) (1.54) (1.32) (1.60) (1.38)
Intercept -0.0577*** -0.0918*** -0.0583*** -0.0934*** -0.0577*** -0.0918***
(-4.12) (-5.12) (-4.12) (-5.16) (-4.11) (-5.11)
# Obs. 118,274 112,263 118,274 112,263 118,274 112,263
Adjusted R2 0.121 0.118 0.121 0.118 0.121 0.118
Panel B: Firm fixed effect
q 0.0266*** 0.0270*** 0.0268*** 0.0272*** 0.0265*** 0.0269***
(30.58) (29.65) (30.64) (29.75) (30.44) (29.51)
CF 0.0860*** 0.0812*** 0.0864*** 0.0817*** 0.0884*** 0.0836***
(14.60) (13.64) (14.68) (13.73) (14.88) (13.91)
RBLK 0.0049* 0.0025 0.0213*** 0.0212*** -0.0049 -0.0067*
(1.79) (0.88) (4.40) (4.29) (-1.38) (-1.88)
q*RBLK -0.0114*** -0.0131***
(-3.83) (-4.30)
CF*RBLK 0.0821*** 0.0764***
(3.82) (3.52)
MKTCAP 0.0242*** 0.0249*** 0.0242*** 0.0249*** 0.0242*** 0.0249***
(34.01) (34.04) (33.94) (33.98) (33.98) (34.02)
LEV 0.0764*** 0.0746*** 0.0765*** 0.0747*** 0.0766*** 0.0749***
(14.48) (13.95) (14.51) (13.97) (14.52) (13.99)
42
RETURN 0.0015* 0.0019** 0.0015* 0.0019** 0.0015* 0.0018**
(1.95) (2.27) (1.93) (2.27) (1.91) (2.24)
1/ASSET 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005*** 0.0005***
(13.50) (13.87) (13.47) (13.84) (13.61) (13.98)
Intercept -0.1620*** -0.1651*** -0.1621*** -0.1651*** -0.1621*** -0.1650***
(-40.62) (-40.42) (-40.62) (-40.43) (-40.66) (-40.45)
# Obs. 118,274 112,263 118,274 112,263 118,274 112,263
# Firms 18,357 17,187 18,357 17,187 18,357 17,187
Adjusted R2 0.100 0.103 0.101 0.103 0.101 0.103
43
Table 7 The Effect of Block Ownership on Investment-q Sensitivity and Investment-CF Sensitivity
during the 2008 Financial Crisis
This table reports estimates of equation (1) around 2008 financial crisis U.S. from January 2006 to August 2009. The
dependent variable is investment (INV) in year t. The main independent variable is BLK in year t-1, the percentage
of shares held by blockholders. Tobin’s q is defined as the market value of equity plus assets minus book value of
equity over assets in year t-1. CF is operating cash flow over beginning-of-year assets in year t. We refer to the
period from September 2008 to August 2009 as the U.S. financial crisis. In Particular, CRISIS takes the value of one
for fiscal year ends between September 2008 to August 2009, and zero otherwise. Other variables are defined in the
Appendix. Panel A reports estimates of pooled OLS regressions with country, industry and year dummies and
standard errors corrected for country-level clustering. Panel B report estimates of firm fixed-effects regressions with
year dummies and standard errors corrected for firm-level clustering. Robusted t-values are reported in parentheses.
***, **, and * indicate a two-tailed test significance level of 1%, 5%, and 10%, respectively.
(1) (2) (3)
Dependent variables INV INV INV
Panel A: Pooled OLS
q 0.0184*** 0.0199*** 0.0184***
(9.77) (5.89) (9.83)
CF 0.1301*** 0.130*** 0.0729**
(5.01) (5.04) (2.40)
BLK 0.0069 0.0119 0.0019
(1.15) (1.65) (0.34)
BLK*CRISIS 0.0094 0.0236* 0.0013
(1.25) (1.92) (0.16)
q*BLK -0.0018
(-0.31)
q*CRISIS 0.0031
(0.72)
q*BLK*CRISIS -0.0121*
(-1.69)
CF*BLK 0.0713**
(2.26)
CF* CRISIS 0.1280***
(3.84)
CF*BLK*CRISIS -0.0383
(-0.71)
MKTCAP 0.0035*** 0.0036*** 0.0032***
(3.58) (3.69) (3.49)
LEV 0.123*** 0.123*** 0.124***
(6.27) (6.28) (6.24)
RETURN -0.0001 -9.34e-05 -0.0003
(-0.08) (-0.06) (-0.18)
1/ASSET 0.0001 0.0001 0.0001
(1.25) (1.31) (1.66)
Intercept -0.1360*** -0.144*** -0.137***
(-6.35) (-6.69) (-6.59)
# Obs. 34,062 34,062 34,062
Adjusted R2 0.171 0.172 0.174
Panel B: Firm fixed effect
q 0.0274*** 0.0366*** 0.0272***
(15.27) (10.61) (15.14)
CF 0.0854*** 0.0866*** 0.0238
(7.95) (8.10) (1.19)
44
BLK -0.0037 0.0407*** -0.0125
(-0.43) (3.08) (-1.30)
BLK* CRISIS 0.0113*** 0.0170** 0.0076
(2.75) (2.56) (1.47)
q*BLK -0.0220***
(-3.55)
q* CRISIS 0.0091***
(4.41)
q*BLK* CRISIS -0.0140***
(-2.94)
CF*BLK 0.1152***
(2.75)
CF* CRISIS 0.1260***
(4.80)
CF*BLK* CRISIS -0.0832
(-1.23)
MKTCAP 0.0284*** 0.0296*** 0.0274***
(13.76) (14.15) (13.16)
LEV 0.116*** 0.116*** 0.116***
(7.30) (7.26) (7.29)
RETURN 0.0027* 0.0028* 0.0028*
(1.69) (1.76) (1.76)
1/ASSET 0.0005*** 0.0006*** 0.0005***
(6.249) (6.54) (6.24)
Intercept -0.186*** -0.219*** -0.180***
(-16.21) (-17.20) (-15.46)
# Observations 34,062 34,062 34,062
# Firms 14,323 14,323 14,323
Adjusted R2 0.090 0.092 0.094
45
Table 8 Block Ownership and External Financing
This table reports estimates of equation (7) from 1991 to 2011. The dependent variable is external finance (EQISS,
DEISS) in year t. The main independent variable is BLK in year t-1, the percentage of shares held by blockholders.
Tobin’s q is defined as the market value of equity plus assets minus book value of equity over assets in year t-1. CF
is operating cash flow over beginning-of-year assets in year t. Other variables are defined in the Appendix. Panel A
reports estimates of pooled OLS regressions with country, industry and year dummies and standard errors corrected
for country-level clustering. Panel B report estimates of firm fixed-effects regressions with year dummies and
standard errors corrected for firm-level clustering. Robusted t-values are reported in parentheses. ***, **, and *
indicate a two-tailed test significance level of 1%, 5%, and 10%, respectively.
(1) (2) (3) (4) (5) (6)
Dependent variables EQISS EQISS EQISS DEISS DEISS DEISS
Panel A: Pooled OLS
q 0.0581*** 0.0764*** 0.0576*** 0.0259*** 0.0273*** 0.0257***
(5.81) (5.35) (5.86) (12.52) (13.87) (12.55)
CF -0.4992*** -0.4981*** -0.6974*** 0.1133*** 0.1131*** 0.1100***
(-3.67) (-3.68) (-4.48) (10.17) (10.26) (8.35)
BLK 0.0495*** -0.0679*** 0.0211*** 0.0149***
(2.74) (-3.78) (3.19) (2.91)
q*BLK -0.0395*** -0.0036
(-2.88) (-1.13)
CF*BLK 0.4650*** 0.0064
(4.31) (0.35)
MKTCAP -0.0003 -0.0003 -0.0007 0.0026*** 0.0030*** 0.0029***
(-0.21) (-0.22) (-0.57) (3.39) (4.00) (3.99)
LEV -0.0584*** -0.0583*** -0.0582*** 0.179*** 0.180*** 0.180***
(-2.860) (-2.874) (-2.916) (8.258) (8.482) (8.480)
Intercept -0.0186 -0.0415 0.0192 -0.0583*** -0.0703*** -0.0672***
(-0.665) (-1.328) (0.812) (-3.449) (-3.916) (-3.744)
# Obs. 143,337 143,337 143,337 143,337 143,337 143,337
Adjusted R2 0.179 0.180 0.182 0.107 0.107 0.107
Panel B: Firm fixed effect
q 0.0500*** 0.0694*** 0.0495*** 0.0299*** 0.0324*** 0.0298***
(20.42) (14.34) (20.28) (28.00) (16.86) (27.91)
CF -0.6700*** -0.6681*** -0.9092** 0.1743*** 0.1744*** 0.140***
(-30.17) (-30.24) (-22.88) (23.83) (23.85) (11.01)
BLK 0.0863*** -0.0483*** 0.0164*** -0.0017
(7.51) (-4.38) (2.77) (-0.36)
q*BLK -0.0429*** -0.0055
(-5.25) (-1.59)
CF*BLK 0.5703*** 0.0791***
(8.01) (2.96)
MKTCAP 0.0368*** 0.0368*** 0.0364*** 0.0222*** 0.0223*** 0.0222***
(26.86) (26.91) (26.62) (26.89) (26.90) (26.78)
LEV -0.0957*** -0.0953*** -0.0939*** 0.2590*** 0.2601*** 0.2602***
(-9.51) (-9.47) (-9.33) (36.98) (37.01) (36.98)
Intercept -0.2001*** -0.2412*** -0.1773*** -0.1704*** -0.1780*** -0.1691***
(-28.28) (-26.58) (-19.79) (-37.48) (-33.11) (-32.87)
# Obs. 143,337 143,337 143,337 143,337 143,337 143,337
# Firms 20,738 20,738 20,738 20,738 20,738 20,738
Adjusted R2 0.163 0.164 0.166 0.093 0.093 0.093
46
Table 9 Block Ownership and Ex post Efficiency
This table reports estimates of equation (8) from 1991 to 2011. The dependent variable is Tobin’s q or GROWTH in
year t, respectively. The main independent variable is BLK in year t-1, the percentage of shares held by blockholders.
Other variables are defined in the Appendix. Panel A reports estimates of pooled OLS regressions with country,
industry and year dummies and standard errors corrected for country-level clustering. Panel B report estimates of
firm fixed-effects regressions with year dummies and standard errors corrected for firm-level clustering. Robusted t-
values are reported in parentheses. ***, **, and * indicate a two-tailed test significance level of 1%, 5%, and 10%,
respectively.
(1) (2) (3) (4)
Dependent variables q q GROWTH GROWTH
Panel A: Pooled OLS
BLK 0.1560** 0.1591** 0.1101*** 0.09690***
(2.16) (2.38) (3.74) (2.95)
CAPX 0.5670* 1.1060***
(1.75) (13.88)
CAPX*BLK -0.1161 -0.3923**
(-0.31) (-2.16)
INV 0.383*** 0.8903***
(2.80) (7.91)
INV*BLK -0.250* -0.3322**
(-1.91) (-2.18)
CF 1.6770*** 1.6981*** 0.2101*** 0.2202***
(8.72) (9.32) (3.30) (3.75)
MKTCAP 0.1250*** 0.1253*** 0.0177*** 0.0161***
(18.37) (18.45) (3.49) (3.20)
LEV -0.6802*** -0.6662*** -0.1202*** -0.1161**
(-5.78) (-5.87) (-2.93) (-2.66)
Intercept 1.3212** 1.3240** -0.2673** -0.2320**
(2.64) (2.67) (-2.56) (-2.17)
# Obs. 143,337 143,337 89,947 89,947
Adjusted R2 0.325 0.325 0.148 0.159
Panel B: Firm fixed effect
BLK 0.1850*** 0.1621*** 0.1202*** 0.1103***
(10.65) (11.05) (5.36) (5.93)
CAPX 0.5571*** 0.5273***
(5.84) (4.86)
CAPX*BLK -0.4372*** -0.3004
(-2.64) (-1.58)
INV -0.0354 0.4001**
(-0.96) (8.65)
INV*BLK -0.0422* -0.2522***
(-1.69) (-3.04)
CF 0.5301*** 0.5583*** 0.0881** 0.0738**
(15.55) (16.30) (2.46) (2.05)
MKTCAP 0.3671*** 0.3732*** -0.1023*** -0.1064***
(86.71) (87.49) (-24.98) (-26.19)
LEV -0.3153*** -0.3012*** -0.3471*** -0.3550***
(-11.26) (-10.74) (-10.96) (-11.17)
Intercept -0.9011*** -0.9052*** 0.6703*** 0.7120***
(-35.63) (-35.90) (6.370) (6.76)
# Obs. 143,337 143,337 89,947 89,947
# Firms 20,738 20,738 15,254 15,254
Adjusted R2 0.340 0.339 0.112 0.115