The Effect of Block Ownership on Investment and Financing ... · PDF fileThe Effect of Block...

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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

[email protected]

(852) 2766-4672

Lei Pang*

Department of Finance & Decision Sciences

Hong Kong Baptist University

[email protected]

(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

mailto:[email protected]

mailto:[email protected]

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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:


+ 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.


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:


+ 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


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.


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.


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:


+ 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


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.


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.


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.


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


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


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


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


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


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


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


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


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

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