Coordination Costs, Institutional Investors, and
Firm Value
Abstract
Coordination costs among institutional investors have a signi…cant impact on corporate governance and …rm value. We use two measures, one based on the geographic
distance among institutional shareholders and the other based on the correlation in
their portfolio allocation decisions, to proxy for coordination costs. We …nd that,
after controlling for other factors, coordination costs are negatively associated with
…rm value as proxied by industry-adjusted Tobin’s q. This e¤ect is robust to controlling for the endogeneity of the institutional ownership structure. Using the 1992
proxy reform as an exogenous shock that relaxes restrictions on communication and
coordination among shareholders, we show that this e¤ect becomes signi…cantly
stronger after the reform. We further show that the ease of coordination among
institutions is associated with fewer anti-takeover provisions adopted by the …rm,
higher equity-based pay for CEOs, and improved CEO turnover-performance sensitivities. Overall, these …ndings suggest that the ease of coordination improves …rm
value by enhancing the governance role of institutional investors.
JEL Classification: G23, G32, G34
Keywords: Coordination costs; Institutional investors; Corporate governance; Firm value
1
Introduction
While institutional investors collectively hold the majority of the U.S. equity market, their in‡uence
on corporate governance and corporate performance remains unclear. Theoretical work suggests
that institutional investors, as large shareholders, can discipline corporate managers through active
monitoring and intervention (Shleifer and Vishny, 1986; Maug, 1998; Kahn and Winton, 1998)
as well as through the threat of exit (Admati and P‡eiderer, 2009; Edmans, 2009). Empirical
research, however, suggests that there is little evidence of improvement in the long-term …rm value
from institutional monitoring.1 One major limitation of institutional monitoring is the free-rider
problem, because institutional equity ownership is widely dispersed. As Figure 1 shows, the median
value of an institution’s equity holdings in a …rm as a fraction of the …rm’s outstanding shares
is 0:07% during 1980 to 2009 and decreases over the years. The di¤used institutional ownership
structure suggests that, in the absence of coordination, the classical “free-rider”problem can prevail
(Grossman and Hart, 1980).
It has been recognized that institutions can play a more e¤ective monitoring role through coordinated activities (see, e.g., Black, 1992). Recent survey evidence of McCahery, Sautner, and Starks
(2010) shows that 59% of institutional investment managers consider coordinating their actions in
disciplining corporate management. Of great importance, and so far largely unexplored, is the
cost of coordinating a group of institutional investors, which includes information production costs
(e.g., to identify trustworthy and cooperative peers), communication and other costs incurred to
reach an agreement, as well as costs associated with monitoring and enforcement of the agreement.
In this paper, we examine the impact of coordination costs on the role of institutional investors
in improving corporate governance and …rm value. We hypothesize that a low coordination cost
improves …rm value by facilitating a stronger governance role provided by institutional investors.
On the one hand, coordination costs can impact the e¤ectiveness of institutional monitoring
and intervention. Although it is not cost-e¢ cient for a small shareholder to monitor managers because of the free-rider problem, low coordination costs enable dispersed institutional shareholders
to conduct coordinated monitoring activities and mitigate managerial agency costs. For instance,
1
See, e.g., Gillan and Starks (2007) and Yermack (2010) for recent surveys of the literature.
1
institutions can form a shareholder coalition to sponsor proxy proposals to e¤ect changes in corporate governance (Gillan and Starks, 2000; Del Guercio, Seery, and Woidtke, 2008) as well as
to engage in direct negotiation with corporate management seeking governance changes (Becht,
Franks, Mayer, and Rossi, 2009). This predicts that a low coordination cost should enhance the
monitoring role of institutions and lead to higher …rm valuation.
On the other hand, the ease of coordination can also intensify the threat of exit. Admati
and P‡eiderer (2009) argue that the threat of exit by a large shareholder can have a disciplinary
impact if the shareholder possesses private information about corporate managers’ extraction of
private bene…ts (and hence her trading can have an impact on the stock price on which managerial
compensation is based). In the absence of coordination, institutions may be limited in using the
threat of exit as a disciplinary device, because, as mentioned above, the individual equity stake by
an institution is very small and because information production is costly. Thus a low coordination
cost enables institutional investors to share information and to conduct coordinated selling, which
can strengthen the disciplinary e¤ect of the threat of exit. This again predicts that the ease of
coordination should be related to improved corporate governance and …rm value.
Coordination costs are hard to observe or quantify. In this paper, we use data on institutional
shareholders and construct two measures to capture the ease with which they conduct coordinated
actions (in monitoring and selling). The …rst measure is the geographic distance among a …rm’s
institutional shareholders. If a …rm’s institutional shareholders are geographically close to one
another, they are more likely to communicate and thus coordinate their actions in major corporate events such as takeovers. This arises because geographic proximity facilitates word-of-mouth
communication among professional money managers (Hong, Kubik, and Stein, 2005) and because
geographic proximity can promote cooperation among agents through repeated interaction and mutual trust (Leamer and Storper, 2001). The second measure is the correlation in portfolio allocation
decisions among institutional shareholders. Institutional asset managers with similar portfolio allocations are likely to form strong ties among themselves because of the homophily e¤ect. A high
portfolio correlation can be the consequences of social connections as well, because institutions
in the same social networks have access to the same information sources (Cohen, Frazzini, and
Malloy, 2008) and because they engage in direct communication with one another (Hong, Kubik,
2
and Stein, 2005; Stein, 2008). Thus, a high portfolio correlation indicates greater homophily and
stronger social ties among institutional asset managers, which should facilitate coordination.
Using a comprehensive sample of stocks from 1980 to 2009, we …nd that …rm valuation (proxied
by an industry-adjusted Tobin’s q) decreases with coordination costs among institutional shareholders. The economic magnitude of this e¤ect is meaningful: Moving from the 10th percentile in the
geographic distance (portfolio correlation) among institutional shareholders to the 90th percentile
decreases (increases) the industry-adjusted Tobin’s q by 0:055 (0:173), as compared to the median
Tobin’s q of 1:29. The e¤ect is robust to controls for other institutional shareholder characteristics (such as aggregate institutional ownership, institutional ownership concentration, investment
horizons of institutional shareholders, and the distance between institutional shareholders and the
…rm), …rm size, growth opportunities, diversi…cation, …nancial performance, managerial ownership,
and …rm-speci…c e¤ects. Furthermore, this e¤ect is driven mainly by independent institutions
and non-transient institutions, both of which are more likely to play an active governance role.
These results are consistent with the hypothesis that the ease of coordination among institutional
shareholders enhances …rm value.
Institutional investors do not randomly invest in …rms, which suggests that institutional ownership structure and hence our coordination cost measures may be endogenous. For instance, institutions that are located nearby to one another may share similar preferences and invest in high-q
stocks. This will result in a reverse causality from …rm valuation to the geographic clustering of
institutional shareholders.
We use two approaches to address this potential endogeneity e¤ect. The …rst is an instrumental
variable approach. The instruments are based on whether or not the top institutional shareholder
is from New York or Boston and on the inclusion of a …rm’s stock in the Standard & Poor’s 500
index. If the top institutional shareholder of a …rm is located in cities with highly concentrated
institutional asset managers, the …rm is likely to have a low coordination cost among the institutional shareholder. The addition of a stock to the S&P 500 index can attract institutions that
are benchmarked against the index, thereby resulting in a more homogeneous institutional shareholder base and hence reduced coordination costs. On the other hand, it is reasonable that these
instruments do not a¤ect our outcome variable through channels other than institutional ownership
3
structure. We …nd that the above relation between coordination costs and …rm value persists even
after controlling for the endogeneity of coordination costs.
The second is a di¤erence-in-di¤erences approach to gauge the impact of exogenous shocks on the
relation between coordination costs and …rm value. We use the proxy reform in 1992 as an exogenous
shock that eases coordination among shareholders. We show that the e¤ect of coordination costs on
…rm value becomes signi…cantly stronger after the reform. In addition, we use the decimalization in
2001 as an exogenous shock that reduces trading costs and hence strengthens the disciplinary impact
of the threat of exit. We …nd that the e¤ect becomes signi…cantly stronger after decimalization,
suggesting that the threat of exit is one of the channels through which coordination cost impact
…rm value.
We then consider how the ease of coordination among institutional shareholders might add
value by focusing on corporate governance mechanisms and governance outcomes. We …nd that
…rms with low coordination costs are associated with better corporate governance, as proxied by
the number of anti-takeover provisions (i.e., the G-index and the E-index). We also show that low
coordination costs are associated with higher CEO equity-based pay and improved CEO turnoverperformance sensitivities. These results strengthen our interpretation that the ease of coordination
enhances the role of institutional investors in corporate governance.
This paper is related to two strands of empirical literature, of which the …rst is the literature
on institutional monitoring. A number of studies suggest that institutional investors in‡uence
corporate policies through costly monitoring or intervention (see, e.g., Hartzell and Starks, 2003;
Chen, Harford, and Li, 2007; Gillan and Starks, 2000; Del Guercio, Seery, and Woidtke, 2008) as
well as through the threat of exit (see, e.g., Parrino, Sias, and Starks, 2003). Much of the literature,
however, implicitly treats institutional investors (or certain types of institutions) as a monolithic
entity. Our paper is the …rst in the literature to study the impact of coordination costs on the role
of institutional investors in improving corporate governance and …rm value.
This paper also connects to the growing body of …nance literature on geography. Hong, Kubik,
and Stein (2005) show that mutual fund managers located close by make similar portfolio decisions, suggesting that geographic proximity facilitates communication among professional money
4
managers. Coval and Moskowitz (1999, 2001) …nd that mutual fund managers exhibit a strong
bias towards locally headquartered …rms and deliver superior returns on their local investments
than distant investments, indicating an information transfer from …rm managers to mutual fund
managers located nearby. Gaspar and Massa (2007) show that mutual funds located near their
portfolio companies play the role of informed monitors. Kang and Kim (2007) …nd that, in partial block acquisitions, acquirer …rms tend to pursue geographically proximate targets and play a
strong monitoring role in such targets post-acquisition. Our paper adds to the literature by showing that the geographic proximity among shareholders matters by a¤ecting the governance role of
shareholders.
The rest of the paper is organized as follows. Section 2 describes the data and summary
statistics. Section 3 presents the empirical results, and Section 4 concludes.
2
Data and Summary Statistics
We retrieve the data for our study from the Center for Research in Stock Prices (CRSP) database,
COMPUSTAT, and Thomson Reuters’13F institutional ownership database. Our sample includes
all common stocks listed on the NYSE, AMEX or NASDAQ during the period from 1980 to 2009
for which su¢ cient information is available in the three databases. There are 105; 454 …rm-year
observations in the sample.
We construct two measures to capture the coordination cost among institutional shareholders
of a …rm. The …rst measure is based on the geographic concentration of institutional ownership.
The premise is that asset managers located close to one another are more likely to come into
direct contact (Hong, Kubik, and Stein, 2005) and hence to take coordinated actions. Moreover,
geographic proximity can promote cooperation among institutional asset managers by facilitating
repeated interaction and cultivating trust (Leamer and Storper, 2001).
To construct the geographic distance measure, we …rst manually identify the location (zip code)
of institutional investors using the Nelson’s Directory of Investment Managers and by searching
the …lings by institutional investors on the SEC Edgar website. We then calculate, for each …rm-
5
quarter, the weighted-average geographic distance among institutional shareholders of the …rm. In
particular, for each institutional shareholder in the …rm, we calculate the geographic distance between the institution and all institutions in the …rm, weighted by their respective fractional holdings
in the …rm. This measure captures the average distance between an institutional shareholder and
its peers. We then calculate a weighted-average of these distances across all institutional shareholders of the …rm, again weighted by their fractional holdings. This weighting scheme ensures
that institutions that are likely to be more in‡uential, i.e., those with larger holdings in the …rm,
receive greater weights in determining the distance among shareholders. Last, we take a simple
average of the geographic distance among shareholders for each …rm over the four quarters in a
year. Speci…cally,
Geographic distance among institutional shareholders for …rm c =
0
13
2
4
X
X
X
1
4
wc;i;q l @
wc;j;q l Distij;q l A5 , (1)
4
l=1
i2S
j2S
where S is the set of institutional shareholders in …rm c, wc;i;t is the weight of institution i in the total
percentage held by institutions in …rm c at quarter q, and Distij;t is the geographic distance between
institutions i and j at quarter q. To reduce the skewness of the variable, we use the logarithm of
one plus the geographic distance among institutional shareholders, Log(1 + Shareholder distance),
as an explanatory variable in the regressions.
The second measure is based on the portfolio correlation among institutional shareholders of the
…rm. This variable is intended to capture the extent of homophily and social ties among institutional
shareholders. A large body of literature on homophily in social networks suggests that individuals
tend to build connections with others similar to themselves (see McPherson, Smith-Lovin, and
Cook, 2001 for a review of research on homophily in social networks). Thus, institutional asset
managers that share similar views about certain stocks, i.e., a high correlation in their portfolio
allocations, are more likely to exhibit homophily and form strong ties among themselves, which
should facilitate coordination. Moreover, a high portfolio correlation can be the consequences of
social ties, because institutions in the same social networks have access to the same information
sources, e.g., through shared educational ties (Cohen, Frazzini, and Malloy, 2008) and geographic
6
proximity (Coval and Moskowitz, 2001), and because they engage in direct communication with one
another (Hong, Kubik, and Stein, 2005; Stein, 2008). To the extent that the portfolio correlation
measure captures homophily and social connectedness among institutional asset managers, it should
be negatively related to shareholder coordination costs.
To construct the portfolio correlation measure, we retrieve the entire portfolio holdings of all
institutional shareholders of our sample …rms in each quarter. For each pair of institutional shareholders, we calculate the correlation coe¢ cient of the excess portfolio weights on common holdings,
i.e., stocks that are held by both institutions.2 The excess portfolio weights are calculated as
the actual portfolio weight assigned to a stock relative to the weight of the stock in the market
portfolio. We use the excess portfolio weights, rather than the actual weights, to focus on active
portfolio allocation decisions of institutional asset managers. Similar to the construction of the
geographic distance variable, we …rst calculate, for each institutional shareholder, the portfolio
correlation between the institution and all institutions in the …rm, weighted by their respective
fractional holdings. We then calculate the weighted-average of these correlations across all institutional shareholders, again weighted by each institution’s fractional holdings in the …rm. We take a
simple average of the institutional portfolio correlation for the stock over four quarters in a year.
Speci…cally,
Portfolio correlation among institutional shareholders for …rm c =
2
0
13
4
X
X
X
1
4
wc;i;q l @
wc;j;q l Corrij;q l A5 , (2)
4
l=1
i2S
j2S
where S is the set of institutional shareholders in …rm c, wc;i;t is the weight of institution i in the
total percentage held by institutions in …rm c at quarter q, and Corrij;t is the correlation coe¢ cient
of the excess portfolio weight (measured as the actual weight relative to the weight in the market
portfolio) allocated to overlapping holdings between institutions i and j at quarter q.
Panel A of Table 1 presents summary statistics for the two measures of shareholder coordination
costs for all sample …rms. The average geographic distance among institutional shareholders is
2
If two institutions have less than …ve common holdings, we set the correlation to zero. The results are robust to
using a di¤erent cuto¤ or setting it to missing.
7
878:1 miles.3 The average portfolio correlation among institutional shareholders is 0:30. Both of
the coordination cost measures exhibit a fair degree of cross-sectional variation across …rms.
Panel A of Table 1 also presents summary statistics for other shareholder characteristics and
…rm characteristics. In particular, since institutions located close to their portfolio companies are
likely to play a monitoring role (Gaspar and Massa, 2007; Chhaochharia, Kumar, and Niessen,
2009), we calculate the weighted-average geographic distance between institutional shareholders
and …rms, weighted by institutions’fractional holdings. The average distance between institutional
investors and the …rm is 945:9 miles. Institutional shareholders, in aggregate, own 33% of the outstanding shares of the average …rm. Following Hartzell and Starks (2003), we calculate institutional
ownership concentration as a Her…ndahl Index of institutional ownership concentration based on
the percentages of institutional holdings by all 13F institutions. The average institutional ownership concentration for the …rms is 0:012. Following Gaspar, Massa, and Matos (2005), we calculate
shareholder turnover of a …rm as the weighted-average of the average total portfolio turnover rate
of the …rm’s institutional shareholders. The average shareholder turnover rate for the …rms is 0:27.
We calculate Tobin’s q as the ratio of market value of assets to book value of assets, where
market value of assets is measured as the market value of common equity plus the book value of
preferred stock (carrying value) plus the book value of long-term debt minus deferred taxes and
investment tax credit.4 The industry-adjusted Tobin’s q is calculated as the di¤erence between the
…rm’s Tobin’s q and its industry median using the three-digit SIC code (McConnell and Servaes,
1990). The mean Tobin’s q is 1:83, and the mean industry-adjusted Tobin’s q is 0:30.
Panel B of Table 1 presents a correlation matrix of the main variables. The two coordination
cost proxies are highly negatively correlated, with a correlation coe¢ cient of
0:786, suggesting
that institutional shareholders located close to one another tend to have correlated portfolio allocations. This is consistent with the “word-of-mouth” e¤ect documented by Hong, Kubik, and Stein
3
The magnitude of this distance appears large. However, it should be noted that it is measured across all
institutions that hold shares in the …rm. Consider a hypothetical …rm with 50 institutional shareholders from the 50
states in the U.S. (assuming they are located in the state capitals), each holding 2% of the …rm’s outstanding shares.
The shareholder distance for the …rm would be 1; 822 miles. Thus, the seemingly large distance among institutions
is driven mainly by the fact that the U.S. is geographically large.
4
A more sophisticated approach to estimating Tobin’s q is to calculate the replacement cost of assets (Lindenberg
and Ross, 1981). We use the simple approach, instead of the more sophisticated one because the latter requires
arbitrary assumptions about depreciation and in‡ation rates and because the two approaches deliver highly correlated
estimates of Tobin’s q (Villalonga and Amit, 2006).
8
(2005). Furthermore, both Tobin’s q and industry-adjusted Tobin’s q are signi…cantly negatively
correlated with the geographic distance measure, and both are signi…cantly positively correlated
with the portfolio correlation measure. These results give a preliminary indication that the ease of
coordination cost may improve …rm value.
In addition, we retrieve various corporate goverance-related variables, such as managerial ownership, board characteristics, and executive compensation, from RiskMetrics and ExecComp. We
report the summary statistics as well as the correlation matrix for these variables in Table 1.
[Insert Table 1 about here]
3
Empirical Results
3.1
Coordination Costs and Firm Value
In this section, we …rst examine the relation between …rm value and coordination costs using …rm…xed e¤ects regressions. We then address endogeneity concerns by using an instrumental variable
approach and by using a di¤erence-in-di¤erences approach to gauge the impact of two exogenous
shocks. Last, we conduct robustness checks of the regression results.
3.1.1
Firm-…xed E¤ects Regressions
To examine the e¤ects of coordination costs on …rm valuation, we run …rm-…xed e¤ects regressions
of industry-adjusted Tobin’s q on our coordination costs proxies and control variables. We lag all
our explanatory variables by one year to mitigate any confounding e¤ects due to contemporaneous
measurement. Speci…cally,
q j;t =
+
j
+ Coordination Costs j;t
1
+
X
i xi;j;t 1
where q j;t is …rm j’s industry-adjusted Tobin’s q at the end of year t,
ordination Costs j;t
1
j
+ "j;t ,
is …rm-…xed e¤ects, Co-
is one of the two measures of coordination costs for …rm j in year t
9
(3)
1, and
xi;j;t
1
includes standard control variables for Tobin’s q such as …rm size, pro…tability, capital ex-
penditure, leverage, R&D expenses, institutional ownership, institutional ownership concentration,
investment horizons of institutional shareholders, the distance between institutional shareholders
and the …rm, and year dummies. We cluster the standard errors at the …rm level (Petersen, 2009).
As Panel B of Table 1 shows, the two coordination cost proxies are highly negatively correlated,
we include them in the regressions one at a time.
The regression results, shown in Panel A of Table 2, indicate that the ease of coordination has a
positive e¤ect on …rm value. The economic magnitude of this e¤ect is meaningful: Based on the full
speci…cation (i.e., the last two columns of Table 2, Panel A), moving from the 10th percentile in the
geographic distance (portfolio correlation) among institutional shareholders to the 90th percentile
decreases (increases) the industry-adjusted Tobin’s q by 0:055 (0:173), as compared to the median
Tobin’s q of 1:29.
Since institutions may di¤er in their incentives and abilities to play a governance role, we partition institutional investors into groups in two di¤erent ways. First, we classify institutions into
independent institutions and “grey” institutions following Chen, Harford, and Li (2007). Independent institutions include investment companies, independent investment advisors, and public
pension funds, which do not have business relationships with their portfolio companies and hence are
more likely to engage in active monitoring. Grey institutions include insurance companies, banks,
and private pension funds, which are less likely to play a governance role because of their business
ties with the …rms they invest in. Second, we divide institutions into transient and non-transient
categories following Bushee (1998). Non-transient institutions are dedicated and quasi-indexer
based on Bushee’s de…nition, which are likely to be more e¤ective monitors. We expect that the
e¤ect of coordination costs on …rm value should be driven mainly by independent institutions and
non-transient institutions.
We reconstruct the coordination cost measures separately for each category of institutions.
We replace the aggregate coordination cost measures in Eq. (3) with separate coordination cost
measures for each category of institutions, and re-estimate the regressions. Panel B of Table 2
reports the results. Consistent with our expectation, the negative e¤ects of coordination costs on
…rm value are driven mainly by independent institutions and by non-transient institutions.
10
We further add control variables related to managerial ownership and board structure in our
…rm-…xed e¤ects regressions to examine whether the negative relation between Tobin’s q and coordination costs are driven by these factors. In particular, we include managerial ownership, managerial
ownership squared, indicator variables for small boards (board size less than 9), independent boards
(independent outside directors account for more than 75% of the board), and CEO/Chairman duality. The sample size for these variables is 31; 559, about a third of our sample size in the baseline
regressions.
The results, reported in the last two columns of Table 2, Panel B, suggest that the negative
e¤ects of coordination costs on …rm value are robust to adding these controls. The coe¢ cient on
managerial ownership is positive and signi…cant, whereas that on managerial ownership squared is
negative and signi…cant. These results are consistent with an inverted U-shaped relation between
Tobin’s q and managerial ownership (e.g., Morck, Shleifer, and Vishny, 1988).
[Insert Table 2 about here]
3.1.2
Addressing Endogeneity Concerns
The panel regression results presented above may raise endogeneity concerns, because institutional
investors do not invest randomly. For instance, institutions that are located nearby to one another
may share similar preferences and invest in high-q stocks. This will result in a reverse causality from
…rm valuation to the geographic clustering of institutional shareholders. We use two approaches to
address this potential endogeneity e¤ect. The …rst is an instrumental variable approach, and the
second is to exploit regulatory changes as exogenous shocks to shareholder coordination.
A. Instrumental Variable Regressions. We use two instruments. The …rst is an indicator
variable for whether the largest institutional shareholder is headquartered in New York City and
Boston. Intuitively, if the top institutional shareholder is from a city with highly concentrated institutional investment managers, the institution can more easily coordinate with other institutional
shareholders, because they are likely to be located in the same city and hence share similar portfolio
allocations (Hong, Kubik, and Stein, 2005). This should lead to a lower coordination cost for the
…rm. We use metropolitan statistical areas (MSAs) to de…ne the location of institutional asset
11
managers. We choose New York and Boston, because these two cities dominate the institutional
asset management landscape, representing 19:3% and 16:0% of the total dollar holdings by all 13F
institutions, respectively. We indentify the largest institutional shareholder of a …rm at the start of
year t
1, i.e., 24 months prior to measuring Tobin’s q, based on holdings in the …rm’s stock. The
exclusion restriction for an instrument— that it should not directly a¤ect or be directly a¤ected by
the dependent variable— is also satis…ed, since the change in the fundamental value of a …rm should
not be directly related to whether or not the …rm’s the top institutional shareholder is located in
New York or Boston.
The second is an indicator variable for S&P 500 index inclusion that equals one if the stock is
included in the S&P 500 index in year t
1 and zero otherwise. The addition of a stock to the
S&P 500 index can attract institutions that are benchmarked against the index, thereby resulting
in a more homogeneous institutional shareholder base and hence reduced coordination costs. The
reverse is true for index deletions. On the other hand, the S&P500 inclusion or deletion seems
to satisfy the exclusion restriction for a valid instrument, because a large literature contends that
index inclusion is unrelated to any change in the fundamental performance of the included stock
(see, e.g., Shleifer, 1986; Kaul, Mehrotra, and Morck, 2000).5
We use the two-stage least square (2SLS) procedure to account for the endogeneity of coordination costs. In the …rst stage, we regress coordination costs measures on the two instruments
and other exogenous variables. In the second stage, we run a regression of the industry-adjusted
Tobin’s q on the …tted values from the …rst stage regression as the instrument for coordination
costs. Speci…cally, we estimate the following 2SLS model:
First Stage: Coordination Costs j;t
Second Stage: q j;t =
where NYC/Boston j;t
+
1
j
1
= c+ j + NYC/Boston j;t
1+
+ Instrumented Coordination Costs j;t
S&P500 j;t
1
+
P
P
1+
i=1;k #i xi;j;t 1 + j;t 1
i=1;k i xi;j;t 1
+ "j;t
(4)
is an indicator variable that equals one if stock j ’s largest institutional
shareholder as of the start of year t
1 is located in New York or Boston and zero otherwise;
5
Standard and Poor’s explicitly states that “the decision to include a company in the S&P 500 Index is not an
opinion on that company’s investment potential.”
12
S&P500 j;t
1
is an indicator variable that equals one if stock j is included in the S&P 500 index
in year t
1 and zero otherwise; Instrumented Coordination Costs j;t
coordination costs measures from the …rst-stage regressions,
j
and
j
1
is the …tted value of the
are …rm-…xed e¤ects, xi;j;t
1
is the same set of control variables as in Eq. (3).
Table 3 report the results from the 2SLS model. Columns 1 and 2 of Table 3 reports the
results of the …rst-stage regression with the dependent variable being one of the two measures of
coordination costs. Consistent with economic intuition, …rms whose largest institutional shareholder is from New York or Boston are associated with signi…cantly lower coordination costs, i.e.,
a smaller geographic distance and a higher portfolio correlation, among the institutional shareholders. Moreover, the addition of a stock to the S&P 500 index has a signi…cant negative e¤ect
on the coordination costs among institutions. These e¤ects are economically signi…cant as well.
For instance, the geographic distance among institutions decreases by 5:7% if the stock’s largest
institutional shareholder switches from a non-New York/Boston institution to a New York/Boston
one; the geographic distance among institutions decreases by 26:1% when the stock is added to
the S&P 500. We conduct F -tests of joint signi…cance of the two instruments. The F -statistics
strongly reject the null hypothesis that our instruments are irrelevant in the …rst-stage regressions.
We also conduct the Stock and Yogo (2005) weak instrument test of the null hypothesis that the
instruments are only weakly correlated with the endogenous variables. The test strongly rejects
the null hypothesis that the instruments are weak. We report these test statistics at the bottom of
Table 3.
Columns 3 and 4 of Table 3 report the second-stage results with industry-adjusted Tobin’s q as
the dependent variable. Consistent with our baseline results from …rm-…xed e¤ects regressions, the
coe¢ cient estimates of the instrumented coordination costs measures remain signi…cant (at the 5%
level) and in the predicted directions. The absolute magnitude of these coe¢ cient estimates appears
greater than those obtained using …rm-…xed e¤ects regressions. Since we use two instruments for
each of the coordination costs variables, we have an overidenti…ed speci…cation. We conduct the
Hansen overidenti…cation test. The Hansen J -statistics cannot reject the joint null hypothesis
that the instruments are uncorrelated with the error term and are correctly excluded from the
second-stage regressions.
13
Overall, the 2SLS regression results suggest that the impact of coordination costs on Tobin’s q
is not driven by the endogenous selection of high-q …rms by coordinated institutions.
[Insert Table 3 about here]
B. The E¤ect of the 1992 Proxy Reform. We now exploit the 1992 proxy reform as an
exogenous shock that reduced the barriers to shareholder coordination in corporate governance
(Choi, 2000; Bradley, Brav, Goldstein, and Jiang, 2010). Prior to the October 1992 changes to the
proxy rules, any communication among a group of 10 shareholders or more under circumstances
reasonably calculated to a¤ect voting decisions would amount to proxy solicitation and was not
allowed until a formal proxy statement was delivered to other shareholders. This communication
restriction was eased with the 1992 proxy reform such that any communication by shareholders
not directly seeking the power to vote as proxy for other shareholders was excluded from the
de…nition of what constitutes a solicitation. The reform thus signi…cantly eased communication
and coordination among shareholders. This predicts that the e¤ects of coordination costs on …rm
value should become stronger in the post-reform period.
We use a di¤erence-in-di¤erences approach to examine the impact of the 1992 reform on the
relation between coordination costs and …rm value. We use a two-year window and de…ne the …scal
year in which the reform occurred as year t. We choose year t
2 for the pre-reform period, and
year t as the post-reform period. We discard the year immediately before the reform, i.e., year t 1,
because the reform was widely discussed in the media before the …nal adoption of the changes and,
as such, …rm value in year t
1 may have factored in the e¤ect associated with coordination costs.
In addition, because we are interested in the e¤ect of proxy reform on coordination costs and …rm
value, we require that each stock be present in both windows around the reform. As a result, for
every stock we note only two observations— one in each window of the event.
We divide the sample of stocks into quintiles based on each of the coordination costs proxies.
Stocks in the bottom quintile of coordination costs constitute a “treatment”group that experiences
an exogenous shock to shareholder coordination. Stocks in the top quintile constitute the control
group. Intuitively, the reform signi…cantly reduces the restrictions on shareholder coordination,
thereby enabling institutions with low coordination costs to conduct coordinated monitoring activ14
ities. In contrast, the reform should have little, if any, impact on …rms whose institutional shareholders face prohibitively high coordination costs, because the institutions are likely to remain passive
post-reform due to the high coordination costs. By comparing the change in industry-adjusted
Tobin’s q after the reform for the treatment and control groups, we allow for both group-speci…c
and time-speci…c e¤ects.
Panels A and B of Table 4 present the results of univariate di¤erence-in-di¤erences comparisons
in industry-adjusted Tobin’s q between low- and high-coordination-cost …rms before and after the
proxy reform. The di¤erence-in-di¤erences estimator indicates a large increase in industry-adjusted
Tobin’s q for …rms with low coordination costs relative to those with high coordination costs after
the reform. In particular, Panel A shows that …rms in the bottom quintile of the geographic
distance among the institutional shareholders (the treatment sample) experience an increase of
0:11 in industry-adjusted Tobin’s q, compared to a change of 0:01 for …rms in the top quintile.
The di¤erence in the change in industry-adjusted Tobin’s q between the two groups, albeit not
statistically signi…cant, is economically large. Panel B shows that when the portfolio correlation
measure is employed as the coordination cost proxy, the di¤erence-in-di¤erences estimator suggests
an increase in industry-adjusted Tobin’s q of 0:15 (signi…cant at the 5% level) for the treatment
…rms relative to the control …rms.
To control for the e¤ect of other factors that may a¤ect …rm value, we estimate multivariate
di¤erence-in-di¤erences regressions on the two-year sample around the reform. In particular, we
add an indicator variable, Post-reform, which equals one for observations after October 1992, and
zero otherwise. We interact our coordination costs variables with the post-reform dummy; the
coe¢ cient on the interaction term captures the di¤erence-in-di¤erences e¤ect of the reform on
…rms with low coordination costs relative to those with high coordination costs.
Panel C of Table 4 presents the results of the di¤erence-in-di¤erences regressions. In all four
speci…cations, the coe¢ cient on the interaction between the coordination costs variables and the
post-reform dummy is signi…cant at the 1% level and in the predicted directions. For instance,
Column 2 shows that, after controlling for other factors that a¤ect industry-adjusted Tobin’s q, the
e¤ect of the geographic distance among institutions on industry-adjusted Tobin’s q is signi…cantly
more negative after the reform as compared to before. These results are consistent with our
15
univariate results, indicating a causal e¤ect of coordination costs on …rm value.
[Insert Table 4 about here]
C. The E¤ect of Decimalization. Institutions can coordinate their selling behavior and
use the threat of exit as a disciplinary device. Admati and P‡eiderer (2009) contend that a liquid
stock market, i.e., lower transaction costs, can improve the e¤ectiveness of the threat of exit as a
corporate governance mechanism. We use decimalization as an exogenous shock that increases stock
market liquidity, which in turn can intensify the disciplinary e¤ect of the coordinated threat of exit.
The stock markets in the U.S. converted to the decimal-pricing system and reduced the minimum
tick size from a sixteenth of a dollar to one cent during the period between August 2000 and April
2001. This led to signi…cant drops in bid-ask spreads following decimalization (Bessembinder, 2003;
Fur…ne, 2003). Institutional investors, due to their sizable holdings, are sensitive to transaction
costs.6 Other things equal, …rms whose institutional shareholders face lower coordination costs
should be more likely to coordinate and use the threat of exit to discipline corporate managers postdecimalization, compared to …rms with widely dispersed institutional shareholders. This predicts
that the e¤ects of coordination costs on …rm value should become stronger following decimalization.
We estimate multivariate di¤erence-in-di¤erences regressions on a two-year sample around decimalization. In particular, we de…ne the …scal year in which decimalization occurred as year t. We
choose year t
1 for the pre-decimalization period, and year t as the post-decimalization period.
We add an indicator variable, Post-decimalization, which equals one for observations after January
2001, and zero otherwise. We interact our coordination costs variables with the post-decimalization
dummy; the coe¢ cient on the interaction term captures the di¤erence-in-di¤erences e¤ect of decimalization on …rms with low coordination costs relative to those with high coordination costs.
Table 5 presents the results of the di¤erence-in-di¤erences regressions. The coe¢ cients on the
interaction between the coordination costs variables and the post-reform dummy are all in the
predicted directions and generally signi…cant. These results suggest that the threat of exit is one
of the channels through which coordination costs a¤ect …rm value.
6
For example, Wermers (2000) …nds that 0:8% of the 2:3% performance di¤erence between mutual funds’ gross
returns and net returns is due to transaction costs.
16
[Insert Table 5 about here]
3.1.3
Robustness Checks
In this section, we conduct a series of robustness checks on the relation between …rm valuation and
coordination costs.
A. Controlling for local institutions. Investors located close to their investments are likely
to have an informational advantage (Coval and Moskowitz, 2001; Baik, Kang, and Kim, 2010) as
well as to provide a strong monitoring role (Gaspar and Massa, 2007; Kang and Kim, 2008). To test
whether the …ndings are driven by local institutional shareholders, we reconstruct the two measures
of shareholder coordination costs by excluding institutional investors located within 100 kilometers
of the …rm’s headquarter and re-estimate Eq. (3). The results, reported in the …rst two columns
of Table 6, show that the e¤ects of coordination costs on …rm value are qualitatively unchanged,
suggesting that the results are not driven by local institutions.
B. Excluding cities with highly concentrated institutional investors. Institutional
asset management is highly geographically concentrated. One concern is that the …ndings are
driven by a few cities with a high concentration of institutional investors. We thus repeat the
analysis by excluding these cities. We use metropolitan statistical areas (MSAs) to de…ne the
location of institutional asset managers. For each MSA and each quarter, we calculate the total
dollar value of equity holdings that are managed by institutions residing in that MSA. New York
and Boston dominate the institutional asset management landscape, representing 19:3% and 16:0%
of the total dollar holdings by all 13F institutions, respectively. We then construct the two measures
of shareholder coordination costs by excluding the two MSAs and re-estimate Eq. (3). Columns
3 and 4 of Table 6 show that the results are again qualitatively unchanged. This …nding suggests
that the results are not driven by the two extreme cities per se.
C. Excluding foreign institutions. The fraction of the total institutional equity holdings
in the U.S. managed by foreign institutions has increased signi…cantly from 3% in 1980 to 15% in
2008. On the one hand, the presence of foreign institutions can increase the geographic distance
among shareholders and, to the extent that they have di¤erent investment objectives from domestic
17
institutions, decrease the portfolio correlation among the institutional shareholders of a …rm. On the
other hand, foreign institutions might be less e¤ective in monitoring management than domestic
institutions due to geographic distance (Kang and Kim, 2008). To test whether the results are
driven by foreign institutions, we reconstruct the two measures of shareholder coordination costs
by excluding foreign institutions and re-estimate Eq. (3). The results, reported in Columns 5 and
6 of Table 6, are essentially unchanged compared to the baseline results reported in Table 2, Panel
A, which suggests that foreign institutions do not drive the results.
D. OLS regressions with lagged dependent variables. We estimate OLS regressions
adding lagged industry-adjusted Tobin’s q as a control variable. The last two columns of Table 6
report the results. As expected, the coe¢ cient on the lagged industry-adjusted Tobin’s q is positive
and highly signi…cant. The coe¢ cients on our key variables, i.e., the coordination costs variables,
remain signi…cant and in the predicted directions. Furthermore, our results are robust to adding
two or three lags of industry-adjusted Tobin’s q in the OLS speci…cation.
[Insert Table 6 about here]
3.2
Coordination Costs and Corporate Governance
We now consider how the ease of coordination among institutional shareholders might add value
by focusing on corporate governance mechanisms and governance outcomes.
3.2.1
Anti-takeover Provisions
A large literature in corporate governance suggests that anti-takeover provisions have a negative
impact on …rm value by insulating corporate managers from the external discipline of takeovers
(e.g., Gompers, Ishii, and Metrick, 2003; Bebchuck, Cohen, and Ferrell, 2009). If coordination costs
are low, a coalition of institutional shareholders can in‡uence the use of anti-takeover provisions
by corporations by coordinating their actions. For example, institutional shareholders can jointly
propose and vote on governance issues, such as removing anti-takeover provisions, in annual shareholder meetings. This predicts that the ease of coordination among institutions should be related
18
to a lower number of anti-takeover provisions.
We use two indices to measure the level of external corporate governance. The …rst is the Gindex proposed by Gompers, Ishii, and Metrick (2003), which is based on 24 anti-takeover provisions.
The second is the entrenchment index (E-index) proposed by Bebchuck, Cohen, and Ferrell (2009).
The entrenchment index consists of six provisions, namely classi…ed boards, limits to shareholder
bylaw amendments, poison pills, golden parachutes, and supermajority requirements for mergers
and charter amendments. For both indices, a low number indicates strong corporate governance.
We estimate multivariate regressions of the corporate governance indices on coordination costs
and control variables. Speci…cally,
Governance Index j;t =
+ $Coordination Costs j;t
1
+
X
i xi;j;t 1
+
j;t ,
(5)
where Governance Index j;t is one of the two corporate governance indices for …rm j in year t;
Coordination Costs j;t
1
is one of the two measures of coordination costs among institutional share-
holders of …rm j in year t 1; and xj;t
1
includes year and industry …xed e¤ects, …rm characteristics,
and other ownership characteristics of …rm j in year t
1.
Table 7 reports the results. In three out of four speci…cations, the coordination costs variables
are signi…cant and in the predicted directions. The economic magnitude is large as well: for instance,
moving from the 10th to the 90th percentiles in the geographic distance (portfolio correlation)
variable increases (decreases) the G-index by 0:71 (4:04), compared to the median G-index of 9.
These results suggest that the ease of coordination enables institutional shareholders to play a
stronger monitoring role by removing barriers to takeovers.
[Insert Table 7 about here]
3.2.2
Equity-based Incentives
An extensive literature suggests that equity-based compensation for corporate managers can improve …rm performance (e.g., Mehran, 1995). Hartzell and Starks (2003) suggests that institutional
investors can enhance the pay-for-performance sensitivity of managers through increased monitor19
ing. We hypothesize that institutional investors, through coordinated monitoring, can improve
corporate governance by increasing corporate managers’equity-based incentives.
We use two measures to capture the equity-based incentives of CEOs. The …rst measure is the
incentive ratio proposed by Bergstresser and Philippon (2006). This ratio employs the total holding
of stock and options rather than annual grants, and is de…ned as follows:
Incentive ratio =
Increase in value of CEO stock and options for a 1% increase in stock price
Increase in value of CEO stock and options + annual salary + annual bonus
(6)
where the numerator is calculated as 0.01 multiplied by the product of the …rm’s share price and
the number of shares and options held by the CEO.
The second measure is the option fraction as in Mehran (1995), which is calculated as the
percentage of total CEO annual compensation comprised of grants of new stock options, with the
options valued by the Black-Scholes formula. Data on option grants, salary, bonus, and other
compensation are available from Standard and Poor’s ExecuComp database, available through
Compustat.
We estimate multivariate regressions of the equity-based incentives on coordination costs and
control variables using a speci…cation similar to Eq. (5). Table 8 reports the results. In all four
speci…cations, the coordination costs variables have the predicted signs, although only two are
signi…cant at the 1% level. These results suggest that a low coordination cost enables institutional
shareholders to in‡uence compensation policies that enhance shareholder value.
[Insert Table 8 about here]
3.2.3
Turnover-performance Sensitivity
A primary outcome of internal monitoring by shareholders and board of directors is CEO turnover
(Huson, Parrino, and Starks, 2001). Coordinated monitoring by institutional investors can exert
pressure on the …rm’s board of directors to identify and terminate incompetent CEOs. We explore
this possibility by testing whether the ease of coordination enhances CEO turnover-performance
sensitivity.
20
We extract data from the ExecuComp database to identify CEO turnover. We classify a …rm
as having experienced a CEO turnover when the CEO in year t is di¤erent from the CEO in year
t
1. We identify 2; 851 (11:8%) CEO turnover events out of 24; 228 …rm-years during the period
from 1993 to 2009. We then run probit regressions to examine the in‡uence of coordination costs
on the likelihood of CEO turnover. Speci…cally,
P rob(Turnover j;t ) =
+ (Coordination Costs j;t
+ Coordination Costs j;t
1
Stock Return j;t
1
+ Stock Return j;t
1
1)
+
X
i xi;j;t 1
+
j;t ,
(7)
where Turnover j;t is an indicator variable that equals one if the CEO of …rm j in year t is di¤erent
from the CEO in year t
1; Coordination Costs j;t
1
is one of the two measures of coordination
costs among institutional shareholders of …rm j in year t
return of …rm j’s stock in year t
1; and xi;t
1
1; Stock Return j;t
1
is the buy-and-hold
includes …rm- and manager-level characteristics
as well as other shareholder characteristics of the …rm. The managerial characteristics we consider
are whether the CEO is above the age of 60, whether the CEO is also the chairman of the board,
and CEO tenure.
The results, reported in the …rst two columns of Table 9, show that the interaction between
the geographic distance variable and stock return has a positive and signi…cant (at the 5% level)
coe¢ cient, which suggests that the ease of coordination improves the CEO turnover-performance
sensitivity. The result using the portfolio correlation variable is insigni…cant. We then create
dummy variables for the two coordination costs measures. Geographically concentrated is an indicator variable that equals one if the geographic distance among institutions is in the bottom
quartile and zero otherwise. Correlated portfolio is an indicator variable that equals one if the
portfolio correlation among institutions is in the top quartile and zero otherwise. We replace each
of the coordination costs variables with the respective indicator variables and re-estimate Eq. (7).
The results, reported in Columns 3 and 4 of Table 9, show that the coe¢ cients on the interaction
terms are signi…cant and in the expected directions. The results lend support to our prediction that
the ease of coordination among institutional shareholders is associated with an increased propensity
to terminate poorly performing CEOs.
21
[Insert Table 9 about here]
4
Conclusion
The ease of coordination has an important impact on the role of institutional investors in corporate
governance. Using measures based on the geographic distance among institutional shareholders and
their portfolio correlation to measure coordination costs, we …nd that …rm value as measured by
industry-adjusted Tobin’s q decreases with coordination costs. This e¤ect is robust to controls for
other institutional shareholder characteristics (such as aggregate institutional ownership, institutional ownership concentration, investment horizons of institutional shareholders, and the distance
between institutional shareholders and the …rm), …rm size, growth opportunities, diversi…cation,
…nancial performance, managerial ownership, and …rm-speci…c e¤ects. Furthermore, this e¤ect is
driven mainly by independent institutions and non-transient institutions, both of which are more
likely to play an active governance role.
We address endogeneity concerns by using an instrumental variable approach and by exploiting
two exogenous shocks to shareholder coordination. The e¤ect of coordination costs on …rm value
still holds after controlling for the endogeneity of institutional ownership structure. We also …nd
that the change in industry-adjusted Tobin’s q after the 1992 proxy reform is signi…cantly greater
for …rms with low coordination costs. Furthermore, the change in industry-adjusted Tobin’s q after
decimalization is signi…cantly greater for …rms with low coordination costs. These results provide
further evidence for the causal e¤ect of coordination costs on …rm value.
Last, we show that the ease of coordination among institutions is associated with fewer antitakeover provisions adopted by the …rm, higher equity-based pay for CEOs, and improved CEO
turnover-performance sensitivities. Overall, these …ndings suggest that the ease of coordination
enhances the governance role of institutional shareholders.
This paper contributes to our understanding of institutional monitoring. While the existing
literature implicitly treats institutional investors (or certain types of institutions) as a monolithic
entity, the di¤use nature of institutional shareholding suggests that coordination among institutions
22
is necessary to limit the free-rider problem. The evidence in this paper highlights the importance
of coordination costs in determining the intensity of institutions’monitoring.
23
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Figure 1: Aggregate institutional ownership and individual institutional stake during the sample
period from 1980 to 2009
40%
Aggregate IO (left scale)
Individual IO (right scale)
0.16%
30%
0.12%
20%
0.08%
10%
0.04%
0%
0.00%
1980
1985
1990
1995
2000
2005
The dark line, on the left scale, indicates the median value of aggregate institutional ownership in a
firm’s equity as a fraction of the firm’s outstanding shares at each year-end during 1980-2009. The
grey line, on the right scale, indicates the median value of an individual institution’s equity holdings
in a firm as a fraction of the firm’s outstanding shares at each year-end.
Table 1: Summary statistics of shareholder coordination costs and other firm characteristics
This table reports the summary statistics and correlation matrix of various firm characteristics,
including coordination costs among institutional shareholders, institutional ownership, firm
valuation, corporate governance structure, and executive compensation and turnover. Shareholder
distance is the weighted-average geographic distance among institutional shareholders over the four
quarters in a year as given in Eq. (1). Log(1+Shareholder distance) is the logarithm of one plus
Shareholder distance. Shareholder portfolio correlation is the weighted-average correlation
coefficient of the portfolio weights (relative to the weight in the market portfolio) on common
holdings between each pair of institutional shareholders of the firm over the four quarters in a year
as given in Eq. (2). Tobin’s q is measured as the market value of common equity plus the book values
of preferred equity and long-term debt minus deferred taxes and investment tax credit, all scaled by
the book value of assets [COMPUSTAT items (fyme+prf+lt–txditc)/at]. The industry-adjusted
Tobin’s q is calculated as the difference between the firm’s Tobin’s q and its industry median using
the three-digit SIC code (McConnell and Servaes, 1990). Inst. ownership is the fraction of shares
outstanding held by institutional investors. Inst. ownership concentration is calculated as a
Herfindahl Index of institutional ownership concentration based on the percentages of institutional
holdings by all 13F institutions (following Hartzell and Starks, 2003). Shareholder-firm distance is
measured as the weighted-average geographic distance between the firm and its institutional
shareholders. Shareholder turnover is the weighted-average of the average total portfolio turnover
rate of the firm’s institutional shareholders (following Gaspar, Massa, and Matos, 2005). Return on
Assets is the ratio of operating income become depreciation to total assets (COMPUSTAT items
oibdp/at). Firm size is the logarithm of total assets [COMPUSTAT item log(at)]. Book leverage is the
ratio of total debt to the book value of total assets [COMPUSTAT items (at–be)/at]. R&D/Sales is the
ratio of R&D expenses to total sales [COMPUSTAT items xrd/sale]. Asset tangibility is the ratio of net
property, plant, and equipment to the book value of total assets [COMPUSTAT items ppent/at].
CapEx/Assets is the ratio of capital expenditure to the book value of total assets [COMPUSTAT items
capx/at]. Number of geographic segments is the number of geographic segments in the firm. G-Index
is the number of takeover defenses as proposed by Gompers, Ishii, Metrick (2003). E-Index is the
entrenchment index based on six provisions: staggered boards, limits to shareholder bylaw
amendments, poison pills, golden parachutes, and supermajority requirements for mergers and
charter amendments as proposed by Bebchuk, Cohen, and Ferrell (2009). Board size is the number
of directors on the board. Board independence is the proportion of the board composed of
independent outside directors. CEO/Chairman duality is an indicator variable which equals one if
the titles of CEO and Chairman are vested in the same individual and zero otherwise. Managerial
stock ownership is the fraction of outstanding shares owned by top five executives of the firm. CEO
incentive ratio is the ratio of the increase in value of CEO stock and options for a 1% increase in stock
price to the sum of the increase in value of CEO stock and options and annual salary and bonus as
proposed by Bergstresser and Philippon (2006). CEO option fraction is the fraction of CEO total
compensation composed of option awards based on Black-scholes value. CEO turnover is an
indicator variable which equals one if the CEO in year t+1 for the firm is not the same as in year t.
Panel A presents the summary statistics, and Panel B presents the correlation matrix for the main
variables. In Panel B, the numbers in bold are significantly different from zero at the 1% level.
Panel A: Summary statistics
N
Coordination cost proxies
Shareholder distance
Log(1+ Shareholder distance)
Shareholder portfolio correlation
Firm valuation
Tobin’s q
Industry-adjusted Tobin’s q
Mean
105,454 878.08
105,454 6.37
105,454 0.30
105,454
105,454
Institutional shareholder characteristics
Inst. ownership
105,454
Inst. ownership concentration (×100)
105,454
Log(1 + Shareholder-firm distance)
105,454
Shareholder turnover
105,396
Firm-level controls
Return on assets
105,454
Firm size
105,454
Book leverage
105,392
R&D/Sales
104,860
Asset tangibility
104,926
CapEx/Assets
105,454
Number of business segments
105,454
Governance structure and managerial ownership
G-Index
23,556
E-Index
25,756
Board size
17,600
Board independence
17,600
CEO/Chairman duality
17,600
Managerial stock ownership
13,986
CEO Compensation structure and turnover
CEO incentive ratio
23,912
CEO option fraction
24,868
CEO turnover
22,198
Std dev Median
10th
90th
437.64
1.50
0.24
912.07
6.82
0.20
210.83 1405.42
5.36
7.25
0.10
0.68
1.83
0.30
1.66
1.49
1.29
0.00
0.86
-0.65
3.28
1.43
0.33
1.17
6.49
0.27
0.27
1.08
0.98
0.11
0.27
0.93
6.61
0.26
0.02
0.00
5.23
0.16
0.73
0.03
7.63
0.39
0.09
5.22
0.51
0.18
0.29
0.06
3.14
0.31
2.14
0.28
1.16
0.24
0.07
3.44
0.12
5.06
0.50
0.00
0.23
0.04
2.00
-0.11
2.56
0.17
0.00
0.03
0.01
1.00
0.28
8.08
0.86
0.16
0.68
0.14
8.00
9.03
2.21
9.33
0.66
0.63
0.07
2.75
1.37
2.62
0.18
0.48
0.12
9.00
2.00
9.00
0.69
1.00
0.02
5.00
0.00
6.00
0.40
0.00
0.00
13.00
4.00
13.00
0.88
1.00
0.21
0.23
0.29
0.12
0.23
0.28
0.32
0.15
0.24
0.00
0.03
0.00
0.00
0.60
0.72
1.00
Panel B: Correlation matrix
(1)
(1) Log(1+ Shareholder distance)
(2)
(3)
(4)
(5)
(6)
(2) Shareholder portf. correlation
-0.786 1.000
(3) Tobin’s q
-0.016 0.036 1.000
(4) Industry-adjusted Tobin’s q
-0.039 0.049 0.943 1.000
(5) Inst. ownership
0.404 -0.617 -0.018 -0.034 1.000
(6) Inst. ownership concentration
0.327 -0.445 -0.045 -0.067 0.855 1.000
(7) Log(1 + Shrhldr-firm distance)
0.104 0.000
(8) Shareholder turnover
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(10) E-Index
0.108 -0.131 -0.093 -0.083 0.212 0.157 -0.010
(11) Board size
0.189 -0.292 -0.017 -0.014 0.467 0.289 -0.052 -0.039 0.125 0.135 1.000
(12) Board independence
0.194 -0.295 -0.008 -0.010 0.512 0.342 -0.031 -0.027 0.135 0.200 0.919 1.000
(13) CEO/Chairman duality
0.153 -0.236 -0.007 -0.008 0.391 0.249 -0.038 -0.021 0.109 0.096 0.748 0.758 1.000
(14) Managerial stock ownership
0.077 -0.112 0.035 0.030 0.120 0.076 -0.011 0.013 -0.123 -0.130 0.167 0.151 0.169 1.000
(17) CEO turnover
(17)
-0.007 -0.021 0.112 0.076 0.096 0.107 0.025 1.000
0.006 -0.202 -0.087 -0.071 0.111 0.014 -0.114 -0.094 1.000
(16) CEO option fraction
(16)
0.063 0.037 -0.044 -0.028 1.000
(9) G-Index
(15) CEO incentive ratio
(15)
1.000
0.016 0.016
0.029 0.729 1.000
0.312 0.289 -0.019 -0.092 0.016 0.051 -0.154 -0.161 0.026 -0.009 0.120 0.433 1.000
-0.033 0.028 0.189 0.141 0.089 0.019 0.057 0.137 -0.017 -0.019 0.087 0.094 0.077 -0.104 0.073 1.000
0.011
0.011 -0.025 -0.031 -0.022 0.000 -0.004 -0.007
0.012
0.010
0.014
0.008 -0.131 -0.031 -0.172 0.073 1.000
Table 2: Regression analysis of the relation between firm value and coordination costs: Firm-fixed
effects models
This table presents regression coefficients from firm fixed effects regressions of firm value on coordination
costs. The dependent variable is industry-adjusted Tobin’s q. Panel A reports the results from baseline
regressions. Panel B reports the results using alternative specifications. In particular, columns 1 and 2 in
Panel B include as regressors separate measures for the coordination costs among independent institutions
and grey institutions (Chen, Harford, and Li, 2007). Similarly, columns 3 and 4 in Panel B include as regressors
separate measures for the coordination costs among transient institutions and non-transient institutions
(Bushee, 1998). The last two columns in Panel B include managerial ownership and board characteristics as
additional controls. Small board is an indicator variable that equal one if the board size is less than 9, and zero
otherwise. Independent board is an indicator variable that equal one if independent outside directors account
for more than 75% of the board, and zero otherwise. See Table 1 for the definition of the variables. Year fixed
effects and firm fixed effects are included in all regressions. Numbers in parentheses are t-statistics based on
robust standard errors clustered at the firm level. Significance on a 10% (*), 5% (**), or 1% level (***) is
indicated.
Panel A: Baseline regressions
Dependent variable =
Log(1 + Shareholder distance)
(1)
-0.062***
(10.68)
Shareholder portfolio correlation
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
-0.083
(1.11)
-8.933***
(7.40)
-0.000
(0.01)
0.602***
(7.90)
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Constant
Observations
Adjusted R-squared
0.612***
(9.51)
124,143
0.42
Industry-adjusted Tobin’s q
(2)
(3)
-0.029***
(4.65)
0.646***
(12.98)
0.147*
1.593***
(1.91)
(17.34)
-11.380***
-24.046***
(9.36)
(17.86)
-0.007
-0.008
(0.78)
(0.88)
0.638***
0.388***
(8.33)
(4.88)
0.004
(0.05)
-0.498***
(27.01)
0.567***
(11.51)
0.053***
(3.44)
-0.440***
(5.01)
0.790***
(6.81)
0.024
(1.57)
-0.013
2.240***
(0.20)
(20.80)
124,143
104,359
0.42
0.45
(4)
0.299***
(5.43)
1.672***
(17.91)
-24.858***
(18.34)
-0.011
(1.17)
0.405***
(5.09)
0.002
(0.03)
-0.491***
(26.48)
0.559***
(11.31)
0.053***
(3.48)
-0.433***
(4.90)
0.810***
(6.98)
0.023
(1.53)
1.921***
(16.65)
104,359
0.45
Panel B: Alternative specifications
Dep = Industry-adjusted Tobin’s q
Independent vs. grey
Transient vs. noninstitutions
transient institutions
(1)
(2)
(3)
(4)
Log(1 + Shareholder distance)
Additional controls
(5)
-0.042**
(1.98)
Shareholder portfolio correlation
Shareholder distance: Independent
Shareholder distance: Grey
0.564***
(4.13)
-0.019***
(3.33)
-0.007
(1.37)
Portfolio correlation: Independent
0.248***
(4.63)
0.078*
(1.77)
Portfolio correlation: Grey
Shareholder distance: Non-transient
-0.036***
(4.85)
0.012***
(3.27)
Shareholder distance: Transient
Portfolio correlation: Non-transient
Portfolio correlation: Transient
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Managerial ownership
Managerial ownership squared
Small board
(6)
1.554***
(16.74)
-23.573***
(17.35)
-0.007
(0.75)
0.452***
(5.32)
0.051
(0.65)
-0.496***
(26.10)
0.531***
(10.42)
0.046***
(3.26)
-0.486***
(5.57)
0.748***
(6.37)
0.020
(1.32)
1.662***
(17.08)
-24.707***
(17.86)
-0.009
(0.96)
0.469***
(5.52)
0.049
(0.64)
-0.487***
(25.31)
0.521***
(10.19)
0.047***
(3.31)
-0.477***
(5.46)
0.771***
(6.55)
0.020
(1.31)
1.499***
(15.97)
-23.190***
(16.92)
-0.010
(0.97)
0.401***
(4.48)
0.031
(0.38)
-0.510***
(25.89)
0.530***
(10.26)
0.043***
(2.98)
-0.455***
(5.19)
0.712***
(5.64)
0.024
(1.55)
0.347***
(6.05)
-0.143***
(3.70)
1.555***
(15.97)
-23.668***
(16.97)
-0.012
(1.19)
0.388***
(4.27)
0.033
(0.40)
-0.505***
(25.57)
0.522***
(10.04)
0.043***
(3.01)
-0.442***
(5.03)
0.719***
(5.70)
0.024
(1.54)
0.818***
(6.78)
-13.022***
(7.62)
-0.009
(0.64)
0.533***
(3.45)
0.134
(0.89)
-0.668***
(15.00)
0.341***
(3.93)
0.025*
(1.66)
-0.679***
(4.15)
0.582**
(2.12)
0.015
(0.67)
0.838***
(3.35)
-0.135***
(2.95)
0.038
0.897***
(7.33)
-13.861***
(8.06)
-0.013
(0.93)
0.565***
(3.65)
0.136
(0.90)
-0.659***
(14.82)
0.333***
(3.85)
0.025*
(1.66)
-0.668***
(4.09)
0.611**
(2.23)
0.014
(0.62)
0.843***
(3.36)
-0.136***
(2.98)
0.038
Independent board
CEO/Chairman Duality
Constant
Observations
Adjusted R-squared
(1.55)
0.035*
(1.81)
-0.000
(0.00)
2.658*** 2.306*** 2.242*** 2.028*** 4.426***
(19.82)
(15.98)
(17.15)
(14.51)
(14.96)
98,281
98,281
93,570
93,570
31,559
0.45
0.45
0.45
0.45
0.57
(1.56)
0.034*
(1.73)
0.002
(0.11)
3.983***
(14.56)
31,559
0.57
Table 3: Regression analysis of the relation between firm value and coordination costs: Instrumental
variable approach
This table presents the results of 2SLS regressions of firm valuation on coordination costs as in Eq. (4).
We use two instruments. The first is Top institution located in NYC/Boston, which is an indicator variable
that equals one if the stock’s largest institutional shareholder as of the start of year t−1 is located in New
York or Boston and zero otherwise. The second is S&P 500 index inclusion, which is an indicator variable
that equals one if the stock is included in the S&P 500 index in year t−1 and zero otherwise.
The left panel reports the results from the first-stage regressions with coordination costs proxies as the
dependent variable. The right panel reports the results from the second-stage regressions of industryadjusted Tobin’s q on instrumented coordination costs and control variables. See Table 1 for the
definition of the variables. Year fixed effects and firm fixed effects are included in all regressions. We
report at the bottom of the table the F-statistics of the test for the joint significance of the instruments in
the first stage, the Wald statistics for Stock and Yogo (2005) weak instrument tests of the null hypothesis
that the instruments are only weakly correlated with the endogenous variables, and the Hansen Jstatistics for overidentifying restrictions. Numbers in parentheses are t-statistics based on robust
standard errors clustered at the firm level. Numbers in square brackets are p-values. Significance on a 10%
(*), 5% (**), or 1% level (***) is indicated.
Dependent =
Top institution located in NYC/Boston
S&P 500 index inclusion
First stage
Shareholder
Portfolio
distance
correlation
(1)
(2)
-0.057***
0.003**
(7.06)
(2.27)
-0.261***
0.046***
(12.40)
(12.03)
Instrumented shareholder distance
Second stage
Industry-adj.
Industry-adj.
Tobin’s q
Tobin’s q
(5)
(6)
-0.365**
(2.43)
Instrumented portfolio correlation
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Observations
F-test
[p-value]
Weak instrument test
[p-value]
Hansen J statistic
[p-value]
-0.018
(0.39)
-0.063
(0.09)
0.155***
(14.23)
0.237***
(2.80)
0.031
(1.20)
0.198***
(17.62)
-0.230***
(7.40)
-0.001
(0.12)
0.207***
(3.07)
0.135
(1.30)
0.001
(0.05)
98,283
99.96***
[0.00]
118.81***
[0.00]
-0.255***
(31.77)
2.704***
(21.85)
-0.007***
(5.51)
-0.083***
(7.53)
0.004
(1.10)
-0.046***
(27.33)
0.050***
(11.40)
-0.002*
(1.84)
-0.044***
(4.60)
-0.084***
(6.29)
-0.002*
(1.84)
98,283
74.46***
[0.00]
150.21***
[0.00]
1.564***
(17.65)
-23.542***
(18.33)
0.047*
(1.86)
0.488***
(5.50)
0.028
(0.43)
-0.444***
(13.15)
0.487***
(8.54)
0.049***
(3.51)
-0.371***
(4.01)
0.779***
(6.67)
0.025*
(1.69)
98,283
2.09
[0.15]
2.732**
(2.48)
2.267***
(7.76)
-30.813***
(9.59)
0.009
(0.70)
0.638***
(5.10)
0.007
(0.10)
-0.392***
(7.54)
0.435***
(6.19)
0.054***
(3.82)
-0.326***
(3.27)
0.964***
(6.52)
0.020
(1.33)
98,283
0.03
[0.87]
Table 4: The effect of the 1992 proxy reform
This table presents results of the difference-in-differences tests of the impact of the 1992 proxy reform on
the relation between firm valuation and coordination costs. We use a two-year window and define the
fiscal year in which the reform occurred as year t. We choose year t−2 for the pre-reform period, and year
t as the post-reform period. We require that each stock be present in both windows around the reform.
We divide the sample of stocks into quintiles based on each of the coordination costs proxies. Stocks in the
bottom quintile of coordination costs, i.e., Geographically concentrated and Correlated portfolios, constitute
a “treatment” group that experiences an exogenous shock to shareholder coordination. Stocks in the top
quintile, i.e., Geographically dispersed and Uncorrelated portfolios, constitute the control group. Panels A
and B present the results of univariate difference-in-differences comparisons in industry-adjusted Tobin’s
q between low- and high-coordination-cost firms before and after the proxy reform. Panel C presents the
results of multivariate difference-in-differences regressions. The dependent variable is industry-adjusted
Tobin’s q. See Table 1 for the definition of the variables. Numbers in parentheses are t-statistics based on
robust standard errors clustered at the firm level. Significance on a 10% (*), 5% (**), or 1% level (***) is
indicated.
Panel A: Univariate analysis using geographic distance as the coordination cost proxy
Geographically concentrated (Treatment)
Geographically dispersed (Control)
Difference (Treatment − Control)
Pre-reform
0.472
Post-reform
0.581
0.231
0.238
0.240***
(3.19)
0.344***
(4.06)
Difference (Post − Pre)
0.110
(1.20)
0.006
(0.10)
0.103
(1.48)
Panel B: Univariate analysis using portfolio correlation as the coordination cost proxy
Correlated portfolios (Treatment)
Uncorrelated portfolios (Control)
Difference (Treatment − Control)
Pre-reform
0.515
0.252
0.263***
(3.69)
Post-reform
0.62
0.206
0.414***
(5.28)
Difference (Post − Pre)
0.105
(1.05)
-0.046
(1.14)
0.151**
(2.23)
Panel C: Multivariate regression results
Dependent variable =
Log(1 + Shareholder distance) × Post reform
Log(1 + Shareholder distance)
(1)
-0.056***
(2.70)
-0.068***
(3.71)
Shareholder portfolio correlation × Post reform
Shareholder portfolio correlation
Post reform
0.330**
(2.38)
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Constant
Observations
Adjusted R-squared
0.724***
(5.96)
7,618
0.01
Industry-adjusted Tobin’s q
(2)
(3)
-0.061***
(3.13)
0.042**
(2.20)
0.358***
(3.14)
0.437***
(4.00)
0.423***
-0.134***
(3.21)
(4.47)
3.849***
(18.47)
-71.579***
(19.33)
-0.024
(1.19)
0.534***
(2.72)
-0.288*
(1.70)
-0.259***
(13.84)
0.585***
(5.80)
0.788***
(5.74)
-0.218***
(2.97)
1.879***
(4.55)
-0.019
(0.60)
0.476**
0.163***
(2.42)
(5.33)
6,486
7,618
0.19
0.01
(4)
0.455***
(3.96)
-0.520***
(3.90)
-0.099***
(3.18)
3.771***
(18.10)
-71.730***
(19.67)
-0.024
(1.17)
0.533***
(2.74)
-0.300*
(1.77)
-0.274***
(13.80)
0.608***
(6.01)
0.780***
(5.70)
-0.216***
(2.96)
1.849***
(4.49)
-0.014
(0.44)
0.976***
(5.03)
6,486
0.19
Table 5: The effect of decimalization
This table presents results of multivariate difference-in-differences regressions of the impact of
decimalization on the relation between firm valuation and coordination costs. The dependent variable is
industry-adjusted Tobin’s q. Post-Decimalization is an indicator variable that equals one if the observation
is in the post-Decimalizationperiod, i.e., after January 2001, and zero otherwise. See Table 1 for the
definition of the variables. Numbers in parentheses are t-statistics based on robust standard errors
clustered at the firm level. Significance on a 10% (*), 5% (**), or 1% level (***) is indicated.
Dependent variable =
Log(1 + Shareholder distance) × Post-Decimalization
Log(1 + Shareholder distance)
Shareholder portfolio correlation × Post-Decimalization
Shareholder portfolio correlation
Post-Decimalization
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Constant
Observations
Adjusted R-squared
Industry-adjusted Tobin’s q
(2)
(3)
-0.036
(1.09)
0.082**
(2.27)
0.275**
(2.29)
-0.366**
(2.52)
0.012
-0.002
-0.407***
(0.08)
(0.01)
(9.09)
5.193***
(23.50)
-84.527***
(21.02)
0.013
(0.45)
0.321
(1.46)
-0.174
(1.19)
-0.327***
(14.03)
0.756***
(6.00)
0.175***
(4.05)
-0.802***
(6.58)
2.577***
(6.18)
-0.072***
(2.58)
0.449***
0.650*
0.798***
(2.62)
(1.87)
(14.75)
10,740
7,786
10,740
0.01
0.15
0.01
(1)
-0.050**
(2.18)
0.035
(1.38)
(4)
0.255*
(1.71)
-0.819***
(4.01)
-0.314***
(6.22)
4.995***
(22.90)
-83.529***
(21.11)
0.017
(0.58)
0.287
(1.32)
-0.166
(1.13)
-0.345***
(13.64)
0.786***
(6.18)
0.171***
(3.93)
-0.788***
(6.50)
2.492***
(5.96)
-0.069**
(2.49)
1.565***
(5.49)
7,786
0.16
Table 6: Regression analysis of the relation between firm value and coordination costs: Robustness checks
This table presents the robustness checks on the relation between firm valuation and coordination costs.
The dependent variable is industry-adjusted Tobin’s q. For all the tests except models 7 and 8, we use the
same set of firm-level control variables as in Table 2. The coefficient estimates for the control variables
are not reported to conserve space. The first two columns report the results when local institutions, i.e.,
those located within 100 kilometers of the firm, are excluded in constructing the coordination cost
measures. Columns 3 and 4 report the results when institutions located in New York and Boston are
excluded. Columns 5 and 6 report the results when foreign institutions are excluded. Columns 7 and 8
report the OLS regression results with industry adjusted Tobin’s q in year t−1 as an additional regressor.
Year fixed effects are included in all regressions. Firm fixed effects are included in all specifications except
the last two. Numbers in parentheses are t-statistics based on robust standard errors clustered at the firm
level. Significance on a 10% (*), 5% (**), or 1% level (***) is indicated.
Excluding local
Excluding NYC and Excluding foreign
OLS with lagged
institutions
Boston
institutions
dependent variable
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
Log(1 + Shareholder distance)
-0.027***
-0.021***
-0.025***
-0.029***
(4.38)
(3.89)
(3.57)
(5.38)
Shareholder portfolio correlation
0.319***
0.230***
0.255***
0.271***
(6.07)
(4.60)
(4.60)
(6.04)
Industry-adjusted Tobin’s q, t−1
0.315*** 0.314***
(24.74) (24.69)
Other firm-level controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Firm FEs
Yes
Yes
Yes
Yes
Yes
Yes
No
No
Year FEs
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Observations
103,880 94,152 103,050 103,050 104,266 104,266 104,330 104,330
Adjusted R-squared
0.45
0.45
0.45
0.45
0.45
0.45
0.51
0.51
Dep = Industry-adjusted Tobin’s q
Table 7: Regression analysis of the relation between corporate governance indexes and coordination costs
This table presents regression coefficients from OLS regressions of corporate governance indexes on
coordination costs measures. The dependent variables are Gompers, Ishii, Metrick’s (2003) G-index and
Bebchuk, Cohen, and Ferrell’s (2009) E-index. The dependent variables are measured in year t+1, while
the independent variables are measured in year t. See Table 1 for the definition of the variables. Year
fixed effects and industry fixed effects are included in all regressions. Numbers in parentheses are tstatistics based on robust standard errors clustered at the industry level. Significance on a 10% (*), 5%
(**), or 1% level (***) is indicated.
Dependent variable =
Log(1 + Shareholder distance)
(1)
0.374*
(1.81)
Shareholder portfolio correlation
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Constant
Observations
Adjusted R-squared
4.716***
(7.99)
-57.978***
(6.38)
-0.182***
(3.03)
-5.816***
(8.73)
-0.784**
(2.29)
-0.387*
(2.01)
-0.010***
(3.02)
-0.066
(1.25)
-2.953***
(5.15)
0.044***
(3.09)
-0.119***
(4.72)
7.180***
(4.54)
19,129
0.14
G-index
(2)
-6.978***
(9.23)
3.478***
(5.68)
-36.446***
(4.03)
-0.160***
(2.79)
-5.305***
(8.25)
-0.625*
(1.78)
-0.480**
(2.37)
-0.009***
(2.97)
-0.075
(1.44)
-2.946***
(5.15)
0.044***
(3.17)
-0.124***
(4.82)
10.733***
(16.53)
19,129
0.15
(3)
0.051
(0.66)
2.096***
(7.37)
-21.428***
(4.69)
0.020
(0.71)
-1.172***
(3.86)
-0.382**
(2.57)
-0.124
(1.08)
-0.002
(0.90)
-0.127***
(4.78)
-0.715
(1.42)
0.011**
(2.15)
-0.033**
(2.46)
1.255**
(2.05)
21,559
0.18
E-index
(4)
-2.054***
(6.09)
1.746***
(5.88)
-15.449***
(3.48)
0.024
(0.87)
-1.009***
(3.35)
-0.329**
(2.22)
-0.150
(1.27)
-0.001
(0.82)
-0.130***
(4.97)
-0.716
(1.42)
0.011**
(2.18)
-0.035**
(2.49)
1.923***
(6.60)
21,559
0.18
Table 8: Regression analysis of the relation between equity-based compensation and coordination costs
This table presents regression coefficients from OLS regressions of equity-based compensation on
coordination costs. We use two measures for CEO compensation structure. The first is Bergstresser and
Philippon’s (2006) incentive ratio, which is the ratio of the increase in value of CEO stock and options for a
1% increase in stock price to the sum of the increase in the value of CEO stock and options and annual
salary and bonus. The second is the percentage of total CEO annual compensation comprised of grants of
new stock options, with the options valued using the Black-Scholes formula (Mehran, 1995). Stock return
is the buy-and-hold return of the stock in year t. CEO is Chair is an indicator variable that equals one if the
CEO is also the chairman of the board and zero otherwise. Tenure is the number of years that the CEO has
held the position with the firm. CEO age is the CEO’s age. The dependent variables are measured in year
t+1, while the independent variables are measured in year t. See Table 1 for the definition of other
variables. Year fixed effects and industry fixed effects are included in all regressions. Numbers in
parentheses are t-statistics based on robust standard errors clustered at the industry level. Significance
on a 10% (*), 5% (**), or 1% level (***) is indicated.
Dependent variable =
Log(1 + Shareholder distance)
Shareholder portfolio correlation
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Stock return
CEO is Chair
Tenure
CEO age
Constant
Observations
Adjusted R-squared
Incentive ratio
(1)
(2)
-0.067***
(6.41)
0.110
(1.57)
-0.165***
-0.147***
(4.83)
(3.84)
-0.843**
-1.002**
(2.19)
(2.36)
0.003
0.001
(0.75)
(0.27)
0.172**
0.184**
(2.41)
(2.65)
0.227***
0.231***
(8.11)
(8.12)
0.031***
0.031***
(7.43)
(7.36)
-0.180***
-0.180***
(10.55)
(10.38)
0.012***
0.012***
(3.45)
(3.35)
-0.151***
-0.152***
(4.09)
(4.03)
0.550***
0.555***
(6.83)
(6.87)
-0.014***
-0.014***
(2.93)
(2.85)
0.028***
0.026***
(4.24)
(4.10)
0.037***
0.037***
(5.99)
(6.04)
0.016***
0.016***
(8.35)
(8.34)
0.000
0.000
(0.31)
(0.33)
0.576***
0.104*
(6.72)
(1.95)
19,828
19,828
0.23
0.23
Option fraction
(3)
(4)
-0.001
(0.08)
0.213***
(3.37)
0.245***
0.277***
(12.76)
(13.86)
-1.577***
-2.107***
(4.69)
(5.94)
0.015***
0.015***
(4.00)
(3.91)
0.206***
0.184***
(3.56)
(3.26)
0.031
0.031
(1.31)
(1.27)
0.028***
0.028***
(8.81)
(8.98)
-0.065***
-0.067***
(3.73)
(3.81)
0.027***
0.027***
(4.92)
(4.82)
-0.146***
-0.143***
(5.25)
(5.09)
0.444***
0.440***
(6.12)
(6.11)
-0.008*
-0.008**
(2.01)
(2.02)
0.011***
0.009***
(3.57)
(3.00)
0.001
0.000
(0.07)
(0.06)
0.002
0.002*
(1.66)
(1.72)
-0.004***
-0.004***
(9.92)
(9.89)
0.129
0.056
(1.16)
(1.27)
20,621
20,621
0.21
0.21
Table 9: Regression analysis of the relation between CEO turnover-performance sensitivity and
coordination costs
This table presents probit regression analysis of the effect of coordination costs on CEO turnoverperformance sensitivity. The dependent variable is an indicator variable for CEO turnover that equals one
if the CEO for the firm in year t+1 is not the same as in year t. Geographically concentrated is an indicator
variable that equals one if the geographic distance among institutions is in the bottom quartile and zero
otherwise. Correlated portfolio is an indicator variable that equals one if the portfolio correlation among
institutions is in the top quartile and zero otherwise. Stock return is the buy-and-hold return of the stock
in year t. CEO is Chair is an indicator variable that equals one if the CEO is also the chairman of the board
and zero otherwise. Tenure is the number of years that the CEO has held the position with the firm. CEO
age > 60 is an indicator variable that equals one if the CEO’s age is greater than 60 and zero otherwise. The
dependent variables are measured in year t+1, while the independent variables are measured in year t.
See Table 1 for the definition of other variables. Numbers in parentheses are t-statistics based on robust
standard errors clustered at the industry level. Significance on a 10% (*), 5% (**), or 1% level (***) is
indicated.
Dependent variable =
Log(1 + Shareholder distance) × Stock return
Log(1 + Shareholder distance)
(1)
0.032**
(1.98)
-0.019*
(1.78)
CEO Turnover
(2)
(3)
0.003
(0.04)
-0.048
(0.92)
Shareholder portfolio correlation × Stock return
Shareholder portfolio correlation
-0.067**
(2.00)
0.002
(0.15)
Geographically concentrated dummy × Stock return
Geographically concentrated dummy
Correlated portfolio dummy × Stock return
Correlated portfolio dummy
Stock return
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
Return on assets
(4)
-0.253**
(2.15)
-0.067***
(3.65)
0.645**
(2.05)
-0.001
(0.45)
0.102***
(3.19)
-0.095***
-0.026*
(1.87)
-0.077***
(4.14)
0.782**
(2.40)
-0.002
(0.75)
0.113***
(3.40)
-0.093***
-0.024**
(2.37)
-0.069***
(3.80)
0.656**
(2.08)
-0.002
(0.75)
0.103***
(3.12)
-0.092***
-0.076*
(1.65)
-0.002
(0.11)
-0.025**
(2.50)
-0.070***
(3.82)
0.668**
(2.10)
-0.002
(0.80)
0.104***
(3.20)
-0.093***
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
CEO is Chair
Tenure
CEO age > 60
Observations
Pseudo R-squared
(3.68)
0.001
(0.88)
0.029**
(2.30)
-0.008***
(2.87)
-0.003
(0.15)
0.030
(0.54)
0.003
(0.89)
-0.041***
(7.59)
0.006***
(7.95)
0.077***
(11.01)
22,027
0.03
(3.72)
0.001
(0.66)
0.029**
(2.35)
-0.008***
(2.85)
-0.003
(0.16)
0.037
(0.64)
0.003
(0.79)
-0.040***
(7.65)
0.006***
(8.71)
0.077***
(10.93)
22,027
0.03
(3.67)
0.001
(0.73)
0.029**
(2.37)
-0.008***
(2.87)
-0.002
(0.10)
0.035
(0.61)
0.003
(0.78)
-0.040***
(7.63)
0.006***
(8.49)
0.077***
(11.04)
22,027
0.03
(3.73)
0.001
(0.73)
0.029**
(2.29)
-0.008***
(2.82)
-0.002
(0.10)
0.034
(0.59)
0.003
(0.78)
-0.040***
(7.61)
0.006***
(8.74)
0.077***
(10.93)
22,027
0.03