Coordination Costs, Institutional Investors, and
Firm Value
Jiekun Huang
First version: October 2011
This version: September 2012
Huang is from Department of Finance, National University of Singapore, phone: 65-6516-7159, fax: 65-67792083, e-mail: [email protected]. I thank Yakov Amihud, Bernard Black, Paul Burik, Mark Chen, Abe de
Jong, Yongheng Deng, Bing Liang, Alexander Ljungqvist, Angie Low, Roni Michaely, Vikram Nanda, Je¤rey
Ponti¤, Qianru Qi, David Reeb, Bernard Yeung, Shan Zhao, Luigi Zingales, as well as conference and seminar
participants at the 2012 European Finance Association annual meetings, the 2012 Financial Intermediation
Research Society annual meetings, Rothschild Caesarea Center 9th Annual Academic Conference, the 2012 CGIO
Academic Conference, the 6th Singapore International Conference on Finance Junior Faculty Workshop, Fudan
University, National University of Singapore, Shanghai Advanced Institute of Finance (SAIF), and University of
Adelaide for helpful comments and suggestions. Financial support from CGIO research grant award (N-311-000433-091) is gratefully acknowledged. I retain responsibility for any remaining errors.
Coordination Costs, Institutional Investors, and
Firm Value
Abstract
Coordination costs among institutional investors have an important impact on corporate governance and …rm value. We use two measures to proxy for coordination
costs, one based on the geographic distance among institutional shareholders and
the other based on the correlation in their portfolio allocation decisions. We …nd
that, after controlling for other factors, coordination costs are negatively associated
with …rm value as proxied by industry-adjusted Tobin’s q. We exploit three exogenous shocks, namely, mergers of asset management …rms, the 1992 proxy reform,
and decimalization in 2001, and …nd evidence consistent with a causal e¤ect of coordination costs on …rm value. Furthermore, we 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 corporate governance 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 tiny 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 0:065 (0:198), 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.
To address this potential endogeneity concern, we apply a di¤erence-in-di¤erences approach
that exploits exogenous shocks to shareholder coordination. The …rst exogenous shock we consider
is mergers of asset management …rms, which result in selling of large positions that the target
institution holds and hence an increase in coordination costs for these stocks (treatment sample).
The mergers are reasonably exogenous to the treatment stocks, because these mergers are generally
unrelated to the fundamentals of the treatment stocks and because the exit decision of the merged
institution from the treatment stocks post-merger does not seem to be driven by the fundamentals.
Using a di¤erence-in-di¤erences approach, we …nd that the industry-adjusted Tobin’s q of treatment
stocks decreases relative to control stocks following mergers of asset management …rms, by 0:075.
3
We further show that the increase in coordination costs after the mergers of asset managers is
driven primarily by treatment stocks that have a low pre-merger coordination cost, and that the
decrease in …rm value post-merger is concentrated among these low coordination cost stocks.
The second exogenous shock we consider is the proxy reform in 1992, which eases communication
and coordination among shareholders (Choi, 2000; Bradley, Brav, Goldstein, and Jiang, 2010).
Prior to the reform, shareholders were restricted in their ability to communicate among themselves
information about voting decisions, because it would amount to proxy solicitation and was not
allowed until the shareholders involved deliver a formal proxy statement to other shareholders. The
1992 proxy reform relaxed this communication restriction such that any shareholder communication
not directly seeking the power to vote as proxy for other shareholders would not be considered as
proxy solicitation. This predicts that the e¤ect of our …rm-level coordination cost measures on
…rm value should become stronger post-reform. Consistent with this, we …nd that, compared to
otherwise similar …rms, …rms whose shareholders can coordinate with relative ease experience an
increase in …rm value post-reform. This result suggests that the e¤ects are driven by explicit
communication and coordination, rather than by “implicit” coordination, such as homogeneous
preferences and behavior, among shareholders as in Kandel, Massa, and Simonov (2011).
The third exogenous shock we consider is decimalization in 2001, which signi…cantly reduces
trading costs for stocks (Bessembinder, 2003; Fang, Noe, and Tice, 2009). Because institutional
investors are sensitive to trading costs (e.g., Wermers, 2000; Edelen, Evans, and Kadlec, 2007), a
decrease in trading costs can enable coordinated institutions to more e¤ectively use the threat of
exit as a disciplining device. Consistent with this, we …nd evidence that the impact of coordination
costs on …rm value becomes signi…cantly stronger after decimalization. Furthermore, consistent with
the notion that trading costs drop more signi…cantly for low-priced stocks post-decimalization, a
triple-di¤erences analysis shows that the e¤ect of decimalization on the e¤ectiveness of the threat
of exit by coordinated institutions is stronger for …rms with a low stock price. Taken together,
the results of the di¤erence-in-di¤erences approach provide evidence that coordination costs have
a causal impact on …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
4
…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.2
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
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 (2008) …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
2
Two papers related to ours explore the idea of coordination costs in other settings. Bradley, Brav, Goldstein,
and Jiang (2010) show that the ease of communication among investors of closed-end funds facilitates activist arbitrage. Kandel, Massa, and Simonov (2011) contend that implicit coordination, proxied using age similarity, among
small individual shareholders can play a corporate governance role. However, neither of these papers examines the
coordination cost among institutional investors, which are the dominant shareholders in the U.S.
5
statistics. Section 3 presents the empirical results on the e¤ect of coordination costs on …rm value.
Section 4 presents the empirical results on the impact of coordination costs on corporate governance
mechanisms and outcomes. Section 5 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 104; 204 …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 …rmquarter, 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
6
year. Speci…cally,
Geographic distance among institutional shareholders for …rm c =
2
0
13
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
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.3 The excess portfolio weights are calculated as
3
If two institutions have less than …ve common holdings, we set the correlation to zero. The results are robust to
7
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
1 X 4X
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
831:7 miles.4 The average portfolio correlation among institutional shareholders is 0:29. 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-Ruenzi,
2011), we calculate the weighted-average geographic distance between institutional shareholders
using a di¤erent cuto¤ or setting it to missing.
4
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.
8
and …rms, weighted by institutions’fractional holdings. The average distance between institutional
investors and the …rm is 943:8 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.5 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 two-digit SIC code. The mean and median of
Tobin’s q are 1:82 and 1:29, respectively, and those of industry-adjusted Tobin’s q are 0:36 and
0:02, respectively.
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
0:78, 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 (2005).
Furthermore, industry-adjusted Tobin’s q is signi…cantly negatively correlated with the geographic
distance measure and signi…cantly positively correlated with the portfolio correlation measure.
These results give a preliminary indication that the ease of coordination may improve …rm value.
In addition, we retrieve various corporate governance-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]
5
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).
9
3
Empirical Results: Coordination Costs and Firm Value
In this section, we …rst examine the relation between coordination costs and …rm value using …rm…xed e¤ects regressions. We then address endogeneity concerns by using a di¤erence-in-di¤erences
approach to gauge the impact of a series of exogenous shocks on …rm value.
3.1
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,
Coordination Costs j;t
1, and xi;j;t
1
1
+ "j;t ,
j
(3)
is …rm-…xed e¤ects,
is one of the two measures of coordination costs for …rm j in year t
includes standard control variables for Tobin’s q, such as …rm size, pro…tability,
capital expenditure, leverage, R&D expenses, and year dummies, and other institutional ownership
characteristics, including institutional ownership, institutional ownership concentration, investment
horizons of institutional shareholders, and the distance between institutional shareholders and the
…rm. We include …rm …xed e¤ects to capture unobserved …rm-speci…c time-invariant factors that
in‡uence …rm value. We cluster the standard errors at the …rm level (Petersen, 2009). Since, 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, reported 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 0:065 (0:198). These
10
numbers translate into a 5% to 15% di¤erence in …rm value, based on the median Tobin’s q of 1:29.
Panel A of Table 2 also reveals a number of other interesting …ndings. Institutional ownership
is positively associated with industry-adjusted Tobin’ q, which is consistent with the …ndings of
McConnell and Servaes (1990). Institutional ownership concentration, however, is negatively associated with industry-adjusted Tobin’ q, possibly because high-q …rms attract a more dispersed
shareholder base. The distance between the …rm and institutional shareholders does not signi…cantly a¤ect …rm value. Shareholder turnover is positively related to industry-adjusted Tobin’ q,
which may be because trading by short-term (high turnover) institutional investors improves stock
price e¢ ciency and hence …rm valuation. Furthermore, high-q …rms are generally smaller in asset
size, have more debt, higher R&D intensities, and higher capital expenditures. These results are
broadly consistent with the literature (see, e.g., McConnell and Servaes, 1990; Bebchuk, Cohen,
and Ferrell, 2009).
Since institutions may di¤er in their incentives and abilities to play a governance role, we partition institutional investors into groups in two 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.6 Consistent with our expectation, the negative e¤ects of coordination costs on
6
Note that the sample size for these regressions is slightly smaller, because, to construct the coordination costs
measures for a category of institutions, we require that a …rm have at least one institution in that category.
11
…rm value are driven mainly by independent institutions and by non-transient institutions.
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, board size, board independence (the fraction of independent outside directors
on the board), and CEO/Chairman duality. Because only about 10% of the sample has non-missing
information on these variables, we create two indicator variables: one equal to 1 for observations
with missing information on managerial ownership and the other equal to 1 for observations with
missing information on board characteristics.
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]
We then conduct a series of robustness checks on the relation between coordination costs and
…rm value. First, we control for the e¤ect of local institutions on …rm value by excluding these
institutions in the construction of the coordination costs measures. 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 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 3, show that the e¤ects of coordination costs on …rm value are
qualitatively unchanged, suggesting that the results are not driven by local institutions.
Second, we exclude cities with highly concentrated institutional investors in the construction
of the coordination costs measures. Institutional asset management is highly geographically concentrated. One concern is that the …ndings are driven by a few cities with a high concentration of
12
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 3 show that the results are again
qualitatively unchanged. This …nding suggests that the results are not driven by the two extreme
cities per se.
Third, we exclude 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
2009. 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
institutions, decrease the portfolio correlation among the institutional shareholders of the …rms
foreign institutions invest in. 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 3, are essentially unchanged compared to
the baseline results reported in Table 2, Panel A, which suggests that foreign institutions do not
drive the results.
Fourth, we use OLS regressions with lagged dependent variables. We estimate OLS regressions
adding lagged industry-adjusted Tobin’s q as a control variable. Columns 7 and 8 of Table 3 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.
Last, we use Arellano-Bond dynamic panel regressions. Since …rm value is persistent, we add
lagged industry-adjusted Tobin’s q to Eq. (3) and reestimate the regressions using Arellano-Bond
13
(1991) dynamic panel estimator. The results, reported in the last two columns of Table 3, are
qualitatively similar to our baseline results.
[Insert Table 3 about here]
3.2
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. To address this potential
endogeneity e¤ect, we use a di¤erence-in-di¤erences approach that exploits exogenous shocks to
shareholder coordination.
We consider three exogenous shocks. The …rst is the mergers of asset management companies
which leads to sales of large holdings by the target institution, and the other two are regulatory
changes that can a¤ect shareholder coordination. We use a di¤erence-in-di¤erences estimator to
compare the change in …rm value for the treatment stocks after the shocks to the change for a
control group of stocks that are not a¤ected by the shocks.
3.2.1
Mergers of Asset Management Firms
We use mergers of asset management …rms as an exogenous shock that increases coordination
costs for stocks in which the target institution is a signi…cant shareholder pre-merger. When two
institutions merge, large holdings by the target institution typically get dissolved, resulting in a more
dispersed ownership structure and hence an increased coordination cost among the shareholders
of these stocks, which constitute the treatment group. The change in coordination costs induced
by mergers of institutions is reasonably exogenous to the treatment stocks because of two reasons.
First, these mergers are generally not driven by the performance of the underlying assets of the
target institution but rather by strategic considerations of the acquiring institution such as to
realign product o¤erings and to achieve economies of scale (Jayaraman, Khorana, and Nelling,
2002). Second, as we show below, post-merger sales of the target institution’s large holdings are
14
driven primarily by the merged institution’s portfolio considerations and are largely unrelated to
the fundamentals of the stocks being sold.
We use stocks in which the target institution owns more than 1% of the outstanding shares
pre-merger as our treatment group. We face a trade-o¤ in choosing the cut-o¤ point to classify
treatment stocks. On the one hand, choosing a too low threshold value can add noise to the tests
because of the inclusion of small shareholders, whose trading decisions would have little impact on
the shareholder base and the coordination costs. On the other hand, choosing a too high threshold
value can sigi…cantly reduce the sample size for our treatment group. We choose 1% as the cut-o¤
point to focus on economically relevant shareholders and to obtain a reasonable sample size. The
1% holding is at the 88th percentile of individual institutional ownership over the entire sample
period.
We retrieve mergers among …nancial institutions from SDC’s Mergers and Acquisitions database
and manually match the acquirers and the targets to 13F …les by name. We require that (1) the
merger is announced during the period between 1980 and 2009; (2) the merger is completed within
one year after the initial announcement; (3) the target institution stops …ling 13F forms within
one year after the completion of the deal; (4) the target institution has at least one equity position
that exceeds 1% of the stock’s outstanding shares. After applying the …lters, we identify 127
mergers between asset management …rms. We then partition the sample of stocks into treatment
and control groups. The treatment group consists of stocks in which the target institution is
a signi…cant shareholder before the merger, i.e., the target institution’s holdings in the stock premerger exceed 1% of the outstanding shares of the stock. We identify 5; 292 stocks as the treatment
group. Panel A of Table 4 reports the number of asset management …rm mergers and the treatment
stocks by year in which the mergers are completed. A summary of the top 20 mergers ranked by
the number of treatment stocks is provided in Panel B of the same table. As is seen in the table,
many of the large mergers between institutional asset management …rms are the result of bank
mergers, such as the merger between Bank of America and FleetBoston Financial in 2004 and that
between Chase Manhattan and J.P. Morgan Chase in 2000.
[Insert Table 4 about here]
15
We gauge the impact of the merger on the merged institution’s holdings of treatment stocks by
exploiting variation in trading across institutions in the same stocks. Speci…cally, we identify, for
each pair of merging institutions in a treatment stock, a matched institution as the institution that
remains independent, holds more than 1% of the treatment stock’s outstanding shares, and has the
closest equity portfolio size to the combined portfolio size of the target and the acquirer institution
pre-merger. To the extent that the merged and the matched institutions respond in the same way
to public information about the fundamentals of the treatment stocks, this matching institution
approach e¤ectively eliminates the possibility that sales of the treatment stocks by merged institutions are driven by public news.7 Speci…cally, we compare the changes in the fractional ownership
of the treatment stocks after the merger between merged and matched institutions. We choose
the quarter immediately before the merger announcement as the pre-merger period, and the …rst
quarter-end one year after the merger completion as the post-merger period. Panel A of Table 5
reports the results. Both merged and matched institutions sell their holdings of treatment stocks
post-merger, but merged institutions sell more aggressively. The di¤erence-in-di¤erences estimate
shows that post-merger sales of treatment stocks by merged institutions are 0:67 percentage points
(or 68%) more than that by matched institutions. This result provides evidence that the merger
leads to a more dispersed shareholder base and hence may increase the coordination costs.
Our identi…cation strategy rests on the premise that post-merger sales of the treatment stocks
by the merged institutions are unrelated to the fundamentals of the stocks. While the matching
institution approach can rule out the possibility that public information about the fundamentals
drives the merged institutions’ selling decision, it remains possible that the merged institutions
sell the treatment stocks because of their superior private information about the fundamentals of
the treatment stocks. We test this by evaluating the merged institutions’trade performance postmerger and compare it with that of the matched institutions. In particular, we infer trades in the
treatment stocks made by the merged and the matched institutions in each of the four quarters
post-merger from the quarterly holdings reports. We regress subsequent abnormal returns of the
treatment stocks on trading by the merged institutions, trading by the matched institutions, lagged
holdings by the merged and the matched institutions, and control variables that can in‡uence
7
If anything, this matching strategy may bias against …nding signi…cant di¤erences in the trading of treatment
stocks post-merger between merged and matched institutions, because selling of the treatment stocks by the merged
institution and the ensuing decrease in the monitoring intensity may prompt other institutions to sell.
16
institutional preferences and stock returns (Gompers and Metrick, 2001; Baik, Kang, and Kim,
2010). If the merged institutions do not possess private information, we expect trading by the
merged institutions to be uninformative in predicting subsequent stock performance.8
The results, reported in Panel B of Table 5, show that the merged institutions’ trading in
the treatment stocks does not positively predict subsequent abnormal returns of the stocks. In
contrast, trading by the matched institution seems to be informative, which is consistent with
the general …nding in the literature (see, e.g., Gompers and Metrick, 2001). We conduct F -tests
to examine whether the coe¢ cients on the trading measures across the merged and the matched
institutions are identical. The F -tests reject the null at the 5% level. These results provide support
for the presumption that post-merger sales of treatment stocks by the merged institution are largely
unrelated to the fundamentals and likely driven by portfolio considerations.
[Insert Table 5 about here]
To gauge the impact of mergers of asset management …rms on …rm value, we follow Hong
and Kacperczyk (2010) to construct the control group and the benchmark-adjusted di¤erence-indi¤erences estimator. Speci…cally, the control group consists all the remaining stocks that are
matched to the treatment stocks based on market capitalization, book-to-market ratio, past returns, and institutional ownership. In particular, we …rst sort stocks into terciles based on market
capitalization. Within each size tercile, we then sort stocks into terciles based on book-to-market
ratio. Within each size and book-to-market tercile, we further sort stocks into terciles based on
past 12 month returns. Last, within each of the above portfolios, we further sort stocks into terciles
based on institutional ownership. This sequential sorting results in 108 benchmark portfolios. We
then compare the change in …rm value for treatment stocks around institution mergers against
that for the benchmark portfolios by constructing the following benchmark-adjusted di¤erence-indi¤erences estimator:
8
Note that for mergers of asset management …rms to have an e¤ect on coordination costs and hence on …rm value, it
is not necessary that trading by the merged institutions positively predict subsequent returns because of two reasons.
First, to the extent that the market correctly anticipates the e¤ect of the mergers on the treatment stocks’shareholder
base and the coordination costs, the decline in …rm value can occur even before the merged institutions actually sell
the treatment stocks. Second, in the cross-section of treatment sample, stocks that the merged institutions sell more
patiently (i.e., in smaller trade sizes) should experience the greatest decline in …rm value, because these stocks are
likely to experience the greatest increase in shareholder dispersion and hence the coordination costs. These stocks,
however, do not necessarily experience the largest magnitude of selling by the merged institutions.
17
BDIDi = (qT;i;post
qT;i;pre )
(qC;i;post
qC;i;pre )
where qT;i;post and qT;i;pre are industry-adjusted Tobin’s q for treatment stock i post- and premerger, respectively, and qC;i;post and qC;i;pre are the average industry-adjusted Tobin’s q for the
benchmark portfolios that are matched to stock i post- and pre-merger, respectively. We choose
the …scal year immediately before the merger as the pre-merger period, and the …rst …scal year-end
at least 12 months after the merger completion as the post-merger period. We take the average
of BDID across all treatment stocks to evaluate the average e¤ect. We expect the average BDID
estimator to be negative and signi…cant.
Table 6 reports the results. Panel A shows that treatment and control groups have the same
pre-merger …rm value, as measured using industry-adjusted Tobin’s q. Firm value drops for both
treatment and control groups, possibly due to the trend toward increased industry competition, but
…rm value drops more signi…cantly for treatment stocks. As a result, the di¤erence-in-di¤erences
tests show that the …rm value of treatment stocks decreases relative to control stocks following
mergers of asset management …rms, by 0:075 (or 5:8% based on the median Tobin’s q of 1:29).
We conduct similar di¤erence-in-di¤erences tests for the coordination costs variables. The
results, unreported, suggest that the geographic distance for the treatment sample increases by
3:2% (signi…cant at the 1% level), relative to that for the control sample. The result using the
portfolio correlation measure is in the predicted direction but insigni…cant. A caveat here is that
the geographic distance and the portfolio correlation measures capture the coordination costs among
institutional shareholders and do not re‡ect the overall coordination costs, i.e., those among all
shareholders. Because the merged institution may sell the treatment stocks to individual investors
with whom coordination may be particularly costly, the above estimated magnitude of changes in
the coordination costs induced by the mergers should be considered as a lower bound of the true
e¤ects.
We conduct further tests by conditioning the di¤erence-in-di¤erences tests on pre-merger coordination costs. When a …rm has a large, widely dispersed shareholder base, i.e., when the
coordination cost among shareholders is high, the exit decision of one institutional shareholder
18
would have a relatively muted impact on the shareholder base and hence the coordination costs.
In contrast, for …rms with a single shareholder or a closely-knit group of shareholders, exit by
one shareholder would have a greater impact on the coordination costs provided that the block
is broken up and sold in pieces.9 We test this by partitioning the treatment group into a high
and low coordination cost subsamples. We sort the treatment stocks into quartiles based on the
pre-merger coordination costs. The low coordination cost subsample consists of treatment stocks
that are in the bottom quartile of geographic distance or in the top quartile of portfolio correlation,
and the high coordination cost subsample consists of the remaining treatment stocks. Panel B of
Table 6 shows that the coordination costs increase signi…cantly for low coordination cost group.
For instance, the geographic distance among institutional shareholders of the treatment stocks with
low pre-merger coordination costs increases relative to control stocks, by 10:2%, and that for the
treatment stocks with high pre-merger coordination costs decreases relative to control stocks, by
4:9%.10 The triple-di¤erences estimate is highly signi…cant. The results for the portfolio correlation
variable are similar.
Because mergers of asset management …rms increase coordination costs more for treatment
stocks with a low pre-merger coordination cost, the e¤ect of the mergers on …rm value should be
driven mainly by these stocks. The results, reported in the same panel, show that the di¤erencein-di¤erences estimate for the low coordination cost group is
whereas that for the high coordination cost group is
0:103 and statistically signi…cant,
0:042 and insigni…cant. The triple-di¤erences
estimate is economically large ( 0:061), but statistically insigni…cant because of high standard
errors associated with the high coordination cost group. These results provide further evidence
that mergers of asset management …rms have an e¤ect on …rm value through coordination costs.
[Insert Table 6 about here]
9
It is possible that the large shareholder sells the entire block to another shareholder, in which case the coordination
costs would not change. Doing so, however, would entail large transaction costs for the selling shareholder (Holthausen,
Leftwich, and Mayers, 1987). Thus, it is more likely that the large shareholder breaks up the block and sell it in
small trade sizes to minimize price impacts.
10
Note that a decrease in the geographic distance among institutions for the treatment stocks with high pre-merger
coordination costs does not necessarily imply a decrease in overall coordination costs for these stocks. Consider
the case in which the merged institution sells its holdings of the treatment stocks to dispersed individual investors
and all other institutions do not change their holdings in the treatment stocks. The coordination costs for the
remaining institutional shareholders can decrease, but those for all shareholders can actually increase because it can
be particularly di¢ cult to coordinate with individual investors.
19
The key identifying assumption for the di¤erence-in-di¤erences approach is that any trends in
outcomes for the treatment and control samples before the treatment are the same (the “parallel
trends” assumption). Figure 2 plots the average industry-adjusted Tobin’s q for treatment and
control groups from three years prior to the merger to three years after the completion of the
merger.11 The …rm value of treatment and control stocks exhibits almost identical trends before
the merger, suggesting that our results are not driven by the pre-merger trends.
Furthermore, the post-merger trend in the …rm value of treatment and control groups suggests
that our results are not driven by a “price pressure” e¤ect, in which the selling institution exerts
temporary downward price pressure through heavy sales of the treatment stocks over a short period
of time. In particular, the reduced valuation associated with treatment stocks persists at least
three years after the merger event. If the result were driven by the merged institutions’ selling
pressure, one would expect the downward pattern in the …rm value of treatment stocks to reverse
in the subsequent year (Coval and Sta¤ord, 2007). Intuitively, since the merged institutions are
generally not liquidity constrained, they are likely to sell the treatment stocks in piecemeal fashion
over time to minimize price impacts. It is through this gradual sale of treatment stocks by the
merged institutions that the shareholder base of these stocks becomes more dispersed and hence
the coordination cost among the shareholders increases.
[Insert Figure 2 about here]
3.2.2
The E¤ect of the 1992 Proxy Reform
We 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
11
The sample size of the treatment group in this test is 3; 676, because we require that the treatment stocks and
the respective control groups have non-missing data on industry-adjusted Tobin’s q for all six years surrounding the
mergers.
20
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 …rm-level coordination costs on …rm value should be particularly strong in the post-reform
period, because institutions, regardless of their geographical proximity and portfolio correlations,
are restricted in their ability to conduct coordinated actions in the pre-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 quartiles based on each of the coordination costs proxies in
year t 2. We de…ne treatment stocks as those that fall in the bottom quartile of geographic distance
or in the top quartile of portfolio correlation. All the remaining stocks constitute the control group.
Intuitively, the reform signi…cantly reduces the restrictions on shareholder coordination, thereby
enabling institutional shareholders with low coordination costs to conduct coordinated monitoring
activities. 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. 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.
Panel A of Table 7 presents the results of univariate di¤erence-in-di¤erences comparisons in
industry-adjusted Tobin’s q between treatment and control …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 treatment …rms experience an increase of 0:069 in industry-
21
adjusted Tobin’s q, compared to a decrease of 0:087 for control …rms. The di¤erence in the change
in industry-adjusted Tobin’s q between the two groups is 0:155, which is statistically signi…cant
and economically large compared to the median Tobin’s q of 1:29. These results are consistent with
the notion that the proxy reform reduces barriers to shareholder coordination and enhances …rm
value especially for …rms whose shareholders are more likely to coordinate. These results also rule
out the possibility that our coordination costs measures merely capture “implicit” coordination,
such as homogeneous preferences and behavior, among shareholders as in Kandel, Massa, and
Simonov (2011). Because the proxy reform only a¤ected shareholders’ ability to conduct explicit
communication and coordination, one would expect the di¤erence-in-di¤erences estimate to be zero
if the results were driven solely by implicit coordination.
To control for the e¤ects of other factors that may a¤ect …rm value, we estimate the following
multivariate di¤erence-in-di¤erences regressions on the two-year sample around the reform:
qj =
+
1 Coordination
Costs j
Post-reform
+
2 Coordination
Costs j +
3 Post-reform
+
X
i xi;j
+ "j , (5)
where q j is …rm j’s industry-adjusted Tobin’s q; Coordination Costs j is one of the two measures
of coordination costs for …rm j measured in year t
2; Post-reform is an indicator variable which
equals one for observations after October 1992, and zero otherwise; and xi;j includes the same set of
control variables as in Eq. (3). The coe¢ cient on the interaction term, Coordination Costs j Postreform, 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 B of Table 7 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. These results
indicate a causal e¤ect of coordination costs on …rm value.
[Insert Table 7 about here]
22
3.2.3
The E¤ect of Decimalization
The third exogenous shock is decimalization, which increases stock market liquidity and in turn can
intensify the disciplinary e¤ect of the coordinated threat of exit. 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. Other things equal, …rms whose institutional
shareholders face lower coordination costs should be more likely to coordinate and use the threat
of exit (i.e., coordinated sales of stocks) to discipline corporate managers post-decimalization, 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.
Decimalization in 2001 reduced the minimum tick size for the stock markets in the U.S. from
a sixteenth of a dollar to one cent, and led to signi…cant drops in bid-ask spreads (Bessembinder,
2003). The decrease in transaction costs post-decimalization can enhace the e¤ectiveness of the
threat of coordinated exit by institutions.12 This arises because transaction costs are an important
consideration for asset managers. For instance, Wermers (2000) …nds that transaction costs account
for 80 basis points of the 2:3 percentage point di¤erence between mutual funds’gross returns and
net returns. A decrease in transaction costs should therefore make coordinated sales by institutions
a more credible threat.
We use a di¤erence-in-di¤erences approach to examine the impact of decimalization on the
e¤ectiveness of the threat of exit by coordinated institutions. We use a two-year window and
de…ne the …scal year in which decimalization occurred as year t. We choose year t
2 for the pre-
decimalization period, and year t as the post-decimalization period. The results are qualitatively
similar if we instead use year t
1 as the pre-decimalization period. In addition, we require that
each stock be present in both windows around decimalization.
12
There is a continuing debate over whether decimalization increases or decreases trading costs for institutional
investors. For instance, Bollen and Busse (2006) compare actual mutual fund returns with the returns of a synthetic
benchmark portfolio that matches the mutual fund’s holdings and …nd an increase in mutual fund trading costs postdecimalization. In contrast, Chakravarty, Panchapagesan, and Wood (2005) use data on actual institutional trades
and …nd a decrease in institutional trading costs following decimalization. It therefore remains an empirical question.
Our tests can be viewed as a joint test that decimalization was perceived by the market as reducing institutional
trading costs and that reduced trading costs enhance the e¤ectiveness of the threat of exit.
23
We divide the sample of stocks into quartiles based on each of the coordination costs proxies in
year t 2. We de…ne treatment stocks as those that fall in the bottom quartile of geographic distance
or in the top quartile of portfolio correlation. All the remaining stocks constitute the control
group. Since decimalization signi…cantly reduces trading costs, a closely-knit group of institutional
investors can more e¤ectively use the threat of exit as a disciplining device, thereby improving …rm
value. In contrast, decimalization should have little impact on …rms whose institutional shareholders
face prohibitively high coordination costs, because the institutions are likely to remain passive postdecimalization.
Panel A of Table 8 presents the results of univariate di¤erence-in-di¤erences comparisons in
industry-adjusted Tobin’s q between treatment and control …rms before and after decimalization.
The di¤erence-in-di¤erences estimator indicates a large increase in industry-adjusted Tobin’s q for
treatment …rms relative to control …rms post-decimalization. In particular, Panel A shows that
both treatment and control …rms experience a decline in …rm value post-decimalization, but the
decline in the …rm value of treatment stocks is signi…cantly smaller than that for control stocks.
The di¤erence in the change in industry-adjusted Tobin’s q between the two groups is 0:30, which
is both statistically and economically signi…cant.
We then estimate multivariate di¤erence-in-di¤erences regressions on the two-year sample around
decimalization. Similar to Eq. (5), 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.
Panel B of Table 8 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 statistically signi…cant. These results suggest that the threat of
exit is one of the channels through which coordinated institutional monitoring a¤ects …rm value.
Since decimalization reduces trading costs for low-priced stocks more than those for high-priced
stocks, the increase in …rm value post-decimalization due to a low coordination cost should be
stronger among …rms with a low stock price. We thus conduct the following triple-di¤erences tests
24
around decimalization:
qj =
+
+
+
1 Coordination
2 Coordination
4 Post-reform
Costs j
Costs j
Post-decimalization
Post-decimalization +
Price j
3 Coordination
Costs j
Price j
X
Price j + 5 Coordination Costs j + 6 decimalization + 7 Price j +
i xi;j + "j ,
(6)
where q j is …rm j’s industry-adjusted Tobin’s q; Coordination Costs j is one of the two measures
of coordination costs for …rm j in year t
2; Post-decimalization is an indicator variable which
equals one for observations after January 2001, and zero otherwise; Price j is the stock price of
…rm j at the end of year t
2; and xi;j includes the same set of control variables as in Eq. (3).
The triple interaction term, Coordination Costs j
P ost-decimalization
P rice j , exploits variation
in coordination costs and variation in stock prices to examine the e¤ect of decimalization on …rm
value.
Panel C of Table 8 presents the results of the triple-di¤erences regressions. The coe¢ cients
on the triple-interaction term are all in the predicted direction and statistically signi…cant. These
results provide evidence that decimalization enhances the e¤ectiveness of the threat of exit by
coordinated institutions particularly for low-priced stocks.
[Insert Table 8 about here]
In sum, the evidence from the di¤erence-in-di¤erences approach exploiting exogenous shocks
suggests that coordination costs among institutions have a causal e¤ect on …rm value.
4
Empirical Results: 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.
25
4.1
Anti-takeover Provisions
Coordinated institutional investors may enhance …rm value by increasing the takeover vulnerability
of their portfolio …rms. 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; Bebchuk, 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 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 Bebchuk, 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 ,
(7)
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 9 reports the results. In all 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 0:83 (3:90), as compared to the median G-index of
26
9. These results suggest that the ease of coordination enables institutional shareholders to play a
stronger corporate governance role by removing barriers to takeovers.
[Insert Table 9 about here]
4.2
Equity-based Incentives
The second governance mechanism is through equity-based compensation. An extensive literature
suggests that equity-based pay for corporate managers can improve …rm performance because of better alignment of manager and shareholder interests (e.g., Mehran, 1995). Hartzell and Starks (2003)
suggests that institutional investors can enhance the pay-for-performance sensitivity of managers
through increased monitoring. We hypothesize that institutional investors, through coordinated
monitoring, can improve corporate governance and …rm value 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
(8)
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. (7). Table 10 reports the results. In three
out of four speci…cations, the coe¢ cients on the coordination costs variables are signi…cant and
27
have the predicted signs. In terms of economic magnitude, for instance, all else equal, moving
from the 10th to the 90th percentiles in the geographic distance (portfolio correlation) variable
decreases (increases) the incentive ratio by 0:11 (0:09), compared to the median incentive ratio
of 0:23. These results suggest that a low coordination cost enables institutional shareholders to
in‡uence compensation policies, which help better align the interests of shareholders and corporate
managers.
[Insert Table 10 about here]
4.3
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.
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; 600 (11:8%) CEO turnover events out of 21; 997 …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 ,
(9)
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
28
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 11, show that the interaction between
the geographic distance variable and stock return has a positive and signi…cant (at the 1% level)
coe¢ cient, which suggests that the ease of coordination improves the CEO turnover-performance
sensitivity. The result using the portfolio correlation variable is in the predicted direction but
insigni…cant. We then create a low coordination cost dummy variable that equals one if the geographic distance among institutions is in the bottom quartile or the portfolio correlation among
institutions is in the top quartile, and zero otherwise. We replace the coordination costs variable
with the indicator variable and re-estimate Eq. (9). The results, reported in column 3 of Table
11, show that the coe¢ cient on the interaction term is signi…cant and in the expected direction.
In terms of economic magnitude, for a one-standard-deviation decrease in stock returns, CEOs of
…rms with low coordination costs are 9:4% more likely to be replaced than those of …rms with
high coordination costs. The magnitude of this e¤ect is large, considering that the unconditional
probability of CEO turnover during 1993 to 2009 is 11:8%. The results lend support to the prediction that the ease of coordination among institutional shareholders is associated with an increased
propensity to terminate poorly performing CEOs.
[Insert Table 11 about here]
5
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
29
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 a di¤erence-in-di¤erences approach that exploits
three exogenous shocks to shareholder coordination, namely mergers of asset management …rms,
the 1992 proxy reform, and the 2001 decimalization. We …nd evidence that these shocks have
an impact on …rm value through coordination costs. Taken together, these results provide strong
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
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.
Because high coordination costs impair the monitoring role provided by institutional shareholders, a policy implication of this research is that removing regulatory restrictions that limit
shareholders’ ability to coordinate can improve the e¤ectiveness of institutional monitoring as a
corporate governance mechanism. For instance, under Rule 13D of the Securities Exchange Act, if
two or more institutions collectively holding 5% of a company’s shares agree to act in concert to in‡uence corporate decisions, the institutions are considered a group and hence are subject to stricter
reporting requirements. Survey evidence of McCahery, Sautner, and Starks (2010) shows that such
legal concerns are the major barrier to coordination among institutions. The results in this paper
suggest that it is important to adapt the regulation to the increasing need for coordination.
30
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33
Figure 1: Aggregate institutional ownership and individual institutional stake during the sample
period from 1980 to 2009
0.16%
70%
0.12%
60%
0.08%
50%
0.04%
40%
Aggregate IO (left scale)
Individual IO (right scale)
30%
1980
1985
1990
1995
2000
0.00%
2005
The dark line, on the left scale, indicates the total dollar value of institutional equity holdings as a
fraction of the total market capitalization of all common stocks listed on NYSE, AMEX, and NASDAQ
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 over the same period.
Figure 2: Trends of firm value in the treatment and control groups around mergers of asset
management firms
The grey (dark) line indicates the average industry-adjusted Tobin’s q for treatment (control)
stocks. We track the firm value of the treatment and control stocks through time from three years
before the announcement of the merger of institutions to three years after the completion of the
merger.
0.5
Control
Treatment
0.4
0.3
0.2
0.1
-3
-2
-1
+1
+2
+3
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 two-digit SIC code. 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 weightedaverage 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
104,204 831.66
104,204 6.29
104,204 0.29
104,204
104,204
Institutional shareholder characteristics
Inst. ownership
104,204
Inst. ownership concentration (×100)
104,204
Log(1 + Shareholder-firm distance)
104,204
Shareholder turnover
104,204
Firm-level controls
Return on assets
104,204
Firm size
104,204
Book leverage
104,204
R&D/Sales
104,204
Asset tangibility
104,204
CapEx/Assets
104,204
Number of business segments
104,204
Governance structure and managerial ownership
G-Index
23,386
E-Index
23,614
Board size
16,696
Board independence
16,696
CEO/Chairman duality
16,696
Managerial stock ownership
14,226
CEO Compensation structure and turnover
CEO incentive ratio
23,905
CEO option fraction
24,859
CEO turnover
21,997
Std dev Median
10th
90th
448.37
1.53
0.24
840.95
6.74
0.19
170.28 1395.32
5.14
7.24
0.10
0.68
1.82
0.36
1.63
1.54
1.29
-0.02
0.86
-0.62
3.24
1.58
0.33
1.17
6.48
0.27
0.27
1.08
0.97
0.11
0.28
0.93
6.61
0.26
0.03
0.03
5.23
0.16
0.73
2.74
7.63
0.39
0.09
5.22
0.51
0.17
0.29
0.06
3.32
0.28
2.13
0.27
1.07
0.24
0.07
3.66
0.12
5.07
0.49
0.00
0.23
0.04
2.00
-0.10
2.59
0.18
0.00
0.03
0.01
1.00
0.29
8.07
0.86
0.16
0.68
0.14
9.00
9.04
2.13
9.35
0.66
0.56
0.07
2.74
1.37
2.64
0.18
0.50
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)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(19)
(1) Log(1+ Shareholder distance) 1.000
(2) Shareholder portf. correlation -0.780 1.000
(3) Industry-adjusted Tobin’s q
-0.019 0.035 1.000
(4) Inst. ownership
0.426 -0.618 -0.028 1.000
(5) Inst. ownership concentration 0.347 -0.448 -0.069 0.853 1.000
(6) Log(1 + Shrhldr-firm distance) 0.092 0.010 0.042 -0.056 -0.037 1.000
(7) Shareholder turnover
0.034 -0.044 0.078 0.119 0.124 0.014 1.000
(8) Return on assets
0.066 -0.155 -0.204 0.198 0.115 -0.056 -0.037 1.000
(9) Firm size
0.424 -0.624 -0.171 0.637 0.377 -0.111 -0.027 0.236 1.000
(10) Book leverage
-0.034 0.049 -0.006 -0.061 -0.054 -0.058 -0.020 -0.115 0.211 1.000
(11) R&D/Sales
0.036 -0.007 0.192 -0.031 -0.009 0.043 0.096 -0.380 -0.117 -0.055 1.000
(12) Asset tangibility
-0.030 -0.041 -0.094 -0.026 -0.072 -0.010 -0.093 0.138 0.147 0.051 -0.104 1.000
(13) CapEx/Assets
-0.051 0.011 0.027 -0.023 -0.044 0.037 0.037 0.117 -0.034 -0.048 -0.049 0.580 1.000
(14) Log # of business segments
0.215 -0.185 -0.041 0.320 0.261 -0.039 0.045 -0.003 0.318 0.018 0.004 -0.035 -0.105 1.000
(15) G-Index
0.010 -0.194 -0.085 0.119 0.023 -0.114 -0.104 -0.011 0.149 0.120 -0.042 0.065 -0.031 0.112 1.000
(16) E-Index
0.088 -0.110 -0.100 0.191 0.140 -0.012 0.008 -0.042 0.037 0.084 -0.004 0.031 -0.018 0.125 0.726 1.000
(17) CEO incentive ratio
0.029 0.010 0.304 -0.018 -0.095 0.007 0.048 0.161 0.060 -0.151 -0.011 -0.110 0.054 -0.036 -0.152 -0.177 1.000
(18) CEO option fraction
0.006 0.027 0.169 0.094 0.024 0.065 0.141 0.064 -0.003 -0.103 0.078 -0.108 0.042 -0.025 -0.020 -0.001 0.070 1.000
(19) CEO turnover
0.009 0.008 -0.029 -0.028 -0.004 -0.004 -0.008 -0.061 0.009 0.033 0.007 -0.001 -0.012 0.013 0.014 0.014 -0.175 0.074 1.000
Table 2: Regression analysis of the relation between coordination costs and firm value: 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. Managerial ownership (Board) missing dummy is an indicator variable
that equal one if managerial ownership (board characteristics) data is not available, and zero otherwise. See
Table 1 for the definition of the variables. Year fixed effects and firm fixed effects are included in all
regressions. The coefficients for other firm and institutional ownership controls are not reported to conserve
space. 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)
-0.069***
(10.21)
Shareholder portfolio correlation
Inst. ownership
Inst. ownership concentration
Log(1 + Shareholder-firm distance)
Shareholder turnover
0.140*
(1.69)
-10.913***
(8.35)
0.006
(0.60)
0.576***
(6.49)
Return on assets
Firm size
Book leverage
R&D/Sales
Asset tangibility
CapEx/Assets
Log number of business segments
Constant
Observations
Adjusted R-squared
0.660***
(8.89)
104,204
0.46
Industry-adjusted Tobin’s q
(2)
(3)
-0.031***
(4.82)
0.810***
(13.64)
0.412***
1.748***
(4.87)
(18.51)
-13.591***
-26.420***
(10.36)
(19.12)
0.001
-0.005
(0.08)
(0.48)
0.615***
0.395***
(6.88)
(4.75)
0.009
(0.12)
-0.535***
(27.72)
0.592***
(11.55)
0.048***
(2.68)
-0.441***
(4.81)
0.794***
(6.57)
0.020
(1.27)
-0.084
2.446***
(1.09)
(21.52)
104,204
104,204
0.46
0.49
(4)
0.341***
(6.08)
1.834***
(19.13)
-27.279***
(19.61)
-0.007
(0.73)
0.414***
(4.97)
0.007
(0.09)
-0.526***
(27.13)
0.583***
(11.32)
0.049***
(2.72)
-0.432***
(4.69)
0.814***
(6.75)
0.019
(1.24)
2.092***
(17.32)
104,204
0.49
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)
-0.028***
(4.37)
Shareholder portfolio correlation
Shareholder distance: Independent
Shareholder distance: Grey
Portfolio correlation: Independent
Portfolio correlation: Grey
Shareholder distance: Non-transient
Shareholder distance: Transient
Portfolio correlation: Non-transient
Portfolio correlation: Transient
Managerial ownership
(6)
0.314***
(5.63)
-0.017***
(3.11)
-0.010**
(2.01)
0.255***
(4.96)
0.118**
(2.55)
-0.045***
(5.87)
0.013***
(3.53)
0.410***
(6.95)
-0.144***
(3.55)
1.077***
(4.37)
Managerial ownership squared
-0.203***
(4.24)
Board size
0.036***
(5.10)
Board independence
-0.055
(0.59)
CEO/Chairman duality
-0.030
(1.04)
Managerial ownership missing dummy
-0.124***
(4.78)
Board characteristics missing dummy
0.260***
(2.66)
Constant
2.468*** 2.067*** 2.477*** 2.197*** 2.342***
(20.68)
(15.52)
(17.82)
(14.95)
(15.02)
Other firm and inst. ownership controls
Yes
Yes
Yes
Yes
Yes
Observations
98,141
98,141
98,439
98,439
104,204
Adjusted R-squared
0.49
0.50
0.49
0.49
0.49
1.090***
(4.42)
-0.207***
(4.31)
0.035***
(4.95)
-0.076
(0.81)
-0.028
(0.96)
-0.120***
(4.63)
0.243**
(2.49)
2.029***
(12.61)
Yes
104,204
0.49
Table 3: Regression analysis of the relation between coordination costs and firm value: Robustness checks
This table presents the robustness checks on the relation between coordination costs and firm value. The dependent variable is industryadjusted Tobin’s q in year t. 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 lagged industry adjusted Tobin’s q as an additional regressor. Columns 9
and 10 report the results of Arellano-Bond dynamic panel regressions with lagged industry adjusted Tobin’s q. Year fixed effects are included in
all regressions. Firm fixed effects are included in all specifications except in columns (7) and (8). 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.
Dep = Industry-adjusted Tobin’s q
Log(1 + Shareholder distance)
Shareholder portfolio correlation
Excluding local
institutions
(1)
(2)
-0.027***
(4.38)
0.319***
(6.07)
Excluding NYC and
Boston
(3)
(4)
-0.021***
(3.89)
0.230***
(4.60)
Excluding foreign
institutions
(5)
(6)
-0.025***
(3.57)
0.255***
(4.60)
Yes
Yes
Yes
103,050
0.45
Yes
Yes
Yes
104,266
0.45
Industry-adjusted Tobin’s q, t−1
Other firm & inst. ownership controls
Yes
Firm FEs
Yes
Year FEs
Yes
Observations
103,880
Adjusted R-squared
0.45
Yes
Yes
Yes
94,152
0.45
Yes
Yes
Yes
103,050
0.45
Yes
Yes
Yes
104,266
0.45
OLS with lagged
Arellano-Bond dynamic
dependent variable
panel GMM
(7)
(8)
(9)
(10)
-0.029***
-0.039***
(5.38)
(4.39)
0.271***
0.382***
(6.04)
(5.00)
0.315***
0.314***
0.375***
0.372***
(24.74)
(24.69)
(21.48)
(21.34)
Yes
Yes
Yes
Yes
No
No
Yes
Yes
Yes
Yes
Yes
Yes
104,330
104,330
90,361
90,361
0.51
0.51
Table 4: Summary statistics for mergers of asset management firms
This table presents summary statistics for mergers of asset management firms. Panel A reports the number
of mergers of asset management firms and treatment stocks by year in which the mergers become effective.
Panel B summarizes the top 20 mergers of asset management firms in terms of the number of treatment
stocks.
Panel A: Mergers of asset management firms and treatment stocks by year
Year
1981
1982
1983
1984
1985
1986
1987
1988
1989
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
All years
# of mergers
Percent of mergers
3
1
0
1
4
6
2
3
3
2
3
7
6
7
4
10
7
10
10
9
8
2
2
4
4
1
4
4
127
2.36%
0.79%
0.00%
0.79%
3.15%
4.72%
1.57%
2.36%
2.36%
1.57%
2.36%
5.51%
4.72%
5.51%
3.15%
7.87%
5.51%
7.87%
7.87%
7.09%
6.30%
1.57%
1.57%
3.15%
3.15%
0.79%
3.15%
3.15%
100.00%
# of treatment stocks
29
3
0
43
36
30
41
82
14
7
98
211
602
307
115
633
273
301
569
452
254
2
110
473
451
35
18
103
5,292
Percent of treatment
stocks
0.55%
0.06%
0.00%
0.81%
0.68%
0.57%
0.77%
1.55%
0.26%
0.13%
1.85%
3.99%
11.38%
5.80%
2.17%
11.96%
5.16%
5.69%
10.75%
8.54%
4.80%
0.04%
2.08%
8.94%
8.52%
0.66%
0.34%
1.95%
100.00%
Panel B: Top 20 mergers of asset management firms ranked by the number of treatment stocks
Announcement
Rank Target institution
Acquirer institution
date
1 Rosenberg Inst. Eq. Management
Equitable Companies Inc.
10/26/1998
2 FleetBoston Corp
Bank of America Corporation
10/27/2003
3 Chase Manhattan Corp
J.P. Morgan Chase & Co.
9/13/2000
4 Shearson Lehman Brothers
Smith Barney
3/9/1993
5 Alliance Capital Management
Equitable Companies Inc.
11/23/1992
6 State Street Research & Management BlackRock Inc.
8/26/2004
7 Strong Capital Management
Wells Fargo & (Norwest Corp)
5/26/2004
8 Sanford C. Bernstein & Co Inc.
AXA Financial, Inc.
6/20/2000
9 Warburg Pincus Asset Management Credit Suisse Asset Management 2/15/1999
10 Bank One Corporation
J.P. Morgan Chase & Co.
1/14/2004
11 Van Kampen American Capital
Morgan Stanley Group Inc.
6/24/1996
12 Evergreen Asset Management
First Union Corporation
3/6/1994
13 Boston Company Inc.
Mellon Bank Corporation
9/14/1992
14 U S Bancorp
First Bank System Inc.
3/20/1997
15 Miller Anderson & Sherrerd
Morgan Stanley Group Inc.
6/28/1995
16 Heine Securities Corp
Franklin Resources Inc.
6/25/1996
17 Citicorp
Travelers Inc.
4/6/1998
18 Oppenheimer & Co LP
PIMCO Advisors LP
11/5/1997
19 Shawmut National Corp
Fleet Financial Group Inc.
2/21/1995
20 Dreyfus Corp
Mellon Bank Corporation
12/6/1993
# of
Effective treatment
date
stocks
1/3/1999
354
4/1/2004
350
12/31/2000
253
7/30/1993
243
7/22/1993
239
1/31/2005
239
1/3/2005
173
10/2/2000
166
7/6/1999
147
7/1/2004
132
10/31/1996
130
6/30/1994
126
5/21/1993
122
8/1/1997
118
1/3/1996
115
11/1/1996
113
10/8/1998
111
12/1/1997
111
11/30/1995
109
8/24/1994
101
Table 5: Changes in the holdings of treatment stocks around mergers of asset management firms
This table presents tests of post-merger sales of treatment stocks. Panel A reports the results of
univariate difference-in-differences comparisons in the fractional ownership of treatment stocks between
merged institutions and matched institutions before and after institution mergers. For each target
institution in a treatment stock, we identify a matched institution as the institution that holds more than 1%
of the treatment stock’s outstanding shares and has the closest equity portfolio size to the combined
portfolio size of the target and the acquirer institution pre-merger. We choose the quarter immediately
before the merger announcement as the pre-merger period, and the first quarter-end 12 months after the
merger completion as the post-merger period. Panel B presents regression analysis of the
informativeness of post-merger trading in treatment stocks by merged institutions and matched
institutions. The dependent variables are buy-and-hold (in the first two columns) and cumulative (in the
last two columns) abnormal returns calculated using the Fama-French-Carhart four-factor model. We
consider two holding periods, a three-month and a six-month holding period, both from the start of
quarter t+1. ∆Ownership by merged (matched) institutions is the change in the fractional ownership of the
treatment stocks by the merged (matched) institutions from quarter t−1 to t. Ownership by merged
(matched) institutions is the fractional ownership of the treatment stocks by the merged (matched)
institutions at the end of quarter t−1. Log market capitalization is the logarithm of market capitalization
at the end of quarter t. Book-to-market is the ratio of book value of equity in the most recent fiscal year to
market capitalization. Return volatility is the variance of monthly returns over the previous two years.
Turnover is the average monthly turnover ratio in quarter t−1. Stock price is the share price at the end of
quarter t. S&P 500 is an indicator variable that equals one if the stock is included in the S&P 500 index at
the end of quarter t, and zero otherwise. Stock return, t is the market-adjusted return of the stock during
quarter t. Stock return, t−3 to t−1 is the market-adjusted return of the stock during the three quarters
prior to the start of quarter t. Dividend yield is cash dividends in the most recent fiscal year divided by
market capitalization. In all regressions, we include quarter fixed effects and cluster the standard errors
by stock. The last row in Panel B reports the F-statistics for the null hypothesis that the coefficient on
∆Ownership by merged institutions and that on ∆Ownership by matched institutions are identical. Numbers
in parentheses are robust t-statistics. Significance on a 10% (*), 5% (**), or 1% level (***) is indicated.
Panel A: Holdings of treatment stocks by merged institutions and matched institutions around mergers
Merged institutions
Pre-merger
3.23%
Post-merger
1.58%
Matched institutions
2.98%
2.00%
Difference (Merged − Matched)
0.25%***
(5.50)
-0.42%***
(9.02)
Difference (Post − Pre)
-1.65%***
(50.48)
-0.98%***
(30.92)
-0.67%***
(14.82)
Panel B: Regression analysis of the informativeness of post-merger trading in treatment stocks by merged
and matched institutions
Dependent variable =
Buy-and-hold abnormal returns
3-mths ahead 6-mths ahead
(1)
(2)
∆Ownership by merged institutions (b1)
-0.286
-0.285
(1.50)
(0.97)
∆Ownership by matched institutions (b2)
0.358
0.690**
(1.43)
(2.00)
Ownership by merged institutions (t−1)
0.056
0.171
(0.70)
(1.14)
Ownership by matched institutions (t−1)
0.114
0.181
(1.50)
(1.27)
Log market capitalization
-0.006***
-0.013***
(3.07)
(3.38)
Book-to-market
0.030***
0.067***
(4.47)
(5.40)
Return volatility
-0.395***
-0.762***
(4.02)
(4.38)
Turnover
-0.075***
-0.171***
(3.60)
(4.77)
Stock price
-0.000
-0.000
(0.66)
(0.92)
S&P 500
0.030***
0.059***
(5.41)
(5.64)
Stock return (t)
-0.020*
-0.035**
(1.87)
(2.20)
Stock return (t−3 to t−1)
-0.004
-0.021**
(0.92)
(2.15)
Dividend yield
0.331***
0.748***
(2.95)
(3.35)
Constant
0.006
0.013
(0.37)
(0.44)
Observations
16,677
16,677
Adjusted R-squared
0.03
0.05
F-test of b1 = b2
4.06**
4.50**
CARs
3-mths ahead 6-mths ahead
(3)
(4)
-0.367*
-0.299
(1.85)
(0.99)
0.374
0.695**
(1.47)
(1.99)
0.081
0.173
(1.01)
(1.16)
0.124
0.215
(1.60)
(1.50)
-0.007***
-0.016***
(3.25)
(3.96)
0.028***
0.059***
(3.90)
(4.53)
-0.173
-0.167
(1.61)
(0.82)
-0.054***
-0.131***
(2.63)
(3.69)
-0.000
-0.000
(0.75)
(0.90)
0.030***
0.065***
(5.25)
(6.08)
-0.027**
-0.050***
(2.33)
(3.01)
-0.003
-0.017*
(0.58)
(1.78)
0.277**
0.592***
(2.33)
(2.75)
0.010
0.036
(0.65)
(1.21)
16,677
16,677
0.02
0.04
5.05**
4.56**
Table 6: Difference-in-differences tests exploiting mergers of asset management firms as natural
experiments
This table presents results of the difference-in-differences tests of the impact of mergers of asset
management firms on the relation between coordination costs and firm valuation. The treatment group
consists of stocks in which the target institution is a significant shareholder before the merger, i.e., the
target institution’s holdings in the stock pre-merger exceed 1% of the outstanding shares of the stock.
The control group consists of all the remaining stocks that are matched to the treatment stocks based on
market capitalization, book-to-market ratio, past returns, and institutional ownership. We use a two-year
window and choose the fiscal year immediately before the merger as the pre-merger period, and the first
fiscal year-end at least 12 months after the merger completion as the post-merger period. We require that
each stock be present in both windows around the merger. Panel A presents the results of univariate
difference-in-differences comparisons in industry-adjusted Tobin’s q between treatment and control
firms before and after institution mergers. Panel B presents the results of the difference-in-differences
tests conditioning on the pre-merger coordination cost of the treatment stocks. Low coordination cost
stocks are stocks in the bottom quartile of geographic distance or in the top quartile of portfolio
correlation, and the rest are classified as High coordination cost stocks. Numbers in parentheses are tstatistics. Significance on a 10% (*), 5% (**), or 1% level (***) is indicated.
Panel A: Difference-in-differences tests of industry-adjusted Tobin’s q
Treatment
Control
Difference (Treatment − Control)
Pre-merger
0.495
0.496
-0.001
(0.04)
Post-merger
0.242
0.318
-0.076***
(3.44)
Difference (Post − Pre)
-0.253***
(8.75)
-0.178***
(8.39)
-0.075**
(2.08)
Panel B: Difference-in-differences tests of coordination costs and industry-adjusted Tobin’s q conditioning on
the pre-merger coordination cost of treatment stocks
Diff-in-diff estimate of
Diff-in-diff estimate of Diff-in-diff estimate of
industry-adjusted
geographic distance
portfolio correlation
Tobin’s q
Low coordination cost stocks
High coordination cost stocks
Difference (Low – High)
0.102***
(6.78)
-0.049***
(5.75)
0.152***
(8.36)
-0.013***
(3.28)
0.012***
(4.89)
-0.026***
(5.15)
-0.103**
(2.14)
-0.042
(0.78)
-0.061
(0.84)
Table 7: 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 coordination costs and firm value. 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 quartiles based on each of the coordination costs proxies. Stocks in the
bottom quartile of geographic distance or the top quartile of portfolio correlation constitute a “treatment”
group that experiences an exogenous shock that eases shareholders’ ability to conduct coordinated
actions. All other stocks constitute the control group. Panel A presents the results of univariate
difference-in-differences comparisons in industry-adjusted Tobin’s q between treatment and control
firms before and after the proxy reform. Panel B presents the results of multivariate difference-indifferences 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 difference-in-differences tests
Treatment
Control
Difference (Treatment − Control)
Pre-reform
0.465
Post-reform
0.534
0.311
0.224
0.154***
(3.15)
0.310***
(6.11)
Difference (Post − Pre)
0.069
(0.94)
-0.087**
(2.57)
0.155**
(2.20)
Panel B: Multivariate difference-in-differences tests
Dependent variable =
Log(1 + Shareholder distance) × Post reform
Log(1 + Shareholder distance)
(1)
-0.059***
(2.67)
-0.063***
(3.28)
Shareholder portfolio correlation × Post reform
Shareholder portfolio correlation
Post reform
0.323**
(2.28)
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
0.746***
(6.04)
7,600
0.01
Industry-adjusted Tobin’s q
(2)
(3)
-0.056***
(2.73)
0.064***
(3.25)
0.344***
(2.87)
0.538***
(4.55)
0.363***
-0.138***
(2.71)
(4.42)
4.328***
(19.16)
-82.292***
(20.22)
-0.022
(1.01)
0.625***
(3.00)
-0.425**
(2.12)
-0.298***
(14.78)
0.540***
(5.13)
1.073***
(6.76)
-0.351***
(4.51)
2.160***
(4.99)
-0.021
(0.62)
0.585***
0.201***
(2.92)
(6.01)
6,463
7,600
0.23
0.01
(4)
0.428***
(3.55)
-0.634***
(4.38)
-0.103***
(3.15)
4.224***
(18.82)
-82.113***
(20.48)
-0.019
(0.89)
0.616***
(2.98)
-0.433**
(2.16)
-0.314***
(14.59)
0.566***
(5.37)
1.066***
(6.75)
-0.352***
(4.55)
2.132***
(4.94)
-0.016
(0.46)
1.238***
(6.04)
6,463
0.24
Table 8: The effect of decimalization
This table presents results of the difference-in-differences tests of the impact of decimalization on the
relation between coordination costs and firm value. We use a two-year window and define the fiscal year
in which decimalization occurred as year t. We choose year t−2 for the pre-decimalization period, and
year t as the post-decimalization period. We require that each stock be present in both windows around
the reform. We divide the sample of stocks into quartiles based on each of the coordination costs proxies.
Stocks in the bottom quartile of geographic distance or the top quartile of portfolio correlation constitute
a “treatment” group that experiences an exogenous shock that enhances the effectiveness of the threat of
exit by institutions. All other stocks constitute the control group. Panel A presents the results of
univariate difference-in-differences comparisons in industry-adjusted Tobin’s q between treatment and
control firms before and after decimalization. Panel B presents the results of multivariate difference-indifferences regressions. 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-Decimalization period, i.e., after January
2001, and zero otherwise. Panel C presents the results of multivariate triple-differences regressions. 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 difference-in-differences tests
Treatment
Control
Difference (Treatment − Control)
Pre-decimalization
0.929
1.235
-0.306***
(2.58)
Post-decimalization
0.395
0.381
0.014
(0.30)
Difference (Post − Pre)
-0.534***
(5.34)
-0.854***
(11.22)
0.320**
(2.50)
Panel B: Multivariate difference-in-differences tests
Dependent variable =
Log(1 + Shareholder distance) × Post-Decimalization
Log(1 + Shareholder distance)
(1)
-0.066***
(3.42)
0.078***
(3.46)
Shareholder portfolio correlation × Post-Decimalization
Shareholder portfolio correlation
Post-Decimalization
0.172
(1.33)
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
0.171
(1.14)
10,500
0.01
Industry-adjusted Tobin’s q
(2)
(3)
-0.054*
(1.79)
0.125***
(3.70)
0.439***
(4.35)
-0.673***
(5.18)
0.164
-0.400***
(0.79)
(10.16)
5.257***
(23.91)
-87.288***
(21.91)
-0.013
(0.44)
0.389*
(1.76)
-0.169
(1.10)
-0.327***
(13.53)
0.592***
(4.39)
0.289***
(6.27)
-0.909***
(8.55)
2.779***
(7.06)
-0.097***
(3.48)
0.652*
0.895***
(1.93)
(17.48)
7,637
10,500
0.19
0.01
(4)
0.472***
(3.83)
-1.207***
(6.19)
-0.335***
(7.29)
4.998***
(23.44)
-85.980***
(22.03)
-0.007
(0.23)
0.315
(1.44)
-0.157
(1.02)
-0.352***
(13.26)
0.629***
(4.62)
0.285***
(6.12)
-0.889***
(8.36)
2.662***
(6.74)
-0.093***
(3.34)
2.016***
(6.90)
7,637
0.20
Panel C: Multivariate triple-differences tests
Dependent variable =
Log(1 + Shareholder distance) × Post-Decimalization × Stock Price
Log(1 + Shareholder distance) × Post-Decimalization
Log(1 + Shareholder distance) × Stock price
Log(1 + Shareholder distance)
Shareholder portfolio correlation × Post-Decimalization× Stock price
Shareholder portfolio correlation × Post-Decimalization
Shareholder portfolio correlation × Stock price
Shareholder portfolio correlation
Post-Decimalization × Stock price
Post-Decimalization
Stock price
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
(1)
(2)
(3)
(4)
0.013*** 0.013**
(3.53)
(2.09)
-0.129*** -0.095***
(5.03)
(3.16)
-0.013*** -0.021***
(2.79)
(2.83)
0.140*** 0.193***
(4.67)
(5.48)
-0.200*** -0.153***
(4.57)
(2.96)
1.536*** 1.026***
(6.37)
(4.87)
0.214*** 0.204***
(4.54)
(3.11)
-1.845*** -1.960***
(6.77)
(6.74)
-0.093*** -0.094**
0.030*** 0.023***
(3.54)
(2.09)
(4.57)
(2.96)
0.630*** 0.460** -0.516*** -0.392***
(3.57)
(2.25)
(11.18)
(7.94)
0.092*** 0.148*** -0.032*** -0.031***
(2.79)
(2.83)
(4.53)
(3.10)
5.247***
5.215***
(23.88)
(23.07)
-87.734***
-90.691***
(22.03)
(20.92)
-0.009
-0.018
(0.30)
(0.60)
0.380*
0.175
(1.72)
(0.80)
-0.168
-0.179
(1.08)
(1.22)
-0.332***
-0.361***
(13.72)
(13.62)
0.602***
0.721***
(4.47)
(5.27)
0.291***
0.275***
(6.33)
(6.07)
-0.887***
-0.860***
(8.35)
(8.29)
2.702***
2.515***
(6.88)
(6.47)
-0.098***
-0.099***
(3.53)
(3.54)
-0.280
0.170
1.019*** 2.207***
(1.36)
(0.50)
(17.61)
(7.51)
10,500
7,637
10,500
7,637
0.01
0.20
0.04
0.22
Table 9: 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 firm level. Significance on a 10% (*), 5% (**),
or 1% level (***) is indicated.
Dependent variable =
Log(1 + Shareholder distance)
G-index
(1)
0.396***
(2.59)
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
4.352***
(10.77)
-49.234***
(7.31)
-0.192***
(3.62)
-5.320***
(7.43)
-1.192***
(3.82)
-0.020
(0.43)
1.379***
(6.96)
-0.133***
(3.06)
1.108***
(3.31)
-2.719***
(3.18)
0.299***
(5.16)
6.723***
(6.07)
23,376
0.13
(2)
-6.728***
(7.33)
3.187***
(7.57)
-28.814***
(4.05)
-0.168***
(3.23)
-4.762***
(6.74)
-1.165***
(3.74)
-0.035
(0.76)
1.463***
(7.44)
-0.144***
(3.28)
1.004***
(3.01)
-2.696***
(3.14)
0.293***
(5.15)
10.321***
(19.11)
23,376
0.14
(3)
0.166**
(2.24)
E-index
1.978***
(10.67)
-15.733***
(4.91)
0.012
(0.47)
-1.081***
(3.07)
-0.738***
(4.53)
-0.101***
(4.60)
0.680***
(6.93)
-0.051*
(1.78)
0.614***
(3.71)
-0.968**
(2.33)
0.108***
(3.88)
4.064***
(6.94)
23,604
0.15
(4)
-2.417***
(6.16)
1.564***
(8.12)
-8.542***
(2.60)
0.021
(0.85)
-0.886**
(2.54)
-0.730***
(4.48)
-0.106***
(4.86)
0.710***
(7.28)
-0.055*
(1.92)
0.576***
(3.48)
-0.958**
(2.30)
0.106***
(3.84)
5.669***
(19.41)
23,604
0.15
Table 10: 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 firm 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)
-0.054***
(4.01)
0.160*
(1.94)
-0.182***
-0.159***
(6.19)
(5.24)
-0.552
-0.847*
(1.31)
(1.95)
0.000
-0.001
(0.12)
(0.37)
0.198***
0.192***
(3.87)
(3.75)
0.232***
0.235***
(9.29)
(9.41)
0.033***
0.032***
(10.70)
(10.57)
-0.179***
-0.178***
(9.39)
(9.34)
0.013**
0.012**
(2.43)
(2.36)
-0.169***
-0.168***
(7.71)
(7.61)
0.592***
0.593***
(8.93)
(8.93)
-0.013***
-0.013***
(3.29)
(3.21)
0.028***
0.026***
(7.91)
(7.31)
0.037***
0.037***
(5.11)
(5.11)
0.015***
0.015***
(12.09)
(12.04)
0.001
0.001
(0.94)
(1.00)
0.470***
0.089
(4.44)
(1.59)
19,750
19,750
0.23
0.23
Option fraction
(3)
(4)
0.002
(0.15)
0.242***
(3.61)
0.237***
0.272***
(9.18)
(9.96)
-1.521***
-2.104***
(3.50)
(4.68)
0.014***
0.014***
(4.32)
(4.27)
0.200***
0.176***
(4.10)
(3.61)
0.028
0.028
(1.10)
(1.11)
0.029***
0.030***
(10.46)
(10.62)
-0.073***
-0.074***
(4.99)
(5.05)
0.032***
0.032***
(3.72)
(3.67)
-0.142***
-0.139***
(7.12)
(6.95)
0.459***
0.452***
(6.91)
(6.81)
-0.009**
-0.008**
(2.33)
(2.32)
0.011***
0.009**
(2.86)
(2.30)
-0.002
-0.002
(0.31)
(0.31)
0.002*
0.002*
(1.70)
(1.80)
-0.004***
-0.004***
(10.08)
(10.09)
0.024
-0.001
(0.25)
(0.01)
20,548
20,548
0.21
0.21
Table 11: 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. Low coordination cost dummy is an indicator
variable that equals one if the geographic distance among institutions is in the bottom quartile or the
portfolio correlation among institutions is in the top quartile, and zero otherwise. Stock return is the buyand-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 firm level. Significance on a
10% (*), 5% (**), or 1% level (***) is indicated.
Dependent variable =
Log(1 + Shareholder distance) × Stock return
Log(1 + Shareholder distance)
(1)
0.055***
(2.97)
-0.019**
(2.19)
Shareholder portfolio correlation × Stock return
CEO Turnover
(2)
-0.025
(0.29)
-0.051
(0.85)
Shareholder portfolio correlation
Low coordination cost dummy × Stock return
Low coordination cost dummy
Stock return
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
CEO is Chair
Tenure
CEO age > 60
Observations
Pseudo R-squared
(3)
-0.415***
(3.15)
-0.070***
(3.42)
0.700*
(1.90)
-0.001
(0.33)
0.101***
(2.69)
-0.093***
(5.12)
0.002
(1.00)
0.027**
(2.44)
-0.007
(1.29)
-0.002
(0.12)
0.029
(0.57)
0.000
(0.20)
-0.039***
(8.37)
0.006***
(7.43)
0.076***
(13.06)
21,971
0.03
-0.022
(1.57)
-0.082***
(3.72)
0.849**
(2.14)
-0.001
(0.59)
0.109***
(2.85)
-0.092***
(5.12)
0.001
(0.73)
0.028**
(2.48)
-0.008
(1.37)
-0.002
(0.13)
0.036
(0.71)
-0.000
(0.01)
-0.039***
(8.24)
0.006***
(7.27)
0.076***
(13.10)
21,971
0.03
-0.147**
(2.48)
0.037
(0.91)
-0.025***
(4.71)
-0.072***
(3.49)
0.696*
(1.88)
-0.001
(0.64)
0.098***
(2.60)
-0.092***
(5.07)
0.001
(0.79)
0.028**
(2.48)
-0.008
(1.35)
-0.001
(0.04)
0.034
(0.68)
-0.000
(0.12)
-0.038***
(8.22)
0.006***
(7.25)
0.076***
(13.14)
21,971
0.03