Takeover Discipline and Asset Tangibility
Julien Sauvagnat∗
April 5, 2013
Abstract
This paper examines whether the threat of liquidation mitigates the negative effect of
bad corporate governance on firm performance. The literature stresses the link between the
threat of liquidation and the tangibility of assets. In support of the hypothesis that the
liquidation threat acts a strong disciplinary device, I find that higher takeover vulnerability
– measured through firm-level takeover defenses and state antitakeover laws – is associated
with higher performance only for firms holding a large share of intangible assets. The analysis
also reveals significant changes in firms’ use of external finance. I find that equity issues
significantly drop after the passage of an antitakeover law, especially for low-tangibility
firms. This has real effects on sales and asset growth, which are significantly reduced for
low-tangibility firms.
Keywords: corporate governance; takeovers; business combination laws; debt; asset
tangibility.
JEL classification codes: G32, G34.
∗
ENSAE, France. E-mail: julien.sauvagnat@gmail.com. This paper is a revised version of the first chapter
of my Ph.D. dissertation at the Toulouse School of Economics. I thank Bruno Biais, Quentin Boucly, Catherine
Casamatta, Christophe de Becdelievre, Xavier Freixas, Denis Gromb, Alexander Gümbel, Augustin Landier,
Massimo Motta, Giovanni Pica, Guillaume Plantin, Patrick Rey, David Sraer, Alminas Zaldokas; and participants
at the EEA (2011) congress and AFSE (2012) congress for helpful comments and suggestions.
1
1
Introduction
An extensive theoretical literature (e.g. Grossman and Hart (1982), Jensen (1986), Harris
and Raviv (1990), Shleifer and Vishny (1992), Hart and Moore (1994)) emphasizes the role
of liquidation in mitigating agency problems and opportunistic behavior between managers
and investors. The credibility of liquidation is strongly related to the liquidation value of
assets. Debt contracts are attractive because they reduce the scope for managerial misbehavior.
However, when cash flows are stochastic, debt triggers default in some states of the world. In
this trade-off, higher liquidation values of assets make default less costly for creditors which, in
turn, allow them to exert more discipline on the management team.
Another strand of the literature stresses the role of (hostile) takeovers in disciplining management (e.g. Manne (1965), Jensen (1988), Scharfstein (1988), Shleifer and Vishny (1997)).
Despite the rich literature analyzing the effect of takeovers and the liquidation threat on firm
behavior, few papers study the interaction between these two disciplinary channels. Is the benefit of good external corporate governance smaller when the liquidation threat exerts discipline
on managers? How do these two disciplinary channels interact with firms’ access to external
finance? These questions are the focus of this paper.
Following recent research (Gompers, Ishii and Metrick (2003), Bertrand and Mullainathan
(2003)), I use firm-level takeover defenses and state antitakeover laws to measure external
corporate governance. Empirical studies present strong evidence that firms with external good
governance perform on average better. Gompers, Ishii and Metrick (2003) and Bebchuk, Cohen
and Ferrell (2009) show that firms with less takeover defenses have higher firm value, higher
operating performance and higher equity returns. Using the passage of state antitakeover laws
as an instrument, Bertrand and Mullainathan (2003) find that a drop in the takeover threat
has a causal and negative effect on firm profitability and productivity. Using a regression
discontinuity design on the outcomes of shareholder proposals, Cuñat, Gine and Guadalupe
(2011) show that the adoption of shareholder-friendly governance provisions has a causal and
positive effect on firm value.1
As mentioned above, creditors exert more influence on the management when the liquidation
value of assets is high. In practice, we do not observe the liquidation value of assets for the whole
1
However, some papers find that antitakeover provisions are in some cases associated with higher firm value
(e.g. Comment and Schwert (1995), Kadyrzhanova and Rhodes-Kropf (2011)).
2
sample of public firms.2 As in previous work (e.g. Harris and Raviv (1991), Rajan and Zingales
(1995), Titman and Wessels (1998), Rauh and Sufi (2012)), the empirical strategy presented
in this paper relies on balance-sheet data and uses the tangibility of assets as a measure of
their liquidation value. In support of the hypothesis that the liquidation threat acts a strong
disciplinary device, I find that the sensitivity of firm performance with respect to the quality of
external corporate governance is increasing in the share of intangible assets on firms’ balance
sheet. The economic effect is significant. When using the adoption of state antitakeover laws as
an exogenous shock to external corporate governance (as in Bertrand and Mullainathan (2003)
or Giroud and Mueller (2010)), I find that the introduction of a business combination law leads
to a decrease in operating performance of around 1.2 percentage points for firms holding a
large fraction of intangible assets. By contrast, the passage of a business combination law has
virtually no effect on the performance of firms holding a large share of tangible assets.
This paper is a contribution to the literature which examines the interaction between
takeover discipline and other governance mechanisms. In this line of research, Giroud and
Mueller (2010, 2011) show that firms in non-competitive industries benefit more from high
takeover vulnerability than do firms in competitive industries. Cremers and Nair (2005) find
that higher takeover vulnerability is associated with higher performance only when the quality
of internal governance, proxied by public pension fund and blockholder ownership, is high.
The empirical literature on external corporate governance occasionally mentions the threat
of bankruptcy as a substitute disciplinary device. Cremers and Nair (2005) show that the
complementarity between the market for corporate control and shareholder activism exists only
in low-leverage firms. Atanassov (2013) shows that the negative effect of antitakeover laws on
innovation is mitigated when leverage is high. Examining a sample of 573 unsuccessful takeover
attempts between 1982 and 1991, Safieddine and Titman (1999) find that the performance
of former targets following failed takeovers is positively related to the change in the target’s
leverage ratio. Their explanation is that leverage commits managers to make the improvements
that would be made by potential raiders.
Using debt levels in order to examine whether the threat of liquidation mitigates the negative
effects of bad external corporate governance raises endogeneity concerns. First, governance
2
Focusing on specific industries, a recent literature (Benmelech, Garmaise and Moskowitz (1995), Benmelech
and Bergman (2008), Benmelech and Bergman (2009)) tries to measure precisely the liquidation value of assets
and studies how financial contracts are affected.
3
and leverage decisions are jointly determined in equilibrium, making it difficult to assess their
relative effects on firm behavior. Second, non-profitable firms have mechanically higher debt
levels which bias any inference regarding the effect of corporate governance on performance
depending on firms’ leverage ratio. The key idea of the identification strategy used in this
paper is that the tangibility of assets is both correlated with the credibility of the liquidation
threat and exogenous to firms’ strategic decisions.
This paper also echoes the literature studying the influence of creditors on firm behavior.
A violation of the debt contract significantly increases the probability of forced CEO turnover
(Gilson (1989), Olzege and Saunders (2012), Nini, Smith and Sufi (2012). Covenant violations
are generally followed by more conservative financial policies and higher performance (Nini,
Smith and Sufi (2012)). In line with this literature, the results presented in this paper suggest
that creditors exert a positive discipline on firms.
Previous research (e.g. Williamson (1988), Hart and Moore (1994), Rajan and Zingales
(1995)) stresses that the mode of external financing – i.e., debt or equity – crucially depends
on the characteristics of firms’ assets. Rajan and Zingales (1998) note that “too much debt
is bad for companies that rely on intangible or specialized assets such as customer confidence,
ideas, or people”. Hart and Moore (1994) mentions (p865, footnote 29) that “the link between
intangibility and equity financing arises because conventional debt finance breaks down with intangible assets”. In the abstract of its influential paper, Williamson (1988) mentions that “debt
and equity are treated not mainly as alternative financial instruments, but rather as alternative
governance structures. [...] whether a project should be financed by debt or equity depends
principally on the characteristics of the assets”. Motivated by this literature, I empirically
examine in the second part of my work the relationship between (equity-centered) corporate
governance, asset tangibility and firms’ use of external finance. A deterioration of corporate
governance is likely to raise the cost of equity financing. In line with theory, I find that equity
issues significantly drop after the passage of an antitakeover law, especially for low-tangibility
firms. Finally, I examine the real effects of the drop in outside financing on the growth of firms.
As expected, the regressions show that sales and asset growth are significantly reduced after
the passage of an antitakeover law, especially for low-tangibility firms.
The evidence presented in this paper has important implications for governance design.
4
For instance, owners of firms holding a large share of intangible assets should avoid installing
takeover defenses at the IPO (Daines and Klausner (2001), Field and Karpoff (2002)).
The rest of the paper proceeds as follows. Section 2 reviews the related literature. Section 3 examines the relationship between takeover defenses, asset tangibility and performance.
Section 4 presents the data and the empirical methodology on business combination laws and
studies their effects on performance, firms’ use of external finance and firms’ growth. Section 5
concludes.
2
Related Literature
This section presents a brief review of the related literature, and the main hypothesis tested in
the paper.
Substantial research stresses the role of the liquidation threat. Earlier work on this topic
includes Harris and Raviv (1990)’s model which links the disciplinary effect of debt with the
liquidation value of assets. The model assumes that i) managers are reluctant to relinquish
control and ii) information about firm quality is revealed to equityholders in case of default. In
Harris and Raviv (1990), debt emerges as a device for investors to force the firm into liquidation.
Default and debt levels are positively correlated with the liquidation value of assets simply
because higher liquidation value increases the probability that liquidation is the optimal policy
compared to continuation.
Incomplete contract theory (e.g. Hart and Moore (1994), Shleifer and Vishny (1992)) shows
that the ability to resell the asset at a high price boosts borrowing capacity. Hart and Moore
(1994) considers an entrepreneur who needs to raise funds, but can ex post threaten creditors to
withdraw his human capital from the project. Because the entrepreneur cannot commit not to
renegotiate the debt contract, creditors are reluctant to finance the project. Higher liquidation
value of assets strengthens creditors’ bargaining power in case of renegotiation. As a result,
when the liquidation value of assets is high, the entrepreneur has less incentives to misbehave,
which eases ex ante the financing of the project.
Shleifer and Vishny (1992) explicitly analyzes the tradeoff between the benefit of debt in
disciplining management and the liquidation costs in default states. The model assumes that
industry peers are the potential buyers with the highest valuation for liquidated assets. The
5
liquidation value of assets becomes endogenous: when shocks are correlated across firms in the
same industry, the resell value of assets is low because a firm in liquidation is likely to face
severely-constrained industry peers. In these industries, firms prefer to renounce to the benefit
of debt discipline – that is, the benefit of preventing managers from launching inefficient projets
– because this imposes too frequent costly liquidation.
The predictions of this theory on the link between the liquidation value of assets and financial
contracts received strong empirical support in recent research. Benmelech et al. (2005) uses
commercial zoning regulation to capture exogenous changes in asset’s redeployability. They
show that more redeployable assets receive larger loans and lower interest rates. Similarly,
Benmelech and Bergman (2009) construct measures of collateral redeployability in the airline
industry and show that more redeployable collateral lowers the cost of external financing, and
increases debt capacity. The empirical relevance of the credibility of the liquidation threat is
well documented in Benmelech and Bergman (2008). They show that aircraft lease contracts are
more likely to be renegotiated when liquidation values are low and airlines’ financial condition is
poor. Using the tangibility of firms’ assets as a proxy for their liquidation value, earlier research
has delivered similar results (e.g., Rajan and Zingales (1995)).
Given that asset tangibility is a key driver of leverage, this paper is also connected to the
literature that examines the effect of debt in agency models of corporate finance. First, debt
limits managerial discretion by forcing the firm to disgorge cash flows (Jensen (1986)). Second,
higher debt levels mechanically concentrate equity ownership in the hands of the management,
which allows to align managers’ objectives with shareholders’ interests (Jensen and Meckling
(1976)). Debt discipline also rests on debtholders’ ability to exercise control when the firm
defaults on its debt contract (Aghion and Bolton (1992), Dewatripont and Tirole (1994)).
Finally, in the presence of debt, payoffs to equity holders are convex in the value of the firm. As
stressed by Jensen and Meckling (1976), managers acting in shareholders’ interests might then
be willing to engage in “asset substitution”, i.e. take excessive risk. In this context, takeover
defenses, by reducing shareholders’ pressure on the management, might reduce shareholderdebtholder conflicts.
The threat of bankruptcy as a powerful disciplinary device also emerges in theoretical models
studying the effect of product market competition on managerial incentives (e.g. Hart (1983),
6
Scharfstein (1988), Schmidt (1997), Raith (2003)). Schmidt (1997) shows that higher product
market competition reduces firms’ profits, which lowers managers’ incentives to exert more
effort, but also increases the probability of liquidation, which by contrast reduces managerial
slack. As a result, product competition has an ambiguous effect on managerial incentives.
This paper is also related to the vast literature on corporate governance, which is too large
to be reviewed in this section. Good corporate governance mitigates moral hazard ex post, and
by the same token relax financial frictions ex ante (Shleifer and Vishny (1997)). Among the
corporate governance mechanisms, hostile takeovers received particular interest in the literature
(e.g. Manne (1965), Jensen (1988), Scharfstein (1988), Shleifer and Vishny (1997))). Takeovers
are more likely to occur following bad performance (Morck et al. (1989), Martin and McConnell
(1991)), in which case managers are more likely to be replaced (Mikkelson and Partch (1997),
Kini et al. (2004)). The prospect of being fired following a takeover pushes ex ante managers
to exert effort. This disciplinary channel is well documented in the empirical literature which
shows a robust and positive relationship between good external corporate governance and high
performance (e.g. Gompers, Ishii and Metrick (2003), Bertrand and Mullainathan (2003)).
Very few theoretical papers study the interaction between the threat of (hostile) takeovers
and the threat of liquidation. An exception is Zwiebel (1996). In his model, the manager
receives private benefit from running the firm and launching new projects. It is shown that
the manager, threatened by the prospect of a takeover, voluntarily chooses to increase the
probability of bankruptcy in order to credibly commit to refrain from launching bad projects.
In Zwiebel (1996), the threat of takeovers and the threat of bankruptcy are complements, in
that the manager has no reason to increase debt in the absence of takeovers.3
Previous research including Williamson (1988), Hart and Moore (1994), Rajan and Zingales
(1995) link the mode of external financing with the characteristics of assets. These three papers
unambiguously argue that debt should not be used to finance intangible assets. This discussion
yields the following predictions:
Hypothesis 1 (Performance). The sensitivity of firm performance with respect to the
quality of external corporate governance is increasing in the share of intangible assets on firms’
3
Grossman and Hart (1982) also discusses the interaction between the threat of bankruptcy and the threat
of takeovers. Manager’s consumption benefits realize only if the firm is not taken over or go bankrupt. Similar
to Zwiebel (1996), the management has an incentive to increase debt because this increases firm value and thus
reduces the probability of a takeover bid.
7
balance sheet.
Following the literature review, the argument in favor of this hypothesis is that the threat
of liquidation may act as a substitute for good external corporate governance in firms holding
a large share of tangible assets. I test for this hypothesis using data on takeover defenses and
business combination laws.
Hypothesis 2 (External Finance). The quality of external governance affects the cost
of equity financing. A drop in takeover threat has a negative effect on firms’ use of equity
financing, in particular for firms holding a large share of intangible assets. In the absence of
substitution effects between debt and equity financing, a drop in takeover threat is not expected
to significantly affect firms’ use of debt.
This hypothesis rests on previous research indicating that good (equity-centered) corporate
governance is crucial for the financing of intangible projects.
Hypothesis 3 (Takeover defenses). The number of takeover defenses decreases with
firms’ share of intangible assets.
When deciding whether to adopt takeover defenses, firms solve a tradeoff between the benefits and the costs of high takeover vulnerability. As discussed above, the benefit of takeover
threats is likely to be higher for firms holding a large share of intangible assets. Assuming that
the cost of high takeover threats is not higher for low-tangibility firms, the number of takeover
defenses is expected to decrease with firms’ share of intangible assets.4
This prediction is also motivated by empirical evidence indicating that takeover defenses
mitigate debtholders-shareholders conflicts. Firms with less takeover defenses face higher bond
and loan spreads (Klock, Mansi and Maxwell (2005), Cremers, Nair and Wei (2007), Chava,
Livdan and Purnanandam (2009)). Similarly, Francis et al. (2010) find that state antitakeover
laws tend to decrease bond yields and increase bond values. As leverage is increasing in asset
4
Stein (1988), Burkart et al. (1997) and Jensen and Meckling (1976) offer theoretical arguments indicating
that reducing takeover pressure might be optimal. Strong takeover exposure might cause managers to behave
myopically (Stein (1998)). In the same vein, shareholders’ control might reduce managers’ initiative (Burkart
et al. (1997)). It is a priori unclear how these motivations to curb shareholders’ control interact with the
characteristics of assets. Finally, takeover threats also reinforce debtholders-shareholders conflicts (Jensen and
Meckling (1976)). As mentioned in the following paragraph, the scope for debtholders-shareholders conflicts is
likely to be larger in high-tangibility firms.
8
tangibility, firms holding a large share of tangible assets are more likely to adopt takeover
defenses in order to mitigate debtholders-shareholders conflicts.
The threat of liquidation is not the only potential driver of the relationship between takeover
discipline, asset tangibility and performance. An alternative force is information asymmetry. In
firms with a large fraction of intangible assets, the relative scarcity of public information is also
likely to make good corporate governance a relatively more important issue for investors. As
mentioned by Almeida and Campello (2007), asset tangibility reduces information asymmetry
because tangible assets’ payoffs are easier to observe.
3
Takeover Defenses
3.1
3.1.1
Data
Sample
The initial sample consists of all firms in the Investor Responsibility Research Center (IRRC)
database that have a match in both CRSP and Compustat. I match 100 percent of the IRRC
sample to CRSP. The IRRC database is available at WRDS and provides information about
corporate governance provisions for the years 1990, 1993, 1995, 1998, 2000, 2002, 2004 and 2006.
Following common practice, I use the information from the latest available publication to fill
in the missing years. In each year, the firms present in the IRRC database account for more
than 90 % of the total US stock market capitalization. Following Gompers, Ishii, and Metrick
(2003), I exclude from the sample all firms with dual-class shares. I also exclude utilities (SIC
codes 4900-4999) and financial firms (SIC 6000 − 6999)5 . The sample period is from 1990 to
2007.
The main measure of takeover vulnerability used in this section is the Entrenchment index
(denoted hereafter, E) proposed by Bebchuk, Cohen and Ferrell (2009) which comprises 6
provisions restricting shareholder rights, namely classified board, limitations to amend bylaws,
limitations to amend the charter, supermajority for merger approval, poison pill and golden
parachute. In robustness checks, I use the Gompers, Ishii and Metrick (2003)’s Governance index
5
Takeover threats are likely to be less relevant for regulated utilities. This might bias the results because
utilities are generally high-tangibility firms. The notion of asset tangibility is not well defined for financial firms.
9
(denoted hereafter, G), the Alternative Takeover Index (denoted hereafter, ATI) by Cremers
and Nair (2005) and a dummy indicating classified board as alternative measures of takeover
vulnerability.6
3.1.2
Definition of variables
Asset tangibility. Following previous research (e.g. Rajan and Zingales (1995), Titman and
Wessels (1998)), I measure asset tangibility with the ratio of property, plant and equipment
(Compustat item PPENT) over total assets. Asset tangibility is frequently used by the empirical
literature as a measure of assets’ liquidation value. Gompers (1995) notes that the liquidation
value of assets is increasing in their tangibility because tangible assets – e.g. machines and
plants – are on average easier to sell than intangible assets – e.g. patents and copyrights.
Asset tangibility might be affected by changes in takeover vulnerability. For instance,
Atanassov (2013) shows that antitakeover laws has a negative impact on innovation and R&D.
Accordingly, the share of intangible assets held by innovative firms is likely to be mechanically
reduced. To deal with this problem, I compute IN T Ai , the share of intangible assets on firm i
balance sheet – which equals one minus the ratio of property, plant and equipment over total
assets – when the firm enters in the sample.
Other accounting variables. As in Gompers, Ishii and Metrick (2003) and Giroud and
Mueller (2011), I examine the effect of takeover defenses on both Tobin’s Q and operating
performance. Tobins’ Q is the market value of assets divided by the book value of assets, where
the market value of assets is the book value of assets plus the market value of common stock
(item CSHO * item PRCC F) minus the sum of the book value of common stock (item CEQ) and
balance sheet deferred taxes (item TXDB). I use the same measures of operating performance
as in Giroud and Mueller (2011). ROA is net income (item NI) over total assets. Net profit
margin (NPM) is net income over sales (item SALE). Return on equity (ROE) is net income
divided by the book value of equity. Sales growth is the growth in sales over the previous five
years. Tobin’s Q, ROA, NPM, ROE and sales growth are industry-adjusted by subtracting the
industry median in a given 48 Fama-French industry and year. To ensure that the results are
6
The G index comprises 24 provisions. The ATI index comprises three provisions: preferred blank check,
classified boards and restrictions on calling special meetings and action through written consent. Bebchuk and
Cohen (2005) shows that classified boards are associated with a significant reduction in firm value.
10
statistically robust, these measures of performance are trimmed at the 1st and 99th percentiles
of their empirical distribution. Industry medians are computed using all available Compustat
firms. I construct the 48 Fama-French industry dummies by matching the firm’s 4-digit Standard
Industrial Classification (SIC) codes of Compustat to the 48 Fama-French industries using the
conversion table in the Appendix of Fama and French (1997).
As in Gompers, Ishii and Metrick (2003), regressions include firm size, firm age, delaware
incorporation and S&P500 inclusion as control variables. Firm size is the logarithm of total
assets. Firm age is the logarithm of one plus the number of years since the firm has been in
Compustat.
Summary statistics. Table 1 presents summary statistics on the takeover defenses sample.
It can be noted that the mean and the median of the industry-adjusted measures of performance
are positive, pointing out that the sample firms, which are relatively large, are more profitable
than the other firms in Compustat.
[Table 1 here]
3.2
3.2.1
Results
Initial evidence on performance
I examine in this section whether takeover defenses have a different effect on performance
depending on firms’ share of intangible assets. Figure 1 provides a first look at the relationship
between the E index and performance separately for “low-tangibility” and “high-tangibility”
firms. Low-tangibility (resp. High-tangibility) firms are defined as firms with INTA above
(resp. below) the sample median. Performance is measured through Tobin’s Q. For each point
of the E index, I present the mean and the median of the industry-adjusted Tobin’s Q for
both low-tangibility and high-tangibility firms. Figure 1 shows that the negative association
between poor takeover vulnerability (high value of the E index) and Tobin’s Q is stronger for
low-tangibility firms. This pattern holds for both the average and the median firm.
[Figure 1 here]
11
I move to a multivariate analysis and estimate the following equation:
Yit = αj + δt + β0 Eit + β1 (Eit × IN T Ai ) + β2 IN T Ai + γ Controlsit + it
(1)
Yit measures industry-adjusted Tobin’s Q of firm i in fiscal year t. δt and αj – j indexes the
48 Fama-French industries – are respectively year- and industry-fixed effects. Using industryfixed effects rather than firm-fixed effects is motivated by the fact that firms rarely change their
governance provisions. Eit is the E index associated to firm i in year t. IN T Ai is the initial
share of intangible assets on firm i balance sheet. The interaction term Eit × IN T Ai captures
the influence of asset tangibility on the sensitivity of performance to takeover vulnerability: the
effect of a one-point increase in the E index on the dependent variable equals β0 + β1 × IN T Ai .
Equation 1 includes the same control variables as in Gompers, Ishii and Metrick (2003), namely
firm size, firm age, whether the firm is incorporated in Delaware and whether the firm belongs
to the S&P 500. I cluster standard errors at the firm level to account for serial correlation of
the error term within the same firm.
[Table 2 here]
Table 2 present the results. As shown in column [1] and consistent with previous research, an
increase in the E index is associated on average with a significant drop in Tobin’s Q. In column
[2], I include the interaction term between the E index and INTA, the share of intangible assets.
The interaction term is negative (-0.106) and significant at the five percent level, indicating
that the negative effect of takeover defenses on performance is stronger for firms holding a large
fraction of intangible assets. In column [3], I replace the variable INTA with a set of three
dummies indicating wether INTA lies in the bottom, medium or top tercile of its empirical
distribution. I then interact the E index with each dummy. In line with results in column
[2], the coefficient on the E index turns out to be monotonic over the terciles. Moreover, the
coefficient on the E index in the highest INTA tercile is twice as large as in the lowest INTA
tercile, and the difference between the two coefficients is statistically significant at the one
percent level. In terms of economic significance, in the highest INTA tercile, a one-standard
deviation increase in E (see table 1, 1.285) is associated with a drop in Tobin’s Q of 0.142, which
12
represents 15% of Tobin’s Q standard deviation.
Operating performance. I reestimate the specification presented in column [3] of Table
2, except that the dependent variable is now either ROA, NPM, Sales Growth, or ROE. Following Gompers, Ishii and Metrick (2003), I include the logarithm of the book-to-market ratio,
Ln(B/M), as an additional control variable. Ln(B/M) is defined as the ratio of the book value
of common stock plus balance sheet deferred taxes over the market value of common stock.
[Table 3 here]
Columns [1], [3], [5] and [7] of table 3 shows the average effect of the E index on each measure
of operating performance. As in Giroud and Mueller (2011), the E index has a statistically
significant effect on the ROA, NPM and sales growth of the average firm, but a statistically
insignificant effect on the ROE of the average firm. Columns [2], [4], [6] and [8] present the
results when the E index is interacted with three INTA terciles. The results are very similar
for each measure of operating performance. The coefficient on the E index is monotonic over
the INTA terciles. Moreover, the coefficient on the E index is always statistically insignificant
in the lowest INTA tercile, and statistically significant in the highest INTA tercile. Finally, the
difference between the coefficients on the E index in the highest and lowest INTA terciles is
always significant at the one percent level.
Taken together, the results in tables 2 and 3 indicate that the negative relationship between
takeover defenses and performance – measured through firm value and operating performance
– is robust only for low-tangibility firms.
3.2.2
Asset tangibility and shareholder rights
Good (equity-centered) corporate governance eases the financing of intangible projects (Williamson
(1988)). By contrast, in firms with high leverage, debtholders might ask for the adoption of
takeover defenses in order to protect the value of their claims against potential raiders. Both
forces predict that the number of takeover defenses should decrease with the share of intangible
assets on firms’ balance sheet. To test for this hypothesis, I estimate the following equation:
13
Eit = αj + αt + β IN T Ai + γ Controlsit + it
(2)
Eit is a measure of takeover vulnerability (E index, G index, ATI index or the classified
board dummy) of firm i in fiscal year t. αj and αt are industry and year-fixed effects. The
control variables are firm age, firm size, whether the firm is incorporated in Delaware, whether
the firm belongs to the S&P 500 and the logarithm of the book-to-market ratio.
[Table 4 here]
Table 4 presents the result. Whether takeover vulnerability is measured using the E index,
the G index, the ATI index or the classified board dummy, firms holding a large share of
intangible assets have on average less takeover defenses. The coefficient is statistically significant
at the first (resp. five) percent level when takeover vulnerability is measured through the E
index, G index and classified board (resp. ATI index). The effect is also economically significant.
A one-standard deviation increase in INTA (see Table 1, 0.216) decreases the E index by 12.8
(=-0.763*0.216/1.285) percent of the E index’s standard deviation. The economic effect is
similar, although smaller, with the other measures of takeover vulnerability: a one-standard
deviation increase in INTA decreases the G index (resp. ATI, classified board) by 9.6 (resp.
6.4, 9.4) percent of the G index (resp. ATI, classified board)’s standard deviation.
3.3
Endogeneity Issues
There are at least two sources of endogeneity in the estimation of equation 1. The first follows a reverse causality argument: the positive association between takeover vulnerability and
performance might be driven by the fact that managers of firms with low performance have
more incentives to adopt takeover defenses. As already mentioned, firms holding a large share
of tangible assets have on average higher debt levels. If leverage is a powerful device to deter takeovers (Stulz (1988), Harris and Raviv (1988)), reverse causality may explain why the
correlation between the number of takeover defenses and bad performance is stronger for lowtangibility firms. Second, the choice of takeover defenses and performance are likely to be jointly
driven by unobservable factors. To address both sources of endogeneity, I use the adoption of
14
state antitakeover laws as an exogenous shock to the market for corporate control.
4
Instrumental Approach: Business Combination Laws
US States enacted three generations of antitakeover laws. Virginia enacted the first antitakeover
statute in 1968. The first generation laws were deemed unconstitutional by the Supreme Court
in 1982 (Edgar v. Mite Corp.) because they applied beyond corporations chartered in the state.
As a response, several states passed second generation laws that were declared constitutional by
the Supreme Court in 1987 (CTS v. Dynamics Corp.), which pushed more states to vote what
was called third generation antitakeover laws.7 Following Bertrand and Mullainathan (2003)
and Giroud and Mueller (2010), I focus on business combination (BC) laws which are the most
stringent among the second and third generation antitakeover laws.8 BC laws were passed in
30 states between 1985 and 1991. They impose a moratorium on certain kinds of transactions
(e.g., asset sales, mergers) between a large shareholder and a firm for a period usually ranging
between three and five years after the shareholder’s stake reaches a pre-specified threshold.
In practice, these laws make any hostile takeover almost impossible, and thus provide a nice
laboratory to study the causal effect of a deterioration in external governance on firm behavior.
4.1
Data
Sample. The initial sample consists of all firms in the Compustat database located and incorporated in the United States. I exclude all observations for which total assets or sales are either
missing or non-positive. As in the previous section, I exclude utilities (SIC codes 4900-4999)
and financial firms (SIC codes 6000-6999). The sample period is from 1976 to 1995, which is
the same period as in Bertrand and Mullainathan (2003) and Giroud and Mueller (2010).
Compustat only reports the state of incorporation for the latest available year. Bertrand
and Mullainathan (2003) checked in a randomly selected sample of 200 firms if any of these
firms had change their state of incorporation. They found only three changes, all of the them
to Delaware and before the passage of the 1988 Delaware antitakeover law.9
7
For more details about antitakeover laws, see Bertrand and Mullainathan (2003).
Karpoff and Malatesta (1989) find that among second generation antitakeover laws, press announcements of
BC laws had the most negative effect on stock prices.
9
Similarly, Cheng, Nagar and Rajan (2005) find that none of the 587 Forbes 500 firms in their sample changed
their state of incorporation during the sample period from 1984 to 1991; and Yun (2009) shows that about 1%
8
15
Definition of variables. I use ROA as the main measure of operating performance, defined
as operating income before depreciation over lagged total assets. To mitigate endogeneity, INTA,
is again computed when the firm enters in the sample. Firm size and firm age are also defined
as in the previous section.
In what follows, I also examine the effect of BC laws on firms’ use of external finance. I
measure equity issuance as sale of common and preferred stock (item SSTK) divided by lagged
assets. Net change in equity is defined as equity issuance minus purchase of common and
preferred stock (item PRSTKC) divided by lagged assets. Finally, I compute debt issues as
long term debt issue (item DLTIS) over lagged assets, and net change in debt as debt issues
minus debt repayment (item DLTR) divided by lagged assets.
Finally, to ensure that the results are statistically robust, all variables defined as ratios are
trimmed at the first and ninety-ninth percentiles of their empirical distribution.
[Table 5 here]
Summary statistics. Table 5 presents summary statistics. Panel A shows firm characteristics for the full sample. Column [1] reports data for the full sample. Panel B show the same
summary statistics separately for firms incorporated in a state that eventually passed a BC law
and firms incorporated in a state that never passed a BC law. We simply remark that SIZE
and AGE statistics are very similar to those in Giroud and Mueller (2010) – i.e., firms incorporated in states that eventually passed a BC law are on average bigger and older than firms
incorporated in a state that never passed a BC law. Panel B also reports the summary statistics separately for low-tangibility and high-tangibility firms. Low-tangibility (High-tangibility)
firms are defined as firms with INTA above (below) the sample median. The spread in asset
tangibility is large: the mean INTA of low-tangibility firms is 50%, compared to 85% for hightangibility firms. Low-tangibility firms tend to have a smaller asset size and to be younger
than high-tangibility firms. To take into account these differences, the regressions presented
below include firm size, the square of firm size and firm age as control variables. Consistent
of the 212 manufacturing firms in his sample changed their state of incorporation during a sample period from
1987 to 2000.
16
with previous studies indicating that leverage increases with asset tangibility (Harris and Raviv
(1991), Rajan and Zingales (1995)), the mean (median) book leverage of low-tangibility firms is
24% (20%), compared to 30% (27%) for high-tangibility firms. Finally, in line with Williamson
(1988), low-tangibility firms issue on average more equity (less debt) than high-tangibility firms.
Finally, Panel C shows a broad industry distribution of our sample based on the 10 FamaFrench industries classification. I construct the 10 Fama-French industries by using the conversion table available on Kenneth French’s website. Low-tangibility and high-tangibility firms
strongly differ in terms of industry distribution. 70% of firms in the “Hi-Tech” industry and
61% of firms in “Healthcare” lie above the sample median of the INTA distribution. Conversely,
86% of “Energy” firms, and 71% of “Telecommunications“ firms are high-tangibility firms. For
the other industries (Nondurables, Durables, Manufacturing, Shops, Other), the proportion of
low-tangibility firms range between 44% and 59%.
Empirical strategy. The empirical approach closely follows Giroud and Mueller (2010),
which in turn is inspired by Bertrand and Mullainathan (2003). Giroud and Mueller (2010)
investigate whether BC laws have a different effect on firms in competitive and non-competitive
industries. I examine whether these laws have a different effect on performance depending on
firms’ asset tangibility. For this, I estimate the following equation:
Yit = αi + δt + β0 BCkt + β1 (BCkt × IN T Ai ) + γ Controlsijklt + it
(3)
Yit measures ROA of firm i in fiscal year t. αi and δt are firm- and year-fixed effects.
Firm-fixed effects control for any unobserved fixed differences between treated firms – i.e., firms
protected by a BC law – and nontreated firms. BCkt is a dummy that equals one if a BC law
has been adopted in state k by time t. IN T Ai is the intial share of intangible assets on firm i
balance sheet. The interaction term BCkt × IN T Ai measures whether asset tangibility affects
the sensitivity of firm performance to the passage of a BC law: the total effect of a BC law
on the dependent variable is β0 + β1 × IN T Ai . Following Bertrand and Mullainathan (2003)
and Giroud and Mueller (2010), I include “state-year” and “industry-year” controls in order to
account for state shocks and industry shocks contemporaneous to the passage of a BC law. The
“state-year” control (respectively “industry-year” control) is the mean value of the dependent
variable in state of location l (respectively in the 3-digit industry j) and year t, excluding firm i
17
itself from the mean. The other control variables are firm size, the square of firm size and firm
age.
As mentioned by Giroud and Mueller (2010), the identification strategy benefits from a
general lack of congruence between a firm’s industry, state of location, and state of incorporation.
As shown in Table 12, the distribution of the firms’ state of incorporation and state of location
is similar to the one in Giroud and Mueller (2010). Only 33.2% of the firms in our sample are
incorporated in their state of location.
Because of serial correlation in the error term, difference-in-differences approaches can seriously understate the standard errors (Bertrand, Duflo and Mullainathan (2004)). We cluster
standard errors at the state of incorporation level. This accounts for arbitrarily correlation
across firms incorporated in the same state, as well as serial correlation within the same firm
(Petersen (2009)). The statistical significance of the coefficients are similar if we report standard
errors clustered at the state of location level.
4.2
Main Results
Performance.
[Table 6 here]
Column [1] of table 6 shows the average effect of BC laws on ROA. The coefficient on the BC
dummy is −0.0087, which implies that ROA drops by 0.87 percentage points on average after
the introduction of a BC law. It is similar to the estimate in Giroud and Mueller (2010), which
equals −0.006.10 In column [2], the coefficient on the BC dummy is not significant whereas
the coefficient on the interaction term between the BC dummy and INTA equals −0.0254 and
is significant at the 10% level. In terms of economic significance, for a one-standard deviation
increase in INTA (see Table 5, 0.224), the effect of the BC laws on ROA increases by 0.56
percentage points (-0.0254*0.224=0.0056). For firms at the first quartile of INTA (i.e., with
IN T A = 0.542), ROA drops by 0.47 (0.0089 − 0.0254 ∗ 0.542 = 0.0047) percentage points after
the passage of a BC law. In contrast, for firms at the third quartile of INTA (i.e., with IN T A =
0.849), ROA drops by 1.26 percentage points. This represents a drop by 11.5 percentage points
10
The coefficients on the control variables are also similar to those in Giroud and Mueller (2010).
18
with respect to the ROA sample average. In column [3], I replace the variable INTA with
a set of three dummies indicating wether INTA lies in the bottom, medium or top tercile of
its empirical distribution. The size of the effects are similar when INTA is replace by three
INTA terciles. The coefficient on BC is small (-0.0031) and insignificant in the lowest INTA
tercile, statistically significant (at the 10% level) in the medium tercile, and large (-0.0141) and
significant in the highest INTA tercile.
Reverse causality. A concern raised by Bertrand and Mullainathan (2003) is that firms
with low performance might lobby for the passage of a BC law in their state of incorporation.
Low-tangibility firms might have more incentives to lobby for the passage of a BC law if for
instance they are more vulnerable to takeovers than high-tangibility firms. If this is the case,
reverse causality might potentially explain the results. As mentioned by Giroud and Mueller
(2010), this alternative story is unlikely: first, the inclusion of state-year and industry-year
controls accounts for lobbying at the state of location and industry level. Second, antitakeover
laws were usually pushed by the management of a single firm that was afraid of being replaced
in a takeover, suggesting that BC laws were exogenous for almost all firms in our sample. As
an illustation, Romano (1987) mentions that the antitakeover law in Connecticut was promoted
by a firm incorporated in Connecticut, the Aetna Life and Casualty Insurance Company.
11
To
fully address the political economy of these laws, I follow Giroud and Mueller (2010) approach
and replace the BC dummy with four dummy variables: BC(−1) is a dummy that equals one
if the firm is incorporated in a state that will adopt a BC law in one year from now, BC(0)
is a dummy that equals one if the firm is incorporated in a state that adopts a BC law this
year, BC(1) and BC(2+) are dummies that equal one if the firm is incorporated in a state
that adopted a BC law one year ago and two or more years ago, respectively. If the coefficient
on BC(−1) ∗ IN T A was negative and significant, this would indicate the possibility of reverse
causality.
[Table 7 here]
11
Giroud and Mueller (2010) and Garvey and Hanka (1999) use newspaper reports in order to identify the
firms which successfully lobbied for BC laws. They mentioned that excluding those firms did not affect their
results.
19
As shown in column 1 of table 7, the coefficient on the interacted term BC(−1) ∗ IN T A
dummy is statistically insignificant. As a robustness check, I split the sample into two subsamples with respect to the INTA median and regress ROA on the BC(−1), BC(0), BC(1) and
BC(2+) and control variables separately for low-tangibility and high-tangibility firms. In line
with the results in table 6, none of the BC dummies have significant effects on the ROA of
high-tangibility firms. By contrast, BC(0), BC(1) and BC(2+) are negative and significant for
low-tangibility firms. The statistical insignificance of BC(−1) shows that there was no decline
in the ROA of low-tangibility firms before the passage of a BC law.
Alternative measures of asset tangibility.
[Table 8 here]
Columns [1] and [2] reproduce the estimation of column [3] in table 6 for two slightly modified
measure of INTA. Column [1] uses a time-varying measure of INTA, which is equal to one minus
the ratio of property, plant and equipment over total assets in fiscal year t. In column [2], asset
tangibility is computed at the industry level. Industry INTA is computed each year by taking
the mean asset tangibility of all firms in Compustat for each 3-digit SIC codes industry. In both
cases, results are very similar to those in column [3] of table 6, both in terms of economic and
statistical significance.
Column [3] uses a cash-adjusted measure of asset tangibility, which is the ratio of property,
plant and equipment over total assets minus cash holdings. Jensen (1986) argues that the scope
for agency problems between managers and shareholders is larger for firms with high free cash
flow. One possible concern might thus be that the ROA of low-tangibility firms is more adversely
affected by BC laws mostly because these firms hoard more cash (see table 5). Adjusting INTA
for cash addresses this concern. The pattern across cash-adjusted INTA terciles is very similar
to the one in column [3] of table 6.
The last measure of asset tangibility follows Almeida and Campello (2007), which conjecture
that assets of firms in durable goods industries are perceived as less liquid by lenders. Sharpe
(1994) documents high cyclicality of durable goods sales. As a consequence, creditors liquidating
a firm in a durable goods industry are likely to face liquidity-constrained industry counterparts.
The link with the credibility of the liquidation threat follows Shleifer and Vishny (1992) insight
20
that a smaller set of potential buyers reduces the liquidation value of assets.12 I follow Almeida
and Campello (2007) and Sharpe (1994) and assign to durable goods industries the firms with
two-digit SIC codes 24, 25, 30 and 33-37. I restrict the sample to manufacturing firms (i.e., with
SIC codes 2000-3999) and assign other firms to nondurable goods industries. I then interact
BC with a dummy which equals one for firms in durable goods industries. As shown in column
[4], the coefficient on the non-interacted term, BC, is virtually zero, whereas the interaction
term between BC and the durable goods industry dummy is negative and significant at the
one percent level. Consistent with Shleifer and Vishny (1992) insight that less liquid assets
make the liquidation threat less credible, the negative effect of a BC law on firm performance
is economically and statistically significant only in durable goods industries.
The pairwise correlations between INTA and the durable goods industry dummy is small
(i.e., 0.04). The sorting of firms thus significantly differs in column [3] of table 6, and in
column [4] of table 8. Finding converging results for both measures of asset tangibility strongly
suggest that the underlying force identified in the regressions is related to the credibility of the
liquidation threat.
As mentioned in the introduction, earlier work uses leverage as a proxy for the threat of
bankruptcy. As a robustness check, in the appendix, I interact the BC dummy with three
leverage terciles. As shown in column [1], the pattern documented in column [3] of table 6 also
emerges across leverage terciles - that is, the coefficient on the BC dummy is monotonic across
the leverage terciles, and large and highly statistically significant for low-leverage firms.
Other robustness checks. Table 9 provides several robustness checks of the specification
presented in column 3 of table 6.
[Table 9 here]
Competition. Mueller and Giroud (2010, 2011) argue that high takeover vulnerability matters only in non-competitive indutries where the lack of competitive pressure fails to discipline
12
Shleifer and Vishny (1992, p.1389) mentions: “Our model predicts that growth assets such as high technology
firms and cyclical assets such as steel and chemical firms are illiquid because industry buyers are likely to be
themselves severely credit constrained when the owners of these assets need to sell.[...] Cyclical and growth
assets are therefore poor candidates for debt finance, unless they are readily understood by deep pocket investors
outside the industry.”
21
managers. Our results might be driven by Giroud and Mueller (2010, 2011) findings if lowtangibility firms are more likely to belong to non-competitive industries. To address this issue,
I reestimate the equation separately for firms in competitive and non-competitive industries in
rows [1] and [2]. For this, I follow Mueller and Giroud (2010) and compute, for each fiscal year
and three-digit SIC code industry, the HHI, defined as the sum of squared market shares:
HHIjt ≡
X
s2ijt
(4)
sijt is the market share of firm i in industry j in fiscal year t. Market shares are computed
from Compustat using firms’ sales (item SALE). I then sort firms into competitive and noncompetitive industries using the sample HHI median. The results turn out to be very similar
in both subsamples: the sensitivity of ROA to the passage of a BC law increases with the share
of intangible assets on firms’ balance sheet in both competitive and non-competitive industries.
Industries. I also investigate whether the results are not driven by any specific industry.
Firms in the “Hi-Tech” sector represent a large fraction of low-tangibility firms (see table 5).
In row [3], I exclude firms operating in the “Hi-Tech” sector and obtain similar results. More
generally, I find in untabulated regressions that removing any of the 10 Fama-French industries
yields similar findings.
Size. Row [4] adds to the equation an interaction term between the BC dummy and firm
initial size. INTA is negatively correlated with SIZE. If small firms are more adversely affected
by the passage of a BC law than big firms, one would mechanically find that the coefficient on
the BC dummy is monotonic over the INTA terciles. Adding an interaction between BC and
firm size addresses this concern. As shown in row [4], the pattern across the INTA terciles
remains very similar.
Miscellaneous. In row [5], I replace the BC dummy by an antitakeover dummy which equals
one if the firm is incorporated in a state that has passed at least one of the following antitakeover
law: Business Combination, Fair Price or Control Share Acquisition.13 Then, following common
practice in this literature, I exclude in row [6] firms incorporated in Delaware, which represent
13
Enactment years of “Fair Price” and “Control Share Acquisition” antitakeover laws are taken from Bertrand
and Mullainathan (2003).
22
more than half of the firms in the sample. Another concern is that the results might be biased
by differences along some uncontrolled dimensions between the treated and the control group.
I therefore restrict the sample in row [7] to firms incorporated in states that eventually passed
a BC law. In both cases, the estimated coefficients are comparable to those obtained in column
[3] of table 6. Finally, in rows [8] and [9], I use alternative measures of performance, namely
ROA after depreciation (defined as item OIBDP - item DP over lagged assets) and net profit
margin (defined as net income over lagged assets). Again, I find similar results.14
The results presented in this section confirm those obtained with firm-level takeover defenses:
the sensibility of firm performance to changes in takeover vulnerability is significantly higher
for firms holding a large share of intangible assets.
4.3
Equity and Debt Financing
I consider in this section the interaction between corporate governance, asset tangibility and
firms’ use of equity and debt financing. Previous research (e.g., Williamson (1988), Hart and
Moore (1995), Rajan and Zingales (1995)) link the mode of external finance (equity or debt)
with the tangibility of firm assets. By protecting equityholders from managerial misbehavior,
better corporate governance eases equity financing, which is crucial for low-tangibility firms.
[Table 10 here]
Table 10 reports results of the effect of BC laws on equity and debt issues. In column [1],
I find that the average firm issues less equity after the passage of a BC law. In line with the
research mentioned above, there is strong cross-sectional heterogeneity in the variation of equity
issues after the adoption of a BC law. As shown in column [2], equity issues of low-tangibility
firms drop significantly while there is no significant effect on the equity issues of high-tangibility
14
Using both definitions of ROA is an important robustness check. Reporting rules about the recognition of
R&D costs differ across industries. For instance, SFAS 86 issued by the FASB in 1985 allows R&D costs for
software to be capitalized and amortized. See Canibano et al. (2000) for a review on the accounting recognition
of intangibles.
23
firms. For firms in the highest INTA tercile, equity issues drop by 3.2 percentage points after
the adoption of a BC law. The results are identical when I look at net change in equity (columns
[3] and [4]), defined as equity issues minus stock repurchases.
Finally, columns [5] to [8] examine the effect of BC laws on debt financing. As shown in
column [5], debt issues of the average firm are basically unaffected by the passage of a BC law.
However, when the BC dummy is interacted with the INTA terciles, I find that debt issues
of firms in the highest INTA tercile significantly increase (at the 10 percent level) after the
passage of a BC law. For these firms, the increase in debt issues (0.6%) represents one-fifth of
the decrease in equity issues. This might suggest that low-tangibility firms partially substitute
equity for debt financing following a deterioration in corporate governance. However, this
substitution effect is not confirmed in columns [7] and [8], where debt issues are replaced by net
change in debt, defined as the difference between debt issues and debt repayment normalized
by lagged total assets.15
4.4
Firm Growth
I investigate the potential real effects - beyond operating performance - of the drop in firms’
equity issues following the passage of a BC law. If BC laws make it more difficulty for lowtangibility firms to raise external finance, this should ultimately be reflected in the growth of
these firms. To investigate this point, I examine the effect of BC laws on sales and asset growth.
Sales growth (resp. asset growth) is the growth in sales (resp. asset) over the last 3 years. To
account for outliers, both measures are trimmed at the first and ninety-ninth percentiles.
[Table 11 here]
15
When I look at the effect on leverage, I find that leverage of the average firm significantly increases after
the passage of a BC law. Moreover, the effect significantly varies across the INTA terciles: BC laws have no
significant effect in the lowest INTA tercile, whereas leverage of firms in the highest tercile significantly increases.
This increase in leverage is consistent with a substitution effect between debt and equity. However, it might also
simply reflect the drop in operating performance documented in the previous tables. Finally, note that previous
research find contrasting results on the effect of antitakeover laws on leverage. Garvey and Hanka (1999) find
that firms protected by second generation state antitakeover laws substantially reduced their use of debt. By
contrast, Long and Wald (2007) and Litov and John (2010) find that firms protected from takeovers have higher
leverage.
24
As shown in table 11, the results present the same patterns as before: the coefficient on
BC is monotonic over the INTA terciles, small and insignificant in the lowest INTA, and large
and significant in the highest INTA tercile. Again, the effect is economically large for firms in
the highest INTA tercile: Sales growth (asset growth) decrease by 16.7 (18.4) percentage points
after the passage of a BC law, which corresponds to a drop of 9.4% (15.4%) in one sales growth
(asset growth) standard deviation.16
5
Conclusion
The threat of liquidation is considered as a powerful device that mitigates agency problems
between managers and investors. The empirical literature has convincingly shown that good
external corporate governance positively affects firm performance. I document in this paper
that higher takeover vulnerability is associated with higher performance only for firms holding
a large fraction of intangible assets, which supports the hypothesis that liquidation acts a strong
disciplinary device.
The analysis also reveals significant changes in the use of external finance following the passage of antitakeover laws. Theory stresses that equity is more appropriate to finance intangible
assets. In line with theory, difference-in-differences regressions show that equity issues drop
after the passage of BC laws, especially for low-tangibility firms. Moreover, this has real effects
on the growth of low-tangibility firms, which is significantly reduced after the passage of BC
laws.
Taken together, the results presented in the paper have important implications for governance design, e.g. owners of firms with a large of intangible assets should avoid installing
takeover defenses at the IPO.
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In the appendix, Table 13 shows that the results are similar when INTA terciles are replaced by leverage
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31
Figure 1: Takeover vulnerability and Tobin’s Q
For each point of the E index, Figure 1 presents the mean and the median of the industryadjusted Tobin’s Q for both “low-tangibility” and “high-tangibility” firms. Low-tangibility firms
are defined as firms with INTA above the sample median. Tobins’ Q is the market value of
assets divided by the book value of assets, where the market value of assets is the book value of
assets plus the market value of common stock (item CSHO * item PRCC F) minus the sum of
the book value of common stock (item CEQ) and balance sheet deferred taxes (item TXDB).
Tobins’ Q is trimmed at the first and ninety-ninth percentiles of its empirical distribution.
32
Table 1: Takeover Defenses - Summary Statistics
This table presents summary statistics on firm characteristics. INTA – defined as one minus the ratio of property,
plant and equipment (Compustat item PPENT) over total assets – is computed when the firm enters in the sample.
SIZE is the logarithm of total assets (item AT). AGE is the logarithm of one plus the number of years since the
firm has been in the Compustat Database. Tobins’ Q is the market value of assets divided by the book value
of assets, where the market value of assets is the book value of assets plus the market value of common stock
(item CSHO * item PRCC F) minus the sum of the book value of common stock (item CEQ) and balance sheet
deferred taxes (item TXDB). ROA is net income (item NI) total assets. Net profit margin (NPM) is net income
over sales. Return on equity (ROE) is net income divided by the book value of common sotck. Sales growth is
the growth in sales (item SALE) over the previous five years. Tobin’s Q, ROA, NPM, ROE and Sales Growth
are industry-adjusted by subtracting the industry median in a given 48 Fama-French industry and year, and then
trimmed at the first and ninety-ninth percentiles of their empirical distribution. E is the Bebchuk, Cohen and
Ferrell (2009) Entrenchment index. The G index is defined as in Gompers, Ishii and Metrick (2003). The ATI
index is defined as in Cremers and Nair (2005). The sample period is from 1990 to 2007.
Tobin’s Q (Ind-adj.)
ROA (Ind-adj.)
NPM (Ind-adj.)
Sales Growth (Ind-adj.)
ROE (Ind-adj.)
INTA
SIZE
AGE
DELAWARE
S&P 500
E
G Index
ATI Index
Classified Board
Mean
0.291
0.054
0.036
0.257
0.016
0.667
7.122
3.077
0.594
0.311
2.193
9.200
1.740
0.599
Median
0.031
0.036
0.023
0.000
0.033
0.712
6.967
3.178
1.000
0.000
2.000
9.000
2.000
1.000
33
SD
0.955
0.114
0.202
1.101
0.334
0.216
1.500
0.664
0.491
0.463
1.285
2.705
0.864
0.490
Min
-1.438
-0.250
-1.415
-1.179
-2.613
0.034
0.644
0.000
0.000
0.000
0.000
2.000
0.000
0.000
Max
5.467
0.500
1.143
9.439
2.340
1.000
13.587
4.060
1.000
1.000
6.000
19.000
3.000
1.000
Obs.
17281
18593
18707
18521
18705
18998
19087
21262
19087
19087
19087
19087
19087
19087
Table 2: Takeover Defenses and Tobin’s Q
This table presents estimated coefficients from panel regressions of (industry-adjusted) Tobin’s Q on the E index
and control variables. All variables are defined in table 1. INTA(Low), INTA(Medium) and INTA(High) are
three dummies indicating wether INTA lies in the bottom, medium or top tercile of its empirical distribution. The
dependent variable is trimmed at the first and ninety-ninth percentiles. Regressions include year and industry
fixed effects. Standard errors, presented in parenthesis, are clustered at the firm level. The sample period is
from 1990 to 2007. The coefficients are multiplied by 100 for expositional convenience. *, ** and *** denotes
significance at the 10%, 5% and 1%, respectively.
Dependent Variable:
[1]
E
-0.0779∗∗∗
(0.0130)
E * INTA
TOBIN’S Q
[2]
-0.00658
(0.0318)
-0.106∗∗
(0.0505)
E * INTA(High)
E * INTA(Medium)
E * INTA(Low)
SIZE
AGE
DELAWARE
S&P 500
-0.119∗∗∗
(0.0158)
-0.163∗∗∗
(0.0272)
-0.0122
(0.0345)
0.610∗∗∗
(0.0466)
INTA
-0.110∗∗∗
(0.0159)
-0.166∗∗∗
(0.0271)
-0.0216
(0.0347)
0.608∗∗∗
(0.0469)
0.587∗∗∗
(0.169)
INTA(High)
Observations
R2
-0.111∗∗∗
(0.0246)
-0.0812∗∗∗
(0.0225)
-0.0464∗∗
(0.0193)
-0.114∗∗∗
(0.0158)
-0.171∗∗∗
(0.0273)
-0.0168
(0.0346)
0.612∗∗∗
(0.0470)
0.179∗
(0.0943)
0.238∗∗
(0.0965)
INTA(Medium)
Industry Fixed Effects
Year Fixed Effects
[3]
Yes
Yes
Yes
Yes
Yes
Yes
17281
0.091
17195
0.097
17195
0.095
34
Table 3: Takeover Defenses and Operating Performance
This table presents estimated coefficients from panel regressions of (industry-adjusted) measures of operating performance on the E index and control variables. All variables are defined in table 1. INTA(Low), INTA(Medium)
and INTA(High) are three dummies indicating wether INTA lies in the bottom, medium or top tercile of its
empirical distribution. The dependent variable is trimmed at the first and ninety-ninth percentiles. Regressions
include year and industry fixed effects. Standard errors, presented in parenthesis, are clustered at the firm level.
The sample period is from 1990 to 2007. The coefficients are multiplied by 100 for expositional convenience. *,
** and *** denotes significance at the 10%, 5% and 1%, respectively.
Dependent Variable:
E
E * INTA(High)
E * INTA(Medium)
E * INTA(Low)
SIZE
AGE
DELAWARE
S&P 500
Ln(B/M)
INTA(Medium)
INTA(High)
Industry Fixed Effects
Year Fixed Effects
Observations
R2
ROA
[1]
[2]
-0.0029∗∗∗
(0.0009)
-0.0066∗∗∗
(0.0020)
-0.0029∗
(0.0015)
-0.0001
(0.0011)
0.0087∗∗∗
0.0088∗∗∗
(0.0016)
(0.0017)
0.0068∗∗∗
0.0061∗∗∗
(0.0021)
(0.0021)
-0.0102∗∗∗ -0.0101∗∗∗
(0.0025)
(0.0025)
-0.0039
-0.0040
(0.0036)
(0.0037)
-0.0334∗∗∗ -0.0330∗∗∗
(0.0024)
(0.0024)
0.0112∗
(0.0058)
0.0115∗
(0.0065)
Yes
Yes
Yes
Yes
16797
16711
0.232
0.233
NPM
[3]
[4]
-0.0031∗∗
(0.0015)
-0.0084∗∗
(0.0035)
-0.0037
(0.0023)
0.0014
(0.0019)
0.0180∗∗∗
0.0178∗∗∗
(0.0026)
(0.0026)
0.0100∗∗∗
0.0096∗∗∗
(0.0035)
(0.0035)
-0.0119∗∗∗ -0.0115∗∗∗
(0.0038)
(0.0039)
-0.0100
-0.0100
(0.0061)
(0.0062)
-0.0278∗∗∗ -0.0275∗∗∗
(0.0033)
(0.0033)
0.0123
(0.0088)
0.0139
(0.0103)
Yes
Yes
Yes
Yes
16742
16657
0.355
0.357
35
Sales Growth
[5]
[6]
-0.0476∗∗∗
(0.0104)
-0.1001∗∗∗
(0.0211)
-0.0211
(0.0166)
-0.0176
(0.0175)
0.1483∗∗∗
0.1587∗∗∗
(0.0142)
(0.0147)
-0.5548∗∗∗ -0.5463∗∗∗
(0.0292)
(0.0292)
-0.0152
-0.0204
(0.0269)
(0.0268)
-0.2348∗∗∗ -0.2425∗∗∗
(0.0398)
(0.0400)
-0.1521∗∗∗ -0.1487∗∗∗
(0.0163)
(0.0163)
0.0202
(0.0691)
0.3657∗∗∗
(0.0773)
Yes
Yes
Yes
Yes
16607
16525
0.142
0.150
ROE
[7]
[8]
-0.0024
(0.0023)
-0.0081∗
(0.0048)
-0.0027
(0.0042)
0.0013
(0.0030)
0.0333∗∗∗
0.0327∗∗∗
(0.0042)
(0.0043)
0.0305∗∗∗
0.0292∗∗∗
(0.0057)
(0.0058)
-0.0173∗∗∗
-0.0164∗∗
(0.0065)
(0.0065)
-0.0314∗∗∗ -0.0316∗∗∗
(0.0095)
(0.0097)
-0.1159∗∗∗ -0.1153∗∗∗
(0.0081)
(0.0081)
0.0102
(0.0155)
0.0006
(0.0155)
Yes
Yes
Yes
Yes
16817
16732
0.147
0.148
Table 4: Asset Tangibility and Takeover Defenses
This table presents estimated coefficients from panel regressions of takeover vulnerability measures on INTA
and control variables. All variables are defined in table 1. Regressions include year and industry fixed effects.
Standard errors, presented in parentheses, are clustered at the firm level. The sample period is from 1990 to
2007. *, ** and *** denotes significance at the 10%, 5% and 1%, respectively.
Dependent Variable:
INTA
SIZE
AGE
DELAWARE
S&P 500
Ln(B/M)
Industry Fixed Effects
Year Fixed Effects
Observations
R2
E INDEX
G INDEX
ATI INDEX
[1]
[2]
[3]
CLASSIFIED
BOARD
[4]
-0.763∗∗∗
(0.191)
-0.0659∗∗
(0.0272)
0.236∗∗∗
(0.0507)
0.217∗∗∗
(0.0629)
0.210∗∗
(0.0816)
0.101∗∗∗
(0.0276)
Yes
Yes
-1.210∗∗∗
(0.384)
0.0375
(0.0569)
1.132∗∗∗
(0.104)
-0.158
(0.132)
0.538∗∗∗
(0.170)
0.0907∗
(0.0515)
Yes
Yes
-0.257∗∗
(0.130)
0.0394∗∗
(0.0182)
-0.0440
(0.0336)
0.426∗∗∗
(0.0404)
0.113∗∗
(0.0540)
0.0260
(0.0178)
Yes
Yes
-0.213∗∗∗
(0.0749)
-0.00779
(0.0108)
-0.0118
(0.0196)
0.0242
(0.0243)
0.0383
(0.0316)
0.0202∗
(0.0105)
Yes
Yes
16975
0.093
16975
0.178
16975
0.120
16975
0.054
36
Table 5: Business Combination Laws - Summary Statistics
Panel A of this table presents summary statistics for the sample of 81,854 firm-year observations with non-missing information on ROA. INTA – defined as one minus the ratio of property, plant and equipment (Compustat item PPENT) over
total assets – is computed when the firm enters in the sample. SIZE is the logarithm of total assets. AGE is the logarithm
of one plus the number of years since the firm has been in the Compustat Database. ROA is operating income before
depreciation and amortization (item OIBDP) divided by lagged total assets. LEV is long term debt (item DLTT) plus short
term debt (item DLC) over total assets. EQUITY ISSUES is sale of common and preferred stock (item SSTK) divided by
lagged assets. CHANGE IN EQUITY is defined as equity issuance minus purchase of common and preferred stock (item
PRSTKC) divided by lagged assets. DEBT ISSUES is long term debt issue (item DLTIS) over lagged assets, and CHANGE
IN DEBT is defined as long term debt issue minus long term debt repayment (item DLTR) normalized by lagged assets.
Panel B also reports the mean, median and standard deviation of the main variables separately for i) firms incorporated in
a state that passed a BC law during the sample period and firms incorporated in a state that never passed a BC law during
the sample period ; and ii) low-tangibility and high-tangibility firms. Low-tangibility firms (resp. High-tangibility firms)
comprise all firms with INTA below (resp. above) the sample median. Finally, Panel C shows a broad industry distribution
of the sample based on the 10 Fama-French industries classification. The share of high-tangibility and low-tangibility firms
as a percent of the total number of firms per industry is reported in parenthesis. All variables defined as ratios are trimmed
at the first and ninety-ninth percentiles of their empirical distribution. The sample period is from 1976 to 1995.
ROA
SIZE
AGE
INTA
LEV
EQUITY ISSUES
CHANGE IN EQUITY
DEBT ISSUES
CHANGE IN DEBT
ROA
SIZE
AGE
INTA
LEV
EQUITY ISSUES
CHANGE IN EQUITY
DEBT ISSUES
CHANGE IN DEBT
ROA
SIZE
AGE
INTA
LEV
EQUITY ISSUES
CHANGE IN EQUITY
DEBT ISSUES
CHANGE IN DEBT
10 FF Industries
Consumer Nondurables
Consumer Durables
Manufacturing
Energy
HiTech
Telecommunications
Shops
Healthcare
Other
Total
Panel A: Firm Characteristics
Mean
Median
SD
Min
Max
Obs.
0.110
0.135
0.206
-1.146
0.708
81854
4.007
3.912
2.232
-6.908
12.435
81854
2.332
2.303
0.804
0.693
3.829
81854
0.675
0.727
0.224
0.070
0.997
80196
0.274
0.240
0.230
0.000
1.472
80994
0.083
0.001
0.293
0.000
3.322
79502
0.075
0.000
0.283
-0.116
3.229
76828
0.094
0.011
0.190
0.000
1.502
78473
0.018
0.000
0.122
-0.381
0.855
76056
Panel B: Firm Characteristics for subsamples
Never BC
Eventually BC
Mean
Median
SD
Mean
Median
SD
0.081
0.115
0.231
0.115
0.138
0.201
3.025
2.873
2.094
4.172
4.074
2.212
2.117
2.079
0.713
2.368
2.398
0.812
0.642
0.708
0.246
0.680
0.729
0.220
0.274
0.242
0.241
0.274
0.240
0.229
0.101
0.001
0.318
0.080
0.001
0.288
0.094
0.000
0.312
0.072
0.000
0.278
0.092
0.005
0.185
0.094
0.012
0.191
0.022
0.000
0.123
0.012
-0.001
0.119
INTA ≤ Median (=0.72)
INTA> Median (=0.72)
Mean
Median
SD
Mean
Median
SD
0.137
0.153
0.175
0.084
0.115
0.231
4.502
4.435
2.325
3.501
3.474
1.998
2.432
2.485
0.817
2.241
2.197
0.783
0.497
0.542
0.179
0.852
0.849
0.074
0.302
0.271
0.229
0.242
0.198
0.227
0.063
0.001
0.241
0.105
0.001
0.338
0.056
0.000
0.237
0.095
0.000
0.324
0.105
0.024
0.195
0.083
0.002
0.184
0.020
-0.001
0.125
0.016
-0.000
0.118
Panel C: Industry Distribution
N
(%)
N
(%)
3028
(46.5%)
3486
(53.5%)
1586
(55.8%)
1255
(44.2%)
7561
(44.7%)
9343
(55.3%)
707
(14.6%)
4137
(85.4%)
10358
(70.2%)
4390
(29.8%)
866
(29.2%)
2096
(70.8%)
6717
(56.4%)
5199
(43.6%)
3920
(60.5%)
2558
(39.5%)
5358
(41.3%)
7631
(58.7%)
40,101
40,095
37
Table 6: Business Combination Laws and Operating Performance
This table presents regression results of the impact of the BC laws on firms’ ROA. ROA is defined as operating
income before depreciation and amortization over lagged total assets. INTA – defined as one minus the ratio of
property, plant and equipment (Compustat item PPENT) over total assets – is computed when the firm enters
in the sample. INTA(Low), INTA(Medium) and INTA(High) are three dummies indicating wether INTA lies in
the bottom, medium or top tercile of its empirical distribution. BC is a dummy that equals one if the firm is
incorporated in a state that has passed a BC law. Control variables are firm size, the square of firm size, firm
age, the mean of the ROA in the firm’s 3-digit industry excluding the firm itself, and the mean of the ROA in
the firm’s state of location excluding the firm itself. Standard errors, presented in parenthesis, are clustered at
the state of incorporation level. The sample period is from 1976 to 1995. *, ** and *** denotes significance at
the 10%, 5% and 1%, respectively.
Dependent variable:
[1]
BC
-0.00870∗
(0.00495)
BC * INTA
ROA
[2]
0.00898
(0.0112)
-0.0254∗
(0.0131)
BC * INTA(High)
BC * INTA(Medium)
BC * INTA(Low)
STATE-YEAR
INDUSTRY-YEAR
SIZE
SIZE2
AGE
Firm Fixed Effects
Year Fixed Effects
Observations
R2
0.243∗∗∗
(0.0354)
0.232∗∗∗
(0.0246)
0.0579∗∗∗
(0.00517)
-0.00449∗∗∗
(0.000381)
-0.0217∗∗∗
(0.00674)
Yes
Yes
81614
0.624
38
[3]
0.241∗∗∗
(0.0324)
0.228∗∗∗
(0.0236)
0.0589∗∗∗
(0.00546)
-0.00462∗∗∗
(0.000418)
-0.0229∗∗∗
(0.00703)
Yes
Yes
79962
0.623
-0.0141∗∗
(0.00554)
-0.00785∗
(0.00463)
-0.00310
(0.00641)
0.241∗∗∗
(0.0323)
0.229∗∗∗
(0.0238)
0.0588∗∗∗
(0.00546)
-0.00461∗∗∗
(0.000417)
-0.0226∗∗∗
(0.00703)
Yes
Yes
79962
0.623
Table 7: Reverse Causality
This table presents regression results of the impact of the BC laws on firms’ ROA. ROA is defined as operating
income before depreciation and amortization over lagged total assets. INTA – defined as one minus the ratio of
property, plant and equipment (Compustat item PPENT) over total assets – is computed when the firm enters
in the sample. Control variables are firm size, the square of firm size, firm age, the mean of the ROA in the firm’s
3-digit industry excluding the firm itself, and the mean of the ROA in the firm’s state of location excluding the
firm itself. BC(−1) is a dummy that equals one if the firm is incorporated in a state that will adopt a BC law
in one year from now, BC(0) is a dummy that equals one if the firm is incorporated in a state that adopts a BC
law this year, BC(1) and BC(2+) are dummies that equal one if the firm is incorporated in a state that adopted
a BC law one year ago and two or more years ago, respectively. Standard errors, presented in parenthesis, are
clustered at the state of incorporation level. The sample period is from 1976 to 1995. *, ** and *** denotes
significance at the 10%, 5% and 1%, respectively.
Dependent variable:
[1]
BC(-1)
BC(0)
BC(1)
BC(2+)
BC(-1)*INTA
BC(0)*INTA
BC(1)*INTA
BC(2+)*INTA
STATE-YEAR
INDUSTRY-YEAR
SIZE
SIZE2
AGE
Firm Fixed Effects
Year Fixed Effects
Observations
R2
ROA
Low-Tangibility
High-Tangibility
-0.00472
(0.00635)
-0.0260∗∗∗
(0.00873)
-0.0268∗∗∗
(0.00923)
-0.0223∗∗
(0.00900)
-0.00153
(0.00408)
0.00327
(0.00537)
0.00440
(0.00701)
0.00807
(0.00853)
0.297∗∗∗
(0.0374)
0.196∗∗∗
(0.0214)
0.0651∗∗∗
(0.00667)
-0.00513∗∗∗
(0.000726)
-0.0224
(0.0136)
Yes
Yes
39023
0.613
0.205∗∗∗
(0.0427)
0.254∗∗∗
(0.0300)
0.0501∗∗∗
(0.00736)
-0.00396∗∗∗
(0.000507)
-0.0226∗∗∗
(0.00524)
Yes
Yes
40939
0.623
0.00728
(0.00870)
0.0185
(0.0136)
0.00575
(0.0184)
0.0106
(0.0117)
-0.0153
(0.0117)
-0.0438∗∗∗
(0.0159)
-0.0247
(0.0223)
-0.0251∗
(0.0132)
0.241∗∗∗
(0.0326)
0.227∗∗∗
(0.0233)
0.0590∗∗∗
(0.00544)
-0.00462∗∗∗
(0.000416)
-0.0229∗∗∗
(0.00696)
Yes
Yes
79962
0.623
39
Table 8: Alternative Measures of Asset Tangibility
This table presents regression results of the impact of BC laws on firms’ ROA. ROA is defined as operating
income before depreciation and amortization over lagged total assets. BC is a dummy that equals one if the firm
is incorporated in a state that has passed a BC law. Control variables are firm size, the square of firm size, firm
age, the mean of the ROA in the firm’s 3-digit industry excluding the firm itself, and the mean of the ROA in the
firm’s state of location excluding the firm itself. In column [1], I use a time-varying measure of asset tangibility,
IN T Ait , which is equal to one minus the ratio of property, plant and equipment over total assets in fiscal year t
for firm i. In column [2], industry INTA is computed each year by taking the mean asset tangibility of all firms
in Compustat for each 3-digit SIC codes industry. In column [3], cash-adjusted INTA is defined as one minus the
initial ratio of property, plant and equipment over total assets minus cash holdings. In column [4], the sample is
restricted to manufacturing firms (SIC 2000-3999). The durable industry dummy equals one for firms that belong
to industries with two-digit SIC codes 24, 25, 30 and 33-37, and zero otherwise. Standard errors, presented in
parenthesis, are clustered at the state of incorporation level. The sample period is from 1976 to 1995. *, ** and
*** denotes significance at the 10%, 5% and 1%, respectively.
Dependent variable:
Time-varying INTA
[1]
ROA
Industry INTA Cash-adj INTA
[2]
[3]
BC
BC* DURABLE INDUSTRY
BC * INTA(High)
BC * INTA(Medium)
BC * INTA(Low)
STATE-YEAR
INDUSTRY-YEAR
SIZE
SIZE2
AGE
INTA(Medium)
INTA(High)
Firm Fixed Effects
Year Fixed Effects
Observations
R2
-0.0151∗∗∗
(0.00498)
-0.00988∗
(0.00504)
0.000233
(0.00626)
0.238∗∗∗
(0.0326)
0.222∗∗∗
(0.0242)
0.0660∗∗∗
(0.00446)
-0.00532∗∗∗
(0.000373)
-0.0244∗∗∗
(0.00622)
0.0175∗∗∗
(0.00223)
0.0423∗∗∗
(0.00608)
Yes
Yes
79977
0.627
-0.0148∗∗∗
(0.00505)
-0.0140∗∗∗
(0.00505)
0.00312
(0.00653)
0.236∗∗∗
(0.0357)
0.227∗∗∗
(0.0248)
0.0583∗∗∗
(0.00524)
-0.00453∗∗∗
(0.000389)
-0.0215∗∗∗
(0.00660)
0.0152∗∗∗
(0.00445)
0.0218∗∗∗
(0.00475)
Yes
Yes
81614
0.624
40
Industry Durability
[4]
0.000638
(0.00559)
-0.0180∗∗∗
(0.00451)
-0.0129∗∗
(0.00556)
-0.0128∗∗
(0.00478)
-0.00186
(0.00595)
0.247∗∗∗
(0.0346)
0.234∗∗∗
(0.0262)
0.0626∗∗∗
(0.00445)
-0.00494∗∗∗
(0.000323)
-0.0251∗∗∗
(0.00644)
0.249∗∗∗
(0.0729)
0.236∗∗∗
(0.0262)
0.0611∗∗∗
(0.00421)
-0.00465∗∗∗
(0.000402)
-0.0248∗∗∗
(0.00645)
Yes
Yes
79849
0.622
Yes
Yes
42851
0.628
Table 9: Business Combination Laws and Operating Performance - Robustness
This table presents estimated coefficients from variants of the specification in column [3] of table 6. Only the
coefficients on the interaction term between the BC dummy and the INTA terciles are reported. In rows [1] and
[2], I split the sample into two groups based on the sample HHI median. HHI is the Herfindahl-Hirschman index,
which is computed as the sum of squared market shares of all firms in a given 3 digit SIC codes industry. Market
shares are computed from Compustat based on firms’ sales. In row [3], I exclude Hi-Tech firms (SIC codes 3570–
3579, 3622, 3660–3692, 3694–3699, 3810–3839, 7370–7379, 7391, 8730–8734). In row [4], I add an interaction term
between the BC dummy and firm initial size. In row [5], I replace the BC dummy by an antitakeover dummy
which equals one if the firm is incorporated in a state that has passed at least one of the following antitakeover
law: business combination, fair price or control share acquisition. I exclude firms incorporated in Delaware in
row [6], incorporated in a state that never passed a BC law in row [7]. In row [8] (resp. row [9]), I use operating
income after depreciation and amortization (resp. net imcome) defined as item OIBDP minus item DP over
lagged total assets (resp. item NI over lagged total assets) as an alternative measure of operating performance.
Standard errors, presented in parenthesis, are clustered at the state of incorporation level. The sample period is
from 1976 to 1995. The coefficients are multiplied by 100 for expositional convenience. *, ** and *** denotes
significance at the 10%, 5% and 1%, respectively.
Dependent Variable:
[1] Competitive Industries
[2] Non-competitive Industries
[3] Excluding Hi-Tech
[4] Controlling for BC*Size0
[5] First Antitakeover Law
[6] Non-Delaware
[7] “Eventually BC”
[8] ROA (after depreciation)
[9] Net Margin
BC*INTA(Low)
ROA
BC*INTA(Medium)
BC*INTA(High)
Obs.
-0.0039
(0.0069)
-0.0023
(0.0078)
-0.00372
(0.0061)
-0.0117
(0.0083)
-0.0011
(0.0059)
0.0029
(0.0070)
0.0023
(0.0046)
0.0012
(0.0067)
-0.0045
(0.0053)
-0.0066
(0.0065)
-0.0085
(0.0058)
-0.00656
(0.0049)
-0.0151∗
(0.0090)
-0.0039
(0.0047)
-0.0080∗
(0.0046)
-0.0029
(0.0025)
-0.0065
(0.0053)
-0.0101∗∗
(0.0049)
-0.0125∗∗
(0.0056)
-0.0133∗∗
(0.0066)
-0.0138∗∗
(0.0069)
-0.0193∗∗
(0.0090)
-0.0133∗∗
(0.0063)
-0.0154∗
(0.0083)
-0.0085∗∗
(0.0035)
-0.0186∗∗∗
(0.0055)
-0.0261∗∗∗
(0.0055)
39646
41
40316
65454
79962
79962
39655
68511
79931
80138
42
Firm Fixed Effects
Year Fixed Effects
N
R2
AGE
SIZE2
SIZE
INDUSTRY-YEAR
STATE-YEAR
BC * INTA(Low)
BC * INTA(Medium)
BC * INTA(High)
BC
Dependent variable:
0.0843∗∗
(0.0338)
0.146∗∗∗
(0.0177)
0.0503∗∗∗
(0.00704)
-0.00537∗∗∗
(0.000587)
-0.264∗∗∗
(0.0136)
Yes
Yes
80577
0.430
-0.0319∗∗∗
(0.00848)
-0.0235∗∗∗
(0.00748)
-0.00842
(0.00701)
0.0880∗∗
(0.0339)
0.147∗∗∗
(0.0175)
0.0521∗∗∗
(0.00744)
-0.00555∗∗∗
(0.000612)
-0.268∗∗∗
(0.0137)
Yes
Yes
78916
0.431
Equity Issues
[1]
[2]
-0.0209∗∗∗
(0.00695)
0.107∗∗
(0.0404)
0.137∗∗∗
(0.0175)
0.0479∗∗∗
(0.00659)
-0.00517∗∗∗
(0.000521)
-0.252∗∗∗
(0.0129)
Yes
Yes
77828
0.432
-0.0287∗∗∗
(0.00897)
-0.0225∗∗∗
(0.00719)
-0.00977
(0.00659)
0.109∗∗
(0.0414)
0.139∗∗∗
(0.0177)
0.0493∗∗∗
(0.00695)
-0.00531∗∗∗
(0.000548)
-0.256∗∗∗
(0.0130)
Yes
Yes
76279
0.432
Net Change in Equity
[3]
[4]
-0.0200∗∗∗
(0.00680)
is from 1976 to 1995. *, ** and *** denotes significance at the 10%, 5% and 1%, respectively.
0.0790∗∗∗
(0.0242)
0.119∗∗∗
(0.0132)
0.0286∗∗∗
(0.00212)
0.000808∗∗∗
(0.000230)
-0.0494∗∗∗
(0.00362)
Yes
Yes
79854
0.359
0.00643∗
(0.00346)
0.00419
(0.00336)
-0.00778
(0.00603)
0.0840∗∗∗
(0.0239)
0.121∗∗∗
(0.0131)
0.0287∗∗∗
(0.00228)
0.000810∗∗∗
(0.000238)
-0.0503∗∗∗
(0.00397)
Yes
Yes
78195
0.357
Debt Issues
[5]
[6]
0.00121
(0.00350)
0.0813∗
(0.0420)
0.149∗∗∗
(0.0162)
0.0155∗∗∗
(0.00212)
0.00103∗∗∗
(0.000175)
-0.0105∗∗∗
(0.00223)
Yes
Yes
77262
0.199
0.0000579
(0.00331)
0.00183
(0.00256)
-0.00341
(0.00475)
0.0971∗∗
(0.0368)
0.149∗∗∗
(0.0157)
0.0160∗∗∗
(0.00202)
0.000985∗∗∗
(0.000169)
-0.0108∗∗∗
(0.00216)
Yes
Yes
75763
0.199
Net Change in Debt
[7]
[8]
-0.000507
(0.00321)
debt repayment (item DLTR) divided by lagged assets. Standard errors, presented in parenthesis, are clustered at the state of incorporation level. The sample period
PRSTKC) divided by lagged assets. Debt issues is defined as long term debt issue (item DLTIS) over lagged assets. Net change in debt is defined as debt issues minus
and preferred stock (item SSTK) divided by lagged assets. Net change in equity is defined as equity issuance minus purchase of common and preferred stock (item
excluding the firm itself, and the mean of the dependent variable in the firm’s state of location excluding the firm itself. Equity issues are defined as sale of common
in a state that has passed a BC law. Control variables are firm size, the square of firm size, firm age, the mean of the dependent variable in the firm’s 3-digit industry
equipment (Compustat item PPENT) over total assets – is computed when the firm enters in the sample. BC is a dummy that equals one if the firm is incorporated
This table presents regression results of the impact of the BC laws on the firms’ access to external finance. INTA – defined as one minus the ratio of property, plant and
Table 10: Equity and Debt Financing
Table 11: Business Combination Laws and Growth
This table presents regression results of the impact of the BC laws on the growth of firms. Sales growth (resp.
asset growth) is the growth in sales (resp. asset) over the last 3 years. Control variables are firm size, the square
of firm size, firm age, the mean of the dependent variable in the firm’s 3-digit industry excluding the firm itself,
and the mean of the dependent variable in the firm’s state of location excluding the firm itself. Standard errors,
presented in parenthesis, are clustered at the state of incorporation level. The sample period is from 1976 to
1995. *, ** and *** denotes significance at the 10%, 5% and 1%, respectively.
Dependent variable:
BC
BC * INTA(High)
BC * INTA(Medium)
BC * INTA(Low)
STATE-YEAR
INDUSTRY-YEAR
SIZE
SIZE2
AGE
Firm Fixed Effects
Year Fixed Effects
Observations
R2
Sales Growth
[1]
[2]
-0.0763
(0.0498)
-0.167∗∗∗
(0.0441)
-0.0531
(0.0394)
-0.0116
(0.0673)
0.0901∗∗∗
0.0879∗∗
(0.0284)
(0.0387)
0.109∗∗∗
0.110∗∗∗
(0.0142)
(0.0150)
0.650∗∗∗
0.663∗∗∗
(0.0620)
(0.0630)
-0.0320∗∗∗ -0.0338∗∗∗
(0.00375)
(0.00387)
-1.667∗∗∗
-1.676∗∗∗
(0.114)
(0.110)
Yes
Yes
Yes
Yes
64829
63548
0.433
0.440
43
Asset Growth
[3]
[4]
-0.0972∗
(0.0500)
-0.184∗∗∗
(0.0505)
-0.0919∗
(0.0486)
-0.0172
(0.0675)
0.128∗∗∗
0.119∗∗∗
(0.0383)
(0.0420)
0.249∗∗∗
0.245∗∗∗
(0.0252)
(0.0274)
0.998∗∗∗
1.002∗∗∗
(0.0904)
(0.0919)
-0.0501∗∗∗ -0.0511∗∗∗
(0.00552)
(0.00590)
-1.390∗∗∗
-1.410∗∗∗
(0.141)
(0.139)
Yes
Yes
Yes
Yes
64824
63507
0.488
0.491
Appendix
44
Table 12: States of incorporation and states of location
This table indicates the number of firms by state of location – i.e., in which a firm’s headquarters are located
– and by state of incorporation. “BC year” is the year in which a business combination law was passed in the
state.
State
Delaware
New York
California
Nevada
Minnesota
Colorado
Florida
Texas
Massachusetts
New Jersey
Pennsylvania
Ohio
Georgia
Virginia
Michigan
Utah
Maryland
Washington
Indiana
Wisconsin
North Carolina
Oregon
Tennessee
Oklahoma
Missouri
Illinois
Arizona
Kansas
Connecticut
Iowa
Louisiana
South Carolina
Rhode Island
Wyoming
Kentucky
New Mexico
Maine
Alabama
Arkansas
Hawaii
Mississippi
Idaho
New Hampshire
West Virginia
District of Columbia
Montana
Nebraska
North Dakota
South Dakota
Vermont
Alaska
Total
BC year
1988
1985
1991
1987
1989
1986
1989
1990
1988
1988
1989
1989
1987
1986
1987
1988
1991
1986
1989
1987
1989
1989
1988
1990
1989
1987
1988
1988
1988
1990
Number of firms
State of
State of
incorporation location
4959
27
468
905
417
1438
272
98
268
301
233
319
231
467
223
880
208
445
206
515
184
347
170
316
112
251
110
202
107
194
97
92
92
151
86
134
82
109
75
111
73
164
69
91
58
124
51
110
49
136
47
392
33
140
33
67
29
209
25
41
22
47
18
45
14
33
14
7
13
52
13
20
10
7
8
44
7
33
7
13
7
28
6
14
6
42
6
9
5
16
5
10
5
26
5
5
5
8
5
12
3
4
9251
9251
45
Number of firms incorporated in...
State of
Delaware
Other states
location
25 (92.6%)
2 (7.4%)
309 (34.1%)
521 (57.6%)
75 (8.3%)
368 (25.6%)
908 (63.1%)
162 (11.3%)
51 (52.0%)
32 (32.7%)
15 (15.3%)
215 (71.4%)
75 (24.9%)
11 (3.7%)
125 (39.2%)
146 (45.8%)
48 (15.0%)
178 (38.1%)
209 (44.8%)
80 (17.1%)
191 (21.7%)
538 (61.1%)
151 (17.2%)
176 (39.6%)
234 (52.6%)
35 (7.9%)
139 (27.0%)
295 (57.3%)
81 (15.7%)
132 (38.0%)
178 (51.3%)
37 (10.7%)
144 (45.6%)
139 (44.0%)
33 (10.4%)
88 (35.1%)
126 (50.2%)
37 (14.7%)
68 (33.7%)
100 (49.5%)
34 (16.8%)
92 (47.4%)
81 (41.8%)
21 (10.8%)
48 (52.2%)
33 (35.9%)
11 (12.0%)
46 (30.5%)
85 (56.3%)
20 (13.2%)
69 (51.5%)
44 (32.8%)
21 (15.7%)
58 (53.2%)
35 (32.1%)
16 (14.7%)
62 (55.9%)
38 (34.2%)
11 (9.9%)
61 (37.2%)
73 (44.5%)
30 (18.3%)
56 (61.5%)
23 (25.3%)
12 (13.2%)
45 (36.3%)
56 (45.2%)
23 (18.5%)
38 (34.5%)
56 (50.9%)
16 (14.5%)
32 (23.5%)
79 (58.1%)
25 (18.4%)
38 (9.7%)
296 (75.5%)
58 (14.8%)
28 (20.0%)
69 (49.3%)
43 (30.7%)
24 (35.8%)
32 (47.8%)
11 (16.4%)
21 (10.0%)
151 (72.2%)
37 (17.7%)
16 (39.0%)
15 (36.6%)
10 (24.4%)
16 (34.0%)
24 (51.1%)
7 (14.9%)
17 (37.8%)
22 (48.9%)
6 (13.3%)
12 (36.4%)
15 (45.5%)
6 (18.2%)
3 (42.9%)
0 (0.0%)
4 (57.1%)
12 (23.1%)
30 (57.7%)
10 (19.2%)
6 (30.0%)
6 (30.0%)
8 (40.0%)
3 (42.9%)
4 (57.1%)
0 (0.0%)
7 (15.9%)
33 (75.0%)
4 (9.1%)
7 (21.2%)
21 (63.6%)
5 (15.2%)
5 (38.5%)
7 (53.8%)
1 (7.7%)
6 (21.4%)
16 (57.1%)
6 (21.4%)
1 (7.1%)
10 (71.4%)
3 (21.4%)
3 (7.1%)
29 (69.0%)
10 (23.8%)
5 (55.6%)
4 (44.4%)
0 (0.0%)
0 (0.0%)
14 (87.5%)
2 (12.5%)
5 (50.0%)
3 (30.0%)
2 (20.0%)
4 (15.4%)
18 (69.2%)
4 (15.4%)
3 (60.0%)
0 (0.0%)
2 (40.0%)
4 (50.0%)
3 (37.5%)
1 (12.5%)
5 (41.7%)
6 (50.0%)
1 (8.3%)
2 (50.0%)
2 (50.0%)
0 (0.0%)
3069 (33.2%) 4934 (53.3%) 1248 (13.5%)
Table 13: Business Combination Laws and Leverage
This table presents regression results of the impact of the BC laws on firms’ ROA, sales growth and asset growth.
ROA is defined as operating income before depreciation and amortization over lagged total assets. Sales growth
(resp. asset growth) is the growth in sales (resp. asset) over the last 3 years. Firm leverage, LEV, is defined as
total debt (compustat item DLC+compustat item DLTT) over total assets, and computed when the firm enters
in the sample. Control variables are firm size, the square of firm size, firm age, the mean of the dependent
variable in the firm’s 3-digit industry excluding the firm itself, and the mean of the dependent variable in the
firm’s state of location excluding the firm itself. Standard errors, presented in parenthesis, are clustered at the
state of incorporation level. The sample period is from 1976 to 1995. *, ** and *** denotes significance at the
10%, 5% and 1%, respectively.
Dependent Variable:
BC * LEV(Low)
BC * LEV(Medium)
BC * LEV(High)
STATE-YEAR
INDUSTRY-YEAR
SIZE
SIZE2
AGE
Firm Fixed Effects
Year Fixed Effects
Observations
R2
ROA
[1]
-0.0154∗∗
(0.00646)
-0.00887
(0.00744)
-0.00246
(0.00577)
0.241∗∗∗
(0.0356)
0.230∗∗∗
(0.0237)
0.0571∗∗∗
(0.00512)
-0.00439∗∗∗
(0.000377)
-0.0234∗∗∗
(0.00699)
Yes
Yes
80888
0.620
46
Sales Growth
[2]
-0.168∗∗∗
(0.0452)
-0.0684
(0.0602)
0.0382
(0.0879)
0.0857∗∗∗
(0.0274)
0.112∗∗∗
(0.0144)
0.653∗∗∗
(0.0604)
-0.0323∗∗∗
(0.00375)
-1.648∗∗∗
(0.110)
Yes
Yes
64291
0.430
Asset Growth
[3]
-0.169∗∗∗
(0.0405)
-0.0919
(0.0588)
-0.0172
(0.0648)
0.127∗∗∗
(0.0363)
0.246∗∗∗
(0.0254)
0.989∗∗∗
(0.0852)
-0.0492∗∗∗
(0.00519)
-1.379∗∗∗
(0.135)
Yes
Yes
64286
0.488