Does Corporate Governance Matter More for Firmswith High Financial Slack? ∗

Kose John † Yuanzhi Li ‡ Jiaren Pang §

This version: June 30, 2015

Abstract

We examine whether and how the effect of corporate governance depends on afirm’s financial slack, financial resources not committed to any specific use. On onehand, financial slack may be spent by self-interested managers for their private ben-efits, so its level is positively associated with the degree of agency conflicts. Thisimplies that corporate governance matters more for high financial slack firms (waste-ful spending hypothesis). On the other hand, financial slack provides insurance againstfuture uncertainties; a low level of financial slack may signal that managers are impru-dent and engage in excess risk taking. Then corporate governance is more effectivefor low financial slack firms (precautionary needs hypothesis). We differentiate thetwo hypotheses using the passage of anti-takeover laws to identify exogenous varia-tion in governance. Consistent with the wasteful spending hypothesis, we find thatthe laws’ passage has a larger negative impact on the operating and stock marketperformance of high financial slack firms. Further analysis of the source of wastefulspending shows that these firms do not invest more but are less efficient at cost man-agement than low financial slack firms after the passage of BC laws. Our findingssuggest that shareholder activism and government regulations aiming to improve cor-porate governance can be more efficient by focusing on firms with high financial slack.

Keywords: corporate governance, financial slack, business combination laws

JEL Codes: G34 G38

∗We are grateful to Xavier Giroud, Kun Huang, David Reebs, David Yermack, Feng Zhang, seminarparticipants at Temple University, University of New Orleans, and Tulane University, and conferenceparticipants at the 2011 Chinese International Conference in Finance for their helpful comments. Weespecially thank Martijn Cremers for sharing G-index data of the 1977-89 period, and David Yermack forsharing various governance data of Fortune 500 firms from 1984 to 1991. All errors are our own.†Kose John is from the Department of Finance, Stern School of Business, New York University. E-mail:

kjohn@stern.nyu.edu. Phone: 212-998-0337.‡Yuanzhi Li is from the Department of Finance, Fox School of Business, Temple University. E-mail:

yuanzhi.li@temple.edu. Phone: 215-204-8108.§Jiaren Pang is from the Department of Finance, School of Economics and Management, Tsinghua

University. E-mail: pangjr@sem.tsinghua.edu.cn. Phone: (+86)10-6279-4800.

Does Corporate Governance Matter More for Firms with High Financial

Slack?

Abstract

We examine whether and how the effect of corporate governance depends on a

firm’s financial slack, financial resources not committed to any specific use. On one

hand, financial slack may be spent by self-interested managers for their private ben-

efits, so its level is positively associated with the degree of agency conflicts. This

implies that corporate governance matters more for high financial slack firms (waste-

ful spending hypothesis). On the other hand, financial slack provides insurance against

future uncertainties; a low level of financial slack may signal that managers are impru-

dent and engage in excess risk taking. Then corporate governance is more effective

for low financial slack firms (precautionary needs hypothesis). We differentiate the

two hypotheses using the passage of anti-takeover laws to identify exogenous varia-

tion in governance. Consistent with the wasteful spending hypothesis, we find that

the laws’ passage has a larger negative impact on the operating and stock market

performance of high financial slack firms. Further analysis of the source of wasteful

spending shows that these firms do not invest more but are less efficient at cost man-

agement than low financial slack firms after the passage of BC laws. Our findings

suggest that shareholder activism and government regulations aiming to improve cor-

porate governance can be more efficient by focusing on firms with high financial slack.

Keywords: corporate governance, financial slack, business combination laws

JEL Codes: G34 G38

1 Introduction

Is corporate governance equally important for the performance of all firms? If not, for which

type of firms does it matter more? These are not trivial questions because they can improve our

understanding of corporate governance and help us design policies to promote better governance

practices and protect shareholder wealth. To answer these questions, this paper attempts to exam-

ine whether corporate governance interacts with certain firm characteristics and exerts differential

impacts on firm performance. While firms are different in many dimensions, we specifically focus

on financial slack, financial resources not committed to any specific use, for two reasons. First,

liquid assets account for a significant fraction of total corporate wealth. Bates, Kahle, and Stulz

(2009) report that the average cash-to-assets ratio of U.S. firms has steadily increased and reached

23.2% in 2006. Second, and more importantly, the use of financial slack is largely at the discretion

of managers, and is considered a central issue among the conflicts between managers and share-

holders (Jensen, 1986). Harford, Mansi, and Maxwell (2008) also suggest that “any discussion of

the efficacy of corporate governance mechanisms to control managers must address this issue.”

Theoretically, corporate governance should matter more for firms with more severe agency

conflicts as its main goal is to mitigate agency problems. However, it is unclear whether and how

the effect of corporate governance depends on financial slack. On one hand, Jensen’s free cash flow

theory points out that financial slack is not subject to the same scrutiny and monitoring by the

capital markets as externally raised funds, and self-interested managers are likely to spend these

excess funds for their private benefits at the expense of shareholders. Because of the potential

wasteful spending, the level of financial slack is positively related to the degree of agency conflicts.

This implies that, everything else being equal, corporate governance has a greater impact on firms

with high financial slack. We call this the wasteful spending hypothesis.

On the other hand, a low level of financial slack may also signal severe agency problems. Due to

financial market imperfections, external financing is generally more costly than internal financing

(Myers and Majluf, 1984). Hence financial slack is beneficial to firms as it provides insurance

against future uncertainties. This is consistent with the precautionary motive for holding cash

1

proposed by Keynes (1936) and elaborated by Opler, Pinkowitz, Stulz, and Williamson (1999),

Lins, Servaes, and Tufano (2010), and Disatnik, Duchin, and Schmidt (2014), among others. In

this view, low financial slack may be an indication of insufficient financial reserves for future

uncertainties and may imply that managers are imprudent and engage in excess risk taking.

If that is the case, the level of financial slack would be negatively associated with the degree

of agency conflicts, and corporate governance should have a greater impact on firms with low

financial slack. This prediction is in contrast to the wasteful spending hypothesis, and we name

it the precautionary needs hypothesis.

To test the two hypotheses, we follow the literature and use the passage of business com-

bination laws (henceforth, BC laws) in different states from 1985 to 1991 to identify exogenous

changes in governance.1 These laws impose stringent restrictions on hostile takeovers of firms in

the legislating state, thus reduce the disciplinary role of capital markets on managers and weaken

corporate governance. They provide a natural experiment to study the effect of corporate gover-

nance as they were exogenous to most firms and were passed in a staggered manner in different

states. Our main proxy for financial slack is excess cash, which is the difference between actual

cash holdings and the predicted amount of cash for future liquidity and investment needs calcu-

lated from a regression as in Dittmar and Duchin (2011), Bates et al. (2009), and Opler et al.

(1999).

Using a triple-difference approach, we find differential impacts of BC laws on firms with

different levels of financial slack. On average, firms’ return on assets (ROA) drops 0.5% after the

passage of BC laws, and a one standard deviation increase in excess cash reduces ROA further

by 0.53%. We also examine the impact of the laws’ passage on firms’ stock market performance

and find similar results. Firms with more financial slack experience a larger decline in stock

prices after the laws’ passage. This also implies that at the time of the laws’ passage, the stock

1The passage of anti-takeover laws is well studied in the literature. Early papers conduct event studiessurrounding the news release date of the laws’ passage (Pound, 1987; Schumann, 1988; Ryngaert andNetter, 1988; Romano, 1987; Margotta, McWilliams, and McWilliams, 1990; Karpoff and Malatesta, 1989).Bertrand and Mullainathan (2003) investigate plant-level data before and after the laws’ passage, and findincreases in worker wages, decreases in destruction of old plants and creation of new plants, and decreasesin productivity and profitability. They conclude that managers choose to enjoy a quiet life rather thanengage in empire building after the passage of anti-takeover laws.

2

market does not completely realize its full impact. Both results suggest that the weakening of

corporate governance leads to a larger decline in performance of firms with higher financial slack,

which is consistent with the wasteful spending hypothesis, but not with the precautionary needs

hypothesis.

Karpoff and Wittry (2014) point out several issues that may lead to biased inference when

using BC laws to identify exogenous changes in governance. Specifically, the coverage of BC laws

is likely endogenous to lobbying firms and firms that used the opt-out or opt-in provisions of

some BC laws to adjust their status. Excluding these firms from the sample does not change our

results. The effect of BC laws may also depend on important court decisions that validated the

constitutionality of these laws, other anti-takeover laws, and firm-level anti-takeover defenses. We

control for these factors and continue to find similar results.

One might be concerned that a firm’s financial slack is endogenous. We address this endo-

geneity concern in three ways. First, we directly control for a number of firm-level governance

measures and other characteristics that may cause omitted variables bias. Second, we use a sticky

measure of financial slack that is not affected by the laws’ passage. Third, we perform 2SLS

estimation using two instruments for financial slack. One is the average of other firms’ financial

slack in the same industry incorporated in states that had not passed BC laws; the other is the

average of sticky financial slack of other firms in the same state but in different industries. We

continue to find supporting evidence for the wasteful spending hypothesis.

Our results also hold with various subsample analysis as robustness checks. (1) Our results

could be driven by reverse causality. Some firms might expect a decline in profitability, and they

lobbied to have the state pass BC laws to protect themselves. Thus the decline in performance

is the cause rather than the result of the laws’ passage. Even though our results hold when we

drop the list of lobbying firms, it is possible that some lobbying activities are not reported and

such firms are not on the publicized list. As managers in larger firms have stronger incentives

and more resources to engage in lobbying activities, we repeat the analysis excluding large firms.

(2) To ensure that the results are not specific to the sample period, we repeat the analysis for

3

different sample periods. (3) Since one half of the sample firms are incorporated in Delaware, our

results could be driven by a Delaware effect. We exclude firms incorporated in Delaware from the

treatment group to address this concern. (4) Some states never passed BC laws in the sample

period, so firms incorporated in these states could be fundamentally different from the rest. We

repeat the analysis excluding those firms from the control group. (5) The entry of new firms and

exit of old firms in the sample period may bias our results, so we analyze a sub-sample of firms

with data available in all sample years. In all robustness checks, we continue to find that the

passage of BC laws exerts a larger negative impact on high financial slack firms.

Finally, we investigate the source of the larger decline in performance for high financial slack

firms. Motivated by Bertrand and Mullainathan (2003), we focus on their investment and cost

management. Due to agency conflicts, both types of activities of high financial slack firms may be

associated with wasteful spending of financial slack that brings private benefits to managers.

The problem can become relatively more severe for these firms after the laws’ passage, and

explains their larger decline in performance. We find that these firms do not increase their

capital expenditure, over-investment, asset growth, PPE growth, or acquisition ratio more than

firms with low financial slack after the laws’ passage, but have higher overhead costs, operating

expenses, costs of goods sold, and more employees relative to sales. These findings suggest that

the larger decline in performance for high financial slack firms is likely due to their managers not

maintaining cost efficiency.

Our study echoes the burgeoning literature on shareholder activism. Brav, Jiang, Partnoy,

and Thomas (2008) show that hedge fund activism tends to target firms with lower growth and

higher cash flows, and the activism leads to increases in payout, operating performance, and

higher CEO turnover of the target firms. Our results reinforce their findings and suggest that

policies aiming to improve corporate governance will be more effective by focusing on firms with

high financial slack. We also contribute to the literature that examines the conditional effect

of corporate governance on firm performance. In a similar setting, Giroud and Mueller (2010)

show that corporate governance matters more for firms in noncompetitive industries, because the

agency problem of firms in competitive industries is already mitigated by competition. Duchin,

4

Matsusaka, and Ozbas (2010) find that the effectiveness of outside directors is conditional on the

cost of acquiring information about the firm: when the cost of acquiring information is low (high),

performance improves (worsens) when outsiders are added to the board. We complement this line

of research by suggesting that the importance of corporate governance varies with the level of

financial slack.

Our paper is closely related to several recent papers that study the differential value of cash

conditional on governance. Dittmar and Mahrt-Smith (2007) find that cash is more valuable for

well-governed firms. Fresard and Salva (2010) find that the value that investors attach to excess

cash is substantially larger for foreign firms listed on US exchanges than for their domestic peers.

Kalcheva and Lins (2007) study international data and find that when external country-level

shareholder protection is weak, firm values are lower when controlling managers hold more cash.

We distinguish our paper from these studies in two ways. First, we focus on the conditional

nature of corporate governance. Second, most governance measures used in these studies suffer

from endogeneity, while we identify exogenous variation of corporate governance by using the BC

laws’ passage.

The rest of the paper is organized as follows. Section 2 provides background knowledge

regarding the passage of state anti-takeover laws. Section 3 describes the empirical methodology

and the data. Section 4 discusses the main findings and various robustness checks. Section 5

studies the impact of the laws’ passage on stock market performance for firms with different

levels of financial slack. Section 6 investigates the source of the larger impact of BC laws on high

financial slack firms. Section 7 concludes and discusses the policy implications of our findings.

2 State anti-takeover laws

There are two generations of state anti-takeover laws. In the 1970s, the first generation

state anti-takeover laws were passed by extending the Williams Act, a federal statute enacted in

1968 that regulates tender offers. These laws based their jurisdiction over tender offers on the

5

relationship between the target and the legislating state, which is determined by a number of

factors, such as the target’s state of incorporation, its principal place of business, and where it

holds substantial assets. However, the early laws were invalidated by a 1982 U.S. Supreme Court

decision (Edgar v. Mite Corp.) on the grounds of excessive jurisdictional reach.2 In response

to this decision, states began to pass a second wave of anti-takeover legislation, which was less

aggressive and restricted the jurisdiction to only firms incorporated in the legislating state. The

Supreme Court upheld an Indiana state control share acquisition law in 1987 (CTS Corp. v.

Dynamics Corp. of America), and the U.S. court of Appeals, Seventh Circuit upheld a Wisconsin

BC law in 1989 (Amanda Acquisition Corp. v. Universal Foods Corp.).3 The rulings generated

the presumption that other anti-takeover laws are also valid and stimulated further enactments

of such legislations across the country.4

Most of the second generation anti-takeover statutes can be classified into business combina-

tion (BC laws), fair price, control share acquisition, poison pill, and constituency laws. BC laws

impose a moratorium on certain kinds of transactions (e.g., mergers and asset sales) between a

bidder and the target for a period of three to five years after the stake of the bidder has reached

a threshold level. These statutes make it more costly for successful bidders to realize gains from

a takeover, hence discouraging potential buyers from bidding.

Fair price laws require a bidder, when acquiring shares beyond a pre-specified threshold, to pay

a “fair price”, which is usually determined by share prices prior to the takeover announcement.

Control share acquisition laws require a bidder intending to make a “control share acquisition”,

defined by several threshold levels, to present its offer to the target’s shareholders. If the bidder

fails to comply and purchases a large block of shares, it may be disqualified from voting with

these shares and will not be able to gain control until its voting rights are reinstated. Poison

pill laws allow a firm to grant current shareholders the rights to buy stocks at a low price when

a bidder acquires a significant amount of shares without the approval of the board. This can

2See 457 U.S. 624 (1982).3See 481 U.S. 69 (1987) and 877 F.2d 496 (1989), respectively.4Some scholars name the laws enacted after the Supreme Court’s decision in (CTS Corp. v. Dynamics

Corp. of America) as the third generation laws, but this paper refers to all state anti-takeover laws passedafter 1982 as the second generation laws.

6

substantially increase takeover costs and deter potential bidders. Constituency laws allow the

board of directors to take into account the interests of constituencies besides shareholders, such

as employees, customers, and suppliers, when the board decides how to respond to a takeover bid.

They are considered to be anti-takeover statutes because they provide managers with additional

resources to oppose the takeover.

In order to be comparable to previous studies, we follow the literature and mainly focus on BC

laws in our analysis. In robustness checks, we also control for the influence of other anti-takeover

laws. The passage of BC laws weakens corporate governance by reducing the disciplinary role

of capital markets. It provides an ideal setting to examine the conditional nature of corporate

governance. First, the laws’ passage can be viewed as an exogenous event to most treated firms;

it is endogenous to only a small fraction of the firms, and the endogeneity concern is addressed

in later sections. Second, the laws were passed at different points in time for different states. It

reduces the clustering of observations at the time of the laws’ passage. Lastly, these laws were

passed on a state basis, so they induced a common change of corporate governance in all affected

firms. Holding constant the change of corporate governance in treated firms enables us to take

firms with different levels of financial slack and compare their changes in performance before and

after the laws’ passage.

While the BC laws’ passage has been used extensively to identify exogenous variation in

corporate governance, Karpoff and Whittry (2014) point out several important issues that may

bias the estimation. First, the laws’ coverage may be endogenous to two small groups of firms.

One group is those firms that faced takeover threats and lobbied legislators to pass the laws. The

other group consists of those firms that opted out of or into the coverage of the laws. Specifically,

many anti-takeover laws, including some BC laws, have opt-out provisions that allow affected

firms to opt out of the laws. Meanwhile, the BC law of Georgia requests firms to opt into the

law. Second, the BC laws were not officially declared constitutional until the court ruling on

Amanda Acquisition Corp. v. Universal Foods Corp. in 1989, so they may have differential

effects before and after the ruling. Third, the effect of the BC laws may depend on the coverage

by first-generation and other second-generation anti-takeover laws. Finally, the results may be

7

confounded by existing firm-level anti-takeover provisions. We address all these issues in our

empirical analysis.

3 Empirical methodology and Data

3.1 Empirical methodology

Our research question is how the passage of BC laws affects the performance of firms with

varying levels of financial slack. The main regression equation is as follows:

ROAijkl,t = αi + αt + β1BC lawijkl,t + β2 (BC lawijkl,t × FSijkl,t−1)

+ β3FSijkl,t−1 + γ′Xijkl,t + εijkl,t, (1)

where i, j, k, l, and t index firms, industries, states of incorporation, states of location, and

years, respectively. ROAijkl,t is the return on assets. αi and αt are firm and year fixed effects,

respectively. BC lawijkl,t is equal to one if firm i is subject to the BC law in year t, and zero

otherwise. FSijkl,t−1 is the financial slack of firm i measured in year t − 1. Xijkl,t is a vector of

control variables, and εijkl,t is the error term. In the baseline specification, Xijkl,t includes firm

size, firm age, squared terms of size and age, and two proxies for time-varying local and industry

shocks, state year and industry year.

We follow Bertrand and Mullainathan (2003) and define state year and industry year as the

annual mean of the dependent variable in the firm’s state of location and three-digit SIC industry,

respectively, excluding the firm itself. Including the two controls enables us to separate the effect

of the laws’ passage from other contemporaneous shocks in the state of location and the industry.

It also helps address the concern that a coalition of firms located in the same state or operating

in the same industry lobbied for an anti-takeover law to gain better protection against hostile

takeovers when they expect a decline in profitability.

The marginal effect of BC laws on performance is given by β1 + β2 × FS. We are most

8

interested in whether and how it varies with the level of financial slack, which is captured by the

coefficient of the interaction term, β2. The wasteful spending hypothesis predicts β2 < 0 and the

precautionary needs hypothesis predicts β2 > 0.

In all regressions, standard errors are adjusted for heteroskedasticity and clustered at the state

of incorporation level. Clustering the standard errors at the state of incorporation level accounts

for three types of correlations among the error terms: across different firms in the same state

of incorporation and year (cross-sectional correlation), across different firms in the same state

of incorporation over time (across-firm serial correlation), and within the same firm over time

(within-firm serial correlation). Cross-sectional correlation is likely since firms in the same state

of incorporation are subject to the same shock in corporate governance due to the passage of BC

laws. Serial correlation is a concern since the dummy variable of the laws’ passage is persistent

over time.

The regression specification is essentially a difference-in-differences-in-differences approach.

The first level of difference is the performance difference of firms before and after the laws’ passage.

The second level is the difference of the first-level difference among firms across incorporating

states with and without BC laws. The first two differences can identify the average treatment

effect of the laws’ passage on firm performance. The interaction term BC law×FS captures the

third-level difference. It is the difference in the second-level differences among treated firms with

different levels of financial slack. For example, suppose we want to estimate the differential impact

of the New York BC law passed in 1985 on the performance of firms incorporated in New York

with different levels of financial slack. First, we would compare the performance before and after

1985 for high financial slack firms incorporated in New York. The performance difference could

reflect the effect of the law, but could also be related to other shocks in the economy, such as an

unexpected increase in oil price. To control for the impact of other contemporaneous shocks, we

select a control state that had not passed the law by 1985, such as California. We compare firm

performance before and after 1985 for high financial slack firms incorporated in California. Since

firms incorporated in California are subject to the same economic shocks, but not to the passage

of the law in New York, the difference of the two identifies the average effect of the law on high

9

financial slack firms. Then we repeat the same process for low financial slack firms incorporated in

New York and California. The difference of the final two differences would reflect the differential

impact of the law’s passage on high and low financial slack firms in New York.

3.2 Data and Variables

The exact years of the BC laws’ passage are obtained from Karpoff and Wittry (2014).5 They

also point out that not all firms incorporated in a state that passed an anti-takeover law were

affected by the law. Specifically, many anti-takeover laws, including some BC laws, have opt-out

provisions. For those firms that opted out of the laws’ coverage, the corresponding observations

are coded as not subject to the laws, i.e., the dummy variable BC law is equal to zero. Meanwhile,

the Georgia BC law requires firms to opt into coverage. Therefore, firms incorporated in Georgia

are considered as not subject to the BC law unless they opted into coverage. The data on firms’

opt-out and opt-in decisions are taken from RiskMetrics.

We collect accounting data of all publicly listed US firms from Compustat. We drop obser-

vations with missing values on total assets, sales, or operating income before depreciation. We

also drop observations that have no data on any of the financial slack proxies discussed below.

All financial firms are excluded because their cash reserves and cash flows may have different

interpretations, and utility firms are dropped because they are highly regulated. To make our

analysis comparable to the literature (Bertrand and Mullainathan, 2003; Giroud and Mueller,

2010), we choose the sample period of 1976 to 1995.6 The final sample contains 8,025 firms and

68,008 firm-year observations.

Our main variable is financial slack, which is the extra financial resource not committed to

5A total of 31 states passed BC laws during our sample period of 1976 to 1995. Specifically, New Yorkpassed the law in 1985; Indiana, Kentucky, Missouri, and New Jersey passed the law in 1986; Arizona,Minnesota, Washington, and Wisconsin passed the law in 1987; Connecticut, Delaware, Georgia, Idaho,Maine, Nebraska, Pennsylvania, South Carolina, Tennessee, and Virginia passed the law in 1988; Illinois,Kansas, Maryland, Massachusetts, Michigan, and Wyoming passed the law in 1989; Ohio, Rhode Island,and South Dakota passed the law in 1990; Nevada and Oklahoma passed the law in 1991.

6In robustness checks, we use different sample periods to ensure that our results are not driven by thespecific time period of the sample.

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future specific use such as liquidity and investment needs. Since the exact amount needed for

future specific use is not directly observable, we start by using two gross proxies of a firm’s overall

financial resource. The first proxy is the current ratio, also known as the liquidity ratio. It is

defined as the ratio of current assets to current liabilities, and is a popular measure for financial

slack in management literature.7 The second proxy is cash ratio, defined as the ratio of cash and

short-term investments to total assets. Practitioners in accounting and finance normally use this

ratio as one of the measures for financial slack. Both variables are noisy proxies for financial slack

as they do not take into account the portion reserved for future specific needs.

Our main proxy for financial slack throughout the analysis is excess cash, which is the dif-

ference between actual cash holdings and the amount of cash committed to future specific needs,

normalized by total assets. It is also referred to as “unexpected cash” (Dittmar and Duchin, 2011)

and “cash residual” (Harford et al., 2008). Specifically, it is calculated as follows,

Excess cashi,t = Cash ratioi,t − Cash ratio∗i,t, (2)

where Cash ratio∗i,t represents firm i’s expected needs for cash in year t, which is unobservable

and must be estimated. Following Dittmar and Duchin (2011), we first estimate an empirical

cash model similar to the one in Opler et al. (1999) and Bates et al. (2009) over a rolling five-year

window [t− 5, t− 1]. The dependent variable is cash ratio, and the explanatory variables include

lagged cash flow, cash flow volatility, Tobin’s Q, firm size, net working capital, leverage, capital

expenditure, R&D expenditure, and the dividend payout dummy variable (variable definitions are

given below). We then use the estimated model to obtain the predicted value of cash holdings of

year t, Cash ratio∗i,t. As the regression explicitly controls for future specific financial needs, it is

the cleanest among all three financial slack proxies.

We construct the rest of the variables as follows. ROA is operating income before depreciation

divided by total assets. Firm size is the log of total assets. Firm age is the log of one plus the

number of years between the first year the firm is covered in Compustat and the current year.

7See Daniel, Lohrke, Fornaciari, and Turner Jr (2004) for a review.

11

Cash flow is earnings after interests, dividends, and taxes but before depreciation divided by

total assets. Cash flow volatility is the standard deviation of cash flow in the previous five years.

Tobin’s Q is the ratio of the market value of assets to the book value of assets, where the market

value of assets is equal to the book value of assets, plus the market value of common equity,

minus the sum of the book value of common equity and deferred taxes. Net working capital is

net working capital excluding cash divided by total assets. Leverage is the sum of long-term and

short-term debt divided by total assets. Capital expenditure is the ratio of capital expenditure

to total assets. R&D expenditure is R&D divided by sales, and is set equal to zero if R&D is

missing. The dividend payout dummy equals one in years when a firm pays a common dividend,

and zero otherwise. All continuous variables are winsorzied at 1% and 99% levels to reduce the

influence of outliers.

Panel A of Table 1 provides summary statistics of the main variables used in Equation (1).

The average ROA of our sample is 8.9%. The averages of current ratio, cash ratio, and excess

cash are 1.176, 0.131, and 0.001, respectively. It is not surprising that the average of excess cash

is close to zero as it is similar to a regression residual by construction. Panel B presents the

correlations among main variables. The three financial slack proxies are highly correlated with

each other, and all three have a slightly negative correlation with ROA. They are also negatively

correlated with firm size and age, suggesting that larger and older firms hold less liquid assets.

4 Results

4.1 Baseline regression results

Table 2 presents our baseline regression results. Column (1) does not include financial slack or

its interaction with BC laws’ passage, while other columns include both. Without the interaction

term, the regression imposes the restriction that the laws’ passage affects all firms equally and

the coefficient β1 shows the average effect of the laws’ passage on the performance of all treated

firms. The coefficient on the law dummy is -0.005, suggesting that ROA drops 0.5% after the

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laws’ passage. This is consistent with the finding of Bertrand and Mullainathan (2003) that on

average the passage of BC laws hurts firm performance.

In columns (2) to (4), a proxy for financial slack and its interaction term with the laws’

passage are included, and the proxies are current ratio, cash ratio, and excess cash, respectively.

The interaction term captures the differential impact of the laws’ passage conditional on the level

of financial slack. From column (2) to column (4), we consistently find that the coefficient of

the interaction term is significantly negative. This implies that the negative impact of the laws’

passage on performance is more pronounced for those firms with more financial slack, which is

consistent with the wasteful spending hypothesis, but not with the precautionary needs hypothesis.

The differential effect is also economically meaningful. Take the regression of Column (4)

that uses excess cash as the proxy for financial slack as an example. According to the coefficient

estimates, the marginal effect of BC laws is −0.002 − 0.056 × Excess cash. It implies that an

increase in excess cash by one standard deviation (0.106) is associated with a relative drop of 0.56

percentage points in ROA after the laws’ passage, which is about 6% of the average ROA of our

sample.

In columns (2) and (3), the coefficient estimates on the stand-alone current ratio and cash

ratio are significantly negative. In column (3), the coefficient on the excess cash is negative but

statistically insignificant. Combined with the negative coefficients on the interaction terms, these

results imply that the average effect of financial slack on firm performance is negative. This

is also consistent with the wasteful spending hypothesis, but not with the precautionary needs

hypothesis.

The signs of coefficient estimates on firm size, firm age, and their squared terms are as ex-

pected. As a firm becomes larger or older, its ROA increases first, and then decreases, which

is an inverted U-shaped relationship. We also obtain positive and highly significant coefficient

estimates on the two proxies for local and industry shocks, which confirms the necessity to control

for them.

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4.2 Using BC laws’ passage for identification

As mentioned in Section 2, there are several important issues that may generate biased infer-

ence when using BC laws to identify exogenous variation in corporate governance (Karpoff and

Wittry, 2014).

First, while it is exogenous to most firms, the laws’ coverage is likely endogenous to two small

groups of firms. One group includes those firms lobbying for specific state anti-takeover laws.

In most cases, the lobbying firm was the target of an actual or rumored takeover bid, and the

state passed anti-takeover legislation quickly upon such news. It is obvious that there exists the

possibility of reverse causality for this group of firms. Karpoff and Wittry (2014) identify nearly

30 lobbying firms that motivated BC laws. The small quantity of firms involved in lobbying

activities is consistent with Romano (1987), who concludes that BC laws are unlikely caused by

broad-based lobbying. We exclude the lobbying firms from the analysis to directly address the

endogeneity concern. The results remain qualitatively unchanged and are presented in column

(1) of Panel A, Table 3.

Endogeneity is also a concern for the group of firms that exercised the opt-out or opt-in options

offered by some BC laws because whether they are covered by the laws reflects their endogenous

choice. In our sample, 26 firms opted out of the coverage of their states’ BC laws, and three

firms incorporated in Georgia opted into coverage. The small quantity is probably due to the

significant adjustment costs. For example, opting out of the Ohio BC law requires the approval of

at least two thirds of the outstanding shares and two thirds of the outstanding shares not owned

by a 10% stockholder. Moreover, the opt-out would be ineffective for 12 months and would not

apply to a control transaction of a shareholder with more than 10% shares before the approval

of the opt-out amendment. Therefore, the laws’ coverage is still exogenous to most firms because

the high transaction costs prevent them from making adjustment (Karpoff and Wittry, 2014).

Nevertheless, we drop firms that opted out of or opted into coverage as well as lobbying firms,

and continue to find similar results, as shown in column (2) of Panel A, Table 3.

Second, the impact of anti-takeover laws may depend on the legal environment. The consti-

14

tutionality of BC laws was not established until a ruling by the U.S. Court of Appeals, Seventh

Circuit in Amanda Acquisition Corp. v. Universal Foods Corp. on May 24, 1989. Before the

court ruling, it was uncertain how much protection against takeover bids that BC laws could

offer. Thus it is possible that the impact of BC laws became meaningful only after the ruling. To

differentiate the effects before and after the ruling, we replace the law dummy and the interaction

term in the baseline regression with two dummy variables, BC law before ruling and BC law after

ruling, and their interactions with excess cash in column (3) of Panel A, Table 3. BC law before

ruling is the same as the dummy BC law for any year t < 1989, and is equal to zero for t ≥ 1989.

Similarly, BC law after ruling is the same as BC law for any year t ≥ 1989, and is equal to zero

for t < 1989. By construction, the two dummies capture the effect of BC laws before and after

the ruling, respectively.

The coefficients of the two interaction terms BC law before ruling × Excess cash and

BC law after ruling × Excess cash are -0.045 and -0.077, respectively, and both are signifi-

cant. The results have three implications. First, both before and after the ruling, the negative

impact of BC laws is stronger for firms with more excess cash, which is consistent with our previ-

ous finding. Second, the size of the two coefficients suggests that the conditional effect of BC laws

is indeed larger in magnitude after the ruling. Finally, in the baseline regression the coefficient

of the interaction term BC law×Excess cash is -0.053 in Table 2, somewhat between these two

coefficients. Thus the baseline regression can be considered as an estimation of the average effect

of the interaction term over the whole sample period, since it does not take into account the court

ruling. In column (4), we repeat the analysis excluding lobbying firms and firms that opted out

of or opted into the laws’ coverage, and the results are similar.

Third, the effect of BC laws may be influenced by four other types of state anti-takeover laws

passed during the sample period, which are fair price, control share acquisition, poison pill, and

constituency laws. To control for their influence, we construct four dummy variables: Fair price,

Control share, Poison pill, and Constituency. They are equal to one if the corresponding laws

are effective and zero otherwise. In columns (1) to (4) of Panel B, Table 3, we separately add one

of the four dummies and its interaction with excess cash into the baseline specification. Column

15

(5) includes all of them. When added separately, each new interaction term enters the regression

negatively, and both Fair price × Excess cash and Poison pill × Excess cash are statistically

significant. When added together, only Poison pill × Excess cash is significant among new

interaction terms. This suggests that some of the other anti-takeover laws also exert a negative

impact on firm performance conditional on financial slack. Admittedly, including these other laws

reduces the magnitude of the coefficient estimate on the interaction term BC law×Excess cash,

but it remains significant in all specifications.8

A related concern is that some of the first generation anti-takeover laws were also effective

in the early years of the sample period, and part of the BC laws’ effect we observe could come

from these laws. To address this concern, we re-run the baseline regression excluding observations

during the 1976-1982 period. Because the first generation anti-takeover laws were deemed un-

constitutional in 1982, the sample period after 1982 is free from their impact.9 With the shorter

sample period of 1983-1995, column (6) presents the results of the baseline regression, and column

(7) controls for all other second generation anti-takeover laws and their interactions with excess

cash. The coefficients on BC law × Excess cash remain significant in both regressions. We

conclude that our main findings are not driven by the first generation or other second generation

anti-takeover laws.

4.3 Endogeneity of financial slack

Financial slack, as a part of a firm’s liquidity management, is likely to be endogenous. In

particular, both firm performance and financial slack can be jointly determined at equilibrium, and

are both driven by other firm characteristics. There is omitted variables bias if these characteristics

are not accounted for. In the earlier baseline specification, we address this concern with two

8Like the BC law dummy, the dummy variables for these anti-takeover laws are appropriately coded toaccount for the opt-out and opt-in decisions of firms. We also control for the court rulings that upheldthese laws and find similar results.

9An alternative solution is to control for these first generation laws explicitly. However, it is difficultto find out whether a firm is under the jurisdiction of these laws because it is determined by a number offactors, such as the firm’s state of incorporation, its principal place of business, or where it holds substantialassets.

16

considerations. First, we use excess cash as the main proxy for financial slack, which is the

difference between actual and expected cash holdings. When estimating expected cash holdings,

we control for a number of firm characteristics that capture a firm’s specific needs for cash, so

excess cash is unlikely a response to changes in these firm characteristics. Perhaps this is why it

is called “unexpected cash” in Dittmar and Duchin (2011). Second, we include firm fixed effects

in all regressions to control for unobserved time-invariant firm characteristics.

Nevertheless, excess cash can still be correlated with some other time-varying firm character-

istics that are not included in the regression of estimating expected cash holdings. Our results

are biased if they are correlated with firm performance and are not taken into account. In this

section, we tackle the problem in three ways. First, we control for various firm-level governance

measures and other characteristics that may cause the omitted variables bias. Second, we con-

struct a sticky measure of financial slack that is not affected by the laws’ passage. This sticky

measure can address the concern that the passage of BC laws induces a change in financial slack

that is correlated with time-varying omitted variables. Third, we construct two instrumental

variables to identify exogenous variation in financial slack and conduct 2SLS estimation.

4.3.1 Firm-level characteristics

Firm-level governance measures

Some firms adopt anti-takeover provisions to protect themselves from takeover threats. If

these provisions affect firm performance and are correlated with financial slack, the regression

results will be biased. To address this concern, we control for a firm’s takeover defenses using

the G-index proposed by Gompers, Ishii, and Metrick (2003). Martijn Cremers graciously shared

with us their hand-collected G-index data for the period of 1977-1989, as described in Cremers

and Ferrell (2014). We then complement their data with the G-index data of 1990-1995 provided

by RiskMetrics.10 We control for G-index as a stand-alone variable and interact it with the law

10The data in Cremers and Ferrell (2014) covers 12,366 firm-year observations of 1,297 firms, and 8465observations have data on excess cash. With the additional G-index from RiskMetrics, the final samplecontains 11650 observations.

17

dummy in the regression, and present the results in column (1) of Panel A, Table 4. We find

that firms with higher G-index on average indeed perform worse than their peers, but there is no

differential impact of BC laws conditional on G-index. Meanwhile, the interaction term of BC

law and financial slack continues to be negative and significant.

A related concern is that other governance measures may also be related to financial slack and

affect firm performance. We use data on CEO ownership, CEO compensation structure, CEO

duality, board size, and board independence to control for these effects. CEO ownership is the

percentage of a firm’s equity owned by its CEO. CEO compensation structure is measured by

CEO equity pay, which is one minus the percentage of cash pay including salary and bonus. CEO

duality is a dummy variable that is one if the CEO is also the chairperson of the board, and

zero otherwise. Board size is the log of the number of board directors. Board independence is

the percentage of directors that are classified as outsiders. David Yermack kindly provided us

with his hand-collected data on these governance variables for the largest 500 companies ranked

by Forbes during 1984-1991, as described in Yermack (1996). For the period of 1992-1995, We

obtain CEO compensation data from ExecuComp and ownership and board characteristics from

Compact Disclosure. To be consistent, we only include past and current Forbes 500 companies.

After deleting observations with missing values on excess cash, the final sample contains less

than 5,000 firm-year observations. To avoid multicollinearity, we separately add these governance

variables and their interactions with the law dummy into the regression, and present the results in

columns (2) to (6) of Panel A, Table 4.While CEO duality and board size are negatively associated

with firm performance, we find no evidence that the effect of BC laws on firms performance is

conditional on these governance measures. Meanwhile, the interaction term BC law×Excess cash

is significant at the 10% level in all regressions except the one with board size (t statistics = 1.63

and p value = 0.103). The decrease in statistical significance of these regressions is likely due to

the much smaller sample size, which is about one tenth of that of the baseline regression.11

Other firm characteristics

11We also perform additional robustness checks. Our results continue to hold when we control forownership nonlinearity by including the squared term of ownership, replace CEO ownership with ownershipof all officers and directors, and control for other CEO characteristics such as age and tenure.

18

Giroud and Mueller (2010) show that the passage of BC laws has a larger negative impact

on the operating performance of firms in less competitive industries. Given that firms in less

competitive industries are more likely to enjoy higher profits and have more financial slack, it

is possible that our results just manifest the findings in Giroud and Mueller (2010). To control

for the effect of competition on the importance of corporate governance, we include the industry

Herfindahl-Hirschman Index (HHI) in the regression analysis. The industry HHI is defined as

the sum of squared market shares of firms with the same three-digit SIC code, where the market

shares are calculated based on sales. Column (1) of Panel B, Table 4 adds the HHI and its

interaction with the law dummy variable to the baseline specification. Consistent with the findings

of Giroud and Mueller (2010), the coefficient estimate on the interaction term BC law × HHI

is negative and significant. Meanwhile, we continue to find a significant coefficient estimate on

BC law × Excess cash.

A firm’s financial slack may be related to its investment opportunities. If a firm’s investment

opportunities also affect the importance of corporate governance, our findings could be driven by

the difference in investment opportunities rather than in agency costs. To address this concern,

we control for lagged Tobin’s Q and its interaction with the law dummy variable. The results are

shown in column (2) of Panel B, Table 4. While Tobin’s Q is significant, its interaction term with

the law dummy variable is not. More importantly, controlling for Tobin’s Q does not change our

main findings.

Another issue is that the accounting performance measure ROA is not adjusted by risk. Thus

a decline in ROA may result from lower asset risk. Before the passage of BC laws, firms may

invest excessively in highly risky projects to appear more profitable to deter hostile bidders. After

the laws’ passage, this incentive is weakened, and firms may choose to switch to less risky projects

which command lower returns. Meanwhile firms with abundant financial slack are more likely to

be the targets of hostile takeovers, which implies that they have stronger incentives to take risky

projects than low financial slack firms before the laws’ passage. Our findings may just reflect

changes in the asset risk of high financial slack firms. To test this alternative hypothesis, we

follow Zhang (2006) and use cash flow volatility as a measure for asset risk. As defined in Section

19

3.2, cash flow volatility is the standard deviation of cash flows in the past five years. Column (3)

of Panel B, Table 4 presents the results of the regression that includes cash flow volatility and

its interaction with the law dummy. While the two additional controls are both significant, the

positive coefficient on the interaction of cash flow volatility and the law dummy is inconsistent

with the alternative story. More importantly, our main results remain qualitatively unchanged.

Firms with more financial slack tend to use less debt. Given that external debt is a device of

monitoring managers, it could be a substitute governance mechanism for the market of corporate

control. When the takeover market is weakened by the laws’ passage, overall governance should

be hurt more for low leverage firms. Our results might just reflect the substitution between debt

monitoring and the takeover market. Column (4) of Panel B, Table 4 tests this hypothesis by

including lagged leverage and its interaction with the law dummy variable in the baseline speci-

fication. The coefficient on the interaction term of the laws’ passage and leverage is significantly

positive, suggesting that the laws’ passage hurts firms with lower leverage more. This means

that debt monitoring and the takeover market are indeed substitutes. However, our main results

remain robust.

Suppose firm performance is related to financial slack in a nonlinear way and the laws’ pas-

sage affects all firms equally. A regression that ignores the nonlinear relationship between firm

performance and financial slack could produce a dubious relationship between performance and

the interaction term of the laws’ passage and financial slack. To check this possibility, we add the

squared term of excess cash to the baseline specification. The results in column (5) of Panel B,

Table 4 show that our findings are robust to this alternative specification.

4.3.2 A sticky measure of financial slack

One might be concerned that the laws’ passage drives variation in financial slack and that

the variation is correlated with omitted determinants of firm performance, leading to biased

estimation. To address this concern, we construct a sticky measure of financial slack so that it

is measured by the levels before the coverage of BC laws. Specifically, if a firm is subject to the

20

BC law starting in year τ , then for any t < τ , financial slack is measured in year t; for any t ≥ τ ,

financial slack is measured in year τ − 1.

By construction, this new measure is sticky and not affected by the passage of BC laws.

Furthermore, its sticky nature suggests that it is less likely correlated with omitted variables that

cause the bias unless there are anticipation effects or the omitted variables are persistent. Column

(1) of Table 5 presents the results of the baseline regression using the sticky measure of excess

cash.12 The interaction term between the BC law dummy and excess cash is negative and highly

significant, consistent with our previous findings.

Since the sticky financial slack variable remains constant when the firm is subject to the BC

law, one concern is that it conveys little information about the firm’s actual financial slack many

years after the law’s coverage when using the whole sample period. For example, the BC law was

passed in New York in 1985, and the sample period ends in 1995. This means that the sticky

measure of financial slack does not change from 1985 to 1995 for firms covered by New York’s BC

law. To address this issue, we repeat the analysis and use the sample period that begins n years

before the law change and ends n years after, n = 1, 2, 3, 4. Columns (2) to (5) of Table 5 present

the results. In all regressions, the coefficient estimates of the interaction term remain consistently

negative and highly significant.13

4.3.3 2SLS estimation

A more general solution to the endogeneity problem is to find an instrumental variable for

financial slack, which is correlated with a firm’s financial slack but not its performance. We

construct two instruments for financial slack.

For the financial slack of firm i in year t, the first instrument is peer financial slack, defined as

12The sample size in the regression is smaller because firms are dropped when there is no data availableto construct the sticky measure of excess cash. This is true for firms incorporated in a state that passedthe BC law and it enters the sample after the law’s passage.

13As a robustness check, to construct excess cash in year t ≥ τ , instead of using its level in year τ − 1,we also use the average levels from years τ − 2 to τ − 1 and from τ − 3 to τ − 1. The results continue tohold.

21

the average financial slack of other firms in year t in the same industry as firm i but incorporated

in states that have not passed the BC law by year t.14 Accordingly, the instrument for the

interaction term is the interaction of peer financial slack with the law dummy. Columns (1),

(2), and (3) of Table 6 present the 2SLS regression results using excess cash as the measure for

financial slack. Columns (1) and (2) show the first stage results, where the dependent variables

are excess cash and its interaction with the law dummy, respectively. Column (3) presents the

second stage results. Consistent with the OLS regression results, the coefficient estimate of the

interaction term is negative and highly significant.

The first stage regressions show that the two endogenous variables are highly correlated with

the two instruments. This is not surprising since the peer financial slack is constructed from a

firm’s industry peers and the financial slack of firms in the same industry is likely affected by

common industry shocks. Regarding the exclusion restriction, one concern is that some of those

shocks may also impact firm performance, but the control variable industry year already takes

this into account. Furthermore, this instrument is unlikely to be correlated with performance

changes caused by the laws’ passage, because it is not affected by BC laws by construction.

However, peer financial slack may affect a firm’s performance directly. In particular, it may be

a proxy for rivals’ financial strength and could exert a negative impact on the firm (Bolton and

Scharfstein, 1990). If this is true, then the instrument violates the exclusion restriction and the

inference is biased.

Given that peer financial slack may not satisfy the exclusion restriction, we construct an

alternative instrument, local sticky financial slack, which is the average sticky financial slack of

other firms in the same state but not in the same industry. The sticky measure of financial slack is

defined as in Section 4.3.2. This instrument is likely to be correlated with financial slack because

the financial slack of firms in the same state is probably affected by some common local shocks

at the state level. It is possible that these shocks also affect firm performance, but their impacts

are already accounted for by the control variable state year. Furthermore, this instrument is not

14When a firm is incorporated in a state that has not passed a BC law, other firms in the same state arealso included to calculate the instrument. When a firm is incorporated in a state that has passed a BClaw, other firms in the same state are not included to calculate the instrument.

22

affected by the laws’ passage since it is constructed from the sticky measure of financial slack.

Most importantly, its construction does not include firms in the same industry, so it does not

proxy for rival firms’ financial strength. Therefore, it is likely to satisfy the exclusion restriction.

Accordingly, its interaction with the law dummy serves as the instrument for the interaction of

financial slack and the law dummy.

The 2SLS regression results are presented in columns (4), (5), and (6) of Table 6. The first

stage regressions are in columns (4) and (5), where the dependent variables are excess cash and

its interaction with the law dummy, respectively. The results show that the two endogenous

variables are indeed significantly correlated with the two instruments. Column (6) presents the

second stage regression results and shows that the coefficient estimate of the interaction term is

significantly negative, which is consistent with our previous findings.

In summary, we address the endogeneity concern of financial slack in three ways. While

they can address the endogeneity of financial slack to some extent, they also have their own

shortcomings. First, directly controlling for more firm characteristics can reduce omitted variables

bias, but it is always possible that some unobservable time-varying variables cause the bias.

Second, the sticky measure of financial slack could be correlated with omitted variables that

affect firm performance if there are anticipation effects or the omitted variables are persistent. In

that case, the estimation would be biased. Finally, as the exclusion restriction is untestable, it is

uncertain whether the instruments are truly exogenous to firm performance. Therefore, the three

methods might not be perfect individually. However, the fact that the results are robust in all

these specifications provides support for our findings.

4.4 Robustness checks

4.4.1 Subsample Analysis

We repeat the analysis using different subsamples to address various concerns.

Reverse Causality

23

First, an alternative interpretation of our regression results is reverse causality: the passage of

BC laws could be the result of expected decline in profitability rather than the cause of it. Some

firms might foresee that they will experience a decline in profitability due to common negative

economic shocks, especially the ones with financial slack. As a result these firms lobby the state

to pass BC laws to protect themselves. We addressed this problem in Section 4.2 directly by

excluding lobbying firms. But some lobbying activities might not be publicized and our list of

lobbying firms could be incomplete. Here we address the possibility of reverse causality directly by

excluding large firms from our sample. From a cost and benefit perspective, managers in larger

firms have stronger incentives and more resources to engage in lobbying activities. When the

passage of the laws is indeed driven by lobbying activities of a group of large firms incorporated

in the same state expecting profit decline, the event is still exogenous to smaller firms incorporated

in the same state. If we find the same results with the sample of smaller firms, reverse causality

is unlikely to be the cause of our findings. For each state and each year, we rank firms based on

their total assets, and perform the regression analysis excluding the top 50% of the firms. The

results are presented in column (1) of Table 7.15 We continue to find that the passage of BC laws

hurts the performance of high financial slack firms more.

Different sample periods

Second, in the baseline regression, we choose the sample period from 1976 to 1995 to be

consistent with Bertrand and Mullainathan (2003) and Giroud and Mueller (2010). To check

if our results depend on a specific sample period, we repeat the analysis using different time

intervals. Since the first BC law was passed in 1985 and the last one was passed in 1991, we choose

alternative sample periods that are symmetric around the period of 1985-1991 by expanding the

period by one, two, three, and four years on each end. Our main results still hold for the four

different sample periods. For brevity, we only report the results based on the period of 1984-1992

in column (2) of Table 7. Other sample periods produce similar results.

Non-Delaware firms

15Dropping the top 10%, 20%, 30% or 40% of firms ranked by size produces similar results.

24

Third, since half of the sample firms are incorporated in Delaware, one might suspect that

our results are merely a Delaware effect. To address this concern, we exclude all Delaware firms

from the treatment group and repeat the regression analysis. Column (3) of Table 7 shows that

our main result does not change.

Firms incorporated in states that eventually passed the BC laws

Fourth, some firms are incorporated in states that never passed BC laws during the sample

period. These firms are used as part of the control group in the regression. One might suggest

that firms incorporated in states that never passed the laws are fundamentally different from

those incorporated in other states. This questions the validity of using these firms as controls.

Column (4) of Table 7 conducts the analysis using only firms incorporated in states that passed

BC laws at some point between 1976 and 1995. The control group in any year t only includes

firms incorporated in states that have not passed the law by year t but later did. Our finding is

robust to this specification.

Firms with data available for the whole sample period

Fifth, one might be concerned with the entry of new firms and the exit of old firms during the

sample period. If a firm’s decision regarding where to incorporate is endogenous and affected by

whether a particular state has passed BC laws, including firms that enter the sample during the

sample period could induce a selection bias. Another possibility is that our finding is caused by

survivorship bias in the data. Ex ante the laws’ passage affects all firms equally, but firms with

low financial slack that experience decline in performance enter bankruptcy and drop out of the

sample. As a result, we observe all high financial slack firms, and only a fraction of low financial

slack firms that perform relatively better. On average, it appears that after the laws’ passage,

firms with more financial slack perform worse. To address the issue of firm entry and exit, we

repeat the analysis with the subsample of firms that have performance data available in the whole

sample period and present the results in column (5) of Table 7. Our main finding continues to

hold.

25

4.4.2 More robustness checks

We conduct more analysis to check the robustness of our main finding.16 So far our measure

for accounting performance is ROA before depreciation. We also perform the analysis using

four alternative performance measures: ROA after depreciation, return on equity (ROE), net

profit margin, and sales growth. ROA after depreciation is the ratio of operating income after

depreciation to total assets. ROE is the ratio of net income to common equity. Net profit margin

is defined as operating income before depreciation divided by sales. Sales growth is the annual

growth rate of sales. The results are qualitatively the same.

When estimating excess cash, we normalize cash by total assets, following Bates et al. (2009)

and Dittmar and Duchin (2011). We also find similar results if we normalize cash by net assets

(total assets excluding cash) or sales. Furthermore, there are also studies that use the natural

logarithm of the cash-to-net-assets ratio or cash-to-sales ratio (e.g., Opler et al., 1999; Harford

et al., 2008). Using these measures to estimate excess cash does not change our main finding.

In all regressions, standard errors are clustered at the state of incorporation level because the

BC law dummy is a possible source of both cross-sectional and serial correlations as discussed in

Section 3.1. As a robustness check, we follow Bertrand and Mullainathan (2003) and Bertrand,

Duflo, and Mullainathan (2004) and consider a number of alternative methods to correct corre-

lations in the error term. We find similar results if we cluster the standard errors at the state of

location level, if we use an AR(1) correction method, or if we block bootstrap the standard errors

using 51 blocks with 200 bootstrap samples.

5 Stock market performance

In previous analysis we use the firm accounting performance as the main dependent variable

when studying the effect of the laws’ passage on performance. If the stock market is unable to

perfectly predict the effect of law at the time of the laws’ passage, stock performance should also

16For the sake of brevity, these results are not reported, but available upon request.

26

be affected. We construct characteristics-adjusted annual returns to measure the stock perfor-

mance as in Daniel, Grinblatt, Titman, and Wermers (1997). Specifically, at the end of each

calendar year, we classify stocks into size quintiles based on their market capitalization using the

breakpoints determined by NYSE stocks. Stocks in each quintile are further split into quintiles

based on their book-to-market ratios. We then assign stocks in each of the 25 groups into quintiles

based on their past six-month returns. This generates 125 benchmark portfolios based on size,

book-to-market ratios, and past returns. We calculate the equally-weighted return for each group

and use it as the benchmark portfolio return. A stock’s adjusted annual return is defined as the

difference between its raw return and the return of its benchmark portfolio.

We use the adjusted annual stock return as the dependent variable and estimate a specification

similar to Equation (1). The purpose is to examine how the laws’ passage affects stock market

performance differently across firms with different levels of financial slack. Because size has been

accounted for when calculating the adjusted return, we do not include size or square of size in

the regression. The results are presented in Table 8. For all four measures of financial slack,

the coefficient estimates on the interaction between BC laws and financial slack are negative and

highly significant. This is consistent with our earlier finding using firm accounting performance.

6 Channels of wasteful spending

We have shown that the passage of BC laws leads to a larger decline in the performance of firms

with high financial slack. This is consistent with the argument that the laws’ passage weakens

corporate governance and exacerbates agency conflicts associated with Jensen’s free cash flow

problem, i.e., managers with excess financial resources tend to engage in more wasteful spending.

It would be interesting to find out what kind of wasteful spending is behind the performance

drop of high financial slack firms after the laws’ passage. In particular, managers can engage

in “empire building” to entrench themselves with more resources under control. Alternatively,

because greater financial slack provides a comfort zone to managers, they may just enjoy a “quiet

life” and do not work as hard to minimize costs. They may spend resources beyond the optimal

27

level in order to avoid “cognitively difficult activities” (Bertrand and Mullainathan, 2003), such

as negotiating with suppliers, labor unions, and business units within the firm demanding bigger

overhead budgets. Motivated by these two conjectures, we investigate firms’ investment activities

and cost management to identify the channels of wasteful spending of high financial slack firms

after the passage of BC laws. Table 9 presents the empirical results.

In Panel A, we estimate the baseline specification using five proxies for investment activities as

the dependent variable. Column (1) uses the ratio of capital expenditure to total assets. Column

(2) uses a measure for over-investment, which is the difference between the actual investment and

expected investment calculated from a regression model based on Richardson (2006), as described

in the Appendix. Column (3) uses the annual growth rate of total assets. Column (4) uses the

annual growth rate of fixed assets. Column (5) uses the acquisition ratio, defined as the sum

of the value of all acquisitions made by the firm in a given year divided by the firm’s market

capitalization in that year.

The coefficients on the stand-alone excess cash are consistently positive across all regressions,

implying that indeed firms with more financial slack on average engage in more investment ac-

tivities. However, the average effect of the laws’ passage is insignificant. Most importantly, the

coefficients on BC law × Excess cash are mostly insignificant. This means that, although on

average firms with more financial slack tend to engage in more investment and expansion, such

tendency is not exacerbated by the passage of the BC laws. In column (3) with asset growth

as the dependent variable, the coefficient on the interaction term is significantly negative, which

suggests that the positive relationship between asset growth and financial slack is actually moder-

ated by the laws’ passage. Overall the results do not seem to suggest that the larger performance

drop of high financial slack firms after the laws’ passage is due to relatively more investment and

expansion.

Panel B turns to the cost management of firms before and after the passage of BC laws. We

estimate the same regression using six proxies for cost management as the dependent variable.

The proxies include overhead costs (selling, general, and administrative expenses), advertising

28

expenses, operating expenses, costs of goods sold (COGS), the number of employees, and wages.17

All variables are normalized by sales except that wages are normalized by the number of employees.

Managers in high financial slack firms might not work as hard to maintain cost efficiency after

the passage of BC laws. Indeed, the positive and significant interaction terms in columns (1), (3),

(4), and (5) suggest that firms with high financial slack experience an increase in overhead costs,

operating expenses, costs of goods sold, and the number of employees after the laws’ passage.

This provides direct evidence that inefficient cost management, a type of wasteful spending, is

behind the larger performance drop of high financial slack firms after the laws’ passage.

We want to emphasize that our main goal in this section is not to test the “empire building”

hypothesis vs. the “quiet life” hypothesis. Instead, the analysis aims to examine the channels

of wasteful spending derived from the two hypotheses. Actually, the two sets of proxies are

imperfect and may be related to both empire building and enjoying the quiet life.18 In particular,

the dependent variables of panel A could also represent substantial cash outlays, so an increase

in these proxies might suggest that managers do not prudently monitor the use of financial slack

and thus enjoy a quiet life. On the other hand, all the dependent variables of panel B could also

be positively related to empire building activities. Increasing overhead costs, advertising, and the

number of employees, is often an indication of a greater scale of operations and suggests possible

empire building. Therefore, the empirical evidence might actually provide some support to both

hypotheses. However, whether it is empire building or enjoying the quiet life is not crucial for this

study; what really matters is that the analysis provides direct evidence and identifies the channel

of wasteful spending by managers of high financial slack firms after the laws’ passage.

7 Conclusion

In this paper, we examine whether and how the effect of corporate governance depends on

the level of financial slack. Theoretically, corporate governance matters more for firms with

17Because many Compustat firms do not report wages, the number of observations is much smaller incolumn (6) compared to other columns.

18We thank one anonymous referee for pointing this out.

29

more severe agency problems as its main goal is to mitigates agency conflicts. However, the

relationship between financial slack and agency conflicts is ambiguous. On one hand, the use of

financial slack is largely at the discretion of managers and lacks disciplines from the capital market,

so managers may spend financial slack for their private benefits at the expense of shareholders

(Jensen, 1986). This implies that agency conflicts are potentially more severe for firms with high

financial slack, and corporate governance should be more effective for this type of firms. On

the other hand, financial slack can serve as precautionary savings and protect firms again future

economic and political uncertainties, but self-interested managers can be imprudent and hold

insufficient financial slack. In this case, a low level of financial slack signals more severe agency

problems and corporate governance should matter more for firms with low financial slack.

Using the passage of BC laws to identify exogenous variation in corporate governance, we find

that the weakening of corporate governance causes a larger decline in performance of firms with

high financial slack. This finding has important policy implications. It suggests that governance

mechanisms do not matter equally for all firms; instead, shareholder activism and government

regulations aiming to improve corporate governance can be more effective by targeting firms with

high financial slack.

In a nutshell, our paper investigates a particular aspect of the conditional nature of corporate

governance, i.e., whether and how financial slack affects the effectiveness of corporate governance.

It would be interesting for future research to examine wether and how the functioning of corporate

governance depends on other firm traits, industry characteristics, macroeconomic environment,

and other factors. This would further deepen our understanding of corporate governance and

help implement governance mechanisms to better address the conflicts between shareholders and

managers.

30

Appendix

Following Richardson (2006), we construct the expected investment in new positive NPV projects

I∗new and over-investment Iεnew in two steps.

Step one, total investment Itotal is defined as the sum of all outlays on capital expenditure,

acquisitions, and research and development, less receipts from the sale of property, plant, and

equipment. Total investment Itotal can be decomposed into two components: (i) required expen-

diture to maintain assets in place Imaintenance, and (ii) investment in new projects Inew. A proper

proxy for Imaintenance is amortization and depreciation. Thus investment in new projects Inew

can be computed as:

Inew,t = Itotal,t − Imaintenance,t

= CAPEXt +Acquisitionst +R&Dt − SalePPEt −Depreciationt.

Step two, observed Inew,t is used as the dependent variable to fit the following regression

model:

Inew,t = α+ βV Pt−1 + ϕZt−1 + εt.

The predicted value is the expected investment expenditure in new positive NPV projects I∗new

, and the residual is the measure for over-investment Iεnew. The main explanatory variable in

estimating the expected investment expenditure is a firm’s growth opportunities denoted as V P .

It is calculated as the ratio of firm value (Vaip) to the market value of equity. Vaip is estimated as

Vaip = (1− αr)BV + α(1 + r)X − αrd, where, α = (ω/(1 + r − ω)), r = 12%, and ω = 0.62. ω is

the abnormal earnings persistence parameter from the Ohlson (1995) framework, BV is the book

value of common equity, d is annual dividends, and X is operating income after depreciation. Z

is a vector of control variables including leverage, size, age, stock of cash, past stock returns, prior

firm level investment, year fixed effects, and industry fixed effects.

31

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33

Table 1: Regression Variables

Panel A reports the summary statistics. Panel B reports the correlation matrix. ROA is operatingincome before depreciation divided by total assets. Size is the log of total assets. Age is the log ofone plus the number of years between the first year when the firm is covered by Compustat and thecurrent year. Current ratio is current assets divided by total assets. Cash ratio is cash and short-term investments divided by total assets. Excess cash is the cash ratio minus a predicted cash ratioestimated by a five-year rolling window; the explanatory variables of the estimation regressioninclude lagged measures of cash flow, cash flow volatility, Tobin’s Q, firm size, net working capital,leverage, capital expenditure, R&D expenditure, and the dividend payout dummy variable.

Panel A: Summary Statistics

N MEAN STDEV MIN MAX

ROA 68008 0.089 0.185 −0.883 0.407

Size 68008 4.357 2.116 −2.847 12.008

Age 68008 2.485 0.773 0.693 3.829

Current ratio 68008 1.176 0.515 0 7.45

Cash ratio 68000 0.131 0.169 0 0.808

Excess cash 44045 0.001 0.106 −0.214 0.47

Panel B: Correlation Matrix

ROA Size Age Current ratio Cash Ratio Excess cash

ROA 1

Size 0.305 1

Age 0.148 0.582 1

Current ratio −0.022 −0.257 −0.139 1

Cash ratio −0.129 −0.231 −0.180 0.571 1

Excess cash −0.047 −0.11 −0.110 0.596 0.862 1

34

Table 2: Impact of BC Laws’ Passage on Firm Performance for Different Levelsof Financial Slack

Coefficient estimates and their t-statistics (in parentheses) are presented for the following regression model:

ROAijkl,t = αi + αt + β1BC lawijkl,t + β2 (BC lawijkl,t × FSijkl,t−1) + β3FSijkl,t−1 + γ′Xijkl,t + εijkl,t,

where i, j, k, l, and t index firms, industries, states of incorporation, states of location, and time, respec-tively. BC lawijkl,t is equal to one if firm i incorporated in state k is under the coverage of BC laws inyear t and zero otherwise. FS is financial slack. X is a vector of control variables, which includes firmsize, firm age, squared terms of size and age, the mean ROA of other firms in the same industry-year group(“industry year”), and the mean ROA of other firms in the same state-year group (“state year”). Column(1) estimates the equation without financial slack. In columns (2) to (4), financial slack is measured bycurrent ratio, cash ratio, and excess cash, respectively, which are defined as in Table 1. Firm and yearfixed effects are included. Standard errors are adjusted for heteroskedasticity and clustered at the state ofincorporation level. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively.

(1) (2) (3) (4)

BC law -0.005∗ 0.014∗∗ -0.000 -0.002(-1.70) (2.35) (-0.12) (-0.88)

Current ratio -0.030∗∗∗

(-8.83)BC law × Current ratio -0.015∗∗∗

(-3.80)Cash ratio -0.043∗∗∗

(-3.93)BC law × Cash ratio -0.028∗∗

(-2.31)Excess cash -0.009

(-0.57)BC law × Excess Cash -0.053∗∗∗

(-2.92)Size 0.117∗∗∗ 0.121∗∗∗ 0.117∗∗∗ 0.085∗∗∗

(21.49) (23.02) (22.34) (9.80)Age 0.155∗∗∗ 0.130∗∗∗ 0.140∗∗∗ 0.262∗∗∗

(8.50) (8.06) (8.52) (4.63)Size squared -0.009∗∗∗ -0.010∗∗∗ -0.009∗∗∗ -0.006∗∗∗

(-22.83) (-25.06) (-23.99) (-8.59)Age squared -0.071∗∗∗ -0.064∗∗∗ -0.067∗∗∗ -0.095∗∗∗

(-10.16) (-10.24) (-10.36) (-6.29)Industry year 0.224∗∗∗ 0.222∗∗∗ 0.222∗∗∗ 0.208∗∗∗

(9.91) (9.82) (9.87) (9.01)State year 0.242∗∗∗ 0.238∗∗∗ 0.242∗∗∗ 0.166∗∗∗

(8.12) (7.46) (7.88) (6.56)R2 0.692 0.696 0.693 0.653N 68008 68008 68000 44045

35

Table 3: Using BC Laws’ Passage for Identification

This table reports the regression analyses that deal with various issues of using the passage for identification.We consider the influence of lobbying firms, firms that chose to opt in or out of state anti-takeover laws,the legal regime as reflected in important court decisions, and first-generation and other second-generationstate anti-takeover laws. BC law before ruling equals one if the law was effective in year t < 1989, and zerootherwise. BC law after ruling equals one if the law was effective in year t ≥ 1989, and zero otherwise. Allother variables are defined as in Tables 1 and 2. Firm and year fixed effects are included. Standard errorsare adjusted for heteroskedasticity and clustered at the state of incorporation level. *, **, and *** denotestatistical significance at the 10%, 5%, and 1% level, respectively.

Panel A: Issues of BC Laws

In Panel A, column (1) drops all lobbying firms; column (2) drops all firms that opted in or opted outof the law as well as lobbying firms; column (3) studies the differential effect of BC laws before and afterthe court ruling on Amanda Acquisition Corp. v. Universal Foods Corp. in 1989 that established theconstitutional status of BC laws; column (4) is the same as column (3) except that it drops all lobbyingfirms and firms that opted in or opted out of the law.

(1) (2) (3) (4)

BC law -0.002 -0.003(-0.78) (-0.93)

Excess cash -0.009 -0.008 -0.009 -0.009(-0.53) (-0.50) (-0.57) (-0.53)

BC law × Excess cash -0.053∗∗∗ -0.053∗∗∗

(-2.81) (-2.74)BC law before ruling -0.007 -0.007

(-1.19) (-1.13)BC law after ruling -0.000 -0.000

(-0.11) (-0.03)BC law before ruling × Excess cash -0.045∗∗∗ -0.046∗∗∗

(-2.71) (-2.74)BC law after ruling × Excess cash -0.077∗∗ -0.078∗∗

(-2.48) (-2.45)Size 0.086∗∗∗ 0.087∗∗∗ 0.085∗∗∗ 0.086∗∗∗

(9.45) (9.48) (9.22) (9.44)Age 0.253∗∗∗ 0.252∗∗∗ 0.263∗∗∗ 0.253∗∗∗

(4.19) (4.23) (4.46) (4.33)Size squared -0.006∗∗∗ -0.006∗∗∗ -0.006∗∗∗ -0.006∗∗∗

(-8.44) (-8.47) (-8.04) (-8.38)Age squared -0.093∗∗∗ -0.092∗∗∗ -0.096∗∗∗ -0.093∗∗∗

(-5.73) (-5.75) (-6.10) (-5.94)Industry year 0.214∗∗∗ 0.214∗∗∗ 0.207∗∗∗ 0.213∗∗∗

(7.90) (7.91) (8.55) (7.98)State year 0.167∗∗∗ 0.168∗∗∗ 0.165∗∗∗ 0.168∗∗∗

(6.12) (6.14) (6.20) (6.17)R2 0.653 0.653 0.653 0.653N 43507 43319 44045 43319

36

Panel B: Other State Anti-takeover Laws

Panel B controls for the effect of other state anti-takeover laws. In columns (1) to (4), Fair price, Controlshare, Poison pill, and Constituency are dummy variables that stand for the passage of fair price laws,control share laws, poison pill laws, and constituency laws, respectively. Column (5) controls for all fourtypes of second generation anti-takeover laws simultaneously. In column (6), observations from 1976 to 1982are dropped to exclude the effect of first-generation anti-takeover laws that were deemed unconstitutionalin 1982. Column (7) controls for all four second generation anti-takeover laws and excludes data from 1978to 1982.

(1) (2) (3) (4) (5) (6) (7)

BC law -0.003 -0.002 -0.002 -0.002 -0.004 0.000 0.000(-0.81) (-0.84) (-0.76) (-0.86) (-1.17) (0.12) (0.01)

Excess cash -0.007 -0.008 -0.006 -0.008 -0.006 -0.000 0.002(-0.44) (-0.47) (-0.39) (-0.46) (-0.41) (-0.02) (0.09)

BC law × Excess Cash -0.043∗∗∗ -0.049∗∗ -0.042∗∗∗ -0.044∗∗ -0.039∗∗ -0.056∗∗∗ -0.047∗∗∗

(-2.99) (-2.52) (-2.88) (-2.51) (-2.67) (-3.01) (-2.87)Fair price 0.001 0.005 -0.001

(0.18) (1.47) (-0.49)Fair price × Excess cash -0.043∗ -0.017 -0.027

(-1.70) (-0.62) (-0.96)Control share 0.001 0.002 0.003

(0.24) (0.57) (0.68)Control share × Excess cash -0.022 -0.008 0.021

(-0.89) (-0.28) (0.72)Poison pill -0.006∗∗∗ -0.005 -0.005

(-3.10) (-1.66) (-1.59)Poison pill × Excess cash -0.054∗∗ -0.053∗ -0.045∗

(-2.59) (-1.86) (-1.72)Constituency -0.006∗∗ -0.005 -0.005

(-2.34) (-1.49) (-1.32)Constituency × Excess cash -0.033 0.013 0.008

(-1.55) (0.41) (0.30)Size 0.085∗∗∗ 0.085∗∗∗ 0.085∗∗∗ 0.085∗∗∗ 0.085∗∗∗ 0.122∗∗∗ 0.123∗∗∗

(9.22) (9.24) (9.24) (9.29) (9.23) (11.20) (11.09)Age 0.259∗∗∗ 0.261∗∗∗ 0.262∗∗∗ 0.263∗∗∗ 0.260∗∗∗ 0.373∗∗∗ 0.374∗∗∗

(4.29) (4.37) (4.35) (4.38) (4.28) (6.32) (6.31)Size squared -0.006∗∗∗ -0.006∗∗∗ -0.006∗∗∗ -0.006∗∗∗ -0.006∗∗∗ -0.010∗∗∗ -0.010∗∗∗

(-8.08) (-8.11) (-8.14) (-8.18) (-8.14) (-11.78) (-11.68)Age squared -0.094∗∗∗ -0.095∗∗∗ -0.095∗∗∗ -0.096∗∗∗ -0.095∗∗∗ -0.121∗∗∗ -0.122∗∗∗

(-5.82) (-5.94) (-5.90) (-5.95) (-5.81) (-7.73) (-7.69)Industry year 0.208∗∗∗ 0.208∗∗∗ 0.208∗∗∗ 0.209∗∗∗ 0.208∗∗∗ 0.182∗∗∗ 0.182∗∗∗

(8.50) (8.48) (8.56) (8.46) (8.50) (11.86) (12.07)State year 0.166∗∗∗ 0.166∗∗∗ 0.165∗∗∗ 0.164∗∗∗ 0.163∗∗∗ 0.163∗∗∗ 0.154∗∗∗

(6.17) (6.21) (6.20) (6.17) (6.19) (4.34) (4.42)R2 0.653 0.653 0.653 0.653 0.653 0.694 0.695N 44045 44045 44045 44045 44045 31657 31657

37

Table 4: Controlling for More Firm Characteristics

This table reports the regressions that control for various governance measures and other firm characteris-tics. Coefficient estimates and their t-statistics (in parentheses) are presented for the following regressionmodel:

ROAijkl,t = αi + αt + β1BC lawijkl,t + β2 (BC lawijkl,t × FSijkl,t−1) + β3FSijkl,t−1

+ β4Zijkl,t−1 + β5(BC lawijkl,t × Zijkl,t−1) + γ′Xijkl,t + εijkl,t.

Zijkl,t−1 is the additional firm characteristic to be controlled for. All other variables are defined as inTables 1 and 2. Coefficients on the control variables are not reported for the sake of brevity. Firm andyear fixed effects are included. Standard errors are adjusted for heteroskedasticity and clustered at thestate of incorporation level. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level,respectively.

Panel A: Firm level Governance Measures

In Panel A, Zijkl,t−1 is G-index, CEO ownership, CEO equity pay, CEO duality, board size and boardindependence in columns (1) to (6), respectively. CEO ownership is the percentage of a firm’s equity ownedby the CEO. CEO equity pay is one minus the percentage of cash pay including salary and bonus. CEOduality is a dummy variable that is one if the CEO is also the chairperson of the board, and zero otherwise.Board size is the log of the number of board directors. Board independence is the percentage of directorsthat are qualified as outsiders.

(1) (2) (3) (4) (5) (6)

BC law -0.001 -0.007∗∗ -0.012∗∗∗ -0.004 -0.011 -0.003(-0.22) (-2.17) (-3.22) (-1.10) (-1.37) (-0.38)

Excess cash 0.003 0.018 0.017 0.012 0.003 0.012(0.14) (0.69) (0.62) (0.45) (0.12) (0.44)

BC law × Excess cash -0.058∗∗ -0.061∗ -0.059∗ -0.062∗ -0.055 -0.062∗

(-1.96) (-1.79) (-1.67) (-1.82) (-1.63) (-1.81)G-index -0.002∗∗∗

(-2.91)BC law × G-index 0.000

(0.44)CEO ownership -0.015

(-0.70)BC law × CEO ownership -0.012

(-0.34)CEO equity pay -0.007

(-1.07)BC law × CEO equity pay 0.012

(1.46)CEO duality -0.006∗

(-1.95)BC law × CEO duality 0.004

(1.22)Board size -0.002∗∗∗

(-3.35)BC law × Board size 0.000

(0.64)Board independence 0.001

(0.10)BC law × Board independence -0.005

(-0.38)R2 0.613 0.424 0.418 0.411 0.414 0.412N 11650 4108 4623 4477 4491 4491

38

Panel B: Other Firm Characteristics

In Panel B, Zijkl,t−1 is the lagged value of industry Herfindahl-Hirschman Index (HHI), Tobin’s Q, cashflow volatility, and leverage in columns (1) to (4), respectively. Column (5) includes the squared term ofexcess cash.

(1) (2) (3) (4) (5)

Additional Controls HHI Tobin’s Q Cash FlowVolatility

Leverage Nonlinearity ofExcess Cash

BC law -0.003 0.000 -0.005∗∗ -0.009∗ -0.002(-0.95) (0.12) (-2.14) (-1.89) (-0.82)

Excess cash -0.008 -0.003 -0.010 -0.010 0.004(-0.48) (-0.18) (-0.58) (-0.59) (0.25)

BC law × Excess cash -0.052∗∗ -0.046∗∗ -0.055∗∗∗ -0.048∗∗ -0.052∗∗

(-2.57) (-2.62) (-2.77) (-2.58) (-2.57)HHI 0.016

(1.26)BC law × HHI -0.020∗∗

(-2.35)Tobin’s Q 0.017∗∗∗

(8.25)BC law × Tobin’s Q -0.003

(-1.12)CF volatility 0.096∗∗∗

(2.74)BC law × CF volatility 0.060∗∗

(2.33)Leverage -0.042∗∗∗

(-5.96)BC law × Leverage 0.027∗∗

(2.04)Excess cash squared -0.084

(-1.18)R2 0.658 0.658 0.654 0.654 0.653N 42927 44045 44045 44045 44045

39

Table 5: Sticky Measure of Excess Cash

This table reports the results of the baseline regression using the sticky measure of excess cash, which ismeasured by the level of excess cash before the coverage of BC laws. Specifically, if a firm is subject tothe BC law starting in year τ , then for any t < τ , excess cash is measured in year t; for any t ≥ τ excesscash is measured in year τ − 1. Column (1) estimates the whole sample from 1976 to 1995. Columns (2)to (5) estimate the sub-periods starting n years before and ending n years after the laws’ coverage, withn equal to 1, 2, 3, and 4, respectively. All other variables are defined as in Tables 1 and 2. Firm andyear fixed effects are included. Standard errors are adjusted for heteroskedasticity and clustered at thestate of incorporation level. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level,respectively.

(1) (2) (3) (4) (5)

BC law 0.000 0.003 -0.003 -0.004 0.001(0.17) (0.27) (-0.64) (-1.00) (0.33)

Sticky excess cash 0.003 -0.015 0.010 -0.004 -0.010(0.24) (-0.43) (0.17) (-0.09) (-0.35)

BC law × Sticky excess cash -0.069∗∗ -0.064∗∗ -0.085∗∗∗ -0.093∗∗∗ -0.083∗∗∗

(-2.64) (-2.28) (-3.55) (-4.17) (-4.46)Size 0.074∗∗∗ 0.146∗∗∗ 0.133∗∗∗ 0.108∗∗∗ 0.102∗∗∗

(13.44) (3.90) (9.12) (7.26) (7.76)Age 0.302∗∗∗ 0.824∗ 0.689∗∗∗ 0.662∗∗∗ 0.640∗∗∗

(6.81) (1.92) (3.37) (4.94) (6.30)Size squared -0.006∗∗∗ -0.013∗∗∗ -0.012∗∗∗ -0.010∗∗∗ -0.009∗∗∗

(-12.50) (-4.35) (-9.31) (-8.06) (-8.16)Age squared -0.103∗∗∗ -0.266∗∗ -0.210∗∗∗ -0.193∗∗∗ -0.187∗∗∗

(-9.03) (-2.28) (-3.52) (-4.91) (-6.33)Industry year 0.189∗∗∗ 0.107∗∗∗ 0.208∗∗∗ 0.213∗∗∗ 0.197∗∗∗

(7.08) (3.40) (3.31) (7.09) (10.69)State year 0.192∗∗∗ -0.004 0.132∗∗ 0.159∗∗∗ 0.164∗∗∗

(10.71) (-0.03) (2.32) (4.26) (3.98)R2 0.569 0.869 0.739 0.685 0.648N 34239 3951 7766 11432 14959

40

Table 6: 2SLS Estimation

This table reports the results of 2SLS regressions. In columns (1) to (3), the instrumental variable forexcess cash is peer excess cash, defined as the average excess cash of firms in the same industry butincorporated in states that have not passed BC laws; its interaction with the law dummy serves as theinstrument for the interaction of excess cash with the law dummy. Columns (1) and (2) show the firststage regression results, and column (3) shows the second stage regression results. In columns (4) to (6),the instrumental variable for excess cash is local sticky excess cash, defined as the average sticky pre-lawlevel of excess cash of firms located in the same state but in different industries; its interaction with thelaw dummy serves as the instrument for the interaction of excess cash with the law dummy. Columns (4)and (5) show the first stage regression results, and column (6) shows the second stage regression results.All other variables are defined as in Tables 1 and 2. Firm and year fixed effects are included. Standarderrors are adjusted for heteroskedasticity and clustered at the state of incorporation level. *, **, and ***denote statistical significance at the 10%, 5%, and 1% level, respectively.

Instrument based on industry peer firms Instrument based on local firms

First stage First stage Second stage First stage First stage Second stage

(1) (2) (3) (4) (5) (6)

Dependent Excess cash Excess cash ROA Excess cash Excess cash ROAVariable × BC law × BC law

BC law 0.003 0.001 -0.001 0.003 -0.001 -0.003(0.89) (0.19) (-0.43) (1.35) (-0.13) (-0.93)

Excess cash -0.057 0.330(-1.51) (1.57)

BC law × Excess cash -0.189∗∗∗ -0.120∗∗

(-2.63) (-2.29)Size 0.024∗∗∗ 0.030∗∗∗ 0.093∗∗∗ 0.021∗∗∗ 0.035∗∗∗ 0.081∗∗∗

(5.63) (7.76) (7.95) (5.07) (9.65) (6.33)Age -0.120 -0.319∗∗∗ 0.228∗∗∗ 0.049 -0.418∗∗∗ 0.204∗∗

(-1.61) (-4.07) (5.13) (0.71) (-8.48) (2.55)Size squared -0.003∗∗∗ -0.004∗∗∗ -0.007∗∗∗ -0.003∗∗∗ -0.004∗∗∗ -0.006∗∗∗

(-10.54) (-9.48) (-6.66) (-10.21) (-13.26) (-4.41)Age squared 0.032 0.102∗∗∗ -0.085∗∗∗ -0.020 0.132∗∗∗ -0.075∗∗∗

(1.62) (4.84) (-7.07) (-1.10) (10.01) (-3.34)Industry year -0.007 -0.018∗ 0.229∗∗∗ -0.049∗∗∗ -0.005 0.224∗∗∗

(-0.48) (-1.99) (8.68) (-3.73) (-0.41) (7.10)State year -0.049∗ -0.028 0.150∗∗∗ -0.014 0.027 0.176∗∗∗

(-1.82) (-1.11) (5.39) (-0.53) (1.05) (6.43)Peer excess cash 0.615∗∗∗ -0.243∗∗∗

(10.83) (-3.01)BC law × Peer excess cash -0.479∗∗∗ 0.398∗∗∗

(-3.94) (9.08)Local sticky excess cash 0.188∗∗∗ 0.001

(4.13) (0.03)BC law -0.037 0.501∗∗∗

× Local sticky excess cash (-0.82) (12.81)R2 0.737 0.648 0.711 0.629N 40745 40745 40745 43577 43577 43577

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Table 7: Subsample Analysis

This table reports the results of the baseline regression model as in Table 2 using various subsamples.All variables are defined as in Tables 1 and 2. Column (1) excludes large firms whose assets are abovesample median. Column (2) examines a symmetric time period of 1984-1992. Column (3) excludes firmsincorporated in Delaware from the treatment group. Column (4) excludes firms incorporated in states thatnever passed BC laws in the sample period. Column (5) includes only firms with observations availablein the whole sample period from 1976 to 1995. Firm and year fixed effects are included. Standard errorsare adjusted for heteroskedasticity and clustered at the state of incorporation level. *, **, and *** denotestatistical significance at the 10%, 5%, and 1% level, respectively.

(1) (2) (3) (4) (5)

BC law -0.006 0.002 -0.004 0.001 -0.001(-0.98) (0.68) (-1.33) (0.43) (-0.33)

Excess cash -0.013 0.009 -0.025 0.013 -0.004(-0.49) (0.47) (-1.59) (1.04) (-0.18)

BC Law × Excess Cash -0.056∗∗∗ -0.083∗∗∗ -0.056∗∗ -0.071∗∗∗ -0.047∗∗

(-2.72) (-5.10) (-2.22) (-3.21) (-2.19)Size 0.156∗∗∗ 0.131∗∗∗ 0.072∗∗∗ 0.091∗∗∗ 0.073∗∗∗

(5.35) (12.49) (9.65) (12.45) (7.13)Age 0.084 0.577∗∗∗ 0.217∗∗∗ 0.281∗∗∗ 0.339∗∗∗

(0.58) (6.39) (3.30) (5.02) (4.64)Size squared -0.017∗∗∗ -0.011∗∗∗ -0.005∗∗∗ -0.007∗∗∗ -0.005∗∗∗

(-4.16) (-12.11) (-7.89) (-11.36) (-6.29)Age squared -0.039 -0.171∗∗∗ -0.084∗∗∗ -0.101∗∗∗ -0.109∗∗∗

(-0.89) (-7.52) (-4.62) (-7.00) (-5.50)Industry year 0.205∗∗∗ 0.185∗∗∗ 0.233∗∗∗ 0.199∗∗∗ 0.190∗∗∗

(6.76) (10.04) (11.53) (8.61) (8.60)State year 0.267∗∗∗ 0.164∗∗∗ 0.159∗∗∗ 0.191∗∗∗ 0.167∗∗∗

(4.81) (3.80) (5.07) (9.20) (5.78)R2 0.658 0.708 0.637 0.656 0.649N 22021 21605 35210 38650 35542

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Table 8: Impact of the Laws’ Passage on Stock Performance

Coefficient estimates and their t-statistics (in parentheses) are presented for the following regression model:

Rijkl,t = αi + αt + β1BC lawijkl,t + β2 (BC lawijkl,t × FSijkl,t−1) + β3FSijkl,t−1 + γ′Xijkl,t + εijkl,t,

where Rijkl,t is the characteristics-adjusted annual stock return of firm i, constructed using benchmarkportfolios based on size, book-to-market ratios, and past returns. “Industry year” is the mean adjustedreturn of other firms in the same industry-year group. “State year” is the mean adjusted return of otherfirms in the same state-year group. All other variables are defined as in Tables 1 and 2. Firm and yearfixed effects are included. Standard errors are adjusted for heteroskedasticity and clustered at the state ofincorporation level. *, **, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively.

(1) (2) (3)

BC law 0.019 0.016 -0.007(1.39) (1.19) (-0.62)

Current ratio -0.008∗∗∗

(-3.92)BC law × Current ratio -0.009∗∗∗

(-3.34)Cash ratio -0.001

(-0.03)BC law × Cash ratio -0.172∗∗∗

(-2.98)Excess cash 0.013

(0.18)BC law × Excess cash -0.263∗∗∗

(-3.61)Age -0.131∗∗ -0.082 0.039

(-2.30) (-1.54) (0.13)Age squared 0.021 0.005 -0.025

(1.12) (0.26) (-0.32)Industry year 0.432∗∗∗ 0.429∗∗∗ 0.396∗∗∗

(24.55) (24.72) (20.84)State year 0.310∗∗∗ 0.308∗∗∗ 0.283∗∗∗

(10.23) (10.94) (9.21)R2 0.190 0.188 0.179N 51609 53026 35921

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Table 9: Channels of Wasteful Spending

Coefficient estimates and their t-statistics (in parentheses) are presented for the following regression model:

Yijkl,t = αi + αt + β1BC lawijkl,t + β2 (BC lawijkl,t × FSijkl,t−1) + β3FSijkl,t−1 + γ′Xijkl,t + εijkl,t.

In Panel A, column (1), Y is capital expenditure divided by total assets. In column (2), Y is over-investment divided by total assets, where over-investment is constructed with the regression model ofRichardson (2006) as described in the Appendix. In column (3), Y is asset growth, the percentage increasein total assets from one year to the next. In column (4), Y is PPE growth, the percentage increase infixed assets. In column (5), Y is acquisition ratio, defined as the sum of the value of all acquisitions madeby the firm in a given year divided by the firm’s market capitalization in that year. The acquisition dataare collected from the Securities Data Corporation’s (SDC) database.

In Panel B, column (1), Y is the overhead costs (selling, general, and administrative expenses) divided bysales. In column (2), Y is the advertising expenses divided by sales. In column (3), Y is the operatingexpenses divided by sales. In column (4), Y is the costs of goods sold (COGS) divided by sales. In column(5), Y is the number of employees divided by sales. In column (6), Y is wages, measured by the naturallogarithm of labor and related expenses divided by the number of employees.

All other variables are defined as in Tables 1 and 2. For the sake of brevity, coefficients on the controlvariables are not reported. Firm and year fixed effects are included. Standard errors are adjusted forheteroskedasticity and clustered at the state of incorporation level. *, **, and *** denote statisticalsignificance at the 10%, 5%, and 1% level, respectively.

Panel A: Investment Activities

(1) (2) (3) (4) (5)

Dependent Variable Capital Expenditure Over-investment Asset Growth PPE Growth Acquisition Ratio

BC law 0.003∗ 0.001 -0.006 -0.006 -0.002(1.79) (0.48) (-0.60) (-0.52) (-0.95)

Excess cash 0.021∗∗∗ 0.089∗∗∗ 0.052∗ 0.572∗∗∗ 0.046∗∗∗

(4.33) (8.07) (1.76) (17.69) (5.62)BC law × Excess cash 0.011 0.000 -0.135∗∗∗ 0.004 0.012

(1.36) (0.02) (-5.35) (0.10) (1.30)R2 0.570 0.305 0.474 0.364 0.245N 43931 24086 44071 44016 40355

Panel B: Cost Management

(1) (2) (3) (4) (5) (6)

Dependent Variable Overhead Advertising Operating COGS Number of WagesCosts Costs Expenses Employees

BC law -0.002 0.000 0.003 0.004 -0.000 -0.001(-0.93) (0.30) (1.14) (1.39) (-0.51) (-0.22)

Excess cash 0.041∗∗∗ 0.010∗∗∗ -0.001 -0.012 -0.003 0.000(3.78) (3.51) (-0.16) (-1.63) (-0.87) (0.00)

BC law × Excess cash 0.052∗∗∗ 0.001 0.059∗∗∗ 0.034∗∗ 0.008∗ -0.014(4.13) (0.17) (4.17) (2.47) (1.75) (-0.74)

R2 0.840 0.853 0.765 0.708 0.606 0.896N 36940 16470 38937 43138 43117 5583

44