Product Market Competition and Agency Conflicts: Evidence from the Sarbanes Oxley Law

Vidhi Chhaochharia University of Miami

Yaniv Grinstein Cornell University

Gustavo Grullon Rice University

Roni Michaely Cornell University

Abstract

We study the effect of product market competition on the alignment of incentives between management and shareholders in public U.S. firms. We find that competition reduces agency conflicts. Firms in less competitive industries are less efficient than firms in more competitive industries and the Sarbanes Oxley regulation, aimed at mitigating agency conflicts, improved efficiency mainly in the less competitive industries. Product market competition is also a substitute for other governance mechanisms as firms in less competitive industries are more likely to employ stronger governance mechanisms to align management interests with those of the shareholders.

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1. Introduction

The agency conflicts between management and shareholders and the importance of having

mechanisms to overcome the conflict have been the center of attention in corporate finance

research in the last thirty years. Among these mechanisms are compensation contracts (e.g.

Holmstrom 1979), financial contracts (e.g., Jensen and Meckling 1976), monitoring by the

board of directors (Fama and Jensen 1983), the market for corporate control (Shleifer and

Vishny 1986), and others.

While research on the importance of these mechanisms on incentive alignment is abundant,

we still find ourselves perplexed by how well corporations do even when many of these

mechanisms are not in place. For example, Gomes (1997) shows that firms are adhering to

protecting the rights to shareholders even in environments in which there is little legal

protection and very few mechanisms to align incentives. Allen and Gale (2000) point to the

phenomenal success of foreign car maker Toyota, despite the lack of internal governance

mechanisms or takeover threats to align incentives. Clearly, in these cases, management finds

itself motivated not to waste corporate resources because other channels are used to align

incentives, even though no formal mechanisms are in place. One such a potential channel is the

product market competition that the firm faces.

In this study we explore the role of product market competition in aligning managerial

incentives in public U.S. corporations. Looking at U.S. public firms between 2001 and 2005, we

study the relation between product market competition and the level of efficiency in these firms.

We use two main measures of the level of efficiency in firms. The first measure is the ratio of

general, sales, and administrative expenses to sales. This measure captures the overhead costs to

the firm as a fraction of its revenues. The second measure is the ratio of sales to assets. This

measure captures the amount of resources that are put in place to generate a given revenue level.

Both measures have been used extensively in past studies to capture corporate efficiency.

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Consistent with previous studies, we find that firms in less competitive industries are less

efficient than firms in more competitive industries. The results are significant in each and every

year in the sample and are robust after controlling for industry sectors, size, and firm age.

However, the relation between industry and corporate efficiency could be driven by the

mere fact that different industries have different production functions. Thus, it is possible that

firms in less competitive industries are required to spend more resources to achieve the optimal

level of efficiency than firms in more competitive industries. To account for this possibility we

study the effect of the Sarbanes Oxley law on the level of efficiency in more competitive and

less competitive industries. The Sarbanes-Oxley law was enacted as a result of the corporate

scandals in companies like Enron, Tyco, and others. The law included provisions such as

increased penalties for officers who are charged with fraud, enhanced scrutiny over external and

internal auditors, enhanced disclosure of financial statements, enhanced disclosure on insider

trading, and other restrictions. To the extent that the incentives of managers in firms in more

competitive industries are more aligned with shareholders’ interests, they should be less

affected by the monitoring requirements than managers in firms in more competitive industries.

We find that after the approval of the Sarbanes Oxley law in 2002, firms that belong to less

competitive industries experienced a significantly larger increase in efficiency than firms that

belong to competitive industries. This finding is consistent with the hypothesis that governance

mechanisms are more relevant to firms that face lower product market competition.

We further explore the role of product market competition in aligning incentives by

decomposing the efficiency gains of incentive alignment between small and large firms. There

are two important reasons for differentiating between small and large firms. First, smaller firms

might be more vulnerable to competition than larger firms (e.g., Bolton and Scharfstein 1990).

Second, governance regulations could have a different effect on small firms and large firms. We

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find that the positive effect of the Sarbanes Oxley law on efficiency affected both large and

small firms.

We also explore the relation between product market competition and the use of other

governance mechanisms to align managerial incentives. We find that product market

competition substitutes for the need to align managerial incentives through other governance

mechanisms. Firms in less-competitive industries have more mechanisms in place than firms in

competitive industries. For example, firms in less-competitive industries have less anti-takeover

provisions, and their board is more independent than firms in more competitive industries.

Our study is reminiscent of the study of Giroud and Mueller (2009) who explore the effect

of the business combination laws in the U.S. in the 1980’s on firms in more competitive and

less competitive industries. Business combination laws reduced the threat of hostile takeovers,

thereby mitigating the disciplinary forces of the takeover markets. Giroud and Mueller (2009)

find that the business combination laws in the 1980’s had negative effect on the efficiency and

the profitability of firms in less competitive industries. The interpretation of their finding is that

the disciplinary role of the takeover market is more important in firms in less competitive

industries. Our results are similar to the results of Giroud and Mueller (2009) since we also find

a stronger effect of governance rules on efficiency in less competitive industries. However, the

ruling event in Giroud and Mueller (2009) is different than the ruling event in our study – a

difference that allows us to shed additional light on the role of governance mechanisms in firms.

In their study, the ruling event is related to external governance mechanisms (the takeover

market). In our study the ruling event is related to internal governance mechanisms (enhanced

accounting reports, enhanced disclosure of insider trading, higher penalties on fraud etc). We

find that the main effect of the anti-takeover laws in the 80’s was on small firms in concentrated

industries, but did not have any effect on large firms in concentrated industries. In contrast, both

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large firms and small firms in less competitive industries were affected by the Sarbanes Oxley

law.

Our study belongs to a growing literature that studies empirically the role of product market

competition in aligning managerial incentives. Several studies have been focusing on the direct

relation between product market competition and efficiency. For example, Graham, Kaplan, and

Sibley (1983) find that the deregulation of the airline industry in the U.S. was associated with

efficiency gains. Nickel (1996) studies differences in production efficiency between eastern

European firms, which faced less competition, and western European firms, which faced more

competition. Caves and Barton (1998), Green and Maers (1991), and Caves (1992) find that

above a certain level of industry concentration, technical efficiency is reduced. Nickel,

Nicolitsas and Dryden (1997) find that the higher the level of competition the higher the level of

growth in productivity.

Other studies have been focusing on the interaction between the design of compensation

contracts and the type of product market competition (e.g., Aggarwal and Samwick (1999). See

also the survey by Gal-Or (1997)). However, these studies by and large do not explore the role

of other governance mechanisms besides compensation contracts in aligning incentives, and

also do not explore the effect of the different mechanisms on efficiency.

More recent studies have looked at the relation between product market competition and

governance mechanisms. For example, Guadalupe and Perez Gonzales (2006) find that stronger

product market competition reduces the premium for voting shares, and Giroud and Mueller

(2008) find that the governance measure of Gompers, Ishii, and Metrick (2003) better explains

differences in stock returns in non competitive industries.

Our findings complement the above studies in several ways. First, our U.S. large-scale study

looks at much more recent data than previous studies. Arguably, public firms in the U.S. have

been given very strong incentives to improve shareholder value in recent years (e.g., Holmstrom

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and Kaplan 2003) and it is plausible that product market competition plays no significant role in

aligning incentives in such environment. In addition, we shed new light on the different role

that different governance mechanisms play in mitigating agency conflicts across less

competitive industries.

The rest of this study continues as follows. In the next section we introduce the literature

review and develop the hypotheses. Section 3 describes the data. Section 4 reports the empirical

results and section 5 concludes.

2. Literature review and development of hypotheses

Early scholars such as Alchian (1950), and Stigler (1958) have argued that competition in

the product market is a very powerful force for ensuring that management does not waste

corporate resources. If management wastes or consumes large amounts of resources in a

competitive market environment, the firm will be unable to compete and will become insolvent.

Later studies by Schmidt (1997), Aghion, Dewatripont, and Rey (1999), and others formalized

this intuition in various models.

Scholars also commented on the role of product market competition in reducing the costs of

incentive alignments. For example, Hart (1983) introduced a model where managerial moral

hazard exists because owners cannot observe effort and the manager can claim that profits are

down not because of effort but because of increased input prices. Product market competition

helps alleviate the agency problem because, when competition exists, owners can observe

profits in other firms and use this information as a benchmark to condition the rewards to the

manager. This benchmarking reduces the asymmetric information problem and the costs of

incentive alignment.1

1 We note that not all theoretical studies agree that product market competition necessarily increases efficiency. Scharfstein (1988), for example notes that because profits are lower in competitive industries and the incentives of the manager to exert effort are lower. See also Hermalin (1992). Raith (2003) resolves some of this ambiguity by endogenizing entry into the product market.

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Our goal in this study is to examine empirically whether indeed product market competition

plays a significant role in aligning incentives. In this section we introduce a tradeoff model to

form the hypotheses. We consider a firm that operates in an industry and faces managerial

agency problems. This firm should weigh the benefits and the costs of different alignment

mechanisms when deciding on which mechanisms to use in order to align incentives (e.g.,

Demsetz and Lehn 1985).

If this firm operates in a competitive industry, then, according to Alchian (1950), Stigler

(1958), and others, the incentives of the manager are already more aligned, and therefore the

firm needs to spend less to further align incentives than they would spend in a non-competitive

industry.

To formalize this intuition, let g≥0 be the firm’s choice of mechanisms to align incentives.

A higher value of g corresponds to more mechanisms in place and a smaller value corresponds

to fewer mechanisms. Let C(g)≥0 be the cost of these incentive mechanisms and let B(g)>0 be

the benefits to the firm from aligning managerial incentives from these mechanisms. The

benefits can be thought of the increased cash flows to shareholders from more efficient

investment decisions and cost reducing activities.2 One can rank these mechanisms in the

analysis assuming that there is a mapping between the choice of mechanisms and the cost of

these mechanisms. These costs could be, for example, increased auditing costs or costs

associated with enhanced disclosure, costs of bringing strong directors to the board to monitor

management, costs associated with structuring more powerful compensation incentives to

management, costs of overcoming managerial entrenchment, etc. We assume that g is additive

in the sense that these mechanisms are additive. We further assume that C’(g)>0, C”(g)>0 for

g>0, C(0)=C’(0) = 0, C’(∞)= ∞. We also assume that B(0) =0, B’(g)>0, and B’’(g)<0 for all

2 Obviously, this reduced form of the choice of mechanisms does not take into account potential interaction among mechanisms and substitutions across them. However, to the extent that there is a cost-benefit efficient frontier among these mechanisms, one can order them on the frontier and map these choices into g.

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g. These assumptions mean that the costs of aligning incentives are convex in g and that the

benefits from alignment mechanisms are concave in g.

We consider the effect of competition on alignment of incentives in the following way: We

assume that industry competition is equivalent to a governance mechanism gc>0, which leads to

added benefits from alignment B(gc) at no cost to the shareholders. A firm that wishes to

implement governance mechanisms g in a competitive industry is therefore facing the benefit

function B(gc +g) rather than B(g). 3

Since B(g) is concave and C(g) is convex, the global solution solves B’(g) = C’(g) for non-

competitive industries and B’(gc +g) = C’(g) in competitive industries. Denote g* the optimal

governance level in competitive industries and g** the optimal governance level in non

competitive industries. Figure 1 illustrates the marginal benefits, marginal costs, and optimal g

for firms in competitive industries and in non-competitive industries.

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MC

MB (non comp)

MB (comp)

g* g**

gc

Figure 1: marginal cost and benefit functions of incentive-alignment mechanisms in competitive and non-competitive industries

3 Note that we abstract from the effect of competition on the amount of rents that are captured by the firm. This simplification is to allow tractability. One way to embed the differences in rents is to assume that the first-best value depends on the level of competition and that the benefits in the model represent the percentage of the value that can be returned back to the shareholders. Another way to think about it is to assume that the level of rents that agents can extract absent alignment mechanisms is constant regardless of the level of competition.

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Proposition 1: Else equal, incentives of agents in firms in non-competitive industries are less

aligned with those of the shareholders than incentives of agents in firms in competitive

industries.

The proof to Proposition 1 as well as the other propositions is in the appendix. The intuition

behind the proposition is quite simple. In competitive industries, the shareholders gain a level of

incentive alignment because the firm competes in the product market. Therefore, the firm saves

on some of the costs of aligning incentives and can therefore allow itself to reach a higher level

of alignment than firms that have to incur all costs of incentive alignment.

Given the above cost and benefit functions, it is easy to see that firms in competitive

industries will find it less beneficial to invest in incentive-alignment mechanisms, and therefore,

in equilibrium, they should have fewer mechanisms in place compared to firms in non-

competitive industries. Figure 1 illustrates the intuition behind the result. When the incentives

are in place because of product market competition, the marginal utility of adding more

governance mechanisms diminishes, and, to the extent that governance mechanisms bear cost, it

is enough to have fewer governance mechanisms in place. Hypothesis 2 formalizes this

intuition:

Proposition 2: Else equal, firms in non-competitive industries will have more incentive-

alignment mechanisms in place compared to firms in competitive industries.

In this study, we explore also the effect of the Sarbanes Oxley law on firms in competitive

and non competitive industries. The Sarbanes Oxley law required the implementation of several

governance mechanisms such as enhanced auditing of financial statements, and increased

penalties on corporate fraud. To illustrate the effect of such an outside intervention on

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alignment of incentives across competitive and non competitive firms, suppose that firms in

competitive and non-competitive industries are required to have additional governance

mechanism which they did not consider before. These mechanisms will add to the existing

mechanisms a constant value ∆ such that firms in competitive industries will move from a level

of g* to g* + ∆ and firms in non competitive industries will move from a level g** to level g** +

∆. Suppose also that the costs of adding these mechanisms are similar across the two firms. We

hypothesize that since the marginal benefit from these additional mechanisms is going to be

smaller in competitive industries than in non competitive industries, firms in competitive

industries are going to benefit less from the Sarbanes Oxley law than firms in competitive

industries. We formulize this idea in hypothesis 3.

Proposition 3: Else equal, an exogenous requirement to include additional governance

mechanisms that are not in place leads to a larger increase in the benefits of alignment of

incentives in non-competitive industries than in competitive industries.

These three propositions lay down the three hypotheses that we examine in the empirical

analysis.

3. Data and variables

The data for the analysis is obtained from several sources. We use the most recent industry

concentration data (the 2002 data) from the Economic Census Bureau, and examine the relation

between industry concentration, efficiency, and governance in the years 2001-2005. We focus

only on this recent data since the Census Bureau has recently made several changes to the

classifications of industries and in its 2002 data it is using the improved NAICS method to

classify industries. (In previous years, industry concentration was given according to SIC

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codes).4 In addition, the 2002 data includes classification of all industries, while earlier data

focused only on manufacturing industries.5

In the analysis of the relation between product market competition and efficiency we study

the entire Compustat database, merged with the industry concentration data and focus on the

years 2001-2005. In the analysis of the relation between product market competition, efficiency,

and governance mechanisms, we further restrict the original data to firms that also have

governance data (firms that belong to the IRRC database - roughly firms that belong to the S&P

1500 index).

3.1 Industry concentration

We use the data from the U.S. Census Bureau on concentration of different industries in the

U.S. economy. We use the latest data available, from the year 2002. The database relies on the

North American Industry Classification System (NAICS), and it provides two industry

concentration measures. For manufacturing industries, it provides the Herfindahl index (based

on the sales of the largest 50 firms). For non manufacturing industries, it provides the market

share of the top fifty firms in the industry.

To calibrate competition across these two measures, we use separate rankings for each one

of them: We define industries as concentrated if they are among the top 50% of the industries

4 NAICS (North American Industry Classification System) was developed under the auspices of the Office of Management and Budget (OMB), and adopted in 1997 to replace the Standard Industrial Classification (SIC) system. The SIC system was developed in the 1930s at a time when manufacturing dominated the US economic scene. The system has received increasing criticism about its ability to handle rapid changes in the US economy (e.g., developments in information services, expansion of services, and high tech manufacturing). NAICS’ six-digit hierarchical structure allows greater coding flexibility than the four-digit structure of the SIC. NAICS classifies all economic activity into twenty industry sectors. Five sectors are mainly goods-producing sectors and fifteen are entirely services-producing sectors. The system allows for the identification of 1,170 industries compared to the 1,004 found in the SIC system. For more information see http://www.census.gov/eos/www/naics/index.html. 5 However, in the robustness section, we also run several specifications with earlier data using the old classification system. Our results are robust to earlier periods.

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which use the same concentration criteria (Herfindahl for manufacturing firms and market share

for non-manufacturing firms).

3.2 Incentive-alignment mechanisms

We consider two main governance mechanisms that align managerial interest with those of

the shareholders. The first mechanism is anti-takeover provisions in the corporate charter. Firms

that do not include these anti-takeover mechanisms become more vulnerable to takeovers, and

would therefore be more disciplined by the market for corporate control (Jensen 1986). We use

the anti-takeover charter amendment index of Gompers, Ishii and Metrick (2001) and the index

of the five main anti takeover provisions of Bebchuk, Cohen, and Ferrel (2006).

The second mechanism is the structure of the board. The board of directors is the

representative of the shareholders and as such is monitoring managerial activities. Past studies

have identified a relation between the existence of outsider directors on the board and a weaker

bargaining position of the CEO vis a vis the board (e.g., Hermalin and Weisbach 1988, Boone et

al. 2007). We consider board independence, defined as a majority of independent directors on

the board, and committee independence, defined as a committee composed fully of independent

directors.

3.3 Efficiency

Hypothesis 1 suggests that agency conflicts in firms that face product market competition

should be lower than agency conflicts in firms that do not face such competition. To test the

hypothesis, we need to find a measure of the level of agency conflicts in firms. Much of the

literature on agency conflicts in firms has been focusing on the tendency of management to

capture managerial slack by spending money on non profitable project and spending money on

themselves rather than on returning cash to shareholders (e.g., Jensen and Meckling 1976,

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Jensen 1986). We therefore focus our attention in this study on efficiency measures. We use two

measures that were used in the past to measure efficiency in firms (e.g., Ang, Cole, and Lin,

2000). The first measure is the ratio of general, sales, and administration costs to sales, and the

second measure is the ratio of sales to assets. The first measure captures the costs that the firm

incurs, not directly related to production. Else equal, firms that have a higher ratio of

administrative expenses to sales spend more of their revenues on overhead administrative costs

and therefore are less efficient. The second measure captures how efficiently assets are used to

generate sales. Firms that generate higher sales relative to their assets are more efficient in

utilizing their assets.

We note that, by definition, firms that operate in less competitive industries are more likely

to capture higher rents from selling their product than firms in more competitive industries.

Therefore, else equal, firms in more competitive industries should bear higher costs to sales and

have higher sales-to-asset ratios than firms in less competitive industries. However, to the extent

that we find higher costs in firms in less competitive, we can still infer that firms in less

competitive industries are less efficient. In addition, our test of the effect of the Sarbanes Oxley

act on firm efficiency controls for firm-specific product market attributes to circumvent this

issue.

3.4 Control variables

Several control variables are used in the analysis. To control for size, we use the market

capitalization of the firm. To control for different production functions and growth opportunities

across industry sectors, we use the 48 industry dummies of Fama and French (1997). In some of

the specifications we also control for growth opportunities using the market to book ratio, and

for firm age.

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4. Results

4.1 Product market competition and Corporate Efficiency

Table 1 shows the results of the relation between product market competition and corporate

efficiency. Panel A shows the univariate results. This panel shows that for each of the years

2001-2005, the median ratio of sales, general, and administrative costs to sales is significantly

lower in non concentrated industries than in concentrated industries. A similar pattern exists

also in mean ratios although significance varies across the years.

Panel A also shows that the average ratio of sales to assets is significantly larger in

concentrated industries than in non-concentrated industries. The result is significant in each of

the years 2001-2005. The difference in the median ratio is also significant at the 1% level. Panel

B shows the results across the 5 size quintiles. For each year we rank the firms in the sample

into 5 size quintiles based on their market capitalization. We then aggregate all firm-years

within each quintile and calculate average and median ratios. The results show that the

difference in mean and median ratios between concentrated and non concentrated industries

appear across the different quintiles.

The univariate results do not control for other potential reasons for the differences in the

efficiency ratios across industries. These reasons could be related to differences in production

functions across different industries or to differences in where firms stand in the firm’s life

cycle. To account for these potential effects, we run a panel regression where the independent

variables include firm size, dummy for the 48 industry sectors, and firm age. We show the

results in panel C of Table 1. The standard errors in the panel regression (as well as all other

regressions in this paper) are clustered at the firm level to account for correlation in errors

within firms.

Panel C column 1 shows the regression result where the dependent variable is the natural

log of one plus the general, sales, and administrative expenses to sales ratio. The coefficient of

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the concentrated industry dummy is positive and significant, suggesting that firms in

concentrated industries have larger expenses relative to sales compared with firms in non

concentrated industries. Since the dependent variable is in logs, the coefficient of 0.057 suggests

that the dependent variable is on average 5.7% larger in concentrated industries than in non-

concentrated industries. We also find that size and age are significantly related to the ratio.

Firms that are smaller tend to have lower expense ratios, and firms that are younger tend also to

have smaller expense ratios.

Column 2 decomposes the concentration variable into large firms and small firms. The

column shows that concentration is associated with higher administrative expenses to sales

across both large and small firms.

Column 3 shows the results where the dependent variable is the natural log of sales to

assets. The coefficient of the concentrated industry dummy is negative and significant,

suggesting that firms in concentrated industries have lower sales-to-assets ratios compared with

firms in non concentrated industries. The coefficient of -0.067 implies that firms in more

concentrated industries have sales-to-assets ratios that are about 6.7% smaller than the ratios in

less concentrated industries. Size is negatively related to the ratio of sales to assets and firm age

is positively related to it. When we decompose the effect of concentration into large and small

firms we again find that the effect on the Sales to Assets ratio is about the same across both

small and large firms.

Overall, the results support Hypothesis 1 that firms in concentrated industries are less

efficient than firms in non-concentrated industries.

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4.2 The effect of the Sarbanes Oxley law on Corporate Efficiency in concentrated and non concentrated industries

The Sarbanes-Oxley law was enacted as a result of the corporate scandals in companies like

Enron, Tyco, and others. The law included provisions such as increased penalties for officers

who are charged with fraud, enhanced scrutiny over external and internal auditors, enhanced

disclosure of financial statements, enhanced disclosure on insider trading, and other restrictions.

To the extent that the incentives of managers in firms in more competitive industries are more

aligned with shareholders’ interests, they should be less affected by the monitoring requirement

than managers in firms in more competitive industries.

To test this hypothesis (based on Proposition 3), we use the difference-in-difference

approach on a panel of firms between 2001-2005.

Specifically, we run the following regression:

Efficiencyit = firm fixed effecti + sector*year effectt + concentrated industry dummyi *

year>2002 dummy + controlsit + eit. (1)

We test whether the coefficient of the concentrated industry dummy interacted with the year

dummy is significantly different from zero. A significant coefficient would imply that firms in

concentrated industries have changed their efficiency differently than firms in less concentrated

industries. We further control for year-sector dummies, firm size, firm age, and for firm fixed

effects. Our empirical specification closely follows the Giroud and Mueller (2009) specification

which studies the effect of the anti-takeover laws in the 1980’s on firm performance across

concentrated and non concentrated industries.

The results appear in Table 2. Column 1 shows the results where the dependent variable is

the natural log of the sales, general, and administrative costs to sales, and column 3 shows the

results where the dependent variable is the natural log of sales to assets.

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The coefficient of the Concentration*year>2002 dummy is significantly negative in column

1. The coefficient is -0.025, implying that firms in concentrated industries have reduced their

sales, general, and administrative costs to sales ratio after the SOX law more than firms in less

concentrated industries by about 2.5%. Column 2 shows the decomposition of the effect across

small and large firms. The results suggest that the effect is about similar for both small and large

firms.

Column 3 shows that these firms have also increased their sales to assets ratio by more than

firms in less concentrated industries by about 1.3%. The effect is significant only in small firms

(Column 4).

The analysis in Table 2 shows that firms in concentrated industries tend to have larger

efficiency gains from the Sarbanes Oxley law. While our model assumed that the Sarbanes

Oxley law imposed new mechanisms over and above existing mechanisms, it is possible that

some firms in concentrated industries were more compliant with the rules to begin with and

therefore they should not be affected by the rule as much as firms in concentrated industries that

were less compliant. Thus, it would be interesting to see whether the Sarbanes Oxley law

affected firms in concentrated industries differently, depending on their governance compliance.

To that end, we repeat the regression (1) except that we interact the concentration*year>2002

dummy with a dummy for whether the firm was more compliant or less compliant with the

regulations to begin with. Our measure of compliance is the level of independence of the board

and its committees, and is taken from Chhaochharia and Grinstein (2007). The measure is the

sum of the dummy variables for whether the firm has a majority of independent directors, and

whether it has an independent compensation and nominating committees. We define a low score

as a measure of 0 or 1, and a high score as a measure of 2, 3. The regression specification

includes the interaction of concentration with score 0 and 1 which represents low governance

and the interaction of concentration with medium score (2 or 3).

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Table 3 shows the regression results. The table shows that the Sarbanes Oxley law improved

sales to assets and reduced administrative expenses to sales the most for firms that were the

least compliant with the rule to begin with.6 This result corroborates our initial efficiency results

and relates them more directly to the existence of governance mechanisms.

4.3 Comparison of governance rules: the Sarbanes Oxley law and the anti-takeover legislation of the 1980’s.

In a separate study, Giroud and Mueller (2009) examine the effect of the anti-takeover

legislations in the 1980’s on firms in concentrated and non concentrated industries. The anti-

takeover legislations were state-level rules whose purpose was to reduce the ability of raiders to

take over firms through hostile takeovers. Giroud and Mueller conjecture that, to the extent that

firms in concentrated industries face stronger agency conflicts, immunizing these firms from the

disciplinary force of takeovers should have worse effect on them than on firms in more

competitive industries, whose incentives are more naturally aligned.

The main variable of interest in their study is the return on assets. In table 5 panel A we

replicate the results of the analysis of Giroud and Mueller. The regression is a differences-in-

differences panel regression, similar to the one used by us for the Sarbanes Oxley legislation,

except that the variable BC is a dummy for whether the business combination law was enacted

in the state in which the company is incorporated.7 The interaction BC*Concentration is a

measure of whether firms in more concentrated industries suffered more from the rule than

firms in less concentrated industries.

6 The difference is significant for the Sales to Assets variable. 7 Note also that Giroud and Mueller included beyond the interaction term also the concentration variable in their regression. We did not include such measure since our sample spans a shorter period of time than theirs (five years vs. 20 years), during which our concentration measure did not change. In addition, their concentration variable is the Herf. measure (rather than our ranking measure), since they consider only manufacturing firms for which the Herf. measure is available. We also use the log(1+ROA) variable rather than the ROA variable to account for non-linearities in the data.

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Table 4 panel A column 1 shows that the overall effect of the anti-takeover legislation in

their sample was a 3.7% decrease in (1+ ROA). This number is similar to the one that Giroud

and Mueller find in their sample (Table 3 column 2 in their paper, a 3.3% decrease in ROA).

The other variables are also similar in magnitude and significance.

When we decompose the effect on ROA into large and small firms we find that the large

decrease is due to small firms only. Small firms saw a decrease of 9.4% in ROA during that

time, while large firms did not see any significant decrease in ROA. The result is interesting,

since it suggests that the legislation had a different effect on small and large firms. One

possibility is that the rule had a bite only in small firms, since large firms are inherently harder

to be taken over and therefore were already immuned from hostile takeovers before the

legislation. It is also possible that the rule had affected the nature of product market competition

since firms can no longer decrease the level of competition by simply acquiring small firms.

To further explore the reason for the differences across large and small firms, we

decompose the Return on Assets variable into the following ratios: Sales to Asset,

Administration expenses to Sales, Cost of Goods Sold to Sales, and Operating income to Sales.

We present the results in columns 2-6.

The results suggest that sales to assets have decreased in both small and large firms,

consistent with the hypothesis that both large and small firms experienced a decrease in

efficiency after the regulation. However, the administrative expenses to sales did not changed

for any type of firm. And while operating income to sales decreased in small firms, it actually

increased in large firms. Cost of goods sold increased, but only in small firms.

Our conclusion from this decomposition is that large firms, which saw a decrease in sales to

assets, actually experienced an increase in earnings to sales. This increase in earnings to sale

could come from the fact that costs went down, or from the fact that margins on sales went up

due to higher prices. Since the effect on costs is not significant, it is possible that margins on

19

sales went up due to higher prices. In contrast, small firms saw a decline in sales to assets, as

well as an increase in cost of goods sold to sales. Thus, it seems that inefficiency in investment

as well as the decrease in costs of goods sold have led to a decline in returns. However, the

magnitudes of the changes in cost-of-goods sold are not enough to produce such a large

decrease in margins. It is therefore plausible that the gain in market power for larger firms was

at the expense of smaller firms.

Panel B decomposes the efficiency gains due to SOX. We start with the ROA measure to

allow for a more complete comparison with panel A. As expected, the gains in ROA were in

both large and small firms. The gains are due to a decrease in administrative expenses to sales in

both large and small firms, and to increase in sales to assets, but only in small firms. There was

no effect on the cost of goods sold in either large or small firms.

Thus, it looks from the above analysis that changes in external governance mechanisms

have a different effect on alignment of incentives than changes in internal governance

mechanisms. External governance mechanisms had an impact on the efficiency of investment,

but did not have much effect on the production or overhead efficiencies. In addition, external

governance mechanisms seem to be associated with changes in nature of product market

competition. In contrast, internal governance mechanisms reduce overhead costs in both small

and large firms, and also investment inefficiencies, but only in small firms.

4.4 The interaction between product market competition and governance mechanisms

The second hypothesis states that, to the extent that product market competition reduces

agency conflicts, firms in non-concentrated industries should be less concerned with managerial

incentive alignment and should therefore not require additional governance mechanisms to align

them.

20

Table 5 panel A shows univariate comparison of differences in governance mechanisms

between firms in concentrated and non concentrated industries. The sample is from the IRRC

database, which includes most of the S&P1500 firms. The anti-takeover provision database has

data every other year and so we look at the five years from 1998-2006. Our data for the director

database includes the years 2000-2005.

Panel A shows that for each year in our dataset there are more anti-takeover provisions in

non-concentrated industries than in concentrated industries. The effect is statistically significant

in the majority of the years. We find a similar relation between industry concentration and the

entrenchment index: Firms in non-concentrated industries have on average an entrenchment

index that is higher than firms in concentrated industries in each and every year of our sample.

The effect is significant in three of the five years that we examine.

Panel A also shows differences in director independence between firms in concentrated and

non-concentrated industries. A larger fraction of firms in concentrated industries have

independent boards and independent committees compared to firms in non concentrated

industries. This result is consistent with previous studies that have looked at the relation

between director independence and product market competition in other countries. For example,

Randøy and Jenssen (2004) find that boards are more likely to be independent in non

competitive industries.

To better account for differences in firm attributes that could lead to the above differences,

we run several regressions where the dependent variables are the governance measures and the

independent variables are a dummy for whether the firm is in concentrated industry and a host

of controls. We present the results in Panel B.

Panel B shows that in all regressions the sign of the industry-dummy coefficient is in the

direction suggesting more controls in concentrated industries. The effect is statistically

significant (5% level or higher) in five of the six regressions.

21

4.5 Robustness Tests

As a first robustness test, we check whether our results regarding the relation between

product market competition and efficiency and product market competition and governance

hold outside the period we examine. To that end, we use industry concentration data from 1970

until 2005 from the Census and run the regression (2) on the entire sample period. Due to data

availability, we can only look at manufacturing firms and use the Herfindahl measure at the

four-digit SIC code level (rather than at the NAICS level). Table 6 panel A shows the results.

Firms in concentrated industries have lower sales to assets and higher administrative expenses

to sales. The results are consistent with our previous results. However, only the effect on sales-

to-assets is significant at the 5% level or better.

Panel B shows the results where we run the regression in (4) on the Gompers, Ishii, Metrick

index and on the Entrenchment index. The data is extended to include the entire IRRC data

(1990-2005). The results show that the concentration has a significant effect on the imposition

of anti-takeover mechanisms. Firms in more concentrated industries have a significantly lower

score over the period 1990-2005, implying that they have fewer anti-takeover mechanisms in

place.

In addition to the above tests, we include as a robustness check another measure of industry

concentration, which captures the level of entry and exit of firms in the industry. Industries that

are more concentrated face barriers of entry and are less likely to have additional firms entering

and leaving them. In contrast, the more competition firms face, the higher the potential for new

firms to enter the industry and for firms to leave the industry.

We define entry rate as the number of firms in a given year that enter the industry as a

percentage of the existing firms in the beginning of the year. Similarly, exit rate measures the

percentage of firms in an industry that seized to exist. We sort the exit rate and the entry rate

across industries and generated a dummy variable which equals 1 if the industry is in the bottom

22

50% of the industries in entry rates and exit rates. This variable is created at the 2 digit SIC code

level. We obtain data on entry and exit from Klapper, Laeven and Rajan (2005)8. Our results

(not shown) are generally consistent with the results using the Herfindahl measure, although

significance varies.

5. Conclusion

This study explores the role of product market competition in aligning managerial incentives

in public U.S. corporations. We find that firms in less competitive industries are less efficient

than firms in more competitive industries. The results are significant in each and every year in

the sample and are robust after controlling for industry sectors, size, and firm age. In addition,

we find that the Sarbanes Oxley law was associated with significantly larger increase in

efficiency in firms that belong to less competitive industries than in firms that belong to

competitive industries.

We contrast the effect of the Sarbanes Oxley act on efficiency to that of the anti-takeover

legislations of the 1980’s. We find that the anti-takeover legislations in the 1980’s affected

efficiency mainly in small firms, while the Sarbanes Oxley act affected efficiency in both large

and small firms. In addition, it seems that the anti-takeover legislation had a side effect on the

division of rents between large and small firms in concentrated industries.

Finally, we find that firms in competitive industries employ more lenient governance

mechanisms than firms in less competitive industries. These findings suggest that product

market competition substitutes for the need to discipline managers through other mechanisms.

8 Klapper, Laeven and Rajan (2005) provide it at the 2 digit SIC code level and obtain the data from Dun and Bradstreet.

23

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26

Appendix

Proof of Proposition 1:

Need to show that B(gc + g* )-C(g*)> B(g** )- C(g** ). Proof by contradiction. Suppose that B(gc + g* ) - C(g* )< B(g** ) - C(g** ). Consider a candidate solution to the competitive firm g= g** (the optimal governance of the firm in the non competitive industry) . The net benefit to the competitive firm from the candidate solution is B(gc + g** )- C(g**). But this net benefit is larger than B(g** )-C(g** ) since B(gc + g** )> B(g** ) . However, by assumption, the optimal solution g* leads to net benefits that are smaller than B(g** ) - C(g** ). Therefore, the candidate solution g** leads to higher benefits than the optimal solution g*. Contradiction. Therefore, B(gc + g* ) - C(g* )> B(g** ) - C(g**). QED Proof of Proposition 2:

The optimal governance structure for the competitive industry solves: B’ (gc + g* )=C’(g* ). The optimal structure for the non competitive industry solves B’(g** )=C’(g** ). We need to show that g*< g**. Suppose that g*≥ g** . Then B’(gc +g* )<B’(g**) which implies that C’(g* ) < C’(g**). But since C’(g ) >0 and C’’(g ) >0, C’(g* ) < C’(g**) implies that g* < g** . Contradiction. QED Proof of Proposition 3: Need to show that B (gc +g*+ ∆ )- B (gc +g* )< B (g** + ∆)- B (g** ) which can be rewritten as B (gc +g*+ ∆ )- B (gc +g* )-[B (g** + ∆)- B (g** )]<0.We can write the inequality as follows:

0d)g('B)gg('B **

0

*c <+−++∫ δδδ

∆

.

Since B is concave, it is sufficient to show that gc+g*>g** to ensure that the integrand is negative for every δ and therefore the entire integral is negative. Suppose that gc+g*≤g**. Then B’(gc + g* )≥ B’(g** ). But this implies that C’(g*)≥ C’(g**) (since in equilibrium B’(gc + g* )= C’(g*) and B’(g** )= C’(g**)). Since C() is convex, this implies that g*> g**. But then gc+g*>g** since gc>0. Contradiction. Therefore, gc+g*>g**, which implies that the inequality holds. QED

27

Table I Product market competition and corporate efficiency

The table presents univariate and regression results of corporate efficiency in public firms in concentrated and non concentrated industries. The sample consists of all Compustat firms between the years 2001-2005. Administrative/Sales is the ratio of Selling, General, and Administrative costs (Compustat data # 189) to Sales (Compustat data # 12). Sale/Assets is the ratio of Sales (Compustat data # 12) to Assets (Compustat data #6). The sample is winsorized at the 1% and 99% levels for each of the variables. Concentration index is obtained from the Economic Census (2002). For manufacturing industries, the Census provides the Herfindahl index at the NAICS code level, and for non manufacturing industries it provides the market share of the largest 50 firms at the NAICS industry level. Concentrated industry (Non concentrated industry) in our sample is defined as any industry whose concentration index is at the top half (bottom half) of all industries that use the same concentration index.. In panel B firms are divided into size quintiles based on market cap in any given year. In panel C, Size is the natural log of the market capitalization of the firm and age is the number of years that the firm exists in Compustat. Year-sector fixed effects are dummy variables for the 48 Fama French sectors multiplied by each of the years 2001-2005. Standard errors are clustered at the firm level. *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

Panel A: Univariate

Year Non Concentrated Concentrated Diff

(t-test) Diff

(Wilcoxon) Variable: Administrative Exp./Sales N Average Median N Average Median 2001 1655 0.488 0.261 3619 0.545 0.277 ** *** 2002 1624 0.449 0.254 3594 0.483 0.293 *** 2003 1545 0.439 0.255 3571 0.456 0.303 *** 2004 1495 0.492 0.255 3502 0.477 0.304 *** 2005 1421 0.493 0.252 3289 0.451 0.292 ***

7740 0.472 0.256 17575 0.483 0.294 ***

Year Non Concentrated Concentrated Diff (t-test)

Diff (Wilcoxon)

Variable: Sales/Assets N Average Median N Average Median 2001 1655 1.275 1.106 3619 0.885 0.671 *** *** 2002 1624 1.277 1.106 3594 0.899 0.699 *** *** 2003 1545 1.262 1.097 3571 0.894 0.700 *** *** 2004 1495 1.231 1.056 3502 0.889 0.695 *** *** 2005 1421 1.243 1.091 3289 0.890 0.689 *** ***

7740 1.259 1.091 17575 0.891 0.689 *** ***

28

Panel B: Univariate by size

Variable: Administrative Exp/Sales Non Concentrated Concentrated Diff Diff

Quintile(1=smallest) N Mean Median N Mean Median (t-test) (Wilcoxon) 1 1903 0.598 0.288 2935 0.732 0.369 *** *** 2 1472 0.597 0.268 3398 0.588 0.325 *** 3 1400 0.475 0.269 3463 0.487 0.306 *** 4 1497 0.315 0.238 3199 0.347 0.258 *** *** 5 1103 0.233 0.208 3530 0.281 0.233 *** ***

Variable: Sales/Assets Not Concentrated Concentrated

Quintile (1=smallest) N Mean Median N Mean Median T-test Z-test 1 1903 1.564 1.368 2935 1.192 1.054 *** *** 2 1472 1.227 1.099 3398 0.793 0.546 *** *** 3 1400 1.104 0.992 3463 0.796 0.616 ** *** 4 1497 1.183 1.054 3199 0.842 0.667 *** *** 5 1103 1.064 0.922 3530 0.847 0.676 *** ***

29

Panel C: Pooled Regression Results

Dependent variable: log(1+Administrative

Expenses/Sales) log(1+Sales/Assets) (1) (2) (3) (4) Concentration 0.057 *** -0.063 *** (0.008) (0.009) Concentration*Large 0.038 *** -0.066 *** (0.008) (0.010) Concentration*Small 0.078 *** -0.060 *** (0.010) (0.011) Size -0.027 *** -0.022 *** -0.024 *** -0.025 *** (0.001) (0.001) (0.001) (0.001) Age -0.042 *** -0.042 *** 0.045 *** 0.045 *** (0.003) (0.003) (0.003) (0.003) Year Sector ( 48 Industry) Fixed Effects + + + + Clustered Standard Errors + + + + N 32905 32905 40463 40463

R2 0.26 0.26 0.52 0.52

30

Table 2 Product market competition and corporate efficiency – the effect of the Sarbanes Oxley Law

The table presents regression results of changes in corporate efficiency in public firms around the Sarbanes Oxley act, in concentrated and non concentrated industries. The sample consists of all Compustat firms between the years 2001-2005. Administrative/Sales is the ratio of Selling, General, and Administrative costs (Compustat data # 189) to Sales (Compustat data # 12). Sale/Assets is the ratio of Sales (Compustat data # 12) to Assets (Compustat data #6). Concentrated industry is as defined in Table 1. Size is the natural log of the market capitalization of the firm and age is the number of years that the firm exists in Compustat. Year-sector fixed effects are dummy variables for the 48 Fama French sectors multiplied by each of the years 2001-2005. Large (small) firms are defined as firms that belong to the top half (bottom half) of the sample in terms of market capitalization. Standard errors are clustered at the firm level. *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

31

Dependent variable: log(1+Administrative expenses/Sales) log(1+Sales/Assets) (1) (2) (3) (4) Concentration*Year>2002 -0.025 *** 0.013 ** (0.006) (0.006) Concentration*Year>2002*Large -0.026 *** 0.007 (0.006) (0.006) Concentration*Year>2002*Small -0.023 *** 0.023 ** (0.009) (0.008) Size -0.016 *** -0.016 *** -0.027 *** -0.027 *** (0.003) (0.002) (0.002) (0.002) Age -0.065 *** -0.066 *** 0.037 *** 0.037 *** (0.008) (0.007) (0.005) (0.005) Year Sector ( 48 Industry) Fixed Effects + + + + Firm Fixed Effects + + + + Clustered Standard Errors + + + + N 32905 32905 40463 40463

R2 0.89 0.89 0.85 0.85

32

Table 3: The effect of SOX across more compliant and less compliant firms The table presents regression results of changes in corporate efficiency in public firms around the Sarbanes Oxley act, in concentrated and non concentrated industries. The sample consists of S&P 1500 firms between the years 2001-2005 with data on board structure from the IRRC database. Administrative/Sales is the ratio of Selling, General, and Administrative costs (Compustat data # 189) to Sales (Compustat data # 12). Sale/Assets is the ratio of Sales (Compustat data # 12) to Assets (Compustat data #6). Concentrated industry is as defined in Table 1. Year-sector fixed effects are dummy variables for the 48 Fama French sectors multiplied by each of the years 2001-2005. For each firm the score is the sum of three dummy variables for whether the firm has a majority of independent directors, an independent compensation committee in the year 2001 and an independent nominating committee. Standard errors are clustered at the firm–period level. *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

Dependent variable: log(1+Administrative

Expenses/Sales) log(1+Sales/Assets) (1) (2) Concentration*Year>2002*Score(0,1) -0.016 * 0.018 * (0.009) (0.010) Concentration*Year>2002*Score (2,3) -0.006 ** 0.0001 (0.002) (0.005) Size -0.017 *** -0.0088 *** (0.002) (0.004) Age 0.004 0.0013 (0.003) (0.009) Year Sector ( 48 Industry) Fixed Effects + + Firm Fixed Effects + + Clustered Standard Errors + + N 5745 6915

R2 0.8 0.75

33

Table 4: Comparison of the effect of SOX to the effect of the anti-takeover legislation in the 1980’s

Panel A shows effect of the antitakeover legislation in the U.S. in the 1980’s on performance in competitive and non competitive sector. The regression specification follows Giroud and Mueller (2009) and the sample consists of all Compustat firms between 1976-1995. The Herfindahl index is defined from Compustat at the four-digit SIC level using sales (Data 12). The dependent variable is Log(1+ROA) where ROA is Data13/Data6. Administrative/Sales is the ratio of Selling, General, and Administrative costs (Compustat data # 189) to Sales (Compustat data # 12). Sale/Assets is the ratio of Sales (Compustat data # 12) to Assets (Compustat data #6). Operating income to sales is defined as data 13/data12. Cost of goods sold to sales is defined as Data 41/Data12. All dependent variables are winsorized at the 1% and 99% levels. BC is a dummy variable that takes a value 1 if an antitakeover law was passed in a particular state. Size is the log of assets and age is number of years the firm has been in Compustat. State year and industry year is the mean of dependent variable for each state year and industry year combination. The standard errors are clustered at the state of incorporation level and firm and year fixed effects are included. In panel B, the concentrated industry is defined as the three-digit SIC code industry that belong to the top half in terms of industry concentration index across industries in the sample. Concentration index is obtained from the Economic Census (2002). For industrial firms, concentration index is defined as the Herfindahl index. For non-industrial firms, concentration index is defined as the share of largest four companies in the industry. Size is the natural log of the market capitalization of the firm and age is the number of years that the firm exists in Compustat. Year-sector fixed effects are dummy variables for the 48 Fama French sectors multiplied by each of the years 2001-2005. Large and small firms are defined on the median value of market capitalization. *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

34

Panel A: The effect of the anti-takeover legislation of the 1980’s in competitive and non competitive industries

Log(1+ROA) Log(1+Sales/Assets) Log(1+Administrative

Exp/Sales) Log(1+Operating

Income/Sales) Log(1+Cogs/Sales) (1) (2) (3) (4) (5) (6) BC -0.001 0.0006 0.024 *** 0.006 -0.004 0.003 (0.006) (0.006) (0.007) (0.003) (0.004) (0.008) Herfindahl 0.039 ** 0.0408 ** 0.009 0.013 -0.005 *** 0.009 (0.019) (0.019) (0.015) (0.021) (0.009) (0.018) Herfindahl * BC -0.037 * (0.009) Herfindahl * BC*Small -0.094 *** -0.085 *** 0.007 -0.098 *** 0.03 *** (0.029) (0.011) (0.016) (0.016) (0.011) Herfindahl * BC*Large 0.0015 -0.126 *** -0.004 0.022 *** -0.015 (0.016) (0.014) (0.004) (0.011) (0.011) Size 0.14 *** 0.139 *** -0.026 *** -0.028 *** 0.091 *** -0.016 (0.009) (0.009) (0.011) (0.004) (0.016) (0.011) Size ( squared) -0.01 *** -0.0103 *** -0.003 *** 0.0012 *** -0.005 *** 0.00005 (0.001) (0.001) (0.001) (0.000) (0.000) (0.0006) Age -0.129 *** -0.1221 *** 0.132 *** -0.002 -0.102 *** 0.015 (0.009) (0.009) (0.017) (0.007) (0.017) (0.013) State-year 0.282 *** 0.276 *** 0.039 *** 0.053 *** 0.013 *** 0.0006 (0.036) (0.035) (0.009) (0.011) (0.008) (0.001) Industry-year 0.276 *** 0.273 *** 0.085 *** 0.079 *** 0.0127 -0.0001 (0.033) (0.033) (0.006) (0.015) (0.005) (0.001) Year Fixed Effects + + + + + + Firm Fixed Effects + + + + + + Clustered Standard Errors + + + + + + N 83629 83629 83896 72073 79737 83896

R2 0.7 0.7 0.8 0.7 0.7 0.7

35

Panel B: The effect of SOX in competitive and non competitive industries

Dependent variable:

Log(1+ROA) log(1+Administrative

Expenses/Sales) log(1+Sales/Assets) log(1+Operating

Inc/Sales) log(1+Cogs/Sale) (1) (2) (3) (4) (5) (6) Concentration*Year>2002 0.036 *** (0.007) Concentration*Year>2002*Large 0.033 *** -0.026 *** 0.007 0.3 *** -0.009 (0.007) (0.006) (0.006) (0.007) (0.006) Concentration*Year>2002*Small 0.044 *** -0.023 *** 0.023 ** 0.032 *** -0.010 (0.010) (0.009) (0.008) (0.010) (0.009) Size 0.057 *** 0.058 *** -0.016 *** -0.027 *** 0.05 *** -0.008 *** (0.003) (0.003) (0.002) (0.002) (0.003) (0.003) Age 0.021 *** 0.021 *** -0.066 *** 0.037 *** 0.052 *** -0.031 *** (0.006) (0.006) (0.007) (0.005) (0.008) (0.007) Year Sector ( 48 Industry) Fixed Effects + + + + + + Firm Fixed Effects + + + + + + Clustered Standard Errors + + + + + + N 38089 38089 32905 40463 35715 39678

R2 0.89 0.89 0.89 0.85 0.8 0.85

36

Table 5: Product market competition and corporate governance The table presents univariate and multivariate results for the relation between antitakeover governance mechanisms and product market competition. The sample consists of the S&P 1500 firms from 1998-2006 that have information about their anti-takeover measures the IRRC database.. The entrenchment index is a measure of the level of anti-takeover measures and is defined as the sum of the following anti-takeover measures: staggered boards, limits on amending by-laws, limits on amending charters, supermajority requirements, poison pills, and golden parachutes (see Bebchuk et al.) The GIM index is the Gompers Ishii, Metrick (2003) anti-takeover index and consists of the sum of 24 anti-takeover measures. Concentration Low (High) are firms which are ranked in the bottom half (top half) in terms of industry concentration across all industries in the sample. Concentration index is obtained from the Economic Census (2002). *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

Panel A: Univariate results Year N GIM Index Entrenchment Index

Variable: External Governance Conc. Non Conc. Diff Conc. Non Conc. Diff 1998 1017 8.66 8.77 1.37 1.46 2000 1313 8.96 9.18 1.46 1.6 ** 2002 1846 8..84 9.31 *** 1.48 1.65 *** 2004 1401 9.01 9.47 *** 2.24 2.37 *

2006 1341 9.02 9.32 ** 2.24 2.31 8.9 9.24 *** 1.76 1.89 ***

Year N Majority Independent Independent Nom. Com. Independent Comp.. Com. Score Variable: Internal

Governance Conc. Non Conc. Diff Conc. Non Conc. Diff Conc. Non Conc. Diff Conc. Non Conc. Diff 2000 952 83.26% 78.30% * 28.13% 26.55% 74.41% 72.32% 2.85 2.78 2001 1163 82.22% 77.08% *** 34.22% 31.25% 75.21% 73.21% 2.92 2.81 2002 1650 81.83% 80.23% 39.64% 36.93% 75.18% 75.25% 2.96 2.93 2003 1308 89.06% 85.37% 63.73% 59.31% ** 80.90% 78.72% 3.34 3.23 * 2004 1334 91.88% 91.45% 79.85% 74.87% ** 86.08% 82.90% 3.58 3.49 * 2005 1327 92.92% 90.63% 80.90% 77.97% 87.45% 84.30% 3.61 3.53 *

86.93% 84.93% 56.25% 53.29% 80.20% 78.30% 3.23 3.16 ***

37

Panel B: Regression results External Mechanisms Internal Mechanisms

Entrenchment

Index GIM Index Majority

Independent Independent Nominating

Independent Compensating

Score

Dummy for Concentration -0.031 -0.249 *** 0.17 *** 0.087 ** 0.196 *** 0.114 *** (0.036) (0.077) (0.051) (0.044) (0.047) (0.029) Size 0.008 0.105 *** 0.036 ** 0.065 *** 0.0287 * 0.036 *** (0.011) (0.024) (0.017) (0.014) (0.015) (0.009) Book to Market 0.281 *** 0.52 *** -0.005 0.156 * 0.116 0.091 (0.063) (0.136) (0.093) (0.083) (0.088) (0.056) Age 0.001 0.033 *** 0.012 0.009 *** 0.005 *** 0.006 *** (0.001) (0.002) (0.001) (0.001) (0.001) (0.001) Industry Fixed Effect + + + + + + Year Fixed Effect + + + + + + N 6261 6261 6348 6216 6202 6222

R2 0.16 0.16 0.11 0.16 0.05 0.15

38

Table 6 Robustness: Different time periods

Panel A presents regression results of corporate efficiency in public firms in concentrated and non concentrated industries. The sample consists of all Compustat firms between the years 1970-2005 in the manufacturing sector. Administrative/Sales is the ratio of Selling, General, and Administrative costs (Compustat data # 189) to Sales (Compustat data # 12). Sale/Assets is the ratio of Sales (Compustat data # 12) to Assets (Compustat data #6). Concentrated industry is defined as the four-digit SIC code industry that belong to the top half in terms of industry concentration index across industries in the sample. Concentration index is obtained from the Economic Census (1970-2002). For industrial firms, concentration index is defined as the Herfindahl index. Size is the natural log of the market capitalization of the firm and age is the number of years that the firm exists in Compustat. Year-sector fixed effects are dummy variables for the 48 Fama French sectors multiplied by each of the years 1970-2005. Panel B presents multivariate results for the relation between antitakeover governance mechanisms and product market competition. The sample consists of the S&P 1500 firms from 1990-2006 that have information about their anti-takeover measures the IRRC database.. The entrenchment index is a measure of the level of anti-takeover measures and is defined as the sum of the following anti-takeover measures: staggered boards, limits on amending by-laws, limits on amending charters, supermajority requirements, poison pills, and golden parachutes (see Bebchuk et al.) The GIM index is the Gompers Ishii, Metrick (2003) anti-takeover index and consists of the sum of 24 anti-takeover measures. Standard errors are clustered at the firm level. *, **, *** indicate significance at the 10%, 5%, and 1% respectively.

Panel A: Multivariate regression for manufacturing firms : 1970-2005

Dependent variable: Log(1+Administrative

Exp./Sales) Log(1+Sales/Assets) (1) (2) Concentrated Industry dummy 0.017 ** -0.023 *** (0.008) (0.007) Size -0.03 *** -0.017 *** (0.002) (0.002) Age -0.092 *** 0.105 *** (0.006) (0.006) Year Sector (48 industry) Fixed Effects + + N 42480 47779

R2 0.21 0.31

39

Panel B: Multivariate regression for manufacturing firms: 1990-2005

External Mechanisms

Entrenchment

Index GIM Index (1) (2) Dummy for Concentration -0.186 *** -0.514 *** (0.613) (0.157) Size 0.0314 0.269 *** (0.022) (0.057) Book to Market -0.063 *** -0.208 *** (0.021) (0.053) Industry Fixed Effect + + Year Fixed Effect + + N 3466 3466

R2 0.14 0.09