The Impact of Product Market Competition on Firms’
Payout Policy
by
Gustavo Grullon
Rice University
and
Roni Michaely
Cornell University and IDC
May 2014
We thank Amir Barnea, Yaniv Grinstein, Gerard Hoberg, Gordon Phillips and seminar participants at the American
Finance Association Meetings, the Caesarea Center Annual Conference, Cornell University, Georgia State
University, Rice University, Texas A&M International, Texas State University, and the University of Maryland for
helpful comments. We are grateful to Gordon Philips for providing us with the industry concentration ratios. We
also thank Daniela Covarrubias and Christopher Vincent for their research assistance. An earlier version of this
paper was circulated under the title “Dividend Policy and Product Market Competition”. Please address
correspondence to Gustavo Grullon [email protected] or Roni Michaely [email protected].
The Impact of Product Market Competition on Firms’
Payout Policy
Abstract
This paper investigates the interaction between product market competition and managers’
decision to distribute cash to shareholders. Using a large sample of manufacturing firms, we find
that firms in more competitive industries pay more dividends than firms in less competitive
markets. Further, consistent with agency theory, we find that the effect of product market
competition on corporate payouts became significantly stronger after an exogenous event that
intensifies the agency conflict between managers and shareholders (the business combination
laws adopted by several states in the U.S. during the 1980s and 1990s). Overall, our findings are
consistent with the notion that the disciplinary forces of competition induce managers to pay out
excess cash and with the idea that dividends are the “outcome” of external factors.
1. Introduction
It has been argued in the economic literature that intense product market competition
provides corporate managers with incentives to behave efficiently (e.g., Allen and Gale (2000)).
One of the main rationales for this theoretical argument is that the disciplinary forces of
competition rapidly remove incompetent managers from the market. This is an old idea that was
recognized by Adam Smith in The Wealth of Nations, who wrote that “monopoly…is a great
enemy to good management.” Other examples include Hicks (1935), who acknowledges that
“the best of all monopoly profits is a quiet life,” and Caves (1980), who comments that
economists seem to have a “vague suspicion that competition is the enemy of sloth.” Over the
past few decades, several theoretical papers have tried to formalize this idea by examining the
potential channels through which competition can have an effect on managerial incentives (see,
for example, Holmstrom (1982), Hart (1983), Nalebuff and Stiglitz (1983), Scharfstein (1988),
Hermalin (1992), Schmidt (1997), Aghion, Dewatripont, and Rey (1999), Jagannathan and
Srinivasan (1999), and Raith (2003)).
Allen and Gale (2000) for example, conclude that
competition among firms may be a more effective corporate governance mechanism than either
the market for corporate control or institutional monitoring.
From an empirical perspective, several recent studies seem to support the idea that
competition incentivizes managers to be more efficient and more aligned with shareholders. For
example, Graham, Kaplan, and Sibley (1983) find that airlines experienced significant
productivity improvements after the deregulation of their industry in 1978. Nickell (1996)
documents that total factor productivity growth among a sample of U.K. firms is positively
correlated with proxies for competition intensity.
Berger and Hannan (1998) find a strong
negative relation between cost efficiency and measures of market power in the U.S. banking
1
industry. Griffith (2001) provides evidence that an increase in product market competition leads
to increases in productivity, especially among those firms in which managers are less aligned
with shareholders. More recently, several studies find evidence suggesting that product market
competition has the disciplinary effects that mitigate the need for either internal or external
corporate controls (Giroud and Mueller (2010), and Chhaochharia, Grinstein, Grullon and
Michaely (2011)).
Financial economists also suggest that dividends could be used as a mean to reduce
agency conflict between management and shareholders (e.g., Jensen (1986), Easterbrook (1984),
La Porta, Lopez-de-Silanes, Shleifer, and Vishny (2000)). Higher dividends reduce managers’
ability to enjoy the quiet life (Bertrand and Mullainathan (2003)) or build unjustified empires
(Jensen (1986)). The idea that agency considerations are an important determinant of payout
policy has received empirical support (see, for example, Lie (2000), Grullon and Michaely
(2004), and DeAngelo, DeAngelo, and Stulz (2006)).
In this paper we investigate the link between product market competition, managerial
incentives, and corporate payout policy. There are several potential reasons why product market
competition and payout policy might be related.
Perhaps the most important one is the
interaction between competition and agency conflicts.
It is possible that product market
competition, through its effect on agency conflicts, may be an important determinant of the
decision to pay out excess cash to shareholders. Similar to La Porta, Lopez-de-Silanes, Shleifer,
and Vishny (2000) (henceforth LLSV), we argue that product market competition can potentially
have two opposing effects on payout policies. One possibility is that firms pay dividends
because competition acts as an enforcement mechanism that exerts pressure on managers to
distribute cash to their shareholders by increasing the risk and the cost of overinvesting (e.g.,
2
higher probability of liquidation, greater transparency). The main idea here is that intense
competition can affect corporate payouts in ways similar to a strong legal system—by creating
conditions that pressure managers to pay out rather than invest in non-profitable investments (the
“outcome” model). Alternatively, payout policy can be a substitute for competition: managers
use dividends as a substitute for the external disciplinary factors to establish good reputation in
the capital markets to be able to raise capital on better terms (the “substitution” model).
There are significant disagreements in the literature on how corporate governance affects
payout policy. On the one hand, consistent with the outcome model, LLSV (2000) find that
firms in countries with strong minority shareholder rights tend to pay higher dividends. More
recently, Michaely and Roberts (2012) compare dividend policies of firms with weak corporate
governance (private firms) to dividend policies of firms with strong corporate governance (public
firms), and find that firms with stronger corporate governance pay higher dividends. On the
other hand, consistent with the implications of the substitution model, several recent papers find
evidence supporting the notion that firms use dividend payments to reduce agency costs that are
caused by poor governance (Officer (2006), and John and Knyazeva (2006)). Further, Grinstein
and Michaely (2005) find that firms with high institutional holding (an external governance
proxy) generally pay lower dividends. In this paper we test these competing hypotheses by
examining the relation between product market competition and corporate payout policy.
Using the Herfindahl-Hirschman Index (HHI) from the Census of Manufacturers as a
measure of market concentration, we find that firms in more concentrated industries have
significantly lower dividend payout ratios and are less likely to pay and increase dividends than
firms in less concentrated industries. These results hold even after controlling for other factors
that have been documented to affect corporate payout policy, such as size, profitability, growth
3
opportunities, firm age, leverage, and volatility.
Moreover, we find that the effect of
concentration levels on corporate dividend payouts is not only statistically significant, but also
economically significant.
While the large sample regression analysis control for many of the factors found by prior
literature to affect dividend policy (as well as industry- and time-fixed-effects), given the nature
of the investigation it may not be enough. To address this issue, we use a natural experiment that
exogenously exacerbated the agency problems between managers and shareholders.
Specifically, following Bertrand and Mullainathan (2003) and Giroud and Mueller (2010), we
use the adoption of business combination (BC) laws to examine the how agency issues affect the
relation between corporate payouts and product market competition.
Because these laws
substantially reduced the chance of hostile takeover, they increased the opportunity of
managerial misbehavior among the firms affected by the BC laws. The reason for this is that the
market for corporate control is an external factor that serves as a substitute for the lack of
product market competition (Giroud and Mueller (2010)). Therefore, if our main results are
mainly driven by the interaction of competition and agency issues, then managers in less
competitive markets should become more entrenched after the adoption of the BC laws than do
managers in more competitive markets, which should lead to a larger differential in payout ratios
between firms in concentrated markets and firms in competitive markets.
Using the business combination (BC) laws passed between 1985 and 1991 on a state-bystate basis, we perform a differences-in-differences analysis to investigate whether these antitakeover laws increased the gap in payout ratios between competitive and non-competitive firms.
Consistent with the predictions of agency theory, we find that the negative relation between
industry concentration levels and dividend payout ratios becomes much stronger after the
4
passage of the BC laws. These results are consistent with the conceptual framework in LLSV
(2000) that dividends are the outcome of external factors.
We also investigate the possibility that firms in more concentrated markets pay lower
dividends because they need to hoard cash to fend off predatory behavior from competitors, as in
Bolton and Scharfstein (1990).
Although there is evidence that, especially for financially
constrained firms, strategic product market considerations are an important determinant of
payout policy (Hoberg, Phillips and Prabhala, (2014)), and cash holdings (Haushalter, Klasa, and
Maxwell (2007)), our analysis indicates that these interactions are not the main drivers of our
results. As discussed above, we find that the differential in payout ratios between firms in
competitive and non-competitive markets increases after the passage of the BC laws.
For this
result to be consistent with the predation hypothesis, BC laws should make firms in noncompetitive markets more prone to predatory behavior. From a theoretical perspective, it is not
clear why this should be the case. Moreover, if our results were mainly driven by strategic
interactions, then firms in concentrated markets would not only hoard more cash after the
adoption of BC laws, but would also try to improve their operational efficiency to better cope
with the increased predatory threats.
However, contrary to this prediction and consistent with
the idea that managers decided to enjoy the “quiet life”, Giroud and Mueller (2010) find that
these firms became less efficient after the passage of the BC laws. Finally, we also find that the
negative relation between industry concentration levels and corporate disbursements is much
stronger among large firms. This result is unlikely to be driven by strategic considerations
because predatory issues should be less of a concern for large firms, as they possess more
resources and potentially more market power to fend off any predatory attack than do small
firms.
5
In a recent paper, Hoberg, Phillips and Prabhala (2014) also examine the effect of
industry concentration on corporate payout policy. Using a new measure of the HHI based on
text-based analysis, these authors find that firms in more concentrated industries tend to pay
more dividends. In this paper we reconcile our results with these recent findings by showing that
the HHI based on text-based analysis is not a good instrument for actual industry concentration.
According to theory, concentrated industries (e.g., oligopolistic markets) should be populated by
larger and more profitable firms. Consistent with these predictions, we corroborate the findings
in Ali, Klasa, and Yeung (2008) and show that the HHI from the Census of Manufacturers is
positively correlated with firm size and price-cost margins (Lerner index). In contrast, the
measure constructed by Hoberg, Phillips and Prabhala (2014) is negatively correlated with firm
size and unrelated to price-cost margins. For example, according to this measure, the average
sales of firms in the least concentrated industries are 2.6 larger than the average sales of the firms
in the most concentrated industries.
This evidence suggests that the measure of concentration
we use in this paper is better proxy for competition than the one used in recent studies.
Overall, our settings enable us to analyze the interaction between two corporate
governance mechanisms: an internal one—dividend policy; and an external one—competition.
The higher payouts in more competitive industries suggest that intense product market
competition appears to have induced management to disgorge cash through dividends. These
findings lend further support to the idea that corporate payouts are the outcome of external
disciplinary forces, similar to LLSV (2000) where the external force is the extent of investors’
protection, or Michaely and Roberts (2012) where the external force is the disciplinary force of
public financial markets. The findings also underscore the importance of agency conflicts in the
6
determination of corporate payout policy. Finally, they provide another example of how product
market competition can have a significant effect on corporate financial decisions.
The paper is organized as follows. Section 2 discusses the theoretical arguments linking
corporate payout policy to product market competition. Section 3 describes the sample selection
procedure, defines the variables, and provides summary statistics. Section 4 investigates the
empirical relation between industry concentration levels and corporate payout policy. In Section
5 we investigate whether agency theory can explain the relation between product market
competition and corporate payout policy documented in this paper. In Section 6 we reconcile
our results with the existing literature that indicates that firms in less concentrated markets have
lower payout ratios. Section 7 concludes.
2. The Link between Corporate Payout Policy and Product Market Competition
In this section, we discuss several potential channels through which product market
competition can affect managers’ decision to distribute cash to their shareholders. Building on
the work of LLSV, we explain how corporate payouts could be the outcome of intense product
market competition or, alternatively, a substitute for competition. Further, we explain how
potential predatory behavior in less competitive markets can affect corporate payout policy.
2.1. Outcome Model
The outcome model contends that managers need some inducements to dispense free cash
flows. This inducement can be stricter investors’ protection laws (LLSV (2000)), tighter
regulation and corporate control (Michaely and Roberts (2012)), or product market competition.
Managers in highly competitive markets should distribute more cash to their shareholders
because they are more likely to be penalized by the disciplinary forces of competition if they
mishandle the resources of the firm. The premise of competition as a disciplinary role is not
7
new, and can be based on several theoretical arguments. The first argument is related to the
threat-of-liquidation hypothesis (see, for example, Schmidt (1997) and Aghion, Dewatripont, and
Rey (1999)), which states that if a firm in a highly competitive industry begins investing in
negative NPV projects or overspending, then it will become less competitive (e.g., raising prices
to subsidize the bad projects), and consequently, more likely to be driven out of the market.
Thus, to avoid liquidation, loss of bonuses that are related to stock price performance, or the loss
of their jobs, managers in more competitive markets are more likely to pay higher dividends and
avoid negative NPV projects. The second argument is related to the yardstick competition
hypothesis (see, for example, Holmstrom (1982), Nalebuff and Stiglitz (1983), and Shleifer
(1985)). Under this hypothesis, product market competition reduces asymmetric information and
monitoring costs by generating greater opportunities for outsiders to benchmark the performance
of a firm to the performance of its competitors. Therefore, according to this argument, intense
competition could make dividends more appealing by reducing the likelihood of overinvestment
and management failure.
The “outcome” model has two important implications. On the one hand, it predicts a
negative relation between industry concentration levels and corporate payouts. On the other
hand, it predicts that the negative relation between concentration levels and payouts should be
stronger among firms with severe agency problems of free cash flows. The main rationale for
the latter prediction is that if competition affects corporate payout policy by increasing the risk
and the cost of overinvesting, then its effect on payouts should be stronger among those firms
that are more likely to overinvest. We test this implication in two ways. First, we examine the
effect of business combination laws on the relation between payout policy and product market
competition. As discussed above, these laws exogenously exacerbated the agency conflicts
8
between managers and shareholders by (at least partially) immunizing firms from the market for
corporate control. Since the market for corporate control is viewed as a substitute for product
market competition, one would expect managers in less competitive markets to face less pressure
from external factors than managers in more competitive markets after the adoption of these
laws. Therefore, the “outcome” model predicts that the passage of the business combination
laws should lead to a larger gap in dividends between firms in competitive and non-competitive
markets. Second, we examine whether the relation between concentration levels and corporate
payouts is stronger among large and low-growth firms, which due to their nature are more likely
to have agency problems of free cash flows.
2.2. Substitution Model
Firms in less competitive markets face higher agency costs associated with free cash
flows. One potential reason for this is that these firms have the ability to generate extraordinary
rents, which allows managers to have access to more free cash flows (e.g., Jensen (1986),
Shleifer and Vishny (1997), Giroud and Mueller (2010)). Another potential reason is that
managers in less competitive markets are more likely to overinvest because they are less
susceptible to the disciplinary forces of product market competition. As discussed above, since
value-destroying managers in less competitive markets have more slack to subsidize negative
NPV projects, they are more likely to avoid liquidation than similar managers in more
competitive industries.
Under the “substitution” model, dividends are a substitute for other disciplinary measures
such as laws and regulations (LLSV (2000)), compliance and regulations of public markets
(Michaely and Roberts (2012)), or the disciplinary role of market competition. Additionally,
payout may be used more commonly in less competitive markets (i.e., markets with less
9
information) because of reputation and signaling. Thus, managers in less competitive markets
pay dividends and repurchase shares to mitigate the potential agency costs generated by the lack
of competitive pressure from the product market. According to recent theoretical arguments,
managers of under-valued firms may rationally pay dividends to either establish a reputation for
treating shareholders well so they can raise capital at favorable terms in the future (LLSV
(2000)) or to maximize the value of their holdings in the firm (Gomes (2000)).
The empirical implication is that corporate payouts should be positively correlated with
industry concentration levels because dividends and share repurchases are being used as a
substitute for intense competition.
However, this model does not provide clear predictions on
how the magnitude of potential agency problems should affect the positive relation between
concentration levels and corporate payouts. If managers use corporate payouts to establish a
reputation as good managers to raise capital on better terms in the future (LLSV (2000)), then the
positive relation between concentration levels and payouts should be stronger among highgrowth firms that generate low cash flows because these are the firms that are most likely to
access the capital markets in the future (e.g., small firms). If on the other hand, managers use
corporate payouts to mitigate the agency costs of free cash flows so they can maximize the value
of their holdings in the firm (Gomes (2000)), then we should expect a stronger positive
correlation between concentration levels and payouts among firms with severe agency costs of
free cash flows (e.g., large firms). The main reason for this is that the benefits of reducing
potential agency problems are larger among this type of firms.
2.3. Predation Hypothesis
It is also possible that product market competition and payout policy may interact for
strategic consideration such as predation risk.
10
For example, an implication of Bolton and
Scharfstein (1990) is that firms will tend to pay lower dividends and hoard more cash so that
they are better able to fend off potential predatory behavior. 1 Since this behavior is unlikely to
be effective in competitive markets not only because prices are equal to marginal costs, but also
because there is no gain from having n-1 firms instead of n firms in the market, predatory risk is
higher in less competitive markets. Thus, the major prediction of the predation hypothesis is that
payouts should be lower in more concentrated industries.
Note that the predation hypothesis and the “outcome” model generate similar predictions
regarding the relation between product market competition and corporate payouts: lower level of
dividends in less competitive markets. However, while the predation hypothesis does not have
any clear predictions regarding the effect of the BC laws on the relation between concentration
levels and corporate payout policy, the “outcome” model predicts that the relation between these
two variables should become stronger after the passage of the BC laws. Therefore, our tests
examining the effects of the BC laws on the HHI-payout policy relation allow us to distinguish
between these two hypotheses. Further, the predation hypothesis predicts that the negative
relation between concentration levels and payouts should be stronger among small firms. This
follows from the idea that predation is less likely to occur against large firms since these firms
possess more resources and market power to fend off any predatory attack. Further, because
large firms are less likely to be financially constrained than small firms (e.g., Hadlock and Pierce
(2010)), holding cash to avoid passing profitable investment opportunities is less of a concern for
large firms than it is for small firms (see, for example, Acharya, Almeida, and Campello (2007)).
2.4 Agency Theory and the Form of Payout: Dividends vs. Repurchases
Over the last two decades, share repurchases have become an important payout
mechanism in part as a substitution for dividends (Grullon and Michaely (2002)). At the same
1
Bolton and Scharfstein’s (1990) original argument is about debt, but it also holds for dividends (and repurchases).
11
time, there is clear evidence that repurchases are less persistent than dividends (e.g, Leary and
Michaely, (2011)) and that they are more likely to be used to distribute non-recurring cash flows
than to distribute permanent cash flows (Jagannathan, Stephens, and Weisbach, (2000)).
Because of these differences, dividends are perceived as a stronger commitment device than
share repurchases. As in the case of debt (Zwiebel (1996)), this suggests that dividends are a
more credible mechanism to reduce the amount of free cash flow in the firm than share
repurchases. Therefore, if agency considerations are an important determinant of our results,
then one would expect product market competition to have a stronger effect on dividends than on
share repurchases.
3. Sample Selection, Variable Definitions, and Descriptive Statistics
3.1. Sample Selection
Our initial sample consists of all firms operating in any of the industries covered by the
Census of Manufacturers (SIC code interval 2000-3990). This census reports the results from a
survey taken every five years in which all manufacturing firms in the U.S. are asked to provide
information on their number of employees, payroll, and total output. Unlike most surveys, this is
a fully comprehensive sample since firms are required by federal law to respond to the survey
supplied by the U.S. Census (Title 13 of the U.S. Code). Thus, selection bias is not a significant
issue. Using this information, the U.S. Census calculates several summary statistics, including
the Herfindahl-Hirschman index (HHI) that we use in this paper.
Since the HHIs are only reported every five years, we assume that the indexes stay
constant until the results from a new survey are available. For example, we use the HHIs
reported in 1987 for the observations in years 1987, 1988, 1989, 1990, and 1991. This approach
is unlikely to bias our empirical results because the HHI does not experience large changes over
12
time. For example, the probability that an industry in the smallest HHI quintile moves to the
largest HHI quintile (or vice versa) over a period of 5 years is virtually zero.
From our initial sample of manufacturing firms, we then select those observations that
satisfy the following criteria: (1) the firm appears on the CRSP/Compustat merged files; (2) the
firm operates in an industry covered by the Census of Manufacturers (SIC code interval 20003990); and (3) the firm’s total assets are greater than $1 million. This selection process generates
a final sample of 64,647 firm-year observations over the period 1972 to 2010.
This is a
relatively large sample considering that it only contains manufacturing firms.
3.2. Variable Definitions
3.2.1. Proxy for Industry Concentration
Following Aggarwal and Samwick (1999), Allayanis and Ihrig (2001), Campello (2006),
MacKay and Phillips (2005), Akdoğu and MacKay (2008), and Haushalter, Klasa, and Maxwell
(2007), among others, we use the Herfindahl-Hirschman Index (HHI) from the Census of
Manufacturers as a proxy for product market competition. The U.S. Census calculates this index
by summing up the squares of the individual market shares for the 50 largest firms in the
industry. If the industry has less than 50 firms, then the U.S. Census uses the total number of
firms in the industry. Since in most industrial groups the largest 50 firms capture most of the
market, this index is a reasonable proxy for the overall level of industry concentration. In this
paper, we use four-digit SIC HHIs.
3.2.2. Measures of Corporate Payouts
Using data from Compustat, we construct the following six measures of corporate
payouts: dividends and share repurchases scaled by total sales (item SALE), dividends and share
repurchases scaled by total assets (item AT), and dividends and share repurchases scaled by the
13
market value of equity (item CSHO times item PRCC_F). Dividends (DIV) are equal to the total
dollar amount of dividends declared on the common stock of a company during a year (item
DVC). Share repurchases (REPO) are equal to the expenditure on the purchase of common and
preferred stocks (item PRSTKC). We examine share repurchases because they have become an
important payout method for many firms (see, for example, Grullon and Michaely (2002)).
Finally, to mitigate the effect of outliers, we exclude from our analyses all observations where
the payout ratios are greater than one.
3.2.3. Control Variables
Following the literature on corporate payout policy, we control for the following firm
characteristics in our empirical analyses:
•
Maturity: Our proxies for the level of firm maturity are the market value of equity
(MV) and the age of the firm (AGE). MV is defined as the total number of common
shares outstanding (Compustat item CSHO) times the closing stock price at the end of
the fiscal year (item PRCC_F). AGE is the time (in years) from the firm’s CRSP
listing date.
•
Investment Opportunities: We use the market-to-book ratio (M/B) as a proxy for
investment opportunities. M/B is equal to the book value of assets (item AT) plus the
market value of equity (MV) minus the book value of equity (item CEQ) scaled by
the book value of assets.
•
Risk: Our proxy for risk is the volatility of stock returns (RETVOL). RETVOL is
the standard deviation of monthly stock returns over a one-year period.
14
•
Profitability:
We use the return on assets (ROA) as a proxy for the level of
profitability of the firm. ROA is the operating income before depreciation (item
OIBDP) scaled by the book value of assets.
•
Leverage: We define leverage (DEBT/ASSETS) as long-term debt (item DLTT) plus
short-term debt (item DD1) scaled by the book value of assets.
To mitigate the effect of outliers, M/B and ROA have been winsorized at the 1% and the
99% of their empirical distribution. Further, since there is evidence that corporate payout policy
in the U.S. has significantly changed over the last three decades (see, for example, Fama and
French (2001) and Grullon and Michaely (2002)), we also include year dummies in our
regressions to control for any time trends. Finally, we include industry fixed effects where
industry is defined by the firm’s two-digit SIC code.
Given the well documented fact that large, stable, profitable, old firms with low
investment opportunities are more likely to distribute cash to their shareholders than are other
types of firms, we expect the coefficients of MV, AGE, and ROA to have a positive sign, and the
coefficients of M/B and RETVOL to have a negative sign.
However, the coefficient of
DEBT/ASSETS could be positive or negative depending on whether firms treat leverage as a
substitute for payouts or as a complement to payouts.
3.2.4. Descriptive Statistics
Table 1 reports summary statistics for the firms in our sample. This table shows that the
average sample firm has a dividend yield (DIV/MV) equal to 1.06% and a repurchase yield of
(REPO/MV) equal to 1.05%. These payout ratios are very similar to the ones reported in
Grullon and Michaely (2002). Further, the average firm in our sample is almost 13 years old and
it has a market value of equity (MV) of $1.1 billion, a market-to-book ratio (M/B) equal to 2.0, a
15
debt-to-asset ratio (DEBT/ASSETS) equal to 16.7%, and a return on assets (ROA) equal to
4.3%. The characteristics of the average firm in our sample are very similar to the characteristics
of the average firm in Compustat (not reported in a table). Thus, it seems that our sample is not
biased toward a particular type of firm. Finally, Table 1 also shows that there are large crosssectional differences in payout ratios and firm characteristics. This large dispersion in both
dependent and independent variables should improve the power of our empirical tests to detect
any effect of concentration levels on corporate payouts.
4. The Relation between Industry Concentration Levels and Corporate Payout Policy
In this section, we investigate whether product market competition affects managers’
decision to distribute cash to their shareholders. We perform this analysis by regressing scaled
measures of dividends and repurchases on the Herfindahl-Hirschman Index (HHI), size (MV),
market-to-book ratio (M/B), return on assets (ROA), debt-to-total assets ratio (DEBT/ASSETS) ,
age of the firm (AGE), stock return volatility (RETVOL), year dummies, and industry dummies.
Since our measures of corporate payouts are truncated at zero and one, we estimate the
regression coefficients using a two-sided Tobit model. Following Petersen (2009), we control
for possible cross-sectional dependence in the residuals by adjusting the standard errors for
within-firm correlation, and control for any time series dependence by including time dummies.
We do not include firm-fixed effects in our panel data regressions because the HHIs do not
change much over time. However, we include two-digit SIC code dummies to control for
industry-fixed effects.
The first three columns of Panel A of Table 2 report estimates of regressions relating
scaled dividends to the HHI and other control variables. Consistent with the predictions of the
“outcome” model and the predation risk hypothesis, these columns show that dividend payout
16
ratios are negatively correlated with industry concentration levels. Note that the coefficient of
the HHI is negative and statistically significant in all the specifications. To control for potential
non-linearities in the relation between payout ratios and HHI, we also consider specifications in
which instead of using the continuous variable HHI, we use a dummy variable equal to one if the
HHI is above its median, zero otherwise. We report the results from this alternative specification
in the last three columns of Panel A of Table 2. Consistent with our results using the continuous
variable, we find that firms in high HHI industries have lower dividend payout ratios than firms
in low HHI industries.
As expected, Table 2 shows that dividend payout ratios are positively correlated with size
(MV), maturity level (AGE), and profitability (ROA), and negatively correlated with growth
opportunities (M/B), and volatility (RETVOL). Moreover, there is evidence that dividends are
negatively related to leverage (DEBT/ASSET), which suggests that firms use debt as a substitute
for dividends. In general, these results are consistent with the stylized fact that large, stable,
profitable, old firms with low investment opportunities pay more dividends.
As discussed earlier, dividends have been shown to be a stronger commitment device
than share repurchases (see, for example, Jagannathan, Stephens, and Weisbach (2000), and
Guay and Harford (2000)). Consequently, if agency considerations are an important determinant
of the relation between competition and payouts, then product market competition should have a
weaker effect on share repurchases. To test this hypothesis, we replicate the previous analyses
using share repurchases instead of dividends (Panel B of Table 2). We find that the relation
between repurchases and the HHI appears to be weaker than the relation between dividends and
the HHI. Consistent with the implications of agency theory, these results indicate that product
market competition has the strongest effect on the least flexible payout method.
17
We augment the previous analysis by examining the effect of industry concentration on
the decision to increase distribute cash to shareholders.
We use this alternative approach to
examine whether our main results hold over the following discrete payout decisions (1) the
decision to pay dividends (Div > 0), (2) the decision to increase dividends (change in Div > 0)
and (3) the decision to repurchase shares (Rep > 0). Given the nature of these dependent
variables, we estimate the regression parameters using a Logit model. Table 3 reports the results
from this analysis. Consistent with the results in Table 2, we find that firms in less competitive
markets are less likely to pay dividends and less likely to increase dividends relative to firms in
more competitive markets.
Further, there is some evidence that firms in less competitive
markets are less likely to repurchase shares (see columns 3 & 6), albeit these results are much
weaker than the ones for dividends.
The results in Tables 2 and 3 are also economically significant. For example, Table 2
results imply that an increase of one standard deviation in the HHI reduces the dividend yield
(DIV/MV) by 27% from its mean value and the dividend-to-assets ratio (DIV/ASSETS) by
almost 41% from its mean value. Overall, the empirical results in this section indicate that firms
in more competitive market tend to have significantly higher dividend payout ratios than firms in
less competitive markets.
5. Explaining the Negative Relation between Industry Concentration and Corporate
Payout Policy
In the previous section we show that corporate payouts, particularly dividend payments,
are negatively related to industry concentration levels. These results are consistent with both the
“outcome” model proposed by LLSV (2000) and the predation hypothesis. In the next two subsections we perform several tests to disentangle these two explanations.
5.1. A quasi-natural experiment: The Effect of Business Combination Laws
18
The negative correlation between corporate payouts and HHI supports one of the
implications of agency theory—that payouts are the outcome of external factors. However, this
negative relation could be driven by some unobservable factor that could be unrelated to agency
problems. Therefore, to examine whether agency issues are driving our main results, we need to
show that an exogenous shock to agency conflicts strengthens the negative relation between
payouts and industry concentration levels.
Following the work of Bertrand and Mullainathan (2003) and Giroud and Mueller (2010),
we use the adoption of business combination (BC) laws in the U.S. as a natural experiment to
examine how agency issues affect the relation between corporate payouts and product market
competition. The business combination laws were enacted in more than 25 states between 1985
and 1991.
These laws make hostile takeovers significantly more difficult by imposing a
moratorium of three to five years on certain types of transactions such as asset sales and mergers.
Bertrand and Mullainathan (2003) and Giroud and Mueller (2010) provide empirical evidence
that the BC laws exacerbated the agency problems between managers and shareholders.
Therefore, we use these laws to examine whether our main results are mainly driven by agency
issues. If the market for corporate control serves as a substitute for the lack of product market
competition (Giroud and Mueller (2010)), then the outcome model predicts that the negative
relation between the HHI and payouts should become stronger after the passage of the BC laws.
If on the other hand dividends in concentrated industries are being limited as a consequence of
predatory concerns, then we expect that the adoption of the BC laws to have no effect on
dividend policy.
Further, since the BC laws only reduce the likelihood of a firm becoming a
takeover target, these exogenous events allow us to control for any unobservable factor that
could drive the negative relation between payouts and concentration levels.
19
To investigate the effect of the BC laws on the relation between concentration levels and
payouts, we perform a differences-in-differences analysis using data around the adoption of these
laws (we use data from 1979 until 1997). We do this by including in our regressions a dummy
variable (DUMBC) equal to one if the firm is incorporated in a state that has passed a business
combination law, zero otherwise, and a term in which we interact the HHI with DUMBC. By
design, this latter term should capture the change in the gap in payout ratios between competitive
and non-competitive firms after the passage of the BC laws. Thus, if agency conflicts are driving
force behind the relation between HHI and corporate payouts, then the coefficient on the
interaction term HHI x DUMBC should be negative.
Table 4 reports the results from this analysis using scaled measures of dividends and
share repurchases. Consistent with the predictions of the outcome, we find evidence that the
negative relation between dividends and industry concentration levels becomes significantly
stronger after the passage of the BC laws. Panel A of Table 4 shows that the interaction term
Log(HHI) x DUMBC is negative and statistically significant in most specifications.
Interestingly, when we use the dummy variable DUMHHI to measure concentration, we find that
that the regression coefficient on DUMHHI x DUMBC is statistically significant in all
specifications, but the coefficient on DUMHHI is not.
This indicates that the negative
correlation between dividends and concentration levels is only significant for firms incorporated
in states that have passed business combination laws. On the other hand, Panel B of Table 4
shows that the BC laws did not have any effect on the relation between concentration levels and
share repurchases.
This suggest that the shock to agency conflicts generated by the BC laws
only affected dividends – the payout method that is less flexible, and consequently, more likely
to reduce agency problems, consistent with our findings in the previous section.
20
Finally, we examine whether the effect of the BC laws on the relation between
concentration levels and corporate payouts hold over discrete payout decisions: whether to pay
dividends, whether to increase dividends, and whether to repurchase shares. We report the
results from this analysis in Table 5. Corroborating the results in Table 4, we find that the
negative effect of the HHI on the likelihood of paying dividends or increasing dividends
becomes much stronger after the passage of the business combination laws. Note that the
coefficients on Log (HHI) x DUMBC and DUMHHI x DUMBC are negative and statistically
significant all the specifications using dividend decisions as the choice variable (see columns 1,
2, 4, and 5). Further, consistent with our previous results, we do not find any effect of the BC
laws on the relation between the HHI and the decision to repurchase shares.
Overall, the results in this section support the idea that the negative relation between
concentration and corporate dividend payouts is driven by agency considerations. In particular,
they provide support to the theoretical argument in LLSV (2000) that corporate dividend policy
is the outcome of external factors. Further, since it is not clear why BC laws should only make
firms in less concentrated markets more vulnerable to predatory threats, these results suggest that
strategic interactions in the product market are not a main determinant of the negative relation
between competition and payout ratios. In fact, if our results were mainly driven by predatory
issues, then the firms in concentrated markets that were affected by the BC laws would have
tried to become more efficient to fend off potential predatory attacks by their competitors.
However, the evidence in Giroud and Mueller (2010) indicates managers of these firms decided
to adopt the “quiet life”, which suggests that they were not extremely concerned about predation.
5.2.The Effect of Firm Size
21
In the previous sub-section we examine the effect of the BC laws on the relation between
market concentration and corporate payout policy to distinguish the “outcome” model from the
predation hypothesis. The results from these seem to support the predictions of the “outcome”
model. To further investigate whether agency or predatory issues are driving our main results, in
this sub-section we examine the effect of firm’s position within the industry (proxied by their
relative market cap) on the relation between concentration and payout.
The predation risk hypothesis suggests that the negative relation between the HHI and
payout ratios should be weaker among large firms and stronger among small firms. The main
intuition behind this argument is that the largest firms in a particular industry should be less
concerned about inducing predatory behavior than the smallest firms because the former firms
have more resources and potentially more market power to fend off any predatory attack than the
latter firms.
If corporate payouts are the “outcome” of intense product market competition, however,
then we should expect that the effect of competition on payouts should be stronger among those
firms that are more likely to have high agency cost of free cash flows. 2 Thus, since large firms
tend to be mature firms that generate substantial and stable cash flows, the “outcome” model
predicts that the negative relation between the HHI and payouts should be stronger among this
type of firms.
To investigate whether the relation between the HHI and payout ratios is different
between large and small firms, we include in our regressions a term that interacts the
concentration measure (HHI) with a dummy variable (LARGE) that is equal to one if a firm’s
market value is above its industry (four-digit SIC code) average market value at time t. If the
2
Jensen (1986) argues that agency costs of free cash flows are likely to be more severe among “firms that have
stable business histories and substantial free cash flow (i.e. low growth prospect and high potential for generating
cash flows).”
22
predation risk hypothesis is correct, then the coefficient of the interaction term (HHI x LARGE)
should be positive. However, if agency considerations are the main drivers behind the relation
between concentration levels and corporate payouts, then the coefficient of the interaction term
should be negative.
Table 6 reports the results from this analysis. Consistent with the predictions of the
“outcome” model, Panel A shows that the coefficient of the interaction term (HHI x LARGE) is
negative and statistically significant in all specifications using scaled measures of dividends as
dependent variables.
This result implies that the negative relation between the HHI and
dividend payout ratios is much stronger among large firms. Interestingly, Panel A shows that the
coefficients on Log (HHI) and DUMHHI are not statistically significant, which suggests that the
relation between concentration levels and dividend payout ratios is mainly driven by large firms.
In Panel B of Table 6 we find similar results using scaled measures of share repurchases as
dependent variables, but the results are weaker than the ones using dividends. Table 7 replicates
the analysis in Table 6 using discrete payout decisions as dependent variables. Consistent with
our previous empirical results, Table 7 shows that the effect of the HHI on the decision to pay
dividends, increase dividends, or repurchase shares is generally stronger among the largest firms
in a particular industry.
One again, the economic magnitude of the coefficients reported in Table 6 and 7 is large.
For example, among large firms, an increase of one standard deviation in the HHI reduces the
dividend yield (DIV/MV) by 47% from its mean value and the dividend-to-assets ratio
(DIV/ASSETS) by almost 74% from its mean value. In general, the empirical results in this
section indicate that the negative relation between HHI and corporate payouts is mainly driven
by firms that are likely to have high agency cost of free cash flows. While these findings seem
23
to support the idea that corporate payouts are the “outcome” of external factors, they are
inconsistent with the idea that firms in more concentrated markets tend to pay less cash to fend
off predatory behavior from competitors.
6. Does the Choice of Concentration Measure Matter?
In a recent paper, Hoberg and Phillips (2014) developed an alternative measure of the
HHI (TNIC3 HHI) using industry classifications based on firm pairwise similarity scores from
text analysis of firm 10K product descriptions. Hoberg, Phillips and Prabhala (2014) use this
alternative measure of concentration in their empirical tests and find that firms in more
concentrated industries tend to pay more dividends. Given that these results are at odds with our
main findings, we investigate in this section the source of the discrepancy between the Census
HHI and the TNIC3 HHI.
To illustrate this issue, Table 8 examines the empirical relation between dividend payout
ratios and these two alternative measures of concentration over the period 1997-2010. 3 While
columns 1, 2, and 3 show that our main results are robust over this sample period, columns 4, 5,
and 6 show that dividends are positively related to the TNIC3 HHI. This inconsistency in the
relation between corporate payouts and concentration levels strongly suggests that the Census
HHI and the TNIC3 HHI are measuring different aspects of firms’ product markets.
One potential reason for this discrepancy is that Hoberg and Phillips (2014) construct
their measure of concentration using data from publicly-traded firms that appear on Compustat.
As pointed out by several authors, the concentration measures derived from Compustat data are
less meaningful than the ones reported by the Census of Manufacturers because the former
measures are constructed only using public firms while the latter measure uses both private and
public firms.
3
In a recent study, Ali, Klasa, and Yeung (2009) show that this key difference
We restrict our analysis to this sample period because the TNIC3 HHI is only available after 1996.
24
between these two measures seems to significantly bias the HHI derived from Compustat data.
For example, consistent with the idea that more concentrated industries should be populated by
larger and more profitable firms, Ali et al. (2009) find that the Census HHI is positively
correlated with both average firm size and price-cost margins. Further, they show that the total
number of firms in the industry is negatively related to the Census HHI. However, they do not
find these results when they use the HHI derived from Compustat data. In fact, they find that
industries classified as concentrated using the Compustat HHI tend to be populated by smaller
firms. These findings suggest that the use of Compustat concentration measures as proxy for
industry concentration may lead to incorrect inferences.
To investigate whether the TNIC3 HHI is good proxy for competition, we replicate the
analysis in Ali et al. (2009) for both the Census HHI and the TNIC3 HHI. The results from this
analysis are reported in Table 9. Corroborating the findings in Ali et al. (2009), Panel A shows
that the Census HHI is positively correlated with several proxies for firm size (market
capitalization, total assets, and total sales) and price-cost margins. In contrast, Panel B shows
that the TNIC3 HHI is negatively correlated with all the proxies for firm size and uncorrelated
with price-cost margins.
To better visualize these patterns, Figures 1 and 2 depicts the
characteristics of portfolios sorted by competition measures.
In general, these results indicate
that TNIC3 HHI suffers from the same biases as the Compustat HHI, which raises important
concerns about the validity of this concentration measure.
7. Conclusion
Our study extends the work of LLSV (2000) by arguing that product market competition
can be viewed as an additional external disciplinary factor. Based on the idea that competition
can exert pressure on managers to distribute cash to their shareholders by increasing the risk and
25
the cost of overinvesting, we argue that corporate payouts could either be the result of product
market competition or, alternatively, a disciplinary devise that substitutes for competition. A
third alternative is that payout policy is related to competition through predation risk. Firms in
less competitive markets pay out less and hoard more cash to reduce potential predatory attacks
from competitors.
The results in this paper seem to support the idea that corporate payouts are the outcome
of the disciplinary forces of product market competition. We find that corporate payouts are
positively correlated with the intensity of industry competition even after controlling for
potential confounding effects.
There is no support for the notion that corporations use payout
policy as an alternative governance mechanism to competition. Moreover, consistent with the
implications of agency theory, we find that the effect of product market competition on payouts
becomes much stronger among firms affected by the business combinations laws adopted by
several states in the U.S. during the 1980s and 1990s. Since these laws exacerbated the agency
conflict between managers and shareholders by immunizing firms against the market for
corporate control, our findings indicate that the link between concentration levels and corporate
payouts is mainly driven by agency considerations.
The initial results-that firms in more competitive market have higher payout than
otherwise similar firms in less competitive markets-is also consistent with the predation-risk
alternative. The use of the change in the BC laws sheds some light on this issue as well since
predation risk imply no change in payout policy as a consequence of these law changes. Our
findings of great effect of competition on payout after the passage of the law is not consistent
with the notion that predation is the dominant factor affect the relation between competition and
payout. Further analysis reveals that the difference in payout policy between firms in competitive
26
and less competitive market is primarily driven by large firms. That is, our main results are
driven by firms who are more likely to be subject to free cash flow issues rather than to predatory
issues.
Overall, our results complement the empirical results in LLSV. While they find evidence
suggesting that a strong legal system exerts pressure on corporate managers to distribute excess
cash to their shareholders, we find that intense product market competition appears to have
similar effects.
These findings are important because they further suggest that competition is
indeed a strong disciplinary devise. At the same time, the findings suggest that agency problems
play an important role in managers’ decision to distribute cash to shareholders and that at times,
external disciplinary devises, such as competition induce managers to behave more optimally.
27
References
Acharya, V., H. Almeida, and M. Campello, 2007, “Is Cash Negative Debt? A Hedging
Perspective on Corporate Financial Policies,” Journal of Financial Intermediation 16,
515-554.
Aggarwal, R., and A. Samwick, 1999, “Executive Compensation, Strategic Competition, and
Relative Performance Evaluation: Theory and Evidence,” Journal of Finance 54, 19992043.
Aghion, P., M. Dewatripont, and P. Rey, 1999, “Competition, Financial Discipline, and
Growth,” Review of Economic Studies 66, 825-852.
Akdoğu, E., and P. MacKay, 2008, “Investment and Competition,” Journal of Financial and
Quantitative Analysis 43, 299-330.
Ali, A., S. Klasa, and E. Yeung, 2009, “The Limitations of Industry Concentration Measures
Constructed with Compustat Data: Implications for Finance Research,” Review of
Financial Studies 22, 3839-3871.
Allayannis, G., and J. Ihrig, 2001, “Exposure and Markups,” Review of Financial Studies 14,
805-835.
Allen, F., and D. Gale, 2000, “Corporate Governance and Competition,” published in Corporate
Governance: Theoretical and Empirical Perspectives, edited by X. Vives, Cambridge
University Press, 23-94.
Allen, F., A. Bernardo, and I. Welch, 2000, “A Theory of Dividends Based on Tax Clienteles,”
Journal of Finance 55, 2499-2536.
Ang, J., R. Cole, and J. Lin, 2000, “Agency Costs and Ownership Structure,” Journal of Finance
55, 81-106.
Arabmazar, A., and P. Schmidt, 1981, “Further Evidence on the Robustness of the Tobit
Estimator to Heteroskedasticity,” Journal of Econometrics 17, 253-258.
Arabmazar, A., and P. Schmidt, 1982, “An Investigation of the Robustness of the Tobit
Estimator to Non-Normality,” Econometrica 50, 1055-1063.
Bebchuk, L., A. Cohen, and A. Ferrell, 2009, “What Matters in Corporate Governance?,” Review
of Financial Studies 22, 783-827.
Berger, A., and T. Hannan, 1998, “The Efficiency Cost of Market Power in the Banking
Industry: A Test of the “Quiet Life” and Related Hypotheses,” The Review of Economics
and Statistics 80, 454-465.
28
Bertrand, M and S. Mullainathan, 2003, “Enjoying the Quiet Life? Corporate Governance and
Managerial Preferences,” Journal of Political Economy, 111(5), 1043-1075.
Bolton, P., and D. Scharfstein, 1990, “A Theory of Predation Based on Agency Problems in
Financial Contracting,” American Economic Review 80, 93-106.
Boudoukh, J., R. Michaely, M. Richardson, and M. Roberts, 2007, “On the Importance of
Measuring Payout Yield: Implications for Empirical Asset Pricing,” Journal of Finance,
62, 877-915.
Campello, M., 2006, “Debt Financing: Does it Boost or Hurt Firm Performance in Product
Markets?,” Journal of Financial Economics 83, 135-172.
Caves, R., 1980, “Industrial Organization, Corporate Strategy, and Structure,” Journal of
Economic Literature 18, 64-92.
Chhaochharia, V., Y. Grinstein, G. Grullon, and R. Michaely, 2012, “Product Market
Competition and Internal Governance: Evidence from the Sarbanes Oxley Act,” Working
Paper, Cornell University.
DeAngelo, H., L. DeAngelo, and R. Stulz, 2006, “Dividend Policy and the Earned/Contributed
Capital Mix: A Test of the Lifecycle Theory,” Journal of Financial Economics 81(2),
227-254.
DeFond, M., and C. Park, 1999, “The Effect of Competition on CEO Turnover,” Journal of
Accounting and Economics 27, 35-56.
Fama, E., and K. French, 2001, “Disappearing Dividends: Changing Firm Characteristics or
Lower Propensity to Pay?” Journal of Financial Economics 60, 3-43.
Fee, C., and C. Hadlock, 2000, “Management Turnover and Product Market Competition:
Empirical Evidence from the U.S. Newspaper Industry,” Journal of Business 73, 205243.
Giroud, X., and H. M. Mueller, 2010, “Does Corporate Governance Matter in Competitive
Industries?” Journal of Financial Economics 95, 312-331.
Giroud, X., and H. M. Mueller, 2011, “Corporate Governance, Product Market Competition, and
Equity Prices,” Journal of Finance 66, 563-600.
Gomes, A., 2000, “Going Public without Governance: Managerial Reputation Effects,” Journal
of Finance 55, 615-646.
Gompers, P., J. Ishii, and A. Metrick, 2003, "Corporate Governance and Equity Prices,"
Quarterly Journal of Economics 118, 107-155.
29
Graham, D., D. Kaplan, and D. Sibley, 1983, “Efficiency and Competition in the Airline
Industry,” Bell Journal of Economics 14, 118-138.
Griffith, R., 2001, “Product Market Competition, Efficiency and Agency Cost: An Empirical
Analysis,” Working Paper, Institute for Fiscal Studies.
Grinstein, Y., and R. Michaely, 2005, “Institutional Holdings and Payout Policy,” Journal of
Finance 60, 1389-1426.
Grinstein, Y., and A. Palvia, 2006, “Executive Loans, Corporate Governance, and Firm
Performance - Evidence from Banks,” Working Paper, Cornell University.
Grullon, G., and R. Michaely, 2002, “Dividends, Share Repurchases, and the Substitution
Hypothesis,” Journal of Finance 57, 1649-1684.
Grullon, G., and R. Michaely, 2004, “The Information Content of Share Repurchase Programs,”
Journal of Finance 59, 651-680.
Guay, W., and J. Harford, 2000, “The Cash-Flow Permanence and Information Content of
Dividend Increases versus Repurchases,” Journal of Financial Economics 57, 385-415.
Hart, O., 1983, “The Market as an Incentive Mechanism,” Bell Journal of Economics 14, 336382.
Haushalter, D., S. Klasa, and W. Maxwell, 2007, “The Influence of Product Market Dynamics on
the Firm's Cash Holdings and Hedging Behavior,” Journal of Financial Economics 84,
797-825.
Hermalin, B., 1992, “The Effects of Competition on Executive Behavior,” RAND Journal of
Economics 23, 350-365.
Hicks, J., 1935, “Annual Survey of Economic Theory: The Theory of Monopoly,” Econometrica
3, 1-20.
Hoberg, G., and G. Phillips, 2014, “Text-Based Network Industries and Endogenous Product
Differentiation,” Working Paper, University of Maryland.
Hoberg, G., G. Phillips, and N. Prabhala, 2014, “Product Market Threats, Payouts, and Financial
Flexibility,” Journal of Finance 69, 293-324.
Holmstrom, B., 1982, “Moral Hazard in Teams,” Bell Journal of Economics 13, 324-340.
Jagannathan R., and S. B. Srinivasan, 1999, “Does product market competition reduce agency
costs?” North American Journal of Economics and Finance 10, 387–399.
30
Jagannathan, M., C. Stephens, and M. Weisbach, 2000, “Financial Flexibility and the Choice
between Dividends and Stock Repurchases,” Journal of Financial Economics 57, 355384.
Jensen, M., 1986, “Agency Costs of Free Cash Flow, Corporate Finance, and Takeovers,”
American Economic Review 76, 323-329.
John, K., and A. Knyazeva, 2006, “Payout Policy, Agency Conflicts and Corporate
Governance,” Working Paper, NYU.
Kruse, T., and C. Rennie, 2006, “Product Market Competition, Excess Free Cash Flows, and
CEO Discipline: Evidence from the U.S. Retail Industry,” Working Paper, University of
Arkansas.
La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R. Vishny, 2000, “Agency Problems and
Dividend Policies around the World,” Journal of Finance 55, 1–33.
Lie, E., 2000, “Excess Funds and Agency Problems: An Empirical Study of Incremental Cash
Disbursements,” Review of Financial Studies 13, 219-248.
Linderberg, E., and S. Ross, 1981, “Tobin’s q Ratio and Industrial Organization,” Journal of
Business 54, 1-32.
MacKay, P., and G. Phillips, 2005, “How Does Industry Affect Firm Financial Structure?,” The
Review of Financial Studies 18, 1433-1466.
Massa, M., Z. Rehman and T. Vermaelen, 2007, “Mimicking Repurchases,” Journal of Financial
Economics 84, 624-666.
Michaely, R., and M. Roberts, 2012, “Corporate Dividend Policies: Lessons from Private
Firms,” Review of Financial Studies 25, 711-746.
Nalebuff, B., and J. Stiglitz, 1983, “Prizes and Incentives: Towards a General Theory of
Compensation and Competition,” Bell Journal of Economics 14, 21-43.
Nickell, S., 1996, “Competition and Corporate Performance,” Journal of Political Economy 104,
724-746.
Officer, M., 2006, “Dividend Policy, Dividend Initiations, and Governance,” Working Paper,
University of Southern California.
Petersen, M., 2009, “Estimating Standard Errors in Finance Panel Data Sets: Comparing
Approaches,” Review of Financial Studies 22, 435-480.
Powell, J., 1984, “Least Absolute Deviations Estimation of the Censored Regression Model,”
Journal of Econometrics 25, 303–325.
31
Raith, M., 2003, “Competition Risk and Managerial Incentives,” American Economic Review 93,
1425-1436.
Salinger, M., 1984, “Tobin’s q, Unionization, and the Concentration-Profits Relationship,” Rand
Journal of Economics 15, 159-170.
Scharfstein, D., 1988, “Product-Market Competition and Managerial Slack,” RAND Journal of
Economics 19, 147-155.
Schmidt, K., 1997, “Managerial Incentives and Product Market Competition,” Review of
Economic Studies 64, 191-213.
Skinner, D., 2008, “The Evolving Relation between Earnings, Dividends, and Stock
Repurchases,” Journal of Financial Economics 87, 582-609.
Shleifer, A., 1985, “A Theory of Yardstick Competition,” RAND Journal of Economics 16, 319327.
Shleifer, A., and R. Vishny, 1997, “A Survey of Corporate Governance,” Journal of Finance 52,
737-783.
Zwiebel, J., 1996, “Dynamic Capital Structure under Managerial Entrenchment,” American
Economic Review 86, 1197-1215.
32
Figure 1
Average Total Sales of Portfolios Sorted by Competition Measures
This figure depicts the average total sales of portfolios sorted by competition measures. Census HHI is the fourdigit SIC Herfindahl-Hirschman Index from the Census of Manufacturers. TNIC3 HHI is the Herfindahl-Hirschman
Index developed by Hoberg and Phillips (2014), which uses industry classifications based on firm pairwise
similarity scores from text analysis of firm 10K product descriptions. The sample period is from 1997 to 2010.
4,500
4,000
Millions of $
3,500
3,000
2,500
2,000
1,500
1,000
500
0
1
2
3
4
Quartiles based on Census HHI
4,500
4,000
Millions of $
3,500
3,000
2,500
2,000
1,500
1,000
500
0
1
2
3
Quartiles based on TNIC3 HHI
33
4
Figure 2
Average Lerner Index of Portfolios Sorted by Competition Measures
This figure depicts the average Lerner index sorted by competition measures. The Lerner Index is the price-cost
margin [(total sales - cost of goods sold - selling and general expenses -depreciation)/total sales]. Census HHI is the
four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers. TNIC3 HHI is the HerfindahlHirschman Index developed by Hoberg and Phillips (2014), which uses industry classifications based on firm
pairwise similarity scores from text analysis of firm 10K product descriptions. The sample period is from 1997 to
2010.
9.50%
9.00%
8.50%
8.00%
7.50%
7.00%
1
2
3
4
Quartiles based on Census HHI
9.50%
9.00%
8.50%
8.00%
7.50%
7.00%
1
2
3
Quartiles based on TNIC3 HHI
34
4
Table 1
Summary Statistics
This table reports the summary statistics for the sample firms. To be included in the sample, the observation must
satisfy the following criteria: the firm’s financial data is available on Compustat; the firm operates in an industry
covered by the Census of Manufacturers (SIC code interval 2000-3990); total assets are greater than $1 million.
DIV is the total dollar amount of dividends declared on the common stock. REPO is the expenditure on the
purchase of common and preferred stocks. SALES are total sales. MV is the market value of common stock.
ASSETS are equal to the total book value of assets. AGE is the time (in years) from the firm’s CRSP listing date.
M/B is the market-to-book ratio [(book value of assets + market value of equity - book value of equity) / book value
of assets]. DEBT/ASSETS is equal to long-term debt plus short-term debt scaled by total assets. HHI is the fourdigit SIC Herfindahl-Hirschman Index from the Census of Manufacturers. ROA is the operating income before
depreciation scaled by total assets. CAPEX is the level of capital expenditures. All the payout measures, M/B and
ROA have been winsorized at the 1% and the 99% of their empirical distribution. MV and ASSETS have been
deflated by the Consumer Price Index (1982-1984=100). The sample period is from 1972 to 2010.
Mean
Std. Dev.
5th
Median
95th
N
Payout Measures
DIV / ASSETS
DIV / SALES
DIV / MV
REPO /ASSETS
REPO / SALES
REPO / MV
Proportion of Firms with DIV > 0
Proportion of Firms with REPO> 0
0.80%
0.74%
1.06%
1.02%
1.16%
1.05%
35.9%
29.7%
1.50%
1.52%
1.92%
3.05%
3.78%
3.04%
0
0
0
0
0
0
0
0
0
0
0
0
3.80%
3.57%
5.38%
6.49%
6.96%
6.37%
64,644
63,647
63,616
60,056
59,270
59,026
Firm Characteristics
MV
ASSETS
AGE
M/B
DEBT/ASSETS
HHI
ROA
RETVOL
CAPEX/ASSETS
1,138
1,154
13.4
2.0
0.167
857
0.043
0.155
0.056
5,980
5,958
14.4
1.4
0.170
680
0.246
0.106
0.056
3
3
0
0.7
0
270
-0.500
0.055
0.005
64
62
9
1.4
0.129
647
0.111
0.131
0.041
4,305
4,253
45
5.6
0.494
2,300
0.281
0.330
0.158
63,794
64,866
64,866
61,718
63,500
64,780
64,634
58,731
64,097
35
Table 2
The Relation between Product Market Competition and Corporate Payouts
This table reports estimates of regressions relating scaled measures of dividends and share repurchases to the
Herfindahl-Hirschman Index and other control variables. DIV is the total dollar amount of dividends declared on
the common stock. REPO is the expenditure on the purchase of common and preferred stocks. SALES are total
sales. MV is the market value of common stock. ASSETS are equal to the total book value of assets. HHI is the
four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by 100. DUMHHI is a
dummy variable equal to one if the HHI is above its median, zero otherwise. M/B is the market-to-book ratio [(book
value of assets + market value of equity - book value of equity) / book value of assets]. ROA is the operating
income before depreciation scaled by total assets. DEBT/ASSETS is equal to long-term debt plus short-term debt
scaled by total assets. AGE is the time (in years) from the firm’s CRSP listing date. All the payout measures, M/B,
and ROA have been winsorized at the 1% and the 99% of their empirical distribution. Since the dependent variables
are truncated at zero and one, we estimate the regression coefficients using a two-sided Tobit model. Standard
errors adjusted for within-firm correlation are reported in parentheses below coefficient estimates. Superscripts a, b,
and c denote significantly different from zero at the 1%, 5%, and 10% level, respectively. The sample period is
from 1972 to 2010.
Panel A: Dividends
Dependent Variable
DIV/
DIV/
ASSETS
SALES
DIV/
SALES
DIV/
MV
Intercept
-0.0233a
(0.0029)
-0.0226a
(0.0033)
-0.0209a
(0.0027)
Log (HHI)
-0.0016a
(0.0005)
-0.0015a
(0.0006)
-0.0017a
(0.0005)
DUMHHI
DIV/
MV
DIV/
ASSETS
-0.0251a
(0.0028)
-0.0243a
(0.0032)
-0.0227a
(0.0026)
-0.0020a
(0.0007)
-0.0017b
(0.0080)
-0.0019a
(0.0007)
log (MV)
0.0039a
(0.0002)
0.0043a
(0.0002)
0.0031a
(0.0002)
0.0039a
(0.0002)
0.0043a
(0.0002)
0.0031a
(0.0002)
log (M/B)
-0.0057a
(0.0008)
-0.0243a
(0.0010)
-0.0055a
(0.0008)
-0.0058a
(0.0008)
-0.0244a
(0.0010)
-0.0055a
(0.0008)
ROA
0.0680a
(0.0037)
0.0773a
(0.0044)
0.0837a
(0.0040)
0.0683a
(0.0037)
0.0776a
(0.0044)
0.0840a
(0.0040)
DEBT/ASSETS
-0.0183a
(0.0023)
-0.0166a
(0.0026)
-0.0241a
(0.0021)
-0.0183a
(0.0023)
-0.0165a
(0.0026)
-0.0241a
(0.0022)
log (1+ AGE)
0.0056a
(0.0004)
0.0072a
(0.0005)
0.0063a
(0.0004)
0.0057a
(0.0004)
0.0072a
(0.0005)
0.0064a
(0.0004)
RETVOL
-0.1299a
(0.0075)
-0.1607a
(0.0085)
-0.1330a
(0.0073)
-0.1296a
(0.0075)
-0.1606a
(0.0085)
-0.1328a
(0.0073)
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
54,715
55,343
55,343
54,715
55,343
55,343
N
36
Panel B: Share Repurchases
REPO/
SALES
REPO/
MV
Dependent Variable
REPO/
REPO/
ASSETS
SALES
Intercept
-0.0573a
(0.0056)
-0.0437a
(0.0045)
-0.0472a
(0.0046)
Log (HHI)
-0.0004
(0.0010)
-0.0005
(0.0009)
-0.0002
(0.0009)
DUMHHI
REPO/
MV
REPO/
ASSETS
-0.0574a
(0.0053)
-0.0440a
(0.0044)
-0.0472a
(0.0044)
-0.0027c
(0.0015)
-0.0022c
(0.0012)
-0.0020c
(0.0012)
log (MV)
0.0055a
(0.0005)
0.0033a
(0.0004)
0.0035a
(0.0004)
0.0056a
(0.0005)
0.0033a
(0.0004)
0.0035a
(0.0004)
log (M/B)
-0.0017
(0.0018)
-0.0161a
(0.0013)
0.0009
(0.0014)
-0.0018
(0.0018)
-0.0161a
(0.0013)
0.0008
(0.0014)
ROA
0.0432a
(0.0052)
0.0500a
(0.0037)
0.0636a
(0.0043)
0.0431a
(0.0052)
0.0499a
(0.0037)
0.0636a
(0.0043)
DEBT/ASSETS
-0.0407a
(0.0049)
-0.0202a
(0.0039)
-0.0346a
(0.0038)
-0.0408a
(0.0049)
-0.0202a
(0.0039)
-0.0347a
(0.0038)
log (1+ AGE)
0.0051a
(0.0008)
0.0052a
(0.0007)
0.0056a
(0.0007)
0.0051a
(0.0008)
0.0051a
(0.0007)
0.0056a
(0.0007)
RETVOL
-0.0982a
(0.0100)
-0.0821a
(0.0078)
-0.0843a
(0.0078)
-0.0975a
(0.0100)
-0.0816a
(0.0078)
-0.0839a
(0.0078)
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
51,101
51,608
51,608
51,101
51,608
51,608
N
37
Table 3
Logit Regressions: Product Market Competition and Payout Decisions
This table reports estimates of Logit regressions relating payout decisions to the Herfindahl-Hirschman Index and
other control variables. DIV > 0 is a dummy variable equal to one if the total dollar amount of dividends declared
on the common stock is positive, zero otherwise. Change in DIV > 0 is a dummy variable equal to one if the firm
increases its dividends from year t-1 to t, zero otherwise. REPO > 0 is a dummy variable equal to one if the
expenditure on the purchase of common and preferred stocks is positive, zero otherwise. MV is the market value of
common stock. HHI is the four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by
100. DUMHHI is a dummy variable equal to one if the HHI is above its median, zero otherwise. M/B is the
market-to-book ratio [(book value of assets + market value of equity - book value of equity) / book value of assets].
ROA is the operating income before depreciation scaled by total assets. DEBT/ASSETS is equal to long-term debt
plus short-term debt scaled by total assets. AGE is the time (in years) from the firm’s CRSP listing date. RETVOL
is the standard deviation of monthly stock returns. M/B and ROA have been winsorized at the 1% and the 99% of
the empirical distribution. Standard errors adjusted for within-firm correlation are reported in parentheses below
coefficient estimates. Superscripts a, b, and c denote significantly different from zero at the 1%, 5%, and 10% level,
respectively. The sample period is from 1972 to 2010.
Dependent Variable
DIV > 0
Change in
DIV > 0
REPO > 0
DIV > 0
Change in
DIV > 0
REPO > 0
Intercept
-3.7083a
(0.2596)
-3.9547a
(0.2025)
-1.2362a
(0.1465)
-3.5544a
(0.2568)
-3.7831a
(0.2009)
-1.1803a
(0.1441)
Log (HHI)
-0.1312a
(0.0501)
-0.1468a
(0.0414)
-0.0441
(0.0291)
-0.2028a
(0.0695)
-0.2305a
(0.0582)
-0.0729c
(0.0407)
DUMHHI
log (MV)
0.4984a
(0.0243)
0.3818a
(0.0178)
0.1344a
(0.0127)
0.4986a
(0.0242)
0.3819a
(0.0177)
0.1343a
(0.0127)
log (M/B)
-1.3758a
(0.0790)
-0.8140a
(0.0663)
-0.3352a
(0.0417)
-1.3809a
(0.0788)
-0.8211a
(0.0662)
-0.3356a
(0.0417)
ROA
6.5581a
(0.3023)
8.0272a
(0.2879)
1.7317a
(0.1248)
6.5870a
(0.3029)
8.0652a
(0.2884)
1.7355a
(0.1249)
DEBT/ASSETS
-1.3200a
(0.1949)
-0.6518a
(0.1673)
-0.9733a
(0.1248)
-1.3229a
(0.1954)
-0.6540a
(0.1679)
-0.9730a
(0.1248)
log (1+ AGE)
0.6786a
(0.0420)
0.4850a
(0.0325)
0.1830a
(0.0226)
0.6816a
(0.0420)
0.4875a
(0.0325)
0.1836a
(0.0226)
RETVOL
-12.192a
(0.5268)
-10.083a
(0.4747)
-2.942a
(0.2408)
-12.153a
(0.5246)
-10.028a
(0.4719)
-2.934a
(0.2409)
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
55,343
54,552
51,608
55,343
54,552
51,608
N
38
Table 4
The Effect of Business Combination Laws on the Relation between Product Market
Competition and Corporate Payouts
This table examines the effect of business combination laws on the relation between the Herfindahl-Hirschman
Index and scaled measures of dividends and share repurchases. DIV is the total dollar amount of dividends declared
on the common stock. REPO is the expenditure on the purchase of common and preferred stocks. SALES are total
sales. MV is the market value of common stock. ASSETS are equal to the total book value of assets. HHI is the
four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by 100. DUMHHI is a
dummy variable equal to one if the HHI is above its median value, zero otherwise. DUMBC is a dummy variable
equal to one if the firm is incorporated in a state that has passed a business combination law, zero otherwise. M/B is
the market-to-book ratio [(book value of assets + market value of equity - book value of equity) / book value of
assets]. ROA is the operating income before depreciation scaled by total assets. DEBT/ASSETS is equal to longterm debt plus short-term debt scaled by total assets. AGE is the time (in years) from the firm’s CRSP listing date.
All the payout measures, M/B, and ROA have been winsorized at the 1% and the 99% of their empirical
distribution. Since the dependent variables are truncated at zero and one, we estimate the regression coefficients
using a two-sided Tobit model. Standard errors adjusted for within-firm correlation are reported in parentheses
below coefficient estimates. Superscripts a, b, and c denote significantly different from zero at the 1%, 5%, and
10% level, respectively. The sample period is from 1979 to 1997.
Panel A: Dividends
Dependent Variable
DIV/
DIV/
ASSETS
SALES
DIV/
SALES
DIV/
MV
DIV/
MV
DIV/
ASSETS
Intercept
0.0054
(0.0033)
0.0250a
(0.0043)
0.0150a
(0.0038)
0.0014
(0.0029)
0.0208a
(0.0038)
0.0106a
(0.0034)
Log (HHI)
-0.0021b
(0.0009)
-0.0024b
(0.0011)
-0.0024b
(0.0010)
Log (HHI) x DUMBC
-0.0028a
(0.0010)
-0.0009
(0.0012)
-0.0024b
(0.0010)
DUMBC
0.0064b
(0.0023)
0.0026
(0.0028)
0.0049b
(0.0024)
DUMHHI
-0.0009
(0.0010)
-0.0018
(0.0013)
-0.0011
(0.0011)
DUMHHI x DUMBC
-0.0052a
(0.0012)
-0.0029b
(0.0014)
-0.0045a
(0.0012)
DUMBC
0.0035a
(0.0014)
0.0022
(0.0017)
0.0024
(0.0016)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC IndustryFixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
23,678
23,938
23,938
23,678
23,938
23,938
N
39
Panel B: Share Repurchases
REPO/
SALES
REPO/
MV
Dependent Variable
REPO/
REPO/
ASSETS
SALES
REPO/
MV
REPO/
ASSETS
Intercept
-0.0661a
(0.0078)
-0.0505a
(0.0080)
-0.0540a
(0.0070)
-0.0641a
(0.0065)
-0.0488a
(0.0070)
-0.0525a
(0.0060)
Log (HHI)
0.0010
(0.0022)
0.0006
(0.0021)
0.0007
(0.0019)
Log (HHI) x DUMBC
-0.0021
(0.0026)
-0.0009
(0.0025)
-0.0020
(0.0022)
DUMBC
0.0030
(0.0057)
0.0013
(0.0055)
0.0032
(0.0050)
DUMHHI
-0.0004
(0.0026)
-0.0012
(0.0025)
-0.0006
(0.0024)
DUMHHI x DUMBC
-0.0027
(0.0031)
-0.0015
(0.0030)
-0.0025
(0.0027)
DUMBC
0.0003
(0.0032)
0.0003
(0.0030)
0.0005
(0.0029)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
22,561
22,797
22,797
22,561
22,797
22,797
N
40
Table 5
The Effect of Business Combination Laws on the Relation between Product Market
Competition and Payout Decisions
This table examines the effect of business combination laws on the relation between the Herfindahl-Hirschman
Index and payout decisions. DIV > 0 is a dummy variable equal to one if the total dollar amount of dividends
declared on the common stock is positive, zero otherwise. Change in DIV > 0 is a dummy variable equal to one if
the firm increases its dividends from year t-1 to t, zero otherwise. REPO > 0 is a dummy variable equal to one if the
expenditure on the purchase of common and preferred stocks is positive, zero otherwise. MV is the market value of
common stock. HHI is the four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by
100. DUMHHI is a dummy variable equal to one if the HHI is above its median, zero otherwise. DUMBC is a
dummy variable equal to one if the firm is incorporated in a state that has passed a business combination law, zero
otherwise. M/B is the market-to-book ratio [(book value of assets + market value of equity - book value of equity) /
book value of assets]. ROA is the operating income before depreciation scaled by total assets. DEBT/ASSETS is
equal to long-term debt plus short-term debt scaled by total assets. AGE is the time (in years) from the firm’s CRSP
listing date. RETVOL is the standard deviation of monthly stock returns. M/B and ROA have been winsorized at
the 1% and the 99% of the empirical distribution. We estimate the regression coefficients using a Logit model.
Standard errors adjusted for within-firm correlation are reported in parentheses below coefficient estimates.
Superscripts a, b, and c denote significantly different from zero at the 1%, 5%, and 10% level, respectively. The
sample period is from 1979 to 1997.
Dependent Variable
DIV > 0
Change in
DIV > 0
REPO > 0
DIV > 0
Change in
DIV > 0
REPO > 0
Intercept
-0.1349
(0.4262)
-1.1203a
(0.3455)
-1.6018a
(0.2517)
-0.5539
(0.3756)
-1.5336a
(0.2938)
-1.5502a
(0.2171)
Log (HHI)
-0.2287b
(0.1131)
-0.2436a
(0.0939)
0.0210
(0.0678)
Log (HHI) x DUMBC
-0.1988c
(0.1237)
-0.2172b
(0.1007)
-0.0664
(0.0791)
DUMBC
0.4028
(0.2703)
0.4445b
(0.2207)
0.1515
(0.1748)
DUMHHI
-0.1975c
(0.1221)
-0.2575a
(0.1029)
0.0087
(0.0813)
DUMHHI x DUMBC
-0.4186a
(0.1408)
-0.3444a
(0.1194)
-0.1267
(0.0958)
DUMBC
0.2228
(0.1482)
0.1911
(0.1272)
0.0834
(0.0928)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
23,938
23,572
22,797
23,938
23,572
22,797
N
41
Table 6
The Effect of Being a Large Firm on the Relation between Product Market Competition
and Corporate Payouts
This table examines the effect of being a large firm on the relation between the Herfindahl-Hirschman Index and
scaled measures of dividends and share repurchases. DIV is the total dollar amount of dividends declared on the
common stock. REPO is the expenditure on the purchase of common and preferred stocks. SALES are total sales.
MV is the market value of common stock. ASSETS are equal to the total book value of assets. HHI is the four-digit
SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by 100. DUMHHI is a dummy variable
equal to one if the HHI is above its median value, zero otherwise. LARGE is a dummy variable equal to one if a
firm’s market value is above its industry (four-digit SIC code) average market value at time t, zero otherwise. M/B
is the market-to-book ratio [(book value of assets + market value of equity - book value of equity) / book value of
assets]. ROA is the operating income before depreciation scaled by total assets. DEBT/ASSETS is equal to longterm debt plus short-term debt scaled by total assets. AGE is the time (in years) from the firm’s CRSP listing date.
All the payout measures, M/B, and ROA have been winsorized at the 1% and the 99% of their empirical
distribution. Since the dependent variables are truncated at zero and one, we estimate the regression coefficients
using a two-sided Tobit model. Standard errors adjusted for within-firm correlation are reported in parentheses
below coefficient estimates. Superscripts a, b, and c denote significantly different from zero at the 1%, 5%, and
10% level, respectively. The sample period is from 1972 to 2010.
Panel A: Dividends
Dependent Variable
DIV/
DIV/
ASSETS
SALES
DIV/
SALES
DIV/
MV
DIV/
MV
DIV/
ASSETS
Intercept
-0.0253a
(0.0030)
-0.0242a
(0.0034)
-0.0225a
(0.0028)
-0.0261a
(0.0028)
-0.0251a
(0.0033)
-0.0233a
(0.0026)
Log (HHI)
-0.0007
(0.0006)
-0.0008
(0.0007)
-0.0007
(0.0006)
Log (HHI) x LARGE
-0.0024a
(0.0008)
-0.0018b
(0.0008)
-0.0024a
(0.0008)
LARGE
0.0041a
(0.0017)
0.0027
(0.0018)
0.0050a
(0.0016)
DUMHHI
-0.0002
(0.0008)
-0.0007
(0.0010)
-0.0002
(0.0008)
DUMHHI x LARGE
-0.0050a
(0.0012)
-0.0028b
(0.0012)
-0.0045a
(0.0011)
LARGE
0.0022b
(0.0009)
0.0008
(0.0010)
0.0029a
(0.0009)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
54,715
55,343
55,343
54,715
55,343
55,343
N
42
Panel B: Share Repurchases
Dependent Variable
REPO/
REPO/
ASSETS
SALES
REPO/
SALES
REPO/
MV
REPO/
MV
REPO/
ASSETS
Intercept
-0.0602a
(0.0055)
-0.0462a
(0.0046)
-0.0495a
(0.0046)
-0.0592a
(0.0053)
-0.0449a
(0.0044)
-0.0479a
(0.0044)
Log (HHI)
0.0005
(0.0012)
0.0004
(0.0010)
0.0009
(0.0010)
Log (HHI) x LARGE
-0.0028
(0.0019)
-0.0028b
(0.0014)
-0.0033b
(0.0015)
LARGE
0.0029
(0.0039)
0.0040
(0.0030)
0.0062b
(0.0031)
DUMHHI
-0.0014
(0.0016)
-0.0015
(0.0014)
-0.0008
(0.0014)
DUMHHI x LARGE
-0.0042
(0.0029)
-0.0023
(0.0022)
-0.0040c
(0.0024)
LARGE
-0.0005
(0.0022)
-0.0001
(0.0018)
0.0019
(0.0019)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
51,101
51,608
51,608
51,101
51,608
51,608
N
43
Table 7
The Effect of Being a Large Firm on the Relation between Product Market Competition
and Payout Decisions
This table examines the effect of being a large firm on the relation between the Herfindahl-Hirschman Index and
payout decisions. DIV > 0 is a dummy variable equal to one if the total dollar amount of dividends declared on the
common stock is positive, zero otherwise. Change in DIV > 0 is a dummy variable equal to one if the firm increases
its dividends from year t-1 to t, zero otherwise. REPO > 0 is a dummy variable equal to one if the expenditure on
the purchase of common and preferred stocks is positive, zero otherwise. MV is the market value of common stock.
HHI is the four-digit SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by 100. DUMHHI
is a dummy variable equal to one if the HHI is above its median, zero otherwise. LARGE is a dummy variable
equal to one if a firm’s market value is above its industry (four-digit SIC code) average market value at time t, zero
otherwise. M/B is the market-to-book ratio [(book value of assets + market value of equity - book value of equity) /
book value of assets]. ROA is the operating income before depreciation scaled by total assets. DEBT/ASSETS is
equal to long-term debt plus short-term debt scaled by total assets. AGE is the time (in years) from the firm’s CRSP
listing date. RETVOL is the standard deviation of monthly stock returns. M/B and ROA have been winsorized at
the 1% and the 99% of the empirical distribution. Standard errors adjusted for within-firm correlation are reported
in parentheses below coefficient estimates. Superscripts a, b, and c denote significantly different from zero at the
1%, 5%, and 10% level, respectively. The sample period is from 1972 to 2010.
Dependent Variable
DIV > 0
Change in
DIV > 0
REPO > 0
DIV > 0
Change in
DIV > 0
REPO > 0
Intercept
-3.6469a
(0.2609)
-3.9052a
(0.2034)
-1.2529a
(0.1474)
-3.5782a
(0.2561)
-3.8204a
(0.2016)
-1.2276a
(0.1449)
Log (HHI)
-0.0847
(0.0554)
-0.0789c
(0.0468)
-0.0027
(0.0324)
Log (HHI) x LARGE
-0.1352
(0.0848)
-0.1703a
(0.0661)
-0.1366a
(0.0504)
LARGE
0.0714
(0.0911)
0.0143
(0.0731)
-0.1207b
(0.0540)
DUMHHI
-0.0906
(0.0753)
-0.1113c
(0.0652)
-0.0296
(0.0452)
DUMHHI x LARGE
-0.3741a
(0.1262)
-0.3270a
(0.0974)
-0.1473c
(0.0778)
LARGE
0.3157a
(0.0972)
0.2530a
(0.0784)
0.0135
(0.0614)
Other Controls
Yes
Yes
Yes
Yes
Yes
Yes
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
55,343
54,552
51,608
55,343
54,552
51,608
N
44
Table 8
The Relation between Product Market Competition and Dividends: Alternative Measures
of Concentration
This table reports estimates of regressions relating scaled measures of dividends and share repurchases to measures
of concentration and other control variables. DIV is the total dollar amount of dividends declared on the common
stock. REPO is the expenditure on the purchase of common and preferred stocks. SALES are total sales. MV is the
market value of common stock. ASSETS are equal to the total book value of assets. Census HHI is the four-digit
SIC Herfindahl-Hirschman Index from the Census of Manufacturers scaled by 10,000. TNIC3 HHI is the
Herfindahl-Hirschman Index developed by Hoberg and Phillips (2014), which uses industry classifications based on
firm pairwise similarity scores from text analysis of firm 10K product descriptions. M/B is the market-to-book ratio
[(book value of assets + market value of equity - book value of equity) / book value of assets]. ROA is the operating
income before depreciation scaled by total assets. DEBT/ASSETS is equal to long-term debt plus short-term debt
scaled by total assets. AGE is the time (in years) from the firm’s CRSP listing date. All the payout measures, M/B,
and ROA have been winsorized at the 1% and the 99% of their empirical distribution. Since the dependent variables
are truncated at zero and one, we estimate the regression coefficients using a two-sided Tobit model. Standard
errors adjusted for within-firm correlation are reported in parentheses below coefficient estimates. Superscripts a, b,
and c denote significantly different from zero at the 1%, 5%, and 10% level, respectively. The sample period is
from 1997 to 2010.
Dependent Variable
DIV/
DIV/
ASSETS
SALES
DIV/
SALES
DIV/
MV
Intercept
-0.0607a
(0.0061)
-0.0364a
(0.0043)
-0.0443a
(0.0047)
Census HHI
-0.0282b
(0.0149)
-0.0229b
(0.0115)
-0.0291a
(0.0115)
TNIC3 HHI
DIV/
MV
DIV/
ASSETS
-0.0761a
(0.0069)
-0.0479a
(0.0048)
-0.0570a
(0.0053)
0.0178a
(0.0041)
0.0121a
(0.0031)
0.0151a
(0.0031)
log (MV)
0.0061a
(0.0005)
0.0038a
(0.0003)
0.0041a
(0.0004)
0.0051a
(0.0006)
0.0030a
(0.0004)
0.0035a
(0.0005)
log (M/B)
-0.0043a
(0.0008)
-0.0071a
(0.0007)
-0.0031a
(0.0006)
-0.0035a
(0.0008)
-0.0063a
(0.0071)
-0.0027a
(0.0006)
ROA
0.1029a
(0.0104)
0.0717a
(0.0067)
0.0986a
(0.0086)
0.0928a
(0.0109)
0.0670a
(0.0072)
0.0921a
(0.0092)
DEBT/ASSETS
-0.0241a
(0.0053)
-0.0127a
(0.0040)
-0.0231a
(0.0041)
-0.0152a
(0.0053)
-0.0071c
(0.0042)
-0.0168a
(0.0042)
log (1+ AGE)
0.0124a
(0.0011)
0.0100a
(0.0008)
0.0107a
(0.0008)
0.0159a
(0.0013)
0.0128a
(0.0009)
0.0133a
(0.0010)
RETVOL
-0.1535a
(0.0184)
-0.1107a
(0.0122)
-0.1235a
(0.0139)
-0.1471a
(0.0199)
-0.1088a
(0.0135)
-0.1212a
(0.0153)
Year-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
Two-Digit SIC
Industry-Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
21,755
22,152
22,152
19,386
19,748
19,748
N
45
Table 9
Firm Characteristics of Portfolios Sorted by Competition Measures
This table reports the summary statistics of portfolios sorted by competition measures. MV is the market value of
common stock. ASSETS are equal to the total book value of assets. SALES are total sales. The Lerner Index is the
price-cost margin [(total sales - cost of goods sold - selling and general expenses -depreciation)/total sales].
GS_5YR is the five-year growth rate in total sales. M/B is the market-to-book ratio [(book value of assets + market
value of equity - book value of equity) / book value of assets]. HHI is the four-digit SIC Herfindahl-Hirschman
Index from the Census of Manufacturers. TNIC3 HHI is the Herfindahl-Hirschman Index developed by Hoberg and
Phillips (2014), which uses industry classifications based on firm pairwise similarity scores from text analysis of
firm 10K product descriptions. M/B and GS_5YR have been winsorized at the 1% and the 99% of the empirical
distribution. The sample period is from 1997 to 2010.
Panel A: Quartiles based on Census HHI
2
3
1
Census HHI
MV
ASSETS
SALES
Lerner Index
GS_5YEAR
M/B
298
936
1,138
1,068
7.92%
8.00%
1.53
831
1,331
1,378
1,438
8.58%
10.28%
1.63
Panel B: Quartiles based on TNIC3 HHI
2
3
1
TNIC3 HHI
MV
ASSETS
SALES
Lerner Index
GS_5YEAR
M/B
521
1,290
1,377
1,352
8.26%
9.51%
1.65
914
4,423
3,923
4,321
8.57%
11.89%
1.74
46
1,908
3,396
3,245
2,554
8.94%
10.55%
1.80
2,965
2,240
2,976
2,102
8.82%
9.44%
1.68
4
1,787
2,883
3,781
3,466
9.22%
8.69%
1.62
4
5,085
2,025
2,571
1,631
8.60%
8.79%
1.74