Product Market Competition, Legal Institutions and Accounting
Conservatism
In-Mu Haw*
Texas Christian University
[email protected]
Simon S.M. Ho
Hang Seng Management College
[email protected]
Annie Yuansha Li
University of Massachusetts Lowell
[email protected]
Feida Zhang
Murdoch University
[email protected]
Current Version: May 2014
* Corresponding author; Tel.: +1 817 257 7563; E-mail: [email protected]. We greatly acknowledge the
thoughtful comments and suggestions of the Editor, Michael Welker, two anonymous referees, and the
seminar participants at Hong Kong Baptist University. Zhang would like to express appreciation for the
financial support of the National Natural Science Foundation of China (approval number: 71002058,
71202090, 71202091).
1
Product Market Competition, Legal Institutions and Accounting
Conservatism
Abstract
This paper examines the role played by product market competition in shaping
accounting conservatism in an international setting. Using a large dataset from 38
countries, we find evidence that product market competition is positively associated with
accounting conservatism in countries with strong legal institutions, but not in countries
with weak legal institutions. Moreover, the positive association is significantly more
pronounced in countries with high quality financial reporting environments, which
consist of higher earnings quality, more frequent and greater disclosure practices, and
more stringent enforcement of insider trading regulations. Our empirical findings suggest
that product market competition and strong legal institutions jointly drive accounting
conservatism.
Keywords: product market competition, accounting conservatism, legal institutions,
financial reporting environments, a cross-country study.
2
1. Introduction
Product market competition has long been regarded as an important industry-level
characteristic in economics literature (e.g., Giroud, Mueller, Aggarwal, and Erel 2010).
The extant literature on accounting conservatism primarily focuses on firm- (e.g., LaFond
and Roychowdhury 2008; Ahmed and Duellman 2007; Qiang 2007) and country-level
factors (e.g., Bushman and Piotroski 2006; Ball, Kothari, and Robin 2000; Ball, Robin,
and Wu 2003; Ball, Robin, and Sadka 2008b). However, studies examining the role of
product market competition in determining accounting conservatism are scant. A notable
exception is Dhaliwal, Huang, Khurana, and Pereira (2014) study, which examines the
relation between product market competition and accounting conservatism in the U.S.
and documents robust evidence that asymmetric timeliness of economic loss recognition
(i.e., conditional conservatism) increases with competition intensity. However, we know
little about whether their results in the U.S. (where investors are well protected) can be
generalizable to countries outside the U.S., where considerable variation exists in investor
protection institutions and financial reporting environments. More importantly, there is
little knowledge about how effectively industry-level product market competition
interplays with country-level legal institutions in shaping firms‘ financial reporting
decision. Our cross-country study attempts to investigate this important and unexplored
research question. Specifically, we argue that the role of industry-specific product market
competition in determining accounting conservatism depends upon the strength of a
country‘s legal institutions and financial reporting environments.
1
Well-known prior studies report that more intense product market competition is
related to lower profitability, greater performance volatility, and higher liquidation risk
(Hou and Robinson 2006; Irvine and Pontiff 2007). A recent study by Dhaliwal et al.
(2014) predicts multi-dimensional nature of product market competition in influencing
firms‘ reporting practices. They argue that product market competition affects a firm‘s
reporting decision as it influences a firm‘s competitive position. Several studies predict
that firms have incentives to report negative proprietary news and withhold positive news
in an attempt to dissuade potential entrants (Darrough and Stoughton 1990; Evans and
Sridhar 2002; Wagenhofer 1990). Furthermore, the rivalry between existing competitors
drives firms to disclose bad news and withhold good news (Darrough 1993; Clinch and
Verrecchia 1997). Dhaliwal et al. (2014) find empirical evidence that is consistent with
the strategic consideration view of product market competition: firms strategically adopt
timely loss recognition to improve their competitive position. Their results do not support
political costs or improved governance views of product market competition, which are
likely to demand less conditional conservatism. Despite conflicting conceptual arguments,
prior studies in general suggest that firms have incentives to supply conservative
accounting to mitigate competitive pressure in product markets, predicting a positive
association between product market competition and conditional conservatism.
A growing body of international accounting and finance literature reports that legal
institutions of a country have a significant impact on financial reporting practices.
Bushman and Piotroski (2006) find that a country's legal and judicial system, securities
laws, and political economy create incentives that influence manager‘s reporting behavior
and that ultimately shape the properties of reported accounting numbers. In this paper, we
2
characterize the differential impacts of product market competition on conditional
conservatism across countries where legal institutions and reporting environments
substantially vary. Watts (2003a, 2003b) points out that the demand for conservatism
arises from four different sources: contracting, shareholder litigation, taxation and
accounting regulation. We thus employ four different proxies for legal institutions to
address those different demands for conservatism: investor protection to proxy for the
regulation-related demand; security regulations for the litigation-related demand; public
enforcement for the contracting or contract enforcement-related demand; and tax
compliance for the taxation-related demand. Being motivated by DeFond, Hung, and
Trezevant (2007) and Haw, Hu, Lee, and Wu (2012), we draw on the corporate
governance literature, identify underlying channels through which legal institutions affect
financial reporting environments, and test how those underlying channels influence the
relation between product market competition and conservatism.
Drawing on 84,835 firm-year observations from 38 economies for the 1999 to 2007
period and employing both Basu (1997) and Khan and Watts (2009) measures of
accounting conservatism, our results show that, on average, product market competition
is positively associated with conditional conservatism across our sample countries, which
is consistent with the finding of Dhaliwal et al. (2014) on U.S. firms. More importantly,
the positive association between product market competition and conservatism exists only
in countries with strong legal institutions (proxied by investor protection, security
regulations, public enforcement, and tax compliance). Furthermore, we find that the
positive relation is more pronounced in countries with high quality financial reporting
environments (represented by higher earnings quality, more frequent and greater
3
disclosure practices, and more stringent enforcement of insider trading regulations), all of
which are associated with strong investor protection institutions. It is worth noting that
the impact of firm size and leverage on conditional conservatism remains significant
irrespective of how stringent those accounting environments are, indicating that the
variation in the strength of legal institutions has a significant impact on conservatism
when product market competition is high, but it has a limited impact on firm size or
leverage. Our results suggest that highly competitive industry environment and a
country‘s strong legal institutions jointly drive accounting conservatism. Our findings
highlight the importance of a country‘s strong legal institutions in ensuring the
effectiveness of product market competition in shaping firms‘ financial reporting
environments. Our results are robust to a battery of sensitivity tests, including alternative
models to measure conservatism and different measures of product market competition.
This study contributes to the literature in a number of ways. First, our study extends
the study of Dhaliwal et al. (2014) on U.S. listed companies to a cross-country setting,
which allows us to examine how effectively a country‘s legal institutions and the
industry-specific product market competition interplay in shaping the quality of a firm‘s
financial reporting. A cross-country study provides a suitable setting to investigate
differential roles of product market competition across countries where considerable
variation exists in investor protection and financial reporting environments. Our findings
reveal that product market competition drives managers to adopt accounting conservatism
only when a country‘s legal institutions are effective in protecting investors, and thus
strong legal institutions are necessary conditions to ensure product market competition
functions effectively.
4
Second, our cross-country analysis enables us to explore the role of underlying
channels through which strong legal institutions affect the relation between product
market competition and accounting conservatism. Our evidence highlights the importance
of high quality financial reporting environments within a country in strengthening the
effectiveness of product market competition. Thus, our study adds to the growing body of
literature on international corporate governance by demonstrating the joint effects of high
quality reporting environments embedded in strong legal institutions and product market
competition on the quality of firms‘ reporting decision. Lastly, we extend both Basu
(1997) and Khan and Watts (2009) measures of accounting conservatism to a crosscountry setting, which would enhance the validity of these models.
The remainder of the paper is organized as follows. Section 2 reviews literature and
develops research hypotheses. Section 3 specifies the test design. Section 4 describes the
sample and presents the descriptive statistics. Section 5 provides our main empirical
results. Robustness checks are discussed in Section 6. Section 7 concludes the paper.
2.
Literature Review and Hypothesis
2.1 Product Market Competition and Accounting Conservatism
Researchers agree that conservative accounting helps improve contracting efficiency
and acts as a governance mechanism limiting managerial opportunism (Watts 2003a;
Ahmed, Billings, Morton, and Stanford-Harris 2002; Holthausen and Watts 2001; Watts
and Zimmerman 1986). 1 Conservatism alerts debtholders of the possible unfavorable
1
Watts (2003a) and Holthausen and Watts (2001) argue that conservatism persists because it helps to
address agency problems. Kim and Pevzner (2008) find that accounting conservatism is beneficial to stock
5
situation earlier and helps them make liquidation decisions correctly (Zhang 2008; Li
2010; Haw, Lee, and Lee 2014). However, prior studies on the determinants of
accounting conservatism focus on firm- and country-level factors (for example, LaFond
and Roychowdhury 2008; Bushman and Piotroski 2006; Ball et al. 2003), while the
research on industry-level determinants is relatively scant. A notable exception is
Dhaliwal et al.‘s (2014) study, which examines the relation between product market
competition and accounting conservatism in the U.S. and finds that asymmetric
timeliness of economic loss recognition increases with competition intensity. In this
paper, we argue that the impact of product market competition on accounting
conservatism depends on the strength of a country‘s legal institutions in protecting
investors.
One stream of research shows that more intense product market competition is
related to lower profitability, greater performance volatility, and higher liquidation risk
(Irvine and Pontiff 2007; Hou and Robinson 2006). Firms operating in a concentrated
industry are expected to earn persistently higher profits for a long time before the
earnings revert to the normal level (Lev 1983; Baginski, Lorek, Willinger, and Branson
1999). These firms could benefit from monopoly power, collude with their industry peers
market by reducing information asymmetry. Zhang (2008) documents that firms which apply more
accounting conservatism experience faster debt covenant violations, thus ―triggering the alarm‖ earlier to
borrowers. Wittenberg-Moerman (2008) shows that more conditionally conservative firms enjoy lower bidask spreads on the secondary loan markets. Vander Bauwhede and Gent (2008) find that creditors reward
conditional conservatism but not unconditional conservatism. Bushman et al. (2011) present evidence
showing that the total and incremental investment response to declining opportunities increases with timely
accounting recognition of economic losses. Ahmed et al. (2002) show that debt-holders view conservatism
as means of minimizing agency problems between debt-holders and shareholders and thus accounting
conservatism is negatively related to cost of debt. Ball, Bushman and Vasvari (2008a) show that
conservatism leads under-writers to hold lower stake in issued loans. Altogether, these studies provide
strong evidence that conservatism is an effective tool in reducing information asymmetry and monitoring
managers‘ behavior.
6
to protect their economic rents, or prevent potential new competitors from entering the
market by imposing high entry barriers (Mueller 1977; Eaton and Lipsey 1981). When
facing adverse external shocks, such companies can transfer the negative effects on firm
value onto consumers rather than absorb them and are likely to demonstrate less volatile
performance (Hou and Robinson 2006; Gaspar and Massa 2006).
This line of research suggests that product market competition affects a firm‘s
strategic reporting decisions and the flow of firm-specific information as they influence
its competitive position, and thus affects the timely recognition of good and bad news.
Earlier studies predict that firms have incentives to report negative proprietary news and
withhold positive news in an attempt to dissuade potential entrants (Darrough and
Stoughton 1990; Evans and Sridhar 2002; Wagenhofer 1990). The likelihood that a
potential competitor will join the market and occupy a portion of the incumbent firm‘s
market share increases as the incumbent firm discloses more favorable information. Thus,
the incumbent firm is likely to report more conservatively to dissuade possible new
competitors from entering into the product market. In a similar vein, the rivalry between
existing competitors could lead firms to provide more timely recognition of bad news and
less timely recognition of good news to communicate optimal output to rival competitors
(Li 2010). These arguments predict a positive association between product market
competition and accounting conservatism. In fact, Dhaliwal et al. (2014) provide
evidence consistent with the strategic consideration view of product market competition,
7
suggesting that a firm recognizes losses more quickly to improve its competitive position
vis-à-vis potential entrants and existing rivals. 2
There are other lines of arguments that might predict the negative relation between
competition and conditional conservatism. For instance, product market competition is
likely to affect a firm‘s political costs. Firms in more concentrated industries with fewer
players are subject to higher political costs because they earn excess profits that can be
extracted by a plaintiff or government (Watts and Zimmerman 1986). Moreover, firms
with monopoly power are particularly at risk due to the antitrust concerns and appear to
be subject to greater litigation risk. Anecdotal evidence shows that when Microsoft faced
its antitrust hearing in the late 1990s, it conservatively deferred revenue to future periods
in order to appear less profitable. Other examples include the shareholder class action
lawsuit against Facebook and the Apple lawsuit against Samsung.3 Given these reasoning,
it is argued that the more concentrated the industry, the greater the litigation risk and
presumably the greater demand for conservative accounting to minimize the litigation
risk and avoid regulatory scrutiny and sanctions, suggesting a negative relationship
between product market competition and accounting conservatism.4
2
Dhaliwal et al. (2014) argue that the strategic consideration view predicts that the industry followers in
more competitive industries are likely to exhibit more conditional conservatism because these firms are
more susceptible to competitive pressures than industry leaders (Li 2010). In contrast, firms in more
concentrated industry, if they are industry leaders, might have greater incentive to deter potential entrants
and use accounting conservatism as the new entrants would have great impacts on firms in concentrated
industries.
3
We thank a referee for pointing this out.
4
A competing view to this argument is that collective action theory predicts that the political influence of
specific industries is positively related to industry concentration. Incumbent firms in concentrated
industries have a greater ability to organize and oppose policy changes that could adversely affect them
(Olsen and Dietrich 1985). If the private interest view holds, firms in concentrated industries can more
easily build barriers, succeed in lobbying for favorite policies, oppose disfavored policy changes, and get
government subsidies.
8
Recent studies point out different managerial incentives between competitive
industries and concentrated industries (Giroud and Mueller 2011; Karuna 2007; Giroud et
al. 2010). One might argue that product market competition serves as an informal
governance mechanism and helps reduce the severity of agency problems, and thus
lowers the demand for accounting conservatism (LaFond and Roychoudhury 2008). The
misbehaviors of corporate insiders are more likely to jeopardize the survival of the firm
in a competitive industry. Product market competition thus represents a natural constraint
on the extraction of private benefits and lowers the demand for accounting conservatism
to govern insiders, suggesting a negative relation.5
Despite these conflicting conceptual arguments, Dhaliwal et al. (2014) find a robust
positive relation between product market competition and conditional conservatism in the
U.S., suggesting that strategic considerations, rather than political costs or governance
mechanism of product market competition affect accounting conservatism. Thus, we
predict that, on average, the association between product market competition and
accounting conservatism is likely to be positive across countries. Our first hypothesis
examines whether Dhaliwal et al.‘s (2014) findings can be generalizable to countries
outside the U.S.
H1: Accounting conservatism increases with the intensity of product market
competition across countries.
5
Another stream of research shows that intense competition encourages the disclosure of firm-specific
information and hence reduces managers‘ ability to conceal bad news (Ali et al. 2010; Clarke 1983; Gal-Or
1985). They find that firms in more concentrated industries provide less frequent management earnings
forecasts, receive lower disclosure ratings from analysts, and have more opaque information environments.
Therefore, managers of firms in more competitive industries have lower ability and incentive to conceal
bad news. However, the implications of these studies are related to an increased propensity to provide less
news in general, rather than an increased propensity to be conditionally conservative.
9
2.2 Legal institutions, Product Market Competition, and Accounting Conservatism
Prior studies in a cross-country setting document a positive relation between legal
institutions and accounting conservatism (e.g., Ball et al. 2000; Ball et al. 2003; Bushman
and Piotroski 2006). They argue that firms in countries with stronger legal institutions
face higher ‗‗contracting‘‘ demand for conservative financial reporting. In addition,
strong legal institutions could increase firms‘ potential litigation costs of overstating
economic performance and thus drive more conservative accounting.
Legal institutions of a country are likely to influence the relation between product
market competition and accounting conservatism. In countries with strong legal
institutions, shareholders are well protected (La Porta, Lopez-De-Silanes, Shleifer, and
Vishny 1998, La Porta, Lopez-De-Silanes, and Shleifer 2006). Well-functioning legal
institutions help increase the flow and quality of firm-specific information through
stricter enforcement of insider trading law and securities laws, higher earnings quality,
and greater corporate disclosure environments (Bushman, Piotroski, and Smith 2004,
DeFond et al. 2007, Haw et al. 2012, Morck, Yeung, and Yu 2000), affecting the strategy
of a firm‘s reporting decision given product market competition. Dhaliwal et al. (2014)
find that greater antitrust enforcement in the U.S. promotes the extent of product market
competition by prohibiting a variety of practices that restrain unfair trading, which
induces firms to recognize bad news in a timely manner. Jayaraman (2012) shows that
greater enforcement of insider trading laws, which is a typical characteristic of stronger
legal institutions, increases timely loss recognition in a cross-country setting, consistent
with greater enforcement increasing the demand for higher quality reporting. However,
such association was not found in non-enforcing countries. Garcia Lara, Garcia Osma and
10
Penalva (2009) find an increase in timely loss recognition following stricter regulatory
changes. Another mechanism of protecting shareholders is litigation, especially when
firms go bankrupt because of managers‘ misbehavior. It is expected that the litigation
costs for firms are higher in countries with stronger legal institutions (for example,
through shareholder class action lawsuits and a stricter judicial system). Thus, in those
countries, the association between product market competition and litigation risk is likely
to be stronger, further strengthening the influence of product market competition on
accounting conservatism.
On the other hand, prior studies show that investors are more likely to choose
liquidation more quickly when legal institutions are stronger (Claessens and Klapper
2005; Djankov, Hart, Nenova, and Shleifer 2006), leading to higher liquidation risk for
firms in competitive industries in those countries. Since strong legal institutions protect
investors better, they might lower the insiders‘ supply of conservative accounting to
reduce the liquidation risk or liquidation cost triggered by product market competition. In
this case, firms in greater competitive environments in countries with strong legal
institutions are likely to be less conditionally conservative, so as not to induce bankruptcy
in environments where investors are prone to choose liquidation more quickly. This
argument suggests that strong legal institutions might weaken the positive association
between product market competition and accounting conservatism.
Despite those contradictory conceptual arguments, our overall discussions above and
prior evidence on the positive impact of greater enforcement of laws on competition in
strong legal environments lead us to predict that strong legal institutions strengthen the
11
positive association between product market competition and accounting conservatism.
Our second hypothesis, which is the main hypothesis of our study, is stated as follows:
Hypothesis 2: The association between product market competition and accounting
conservatism is more pronounced in countries with strong legal institutions and high
quality financial reporting environments.
Based on Watts (2003a, 2003b), we use four different proxies for legal institutions to
address four different demands for conservatism: investor protection for the regulationrelated demand; security regulation for the litigation-related demand; public enforcement
for the contracting or contract enforcement-related demand; and tax compliance for the
taxation-related demand.
3.
Research Design
To test H1 on the impact of product market competition on conditional
conservatism around the world, we employ both Basu (1997) and Khan and Watts (2009)
methodologies.6 We anticipate that, on average, product market competition is positively
associated with the incremental timeliness of economic loss recognition across countries
(H1), but the positive association is more pronounced in countries with strong legal
institutions (H2). First, we estimate the following regression models based on Basu
(1997):
NIt=a0+b1Dt+b2RETt+b3Dt*RETt
+b4PMC+b5PMC*Dt+b6PMC*RETt+b7PMC*Dt*RETt
6
Jayaraman (2012) confirms the validity of CSCORE in the international setting, who shows that the
associations between CSCORE and firm characteristics in his cross-country sample are similar with those
shown by Khan and Watts for U.S. firms.
12
+b8SIZEt+b9SIZEt*Dt+b10SIZEt*RETt+b11SIZEt*Dt*RETt
+b12LEVt+b13LEVt*Dt+b14LEVt*RETt+b15LEVt*Dt*RETt
+b16MBRt+b17MBRt*Dt+b18MBRt*RETt+b19MBRt*Dt*RETt
+b20LIT*Dt+b21LIT*RETt+b22LIT*Dt*RETt
+Firm and Year Fixed Effects + ξ
(1)
where NI is net income before extraordinary items, deflated by beginning of period prices
(MVEt-1). D is an indicator variable equal to one if RET is less than zero, and zero
otherwise. RET is holding period market-adjusted return, including dividends, over the
firm‘s fiscal accounting year. PMC is the measure of product market competition and
equals to minus one multiplied by HHI. To calculate HHI, we get the sales data of both
public firms and private firms from the Bureau van Dijk (BvD) Orbis. We use 8,190,848
observations (1,490,106 public and private companies, from 1999-2007) around the
world to calculate HHI, which would more accurately reflect the extent of product market
competition than ratios constructed using data only from Global Vantage, which is
comprised almost entirely of publicly-traded firms (Ali, Klasa, and Yeung 2009). 7, 8 We
also include three firm-level control variables: firm size (SIZE), leverage (LEV), and
market-to-book ratio (MBR). Ball, Kothari, and Nikolaev (2012) show that a simple
inclusion of firm fixed effects mitigates the bias in conservatism coefficients of the
Basu‘s (1997) return model and controls for the potential effect of firm-specific omitted
7
When we follow Dhaliwal et al. (2014) to use sales data of public firms to calculate HHI and PMC, our
main empirical results remain qualitatively unchanged.
8
We calculate HHI using 3-digit SIC codes in the main tests. However, to improve the robustness of our
study, we also calculate HHI using 4-digit SIC code, 5-digit NAICS code, and 6-digit NAICS code in the
sensitivity tests and obtain similar results.
13
variables. We thus include firm and year fixed effects. The measurement and sources of
the variables are detailed in Appendix A.
In addition, we construct CSCORE following Khan and Watts (2009) and describe
details of its estimation process in Appendix B. CSCORE is the firm-year measure of
conservatism, or incremental bad news timeliness. Following Jayaraman (2012), we
estimate the following regression model to test our hypotheses:
CSCOREt= c0+d1PMCt+ d2ROEt+ d3MBRt+ d4GROWTHt + d5RDt+ d6INVCYCLEt
+ d7VOLt+ d8IFRSt+ d9BIG8t+d10SIZEt + d11RETt +d12FOREIGNt+d13GDPt
+ d14EQMKTCAPt+d15GDPGROWTHt + d16INFLATIONt
+d17CREDITRIGHTSt+d18FDIt+Firm/Year Fixed Effects+ξ
(2)
where ROE is return on equity. MBR is a market to book ratio. GROWTH is annual sales
growth, RD is research and development divided by total assets, INVCYLE is depreciation
divided by total assets, and VOL is stock price volatility. IFRS equals one if the
―Accounting Standards‖ field in Global Vantage indicates that the firms has adopted
international standards. BIG8 is an indicator variable to denote the presence of Big Eight
auditors. SIZE is calculated as the natural logarithm of total assets. RET is stock return
which is included to capture firm performance. FOREIGN is foreign income as a
percentage of sales and is included to capture international operations. We also control a
set of country-level factors. GDP is the level of per capita GDP, EQMKTCAP is the ratio
of capital market capitalization to GDP, GDPGROWTH is the growth of per capita GDP,
INFLATION is the annual rate of inflation, CREDITRIGHTS is the time-varying measure
of creditor rights provided by Djankov et al. (2007), and FDI is the amount of foreign
14
direct investment flows. The other variables are the same as those in equation (1). H1
predicts b7 in equation (1) and d1 in equation (2) to be significantly positive.9
To test our main hypothesis (H2) on the role of legal institutions on the association
between product market competition and accounting conservatism, we regress the above two
models (Basu [1997] model and Khan and Watts [2009] model) separately for two
subsamples partitioned by strong and weak legal institutions (proxied by investor protection
(INVPRO), security regulation (SECREG), public enforcement (PUBENF) and tax
compliance (TAXCOM)), and compare their coefficients. 10 INVPRO is the index of investor
protection constructed as the principal component of disclosure, liability standards, and antidirector rights (La Porta et al. 2006). Investor protection is identified as ‗high‘ if INVPRO is
above the sample median, ‗low‘ otherwise. SECREG is the index of enforcement of
securities laws and is measured as the sum of the index of public enforcement of securities
laws and the index of private enforcement of securities laws. Security regulation is identified
as ‗high‘ if SECREG is above the sample median, ‗low‘ otherwise. PUBENF is the index of
public enforcement of securities laws, measured as the arithmetic mean of four underlying
indices: Supervisor Characteristics index, Investigative Powers index, Orders index and
Criminal index. The variable is ranked from 0 (weak public enforcement) to 1 (strong public
enforcement). Public enforcement is identified as ‗high‘ if PUBENF is above the sample
median, ‗low‘ otherwise. TAXCOM measures the time to prepare, file and pay (or withhold)
9
Following Jayaraman (2012), we include MBR and SIZE in equation (2) as addition variables to control
for growth opportunities and voluntary IFRS adoption in an international setting although MB and SIZE are
included in the original model to estimate CSCORE. Our results are robust to excluding control variables
(including MBR and SIZE), as shown in Table 4, columns (1) and (2).
10
Furthermore, we examine the roles of underlying channels through which strong legal institutions affect
the relation between competition and conservatism in Section 5.3. Based on DeFond et al. (2007) and Haw
et al. (2012), we employ insider trading law enforcement, earnings quality, and disclosure as potential
channels.
15
three major types of taxes: the corporate income tax, value added or sales tax, and labor
taxes, including payroll taxes and social security contributions. Tax compliance is identified
as ‗high‘ if TAXCOM is below the sample median, ‗low‘ otherwise. Our second hypothesis
predicts b7 in equation (1) and d1 in equation (2) to be more pronounced in countries with
strong legal institutions than those with weak legal institutions.
4.
Sample Selection and Descriptive Statistics
4.1 Sample Selection
Our sample is taken from Global Vantage database for the listed companies from 38
countries around the world: thirteen in Asia, sixteen in Western Europe, six in North and
South America, and three in Oceania and Africa.11 The sample period spans from 1999 to
2007 because of the data availability to calculate HHI. Accounting income and other
financial data are from the Global Vantage Industrial/ Commercial (IC) files. Stock price
data is drawn from the Global Vantage Issues files. We exclude firm-year observations
without fully consolidated financial statements and those with missing values to compute
dependent and independent variables. We keep only those observations in countries with
legal institution measures for the 49 countries surveyed in La Porta et al. (1998) and La
Porta et al. (2006). We then delete observations in regulated industries, including
financial institutions (SIC 6000-6999) and government-owned companies (SIC 90009999). To mitigate the influence of outliers, we winsorize each firm-level variable at the
11
The 13 Asian countries include Hong Kong, India, Indonesia, Israel, Japan, Korea, the Philippines,
Malaysia, Pakistan, Singapore, Taiwan, Thailand, and Turkey. The 16 European countries are Austria,
Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, the Netherlands, Norway, Portugal,
Spain, Sweden, Switzerland, and the United Kingdom. We also include Argentina, Brazil, Canada, Chile,
Mexico, and United States from North and South America and Australia, New Zealand, and South Africa
from Oceania and Africa.
16
1st and 99th percentile values and delete observations with the absolute value of
studentized residuals greater than three. The final sample consists of 84,835 firm-year
observations as shown in Table 1.
[Insert Table 1 around here]
4.2 Descriptive Statistics
Table 2 summarizes the descriptive statistics by country, industry, and firm and
provides the correlation matrix among the variables used in the regressions. Panel A
reports the mean values of each variable for each country sample and for the total sample.
The median and standard deviation of each variable are also reported for the total sample.
As shown in the second column, the size of the country samples ranges from 106 firmyears for Argentina to 21,046 firm-years for the United States. Accounting earnings (NI)
have positive mean values except for those of Australia (-5.9%), Canada (-1.8%),
Germany (-0.7%), Sweden (-2%), UK (-0.2%), and the U.S. (-0.6%). Product market
competition (PMC) shows considerable variation across countries. Japan has the highest
average level of product market competition (PMC = -0.134), and Turkey has the lowest
(PMC = -0.872). Leverage (LEV) and market-to-book ratio (MBR) also vary significantly
across countries.
Panel B shows the descriptive statistics of the firm-level variables. Consistent with
Bushman and Piotroski (2006), accounting earnings (NI) are negatively skewed and stock
returns (RET) are positively skewed. Moreover, stock returns display greater volatility
than accounting income, suggesting that managers tend to smooth earnings. The mean
and median of product market competition (PMC) is -0.417 and -0.326, respectively. This
indicates that the extent of market competition in our international setting is lower than
17
that in the U.S. The mean (median) of LEV is 0.942 (0.363). The mean (median) value of
CSCORE is -0.004 (0.060), which is close to those reported by Jayraman (2012).
Consistent with Jayraman (2012), the 25th of CSCORE is negative.
Panel C reports descriptive statistics for the country-level variables. Consistent with
Bushman and Piotroski (2006), these statistics are tabulated using only one observation
for every country and reveal significant cross-country variation in institutional features.
Panel D reports a correlation matrix for the country-level variables. Pearson (Spearman)
correlations are presented above (below) the diagonal, and the correlation coefficients in
bold are significant at the 5-percent level. Security regulation (SECREG), public
enforcement (PUBLENF), investor protection (INVPRO), and tax compliance (TAXCOM)
are positively correlated with each other.
Panel E of Table 2 reports the correlation matrix among the industry- and firm-level
regression variables. Consistent with prior literature, accounting earnings (NI) are
positively correlated with stock returns (RET). Product market competition (PMC) is
negatively related to NI, suggesting that product market competition has a negative effect
on firms‘ profitability. In addition, product market competition (PMC) is negatively
associated with firm size (SIZE) and leverage (LEV), indicating that product market
competition shrinks the scale of firms and keeps them from using financial leverage.
CSORE is negatively correlated with NI and SIZE and positively with LEV, as predicted.
Its positive correlation with market to book ratio (MBR) is inconsistent. However, these
results should be interpreted with caution, as the pairwise correlations may suffer from
correlated omitted variables, which are controlled for in the regression analyses.
[Insert Table 2 around here]
18
5.
Empirical Results
Our multivariate tests are estimated using ordinary least squares (OLS). In all the
regressions, we report robust t-statistics using firm clustered standard errors (Petersen
2009).
5.1 Regression Results on How Product Market Competition Affects Accounting
Conservatism
Table 3 summarizes the basic regression results that test the impact of product
market competition on accounting conservatism (H1). It reports the coefficients and
significance levels for the full sample (84,835 firms-years), with column (1) showing the
measure of product market competition only and column (2) showing both the measures
of product market competition and firm-level control variables (firm size, leverage,
market-to-book ratio, and litigation risk) identified in prior studies. We report the
regression results in Panel A based on Basu (1997) model and in Panel B on Kahn and
Watts (2009) CSCORE model, respectively.
As shown in Panel A of Table 3, the coefficients on D*RET are significantly
positive in both columns (1) and (2), indicating, on average, the existence of accounting
conservatism in our cross-country sample. More importantly, the coefficients for
PMC*D*RET are significantly positive (0.043, with p<0.01 in column (1); 0.031, with
p<0.05 in column (2)), thus indicating that product market competition is positively
associated with the incremental timeliness of bad news recognition, our key measure of
accounting conservatism. The result is consistent with H1. 12 The coefficients for
12
To eliminate a potentially spurious relationship between product market competition and conservatism
19
PMC*RET are insignificant (-0.004 in column (1) and -0.003 in column (2)) at
conventional levels. We further find that the coefficient on LEV*D*RET is significantly
positive, while the coefficient on SIZE*D*RET is significantly negative. These results are
consistent with findings of prior studies, strengthening the validity of our empirical
analysis. Overall, the empirical results in Panel A of Table 3 show that, on average, the
association between product market competition and accounting conservatism is positive
across countries, which is consistent with Dhaliwal et al. (2014)‘s findings for U.S. firms.
In Panel B of Table 3, following Kahn and Watts (2009), we estimate the firm-year
measure of accounting conservatism, CSCORE, which serves as the alternative dependent
variable to estimate the effects of product market competition on accounting
conservatism. Product market competition (PMC) is positively associated with CSCORE
(a coefficient of 0.131, with p<0.01; 0.149, with p<0.01) in both columns (1) and (2) with
or without control variables. While the inclusion of firm-and country-level control
variables does not affect our main variable of interest (PMC), the signs of most control
variables are consistent with those of Jayaraman (2012). Our robust results based on Basu
(1997) and Kahn and Watts (2009) in Panels A and B of Table 3 further strengthen the
positive role of competition on conditional conservatism.
[Insert Table 3 around here]
5.2. Regression Results on Legal Institutions, Product Market Competition, and
Accounting Conservatism
as documented in Patatoukas and Thomas (2011), we redo the main test of Table 3 by controlling volatility
of performance and liquidity risks and the results are consistent with those reported here.
20
In this section, we present the results of the tests of our main hypothesis (H2) that
explores the role of country-level legal institutions on the association between product
market competition and accounting conservatism. We compare the effects of competition
across countries based on four proxies for legal institutions (investor protection, security
regulation, public enforcement and tax compliance) and summarize the regression results
in Panel A of Table 4 based on Basu (1997) model and in Panel B of Table 4 on Kahn and
Watts (2009) CSCORE model.
As shown in columns (1) and (2) of Panel A, the coefficient on PMC*D*RET is
significantly positive (0.116, with p<0.01) in high investor protection countries, whereas
insignificant (-0.048) in low investor protection countries, suggesting the joint impact of
product market competition and strong investor protection on conservatism.13 We also
find significantly positive coefficients for the interaction terms in countries with strong
security regulation, public enforcement, and tax compliance as a proxy of the litigationrelated, contracting, and taxation-related demands for conservatism (a coefficient of 0.124,
with p<0.01; 0.133, with p<0.01; 0.069, with p<0.05 in columns (3), (5), and (7),
respectively). However, such relationship does not exist in countries with weak legal
institutions. Taken together, our results imply that the effect of product market
competition on conditional conservatism depends on the strength of the country-level
disciplinary mechanism.
Additionally, the coefficients for PMC*RET are significantly negative (-0.042, with
p<0.01; -0.042, with p<0.01; -0.046, with p<0.01; -0.028, with p<0.10) in countries
13
To eliminate a potentially spurious relationship between product market competition and conservatism
as documented in Patatoukas and Thomas (2011), we redo the main test of Table 4 by controlling volatility
of performance and liquidity risks and the results are consistent with those reported here.
21
with strong legal institutions (strong investor protection, security regulation, public
enforcement and tax compliance) in columns (1), (3), (5), and (7), respectively.
However, none of the coefficients for PMC*RET is insignificant in countries with weak
legal institutions (columns (2), (4), (6), and (8)). Overall, the results suggest that product
market competition facilitates timely recognition of bad news as losses but delays
timely recognition of good news as gains in countries with strong legal institutions. We
report p-values for the differences of the coefficients on PMC*D*RET between strong
and weak legal regimes at the bottom of Panel A of Table 4, which indicate significantly
different coefficients for each of the four proxies of legal institutions (mostly at the 1%
level). Overall, our results highlight the importance of country-level legal institutions in
strengthening the association between product market competition and accounting
conservatism.14, 15
In Panel B of Table 4, we use the CSCORE measure of Khan and Watts (2009) as the
dependent variable for accounting conservatism. After controlling for firm-and countrylevel characteristics, the coefficients on PMC are positive and significant at the 1% level
in countries with strong legal regimes but insignificant in countries with weak legal
14
In this study, we focus on the conditional conservatism models because, consistent with Watts‘s (2003)
assertion, several influential studies (e.g., Qiang 2007, Ball et al. 2008) provide cross-country evidence
suggesting that conditional conservatism is significantly related to debt market demand and unconditional
conservatism is not. Unconditional conservatism recognizes revenues later and expenses sooner, resulting
in a lower value of assets and a higher value of liabilities.
15
Among control variables, our results in Table 4 indicate that the impact of litigation on conservatism
(LIT*D*RET) is statistically significant and induces conservative reporting, especially in countries with
strong legal institutions. Similar patterns exist in countries with weaker legal institutions although the
coefficients of the former are generally greater than those of the latter. The results shed light on the
importance of litigation risk regardless of country-level legal institutions probably due to potentially high
litigation costs if firms are litigated.
22
regimes. The additional results based on CSCORE are consistent with those reported in
Panel A of Table 4 and strengthen the validity of our analysis.
[Insert Table 4 around here]
5.3 Underlying Channels and Alternative Country-level Proxies
In this section, we examine the underlying channels through which a country‘s legal
institutions affect the association between product market competition and accounting
conservatism. We draw on the corporate governance literature to identify those
underlying channels that influence a country‘s financial reporting environments
(Bushman et al. 2004, DeFond et al. 2007, Haw et al. 2012). Following DeFond et al.
(2007) and Haw et al. (2012), we identify insider trading law enforcement, earnings
quality, and financial disclosure as the underlying channels and perform additional
analyses to explore the role of each channel in determining the relationship of product
market competition and conservatism.
5.3.1 Insider trading law enforcement
Prior studies argue that the enforcement of insider trading laws increases the level of
securities law enforcement and the demand for higher quality financial reports (e.g.,
Bhattacharya and Daouk 2002, Bushman and Piotroski 2006). Beny (2005) documents
that common law countries have stricter insider trading laws that are more likely to be
enforced. Since common law legal origin is a characteristic related to strong investor
protection institutions (La Porta et al. 1998), Beny‘s finding indicates that stronger
insider trading law enforcement is one of the institutional factors related to the reporting
environments of countries with strong investor protection.
23
When managers plan to engage in insider trading activities, they are likely to be less
timely in their disclosure of negative news so that they could benefit more from the
insider trading (Billings 2008). Consistent with this argument, Jayaraman (2012) find a
positive relation between insider trading enforcement and timely loss recognition in a
cross-country setting. Therefore, the extent of insider trading law enforcement in a
country could affect a firm‘s strategic reporting decision that is related to its competitive
position in the market. It suggests that insider trading law enforcement is one of the
underlying channels through which a country‘s legal institutions affect the association
between product market competition and accounting conservatism. We measure insider
trading enforcement (ITL) based on Bhattacharya and Daouk (2002), which is an
indicator variable that equals 1 in the years after the first legal case is brought against
insider trading in a country, and is zero otherwise.
5.3.2 Earnings quality
We expect earnings in countries with less earnings management to be of higher
quality in the sense that earnings of higher quality are less likely to distort firms‘
underlying economic performance. Existing literature indicates that a country‘s legal and
political institutions play a pivotal role in shaping managers‘ reporting decisions.
Specifically, strong investor protection institutions constrain insiders‘ incentives to
manipulate earnings to conceal their private benefits extraction (Burgstahler, Hail, and
Leuz 2006; Haw et al. 2004; Leuz, Nanda, and Wysocki 2003). Another line of research
finds that intense product market competition constrains managerial opportunism and
curtails the extent of private benefits of control (e.g., Guadalupe and Perez-Gonzales
2006), which affects the demand for accounting conservatism. Therefore, we predict that
24
earnings quality could affect governance mechanism of product market competition and
serves as one of the underlying channels through which a country‘s legal institutions
affect the relation between competition and accounting conservatism. We measure a
country-level earnings quality (EQ), which is a dummy variable based on the aggregate
earnings management score from Leuz et al. (2003) multiplied by -1 (so that higher
scores indicate higher earnings quality). 16 EQ is equal to 1 if earnings quality in one
country is above the sample country median, and 0 otherwise.
5.3.3 Financial disclosure
Prior studies show that firms in countries with strong investor protection have
greater corporate disclosure, higher transparency, and more timely financial reporting
(Bushman et al. 2004; Hope 2003). Another stream of research reveals that more intense
competition encourages the disclosure of firm-specific information and hence reduces
managers‘ ability to conceal bad news (Ali, Klasa, and Yeung 2010; Clarke 1983; Gal-Or
1985). They argue that firms in concentrated industries are more reluctant to disclose due
to propriety cost reasons and have more opaque information environments because these
firms have interdependent investment strategies and worry that their disclosed
information could be used by their rivals. Thus, we expect that a country‘s disclosure
regime influences corporate information flow and a firm‘s reporting strategy that affects
its competitive position, and thus serves as one of the underlying channels through which
16
The score, based on data over the 1990 to 1999 period, equals the average rank of two earningssmoothing measures and two earnings-discretion measures and has been widely used in prior studies (e.g.,
DeFond et al. 2007, Haw et al. 2012).
25
a country‘s legal institutions affect the relation between competition and accounting
conservatism.17
Following Bushman et al. (2004) and DeFond et al. (2007), we measure a country‘s
financial disclosure environments based on interim reporting frequency and financial
disclosure. Interim reporting frequency (FREQ) is a dummy variable measured based on
the index that measures the frequency of a country‘s financial reporting, with a score of 4
for quarterly reporting, 2 for semi-annual reporting, and so forth (Center for International
Financial Analysis Research, 1995). FREQ is equal to 1 if the reporting frequency index
in one country is above the sample country median, and 0 otherwise. Financial disclosure
(DISCL) is a dummy variable based on the accounting disclosure index constructed by
Center for International Financial Analysis Research (1995).18 DISCL is equal to 1 if the
accounting disclosure index in one country is above the sample country median, and 0
otherwise.
5.3.4 Results on the underlying channels
We re-estimate equations (1) and (2) for each subgroup partitioned by a dummy
variable for each channel. As shown in Panel A of Table 5, PMC*D*RET is positively
associated with asymmetric timeliness of economic loss recognition at the 1% level, only
when a country has stringent enforcement of insider trading laws, higher earnings quality,
17
Prior studies document a positive relation of strong country-level institutions such as strong investor
protection with accounting conservatism and greater disclosure and transparency (e.g., Bushman and
Piotroski 2006; Ball et al. 2003). However, we want to be cautious about our argument because the greater
corporate disclosure available in strong investor protection environments is related to an increased
propensity to provide more news in general, not necessarily to an increased propensity to be conditionally
conservative (see also footnote 5).
18
It is constructed by determining the mean percentage of items, from a pre-specified list of 85 accounting
items, which are included in a sample of fiscal year 1993 annual reports of domestic companies (Center for
International Financial Analysis Research, 1995).
26
more frequent interim reporting, and greater financial disclosure (columns (1), (3), (5),
(7)). However, the association is not found in countries with weak financial reporting
environments. These results suggest that a country‘s well-established reporting
environments are necessary conditions to ensure the association between product market
competition and accounting conservatism. Furthermore, it‘s interesting to find that the
interaction coefficients with firm size (SIZE) and leverage (LEV) with conditional
conservatism remain significant irrespective of how stringent those accounting
environments are. It suggests that the variation in the strength of legal institutions seems
to have a limited impact on the role of firm size and leverage on conditional conservatism.
Panel B of Table 5 reports the regression results with CSCORE as the dependent variable,
which are consistent with those of Panel A. Taken together, Table 5 indicates that the
competition-conservatism relationship is strengthened in countries with higher earnings
quality, greater and more frequent disclosure practices, and stringent enforcement of
insider trading regulations, all of which are associated with strong investor protection
institutions.
[Insert Table 5 around here]
Prior cross-country studies show that conditional conservatism is significantly related
to debt market demand (e.g., Ball et al. 2008b, Qiang 2007) and a country‘s economic
development affects its financial reporting environments (Ball et al. 2000, Leuz et al.
2003). Thus, we re-estimate the regression models on samples partitioned by the level of
economic development (DEVELOP) and the debt versus equity market influence (DEBT).
DEVELOP is a dummy variable that is equal to 1 if the logarithmic of per capita GDP in
one country is above the sample country median, and 0 otherwise (La Porta et al. 2006).
27
DEBT is an indicator variable that is equal to 1 if a ratio of the sum of bank debt of the
private sector and outstanding non-financial bonds to GNP in a country is above the
sample country median, and 0 otherwise (Ball et al. 2008b).
We present the results in Table 6 separately for each subgroup. As shown in Panel A
of Table 6, PMC*D*RET is positively associated with asymmetric timeliness of economic
loss recognition only in countries with greater economic development and higher debt
market demand (a coefficient of 0.039, with p<0.10; 0.043, with p<0.05 in columns (1)
and (3), respectively), suggesting that the competition-conservatism relation is more
pronounced in developed countries and bank-centered economies. The results become
stronger when CSCORE is used as the dependent variable (both positively significant at
the 1% level in columns (1) and (3) of Panel B). Taken together, the results suggest that
the level of economic development and debt market demands play a significant role in
explaining cross-country variation in the competition-conservatism association.
[Insert Table 6 around here]
6.
Robustness Checks
6.1 Re-estimation Using Ball & Shivakumar (2006) Model
An important concern is that the Basu‘s (1997) model we used for our main empirical
tests may be affected by the different extent of market efficiency around the world. To
assuage this concern, we employ Ball and Shivakumar‘ model (2006, 2005) to examine the
asymmetric timeliness of earnings without reference to security prices and estimate the
following model:
ACCRUALSt=e0+f1DCFOt+f2CFOt+f3DCFOt*CFOt
28
+f4PMCt+f5PMCt*DCFOt+f6PMCt*CFOt+f7PMCt*DCFOt*CFOt
+ f8FASSETt+f9FASSETt*DCFOt+f10FASSETt*CFOt+f11FASSETt* DCFOt*CFOt
+ f12ΔSALESt+f13ΔSALESt*DCFOt+f14ΔSALESt*CFOt+f15ΔSALESt* DCFOt*CFOt
+Firm/Year Fixed Effects+ξ
(3)
where ACCRUALSt is current period accruals, CFOt is current period operating cash
flows, and DCFOt is an indicator variable equal to one if CFOt is negative, zero
otherwise. Since Hribar and Collins (2002) argue that current (working capital) accruals
are biased when estimated from changes in the balance sheet data, we use the CFO data
directly from cash flow statement.
Table 7 re-examine our hypotheses based on equation (3). Our interest is in the
variable PMC*DCFO*CFO. Column (1) of Table 7 shows a significantly positive
association between product market competition and conservatism for the full sample.
More importantly, in the subsample analysis with four proxies for legal institutions, the
coefficients on PMC*DCFO*CFO are significantly positive in countries with stronger
institutions for investor protection, security regulations, public enforcement and tax
compliance (columns (2), (4), (6) and (8)). However, none of the coefficients on
PMC*DCFO*CFO is significant in countries with weak legal institutions (columns (3),
(5), (7) and (9)). These results based on Ball and Shivakumar‘s model (2006) corroborate
the findings in our main analyses and highlight the importance of product market
competition in determining the quality of accounting earnings across countries, and the
importance of sound legal institutions as country-level governance mechanisms in
ensuring product market competition functions well.
[Insert Table 7 around here]
29
6.2 Alternative Measures of Product Market Competition
Recent studies on industry competition suggest that competition encompasses
several dimensions, such as product substitutability, market size, and entry costs, given
the level of concentration (Raith 2003, Karuna 2007).19 Following Karuna (2007), we add
into our equation (1) three alternative dimensions of competition as alternative measures
of product market competition – product substitutability (DIFF), market size (MKTSIZE),
and entry cost (ENTCOST) – which are described in Appendix A. Table 8 shows that the
coefficients on MKTSIZE*D*RET and PMC*D*RET are significantly positive, while
those on ENTCOST*D*RET are significantly negative, as predicted in countries with
strong legal institutions (columns (2), (4), (6) and (8)). However, the coefficients on
DIFF*D*RET are insignificant. The results in Table 8 are generally consistent with our
main results.
[Insert Table 8 around here]
6.3 Re-estimation Using Three-Year Basu’s (1997) Specification
Roychowdhury and Watts (2007) argue that the beginning composition of equity
value affects asymmetric timeliness measured over short horizons. Specifically, past
timeliness of earnings with respect to returns influences future earnings timeliness over
short periods, which might affect the results of Basu‘s (1997) model. To mitigate the
19
They argue that concentration by itself may be a poor proxy for competition, as the relation between
concentration and competition is not clear.
30
concern that one-year Baus‘s (1997) model might lead to biased results, we re-estimate
our regression model using earnings and return over three-year periods as follows:
NIt-3,t=g0+h1Dt-3,t+h2RETt-3,t+h3Dt-3,t*RETt-3,t
+h4PMCt +h5PMCt *Dt-3,t+h6PMCt *RETt-3,t+h7PMCt *Dt-3,t*RETt-3,t
+Control Variables +Firm/Year Fixed Effects+ξ
(4)
where NI is equal to the sum of net income before extraordinary items over the estimation
period divided by beginning of estimation period market value of equity. RET is equal to
the market-adjusted buy-and-hold return over the estimation period. D is equal to one if
RET is negative, zero otherwise. The (untabulated) results of replicating Tables 3 and 4
using three-year asymmetric timeliness measures are similar to those of the main tables.
6.4 Other Robustness Check
We conduct a number of other robustness checks. First, in addition to HHI using 3digit SIC codes used for the main tests, we re-calculate HHI using 4-digit SIC code, 5digit NAICS code, and 6-digit NAICS code. Moreover, we adopt two typical industry
measures, a four- and an eight-firm concentration ratio, as alternative industry
concentration measures. Empirical tests using these alternative industry measures
produce consistent results. Second, to accommodate the potential nonlinear relation, we
transform HHI into a fractional rank variable, and re-estimate all the regressions. The
results remain qualitatively unchanged. Third, since the sample size varies across
countries, we apply weighted least squares (WLS) procedures, placing an equal weight on
each country sample. The (untabulated) results are similar to those reported in main
tables.
31
7.
Conclusions
The product market competition has been regarded as an important industry-level
characteristic. The purpose of this study is to examine how product market competition
and country-level legal institutions interplay in shaping conditional conservatism around
the world. Using 84,835 firm-year observations from 38 economies for the 1999 to 2007
period, our results show that, on average, product market competition is positively
associated with timely recognition of bad news as losses across countries. However, the
positive relationship is found only in countries with strong legal institutions (proxied by
investor protection, security regulation, public enforcement and tax compliance) and is
strengthened in countries with high quality financial reporting environments (represented
by higher earnings quality, more frequent and greater disclosure practices, and more
stringent enforcement of insider trading regulations), which are associated with strong
investor protection institutions. Taken together, our findings suggest that highly
competitive industry environments and strong legal institutions of a country jointly drive
accounting conservatism. Our results indicate that sound country-level legal institutions
are necessary conditions in ensuring product market competition functions effectively.
Our results are robust to a series of sensitivity tests, such as alternative models to measure
conservatism and different measures of product market competition.
Our investigation extends the literature that examines the impact of product market
competition on conservative accounting in the U.S. (Dhaliwal et al. 2014) to a crosscountry setting, where considerable variation exists in investor protection and financial
reporting environments. This study highlights the differential role of product market
32
competition in shaping the quality of financial reporting around the world and the
importance of strong legal institutions and high quality financial reporting environments
in a country to ensure the effectiveness of industry competition. This study also extends
the Basu (1997) and Khan and Watts (2009) measures of accounting conservatism to a
cross-country setting and enhances the validity of these models.
33
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37
Appendix A Variable Definitions
Variable
Definition
Country Variables
INVPROt
Index of investor protection, constructed as the principal component of
disclosure, liability standards, and anti-director rights. Scale is from 0 to
10. This data is available from La Porta et al. (2006).
SECREGt
Security regulation in Hail and Leuz (2006) that is the arithmetic mean of
the three La Porta et al. (2006) indices: disclosure requirement, liability
standard, and public enforcement indices.
PUBENFt
Index of public enforcement of securities laws, measured as the arithmetic
mean of four underlying indices: Supervisor Characteristics index,
investigative Powers index, Orders index and Criminal index. The variable
is ranked between 0 (weak public enforcement to 1 (strong public
enforcement). This data is available from La Porta et al. (2006).
TAXCOMt
This index measures the time to prepare, file and pay (or withhold) three
major types of taxes: the corporate income tax, value added or sales tax,
and labor taxes, including payroll taxes and social security contributions.
Tax compliance is identified as ‗high‘ if TAXCOM is below the sample
country median, ‗low‘ otherwise. This data is from World Bank‘s Ease of
Doing Business Index in Djankov et al. (2008).
GDPt
GDP per capita (constant 2000 US$). This data is from World
Development Indicators database.
EQMKTCAPt Market capitalization of listed companies divided by GDP. This data is
from World Development Indicators database.
GDPGROWTHt Annual percentage change of GDP per capita. This data is from World
Development Indicators database of the World Bank.
INFLATIONt Annual inflation rate. This data is from World Development Indicators
database.
CREDITRIGHTSt The time-varying measure of creditor rights. This data is from Djankov et
al. (2007).
FDIt
Net inflows of foreign direct investment divided by GDP. This data is from
World Development Indicators database of the World Bank.
Industry Variables
HHIt
Herfindahl-Hirschman index is the sum of the squared market shares of
the firms competing in each industry-country sample. Industry
membership is classified by the three-digit SIC code. This data is obtained
from Bureau van Dijk (BvD) Orbis.
PMCt
Index of product market competition, which is calculated as minus one
multiplied by the HHIt.
DIFFt
DIFF is equal to the sales/operating costs for each industrial segment:
operating costs include the cost of goods sold; selling, general, and
administrative expenses; and depreciation, depletion, and amortization.
38
MKTSIZEt
ENTCOSTt
CONC4t
CONC8t
Industry segment is classified by the three-digit SIC code. DIFF measures
the extent of product substitutability in the industry.
Natural logarithm of industry sales (industry sales is computed as the sum
of segment sales for firms operating in the industry). Industry segment is
classified by the three-digit SIC code.
Natural logarithm of the weighted average of the gross value of the cost of
property, plants, and equipment for firms in an industry, weighted by each
firm‘s market share in the industry. Industry membership is classified by
the three-digit SIC code.
Proportion of sales in the industry accounted for by the four largest firms
(by sales) in the industry (industry sales are computed as in MKTSIZE
above).
Proportion of sales in the industry accounted for by the eight largest firms
(by sales) in the industry (industry sales are computed as in MKTSIZE
above).
Firm Variables
RETt
Holding period market-adjusted return, including dividends, over the
firm‘s fiscal accounting year. This data is draw from Standard and Poor‘s
Global Vantage Issues files.
MVEt
Market value of equity at the end of a given fiscal year, defined as number
of shares outstanding times closing price available for the last month of
the fiscal year. This data is gathered from Standard and Poor‘s Global
Vantage Issues files.
NIt
Net income before extraordinary items (IC data 32), deflated by beginning
of period prices (MVEt-1). This data is draw from Standard and Poor‘s
Global Vantage Industrial /Commercial files.
Dt
An indicator variable equal to one if RET is less than zero; zero otherwise.
CFOt
Operating cash flow, deflated by beginning of period prices (MVEt-1). This
data is draw from Standard and Poor‘s Global Vantage Industrial
/Commercial files.
ACCRUALSt Total accruals, deflated by the average total assets, defined as Net income
before extraordinary items minus cash flow from operating activities,
scaled by the average total assets. This data is draw from Standard and
Poor‘s Global Vantage Industrial /Commercial files.
NCFOt
An indicator variable equals to one if CFOt is less than zero; zero
otherwise.
LEVt
Leverage is the total debt deflated by the average total assets.
SIZE
Firm size is the natural logarithm of the total assets (in millions of U.S.
dollars) at the end of fiscal year t.
MBRt
Market-to-book ratio is the market value of equity divided by the book
value of equity.
ROEt
Return on equity is computed as net income divided by market value of
equity.
GROWTHt
Annual percentage change in total sales.
39
RDt
FOREIGNt
Research and development expenses divided by sales. Missing value of
R&D are set to 0.
Annual depreciation divided by total assets.
Annual standard deviation of monthly stock prices.
Dummy variable based on the ‗‗Accounting Standards‘‘ field (data item
astd) that takes 1 for codes ‗‗DA‘‘, ‗‗DI‘‘, ‗‗DT‘‘, ‗‗DU‘‘, ‗‗MU‘‘ or
‗‗US‘‘.
Dummy variable that denotes whether the firm is audited by a Big 8
auditor.
Foreign income scaled by total sales.
FASSET
ΔSALESt
Book value of fixed assets scaled by the average total assets.
Change in sales scaled by the average total assets.
INVCYCLEt
VOLt
IFRSt
BIG8t
______________________________________________________________________________
40
Appendix B Construction of CSCORE (Khan and Watts 2009)
In this section, we briefly explain the details of the construction of CSCORE in our
study. Following Khan and Watts (2009), we calculate CSCORE by using Basu (1997)
measure of asymmetric timeliness to estimate a firm-year conservatism measurement. Similar
to Khan and Watts (2009), we specify the following Basu (1997) model and estimate it
individually for each country:
NIt=a0+b1Dt+b2RETt+b3Dt*RETt+ ξ
(a)
Where NI is net income before extraordinary items, divided by beginning of period
prices (MVEt-1). D is a dummy variable that equals one if RET is less than zero, and zero
otherwise. RET is holding period market-adjusted return, including dividends, over the firm‘s
fiscal accounting year.
Khan and Watts (2009) assume that both the timeliness of good news (GSCORE) each
year and the incremental timeliness of bad news (CSCORE) each year are linear functions of
firm-specific characteristics each year. In other words,
GSCOREt=b3=u1+u2SIZEt+u3MBt+u4LEVt+ξ
(b)
CSCOREt=b4=v1+v2SIZEt+v3MBt+v4LEVt+ξ
(c)
CSCORE is the firm-year measure of conservatism, or incremental bad news timeliness.
Although we run regressions by country, both CSCORE and GSCORE are different across
firms and over time within each country through cross-sectional variation in the firm-year
characteristics (i.e., size, market-to-book ratio and leverage). Following Jayaraman (2012),
we retain countries with at least 20 observations to estimate Equation (a). We run the
following regression model to estimate CSCORE:
NI=a0+b1D+RET*(u1+u2SIZE+u3MB+u4LEV)
+ Dt*RETt(v1+v2SIZE+v3MB+v4LEV)
41
+(w1SIZE+w2MB+w3LEV+w4SIZE*D+w5MB*D+w6LEV*D)+ξ
(d)
Then we use the coefficients estimated from regressing equation c (i.e., v1, v2 and v3) to
calculate CSCORE for every firm-year observation.
42
TABLE 1 Sample Selection
Sample-Selection Process
Initial sample from 1999 to 2009 in the Global Vantage
database for the 38 economies in Bushman and Piotroski
(2006)
Obs.
Removed
Obs. Remaining
128,695
After eliminating firms with nonfully consolidated
financial report
(7,125)
121,570
After eliminating firms with missing values of
dependent and independent variables
(33,389)
88,181
After eliminating financial institutions (SIC 6000-6999)
and government-owned companies (SIC 9000-9999)
(1,083)
87,098
After excluding
residuals|>3
84,835
observations
with
|studentized
(2,263)
Notes: This table presents the sample selection process and data requirements for the regressions. The final
sample for these regressions consists of listed companies from 38 economies: thirteen in Asia (Hong Kong,
India, Indonesia, Israel, Japan, Korea, Malaysia, Pakistan, the Philippines, Singapore, Taiwan, Thailand, and
Turkey), sixteen in Western Europe (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland,
Italy, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, and the United Kingdom), six in North
and South America (Argentina, Brazil, Canada, Chile, Mexico, and the U.S.), and three in Oceania and Africa
(Australia, New Zealand, and South Africa from Oceania and Africa).
43
Table 2 Summary statistics and correlations
Panel A: Country-level descriptive statistics (mean)
Country
Obs
NI
RET
PMC
Argentina
106
0.057
-0.065
-0.669
Australia
3,923
-0.059
-0.030
-0.326
Austria
335
0.043
0.001
-0.550
Belgium
538
0.042
0.006
-0.283
Brazil
728
0.045
-0.123
-0.459
Canada
1,774
-0.018
-0.002
-0.430
Chile
447
0.048
-0.002
-0.504
Denmark
515
0.021
-0.046
-0.723
Finland
683
0.030
0.002
-0.331
France
3,288
0.019
-0.042
-0.148
Germany
3,560
-0.007
-0.007
-0.263
Greece
408
0.057
0.041
-0.350
Hong Kong
569
0.034
-0.047
-0.720
India
812
0.112
-0.073
-0.256
Indonesia
782
0.071
-0.061
-0.626
Ireland
182
0.023
-0.060
-0.808
Israel
263
0.033
0.003
-0.571
Italy
1,236
0.002
0.008
-0.183
Japan
17,776
0.023
0.001
-0.134
Korea
1,240
0.104
0.017
-0.213
Malaysia
3,586
0.025
-0.014
-0.352
Mexico
278
0.076
-0.007
-0.620
Netherlands
979
0.030
-0.024
-0.392
New Zealand
276
0.045
0.035
-0.842
Norway
556
0.017
0.025
-0.643
Pakistan
180
0.155
-0.080
-0.564
Philippines
426
0.007
-0.147
-0.668
Portugal
142
0.033
-0.023
-0.281
Singapore
2,265
0.040
0.001
-0.313
South Africa
570
0.091
-0.067
-0.595
Spain
735
0.054
0.000
-0.189
Sweden
1,425
-0.020
-0.026
-0.226
Switzerland
1,057
0.038
0.028
-0.525
Taiwan
3,439
0.039
0.012
-0.264
Thailand
1,659
0.083
-0.005
-0.278
Turkey
116
0.094
-0.027
-0.872
UK
6,935
-0.002
0.001
-0.422
USA
21,046
-0.006
-0.087
-0.200
Total
84,835
44
LEV
1.174
0.280
0.887
0.764
0.415
0.552
0.660
0.639
0.508
0.695
0.701
0.529
0.528
0.862
1.466
0.279
0.873
0.692
1.070
1.265
0.870
0.604
0.491
0.498
0.727
0.644
1.011
1.274
0.566
0.303
0.548
0.320
0.460
0.581
1.082
0.303
0.379
0.606
SIZE
20.347
17.456
19.451
19.594
20.740
19.918
19.917
18.731
19.357
19.223
19.067
20.066
19.694
19.689
18.285
18.645
19.896
20.082
19.828
20.536
18.158
21.200
19.525
18.626
19.120
18.800
18.265
20.216
18.368
19.536
20.547
18.603
19.849
19.147
18.149
20.352
18.844
20.137
MBR
5.289
3.187
1.925
2.508
9.009
2.831
1.895
2.687
2.403
2.589
2.438
3.740
1.625
3.154
2.197
3.076
2.657
2.299
1.561
1.396
1.346
1.850
3.511
2.774
3.028
2.130
1.905
2.253
1.788
3.120
3.123
3.077
3.116
1.712
1.612
2.813
3.112
3.110
Table 2 (continued)
Panel B: Descriptive Statistics of the firm-level variables
Variable
Mean
Median
Std
25th
75th
NI
0.046
0.051
0.137
0.013
0.096
RET
0.155
0.043
0.634
-0.198
0.344
PMC
-0.417
-0.326
0.315
-0.604
-0.153
LEV
0.942
0.363
1.959
0.103
0.952
SIZE
8.422
8.542
3.065
5.803
10.748
MBR
1.781
1.351
1.457
0.805
2.250
LIT
0.175
0.000
0.380
0.000
0.000
CSCORE
-0.004
0.060
1.394
-0.139
0.287
Panel C: Descriptive statistics for country-level variables
Variable n Mean Std.
Min 10th
25th
50th
75th
90th
Max
SECREG 38 1.06 0.43 0.34 0.48 0.75 1.03
1.36
1.71
1.88
PUBLENF 38 0.50 0.26 0.00 0.15 0.29 0.50
0.69
0.88
0.90
INVPRO 38 0.50 0.24 0.00 0.10 0.36 0.48
0.63
0.81
1.00
TAXCOM 22 0.56 0.50 0.00 0.00 0.00 1.00
1.00
1.00
1.00
Panel D: Pearson (above the diagonal) and Spearman rank (below the diagonal) correlations
Variable
SECREG PUBLENF INVPRO TAXCOM
SECREG
1
0.892
0.906
-0.224
PUBLENF
1
0.892
0.747
-0.197
INVPRO
1
0.850
0.733
-0.157
TAXCOM
1
-0.253
-0.204
-0.173
Panel E: Pearson (above the diagonal) and Spearman rank (below the diagonal) correlations
Variable
NI
RET
PMC
LEV
SIZE
MBR
LIT
CSCORE
NI
1
-0.001
0.242
-0.053
-0.064
0.196
0.065
-0.075
RET
1
-0.002
0.001
0.362
-0.131
0.243
-0.007
0.015
PMC
-0.000
1
0.000
-0.089
-0.041
-0.012
0.093
0.032
LEV
0.004
1
-0.100
-0.047
0.234
-0.424
-0.153
0.005
SIZE
1
0.182
0.076
-0.037
0.239
0.100
-0.039
0.008
MBR
-0.011
1
0.006
0.217
-0.418
0.100
0.110
-0.023
LIT
1
0.008
-0.103
-0.040
0.089
-0.153
-0.040
0.111
CSCORE -0.018
0.002
1
0.016
0.029
0.013
-0.111
0.112
Notes: Panel A of this table presents the country-level summary statistics for the research variables. The mean
values of each variable are calculated and reported for each sample country. The last three rows report the crosscountry mean, median, and standard deviation. Panel B of this table presents the mean and median statistics of
the firm-level variables. Panel C of this table reports the descriptive statistics for country-level variables. Panel
D of this table presents correlation matrix of country-level variables. Panel E of this table presents correlation
matrix of firm- and industry-level variables for 84,835 observations over the 1999-2007 period. The correlation
coefficients in bold are significant at the 5-percent level. See Appendix A for variable definitions.
45
Table 3 Evidences on the association between product market competition and accounting
conservatism (test for H1)
Panel A: Basu (1997) Model
Variable
RET
Column (1)
0.033***
(12.705)
0.081***
(10.324)
-0.004
(-0.802)
0.043***
(3.081)
-0.005**
(-2.344)
0.003
(0.778)
0.013***
(2.857)
Column(2)
0.036***
(6.609)
D*RET
0.117***
(7.299)
PMC*RET
-0.003
(-0.629)
PMC*D*RET
0.031**
(2.433)
D
-0.021***
(-3.882)
PMC
0.011**
(2.519)
PMC*D
0.007*
(1.684)
MBR
-0.006***
(-8.351)
MBR*D
0.003***
(3.651)
MBR*RET
-0.003***
(-3.434)
MBR*D*RET
0.003
(0.967)
LEV
-0.009***
(-10.217)
LEV*D
0.002
(1.210)
LEV*RET
-0.003***
(-3.533)
LEV*D*RET
0.038***
(12.110)
SIZE
0.012***
(11.150)
SIZE*D
0.000
(0.931)
SIZE*RET
0.001
(1.432)
SIZE*D*RET
-0.013***
(-8.583)
LIT*D
0.002
(0.582)
LIT*RET
-0.004
(-1.254)
LIT*D*RET
0.003
(0.281)
Constant
0.068***
-0.005
(8.679)
(-0.429)
Firm/Year fixed effects
Yes
Yes
No. of Obs.
84,835
84,835
Adj. R-squared
0.334
0.380
Notes: The dependent variable in the table is NI, which is net income before extraordinary items (#IB), deflated
by beginning of period prices. RET is accumulated market-adjusted stock returns form 9 months before fiscal
year end to three months after fiscal year end. D is a dummy variable which equals one if RET is less than zero,
and zero otherwise. PMC is a measure of product market competition which is calculated as minus one
multiplied by the HHIt. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms
competing in each industry-country sample. Industry membership is classified by the four-digit SIC code.
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level
or better, respectively (two tailed).
46
Panel B: Kahn and Watts (2009) CSCORE Model
Variable
Column (1)
PMC
0.131***
(3.543)
ROE
Column (2)
0.149***
(4.530)
0.006
(0.573)
MBR
-0.002***
(-5.802)
GROWTH
-0.001
(-0.148)
R&D
0.157
(0.998)
INVCYCLE
0.302**
(1.988)
VOL
-0.013
(-0.325)
IFRS
-0.025**
(-2.009)
BIG8
0.035***
(3.935)
SIZE
-0.047***
(-6.404)
RET
0.020***
(4.421)
FOREIGN
-0.000
(-0.919)
GDP
-0.368***
(6.167)
EQMKTCAP
-0.001***
(-12.734)
GDPGROWTH
0.013***
(8.065)
INFLATION
0.008***
(4.198)
CREDITRIGHTS
0.092***
(12.600)
FDI
0.001
(1.241)
Constant
0.126
(1.586)
Firm/Year fixed effects
Yes
Yes
No. of Obs.
84,835
84,835
Adj. R-squared
0.464
0.514
Notes: The dependent variable in the table is CSCORE, which is calculated following Kahn and Watts (2009).
PMC is a measure of product market competition which is calculated as minus one multiplied by the HHI t. HHI
(Herfindahl-Hirschman index) is the sum of the squared market shares of the firms competing in each industrycountry sample. Industry membership is classified by the four-digit SIC code.
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level
or better, respectively (two tailed).
47
Table 4 Evidences on how legal institutions affect the association between product market
competition and accounting conservatism (test for H2)
Panel A:Basu (1997) Model
Variable
RET
D*RET
PMC*RET
PMC*D*RET
D
PMC
PMC*D
MBR
MBR*D
MBR*RET
MBR*D*RET
LEV
LEV*D
LEV*RET
LEV*D*RET
SIZE
SIZE*D
SIZE*RET
SIZE*D*RET
LIT*D
LIT*RET
LIT*D*RET
Constant
Firm/Year FE
No. of Obs.
Adj. R2
Difference of
coefficients on
PMC*D*RET
Investor Protection
(1)
(2)
High
Low
-0.053*** 0.066***
(-2.976)
(4.002)
0.354***
0.125***
(13.468)
(4.172)
-0.042*** 0.006
(-3.855)
(0.503)
0.116***
-0.048
(4.939)
(-1.570)
0.004
0.002
(0.414)
(0.188)
-0.008
-0.032***
(-1.289)
(-4.634)
0.010
-0.005
(1.223)
(-0.522)
-0.067*** -0.053***
(-7.587)
(-7.513)
-0.010
0.021**
(-0.911)
(2.108)
0.022
-0.062***
(1.284)
(-3.924)
-0.251*** 0.015
(-9.545)
(0.469)
-0.078*** -0.051***
(-8.294)
(-6.896)
0.060***
0.013
(4.966)
(1.253)
0.112***
0.073***
(6.462)
(4.560)
0.186***
0.213***
(6.483)
(6.576)
0.075***
0.022***
(10.485)
(2.855)
-0.014
-0.022**
(-1.571)
(-2.120)
0.035***
0.010
(2.993)
(0.721)
-0.282*** -0.334***
(-12.515)
(-10.559)
-0.025*** -0.010*
(-4.955)
(-1.673)
-0.029*** -0.022***
(-4.621)
(-3.016)
0.051***
0.047**
(3.882)
(2.549)
0.087***
0.043***
(9.092)
(5.841)
Yes
Yes
52,449
32,386
0.232
0.206
(1)-(2)
p=0.008***
Security Regulation
(3)
(4)
High
Low
-0.055***
0.066***
(-3.116)
(3.886)
0.353***
0.143***
(13.645)
(4.737)
-0.042***
0.008
(-3.810)
(0.626)
0.124***
-0.059
(5.302)
(-1.413)
0.001
0.009
(0.105)
(1.006)
-0.004
-0.032***
(-0.619)
(-4.665)
0.013
-0.011
(1.499)
(-1.193)
-0.071***
-0.048***
(-8.089)
(-6.985)
-0.007
0.014
(-0.619)
(1.373)
0.024
-0.064***
(1.424)
(-3.948)
-0.249***
0.000
(-9.585)
(0.013)
-0.079***
-0.045***
(-8.371)
(-6.097)
0.060***
0.008
(4.932)
(0.797)
0.124***
0.056***
(7.506)
(3.372)
0.178***
0.215***
(6.329)
(6.569)
0.066***
0.040***
(9.313)
(5.176)
-0.010
-0.027**
(-1.138)
(-2.530)
0.030***
0.021
(2.582)
(1.434)
-0.276***
-0.348***
(-12.289)
(-11.090)
-0.027***
-0.011*
(-5.171)
(-1.925)
-0.030***
-0.020***
(-4.920)
(-2.817)
0.054***
0.036*
(4.134)
(1.931)
0.096***
0.028***
(10.140)
(3.834)
Yes
Yes
53,085
31,750
0.228
0.209
(3)-(4)
p=0.007***
Public Enforcement
(5)
(6)
High
Low
-0.059***
0.065***
(-3.323)
(4.049)
0.356***
0.136***
(13.603)
(4.711)
-0.046***
0.007
(-4.125)
(0.604)
0.133***
-0.045
(5.650)
(-1.528)
-0.002
0.011
(-0.160)
(1.225)
-0.005
-0.023***
(-0.664)
(-3.676)
0.014
-0.018**
(1.504)
(-2.023)
-0.082***
-0.031***
(-8.968)
(-4.700)
-0.000
0.005
(-0.006)
(0.476)
0.030*
-0.063***
(1.796)
(-4.108)
-0.253***
-0.021
(-9.665)
(-0.670)
-0.067***
-0.055***
(-6.850)
(-7.660)
0.053***
0.018*
(4.251)
(1.736)
0.125***
0.045***
(7.495)
(2.820)
0.174***
0.267***
(6.089)
(8.419)
0.070***
0.035***
(9.438)
(4.923)
-0.007
-0.032***
(-0.801)
(-3.211)
0.024**
0.030**
(2.057)
(2.229)
-0.256***
-0.387***
(-11.249)
(-12.990)
-0.032***
-0.008
(-5.785)
(-1.468)
-0.034***
-0.019***
(-5.170)
(-3.136)
0.052***
0.039**
(3.846)
(2.388)
0.098***
0.028***
(9.966)
(4.056)
Yes
Yes
49,047
35,788
0.230
0.211
(5)-(6)
p=0.007***
Tax Compliance
(7)
(8)
High
Low
-0.021
-0.016
(-1.133)
(-0.816)
0.311***
0.255***
(11.339)
(8.143)
-0.028*
-0.014
(-1.913)
(-1.226)
0.069**
-0.020
(2.326)
(-0.562)
0.011
-0.012
(1.253)
(-1.001)
-0.048***
0.016**
(-5.822)
(2.341)
0.009
-0.003
(0.834)
(-0.279)
-0.054***
-0.086***
(-7.615)
(-8.716)
0.003
0.010
(0.299)
(0.812)
-0.001
-0.003
(-0.078)
(-0.176)
-0.197***
-0.149***
(-7.533)
(-4.503)
-0.060***
-0.073***
(-8.160)
(-6.855)
0.020**
0.051***
(1.987)
(3.606)
0.092***
0.115***
(5.459)
(6.107)
0.130***
0.246***
(4.710)
(6.875)
0.040***
0.081***
(4.965)
(10.256)
-0.017*
-0.011
(-1.666)
(-1.065)
0.029**
0.052***
(2.054)
(3.747)
-0.280***
-0.367***
(-10.222)
(-12.685)
-0.019***
-0.030***
(-3.815)
(-4.345)
-0.028***
-0.038***
(-4.129)
(-4.852)
0.069***
0.045***
(4.887)
(2.634)
0.047***
0.103***
(6.098)
(9.228)
Yes
Yes
45,601
35,718
0.213
0.238
(7)-(8)
p=0.047**
Notes: The dependent variable in Panel A of table 4 is NI, which is net income before extraordinary items (#IB),
deflated by beginning of period prices. RET is accumulated market-adjusted stock returns form 9 months before
fiscal year end to three months after fiscal year end. D is a dummy variable which equals one if RET is less than
48
zero, and zero otherwise. PMC is a measure of product market competition which is calculated as minus one
multiplied by the HHIt. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms
competing in each industry-country sample. Industry membership is classified by the four-digit SIC code.
INVPRO is the index of investor protection constructed as the principal component of disclosure, liability
standards, and anti-director rights (La Porta et al. 2006). Investor protection is identified as ‗high‘ if INVPRO is
above the sample median, ‗low‘ otherwise. SECREG is the index of enforcement of securities laws and is
measured as the sum of the index of public enforcement of securities laws and the index of private enforcement
of securities laws. Security regulation is identified as ‗high‘ if SECREG is above the sample median, ‗low‘
otherwise. PUBENF is the index of public enforcement of securities laws, measured as the arithmetic mean of
four underlying indices: Supervisor Characteristics index, Investigative Powers index, Orders index and Criminal
index. The variable is ranked between 0 (weak public enforcement to 1 (strong public enforcement). Public
enforcement is identified as ‗high‘ if PUBENF is above the sample median, ‗low‘ otherwise. TAXCOM
measures the time to prepare, file and pay (or withhold) three major types of taxes: the corporate income tax,
value added or sales tax, and labor taxes, including payroll taxes and social security contributions. Tax
compliance is identified as ‗high‘ if TAXCOM is below the sample median, ‗low‘ otherwise. This data is from
World Bank‘s Ease of Doing Business Index in Djankov et al. (2008).
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level
or better, respectively (two tailed).
49
Panel B: Kahn and Watts (2009) CSCORE Model
Investor Protection
Variable
(1)
(2)
High
Low
PMC
ROE
MBR
GROWTH
R&D
INVCYCLE
VOL
IFRS
BIG8
SIZE
RET
FOREIGN
GDP
EQMKTCAP
GDPGROWTH
INFLATION
CREDITRIGHTS
FDI
0.177***
(3.537)
-0.008
(-0.638)
-0.008**
(-2.226)
-0.003
(-0.362)
-0.056
(-0.270)
0.132
(0.660)
-0.131**
(-2.219)
-0.142***
(-9.277)
0.112***
(10.469)
-0.020***
(-2.608)
0.038***
(5.462)
-0.000
(-0.189)
0.000***
(11.367)
-0.001***
(-6.268)
0.015***
(7.722)
0.010***
(4.510)
0.044***
(2.936)
0.001
(0.987)
-0.057
(-0.783)
0.022
(1.366)
-0.002***
(-4.488)
0.006
(0.622)
0.096
(0.427)
0.422*
(1.904)
0.120**
(2.185)
0.124***
(6.479)
-0.054***
(-3.798)
-0.074***
(-5.969)
-0.001
(-0.234)
-0.000
(-1.584)
-0.000
(-0.079)
-0.002***
(-6.881)
-0.011***
(-3.181)
-0.000
(-0.121)
-0.002
(-0.214)
-0.001
(-0.703)
Security Regulation
(3)
(4)
High
Low
Public Enforcement
(5)
(6)
High
Low
Tax Compliance
(7)
(8)
High
Low
0.173***
(3.140)
0.014
(0.907)
-0.008**
(-2.415)
0.017*
(1.894)
-0.020
(-0.096)
0.374*
(1.832)
-0.079
(-1.327)
-0.157***
(-10.245)
0.111***
(10.799)
-0.038***
(-4.416)
0.035***
(5.232)
0.000
(0.595)
0.000***
(12.210)
-0.001***
(-9.401)
0.015***
(8.354)
0.006***
(2.796)
0.028*
(1.937)
0.002*
(1.912)
0.130***
(3.121)
0.013
(0.852)
-0.002***
(-4.458)
0.023***
(2.586)
-0.065
(-0.315)
0.310
(1.562)
-0.118**
(-2.034)
-0.218***
(-17.286)
0.104***
(10.532)
-0.051***
(-6.008)
0.031***
(4.703)
0.000
(0.315)
0.000***
(14.385)
-0.002***
(-12.729)
0.015***
(7.943)
0.002
(1.103)
0.033**
(2.457)
0.006***
(4.696)
0.257***
(4.903)
0.019
(1.291)
-0.002***
(-4.763)
-0.001
(-0.131)
-0.362
(-1.372)
0.239
(0.958)
0.082
(1.118)
-0.051***
(-2.884)
0.100***
(8.340)
-0.066***
(-7.004)
-0.003
(-0.406)
0.000
(0.101)
0.000***
(11.361)
-0.002***
(-9.682)
0.006**
(2.427)
0.003
(1.020)
0.031**
(2.235)
-0.003**
(-2.019)
-0.078
(-1.200)
0.009
(0.674)
-0.002***
(-4.601)
-0.017*
(-1.684)
0.062
(0.274)
0.189
(0.851)
0.083
(1.504)
0.120***
(6.385)
-0.068***
(-4.457)
-0.052***
(-4.794)
-0.000
(-0.024)
-0.000***
(-2.961)
-0.000*
(-1.862)
-0.001***
(-5.387)
-0.006
(-1.621)
0.009**
(2.037)
-0.002
(-0.301)
-0.001
(-0.673)
50
-0.077
(-1.271)
0.011
(0.822)
-0.008***
(-3.451)
-0.018*
(-1.799)
0.060
(0.265)
0.151
(0.674)
0.114**
(2.001)
0.139***
(7.202)
-0.046***
(-2.903)
-0.047***
(-4.372)
0.002
(0.292)
-0.000**
(-2.506)
-0.000***
(-3.696)
-0.001***
(-3.946)
-0.013***
(-3.249)
0.019***
(3.871)
-0.001
(-0.064)
-0.003**
(-2.445)
-0.031
(-0.402)
-0.017
(-1.170)
-0.014**
(-2.206)
-0.005
(-0.516)
0.389*
(1.853)
0.222
(1.018)
-0.212***
(-4.079)
-0.013
(-0.614)
-0.018
(-1.070)
-0.060***
(-5.349)
0.044***
(7.742)
-0.000***
(-3.611)
-0.000***
(-3.476)
-0.001***
(-3.021)
0.013***
(3.907)
0.039***
(4.982)
-0.001
(-0.074)
0.001
(0.492)
Constant
-0.143*
0.989***
(-1.872)
(5.683)
Firm/Year FE
Yes
Yes
No. of Obs.
52,449
32,386
Adj. R-squared
0.524
0.491
Difference of coefficients on
(1)-(2)
PMC
p=0.004***
-0.106
(-1.434)
Yes
53,085
0.509
(3)-(4)
p=0.011**
1.175***
(7.434)
Yes
31,750
0.483
0.115
(1.180)
Yes
49,047
0.507
(5)-(6)
p=0.024**
1.234***
(6.415)
Yes
35,788
0.479
0.115
(1.180)
Yes
45,601
0.486
1.234***
(6.415)
Yes
35,718
0.487
(7)-(8)
p=0.002***
Notes: The dependent variable in Panel B of Table 4 is CSCORE, which is calculated following Kahn and Watts (2009). PMC is a measure of product market competition
which is calculated as minus one multiplied by the HHIt. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms competing in each industrycountry sample. Industry membership is classified by the four-digit SIC code.
INVPRO is the index of investor protection constructed as the principal component of disclosure, liability standards, and anti-director rights (La Porta et al. 2006). Investor
protection is identified as ‗high‘ if INVPRO is above the sample median, ‗low‘ otherwise. SECREG is the index of enforcement of securities laws and is measured as the sum of
the index of public enforcement of securities laws and the index of private enforcement of securities laws. Security regulation is identified as ‗high‘ if SECREG is above the
sample median, ‗low‘ otherwise. PUBENF is the index of public enforcement of securities laws, measured as the arithmetic mean of four underlying indices: Supervisor
Characteristics index, Investigative Powers index, Orders index and Criminal index. The variable is ranked between 0 (weak public enforcement to 1 (strong public
enforcement). Public enforcement is identified as ‗high‘ if PUBENF is above the sample median, ‗low‘ otherwise. TAXCOM measures the time to prepare, file and pay (or
withhold) three major types of taxes: the corporate income tax, value added or sales tax, and labor taxes, including payroll taxes and social security contributions. Tax
compliance is identified as ‗high‘ if TAXCOM is below the sample median, ‗low‘ otherwise. This data is from World Bank‘s Ease of Doing Business Index in Djankov et al.
(2008).
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. T-statistics are reported in parentheses. ***, **, and * denote
statistically significant at the 1%, 5%, and 10% level or better, respectively (two tailed).
51
Table 5 Evidences on how insider trading law enforcement, earnings quality, frequency of
interim reports and disclosure affect the association between product market competition and
accounting conservatism (test for H2)
Panel A: Basu (1997) Model
Column (1) Column (2)
ITL=1
ITL=0
-0.017
0.063
(-1.292)
(0.731)
D*RET
0.282*** 0.183
(13.892)
(1.309)
PMC*RET
-0.024*** 0.018
(-2.797)
(0.490)
PMC*D*RET
0.052*** -0.074
(2.795)
(-0.744)
D
0.003
-0.043
(0.447)
(-0.909)
PMC
-0.013*** 0.004
(-2.652)
(0.181)
PMC*D
0.001
-0.026
(0.136)
(-0.903)
MBR
-0.056*** -0.105***
(-9.981)
(-2.755)
MBR*D
-0.001
0.057
(-0.107)
(1.204)
MBR*RET
-0.005
-0.067
(-0.423)
(-0.822)
MBR*D*RET
-0.164*** -0.060
(-7.957)
(-0.496)
LEV
-0.064*** -0.130***
(-10.742) (-3.051)
LEV*D
0.033*** 0.084
(4.103)
(1.620)
LEV*RET
0.100*** 0.090
(8.229)
(1.139)
LEV*D*RET
0.180*** 0.252*
(8.306)
(1.682)
SIZE
0.051*** 0.049
(9.502)
(1.546)
SIZE*D
-0.012*
-0.067*
(-1.802)
(-1.715)
SIZE*RET
0.036*** 0.021
(3.898)
(0.417)
SIZE*D*RET
-0.316*** -0.416***
(-16.994) (-4.031)
LIT*D
-0.020*** -0.024
(-5.078)
(-0.981)
LIT*RET
-0.032*** -0.027
(-6.531)
(-0.634)
LIT*D*RET
0.064*** 0.019
(5.916)
(0.275)
Constant
0.054*** 0.181***
(8.992)
(4.265)
Firm/Year fixed effects Yes
Yes
No. of Obs.
82,369
2,389
0.216
0.259
Adj. R-squared
Difference of
coefficients on
(1)-(2)
PMC*D*RET
p=0.007***
Variable
RET
Column (3)
EQ=1
-0.050**
(-2.576)
0.358***
(12.830)
-0.038***
(-3.056)
0.108***
(4.289)
0.009
(0.752)
-0.011
(-1.538)
0.016*
(1.829)
-0.058***
(-5.993)
-0.011
(-0.911)
0.021
(1.128)
-0.250***
(-8.870)
-0.069***
(-6.873)
0.056***
(4.256)
0.108***
(5.594)
0.196***
(6.317)
0.080***
(11.022)
-0.013
(-1.345)
0.024*
(1.909)
-0.273***
(-11.516)
-0.032***
(-5.757)
-0.029***
(-4.455)
0.037***
(2.686)
0.075***
(7.308)
Yes
45,706
0.248
Column (4)
EQ=0
0.053***
(2.908)
0.087***
(2.802)
-0.001
(-0.114)
-0.028
(-0.817)
-0.010
(-1.021)
-0.027***
(-3.311)
-0.012
(-1.101)
-0.037***
(-5.042)
0.010
(0.941)
-0.053***
(-3.111)
-0.026
(-0.756)
-0.057***
(-7.501)
0.026**
(2.402)
0.064***
(3.840)
0.264***
(7.954)
0.005
(0.592)
-0.010
(-0.893)
0.021
(1.450)
-0.296***
(-9.090)
-0.007
(-1.225)
-0.020***
(-2.616)
0.048**
(2.531)
0.049***
(6.373)
Yes
30,864
0.186
(3)-(4)
p=0.000***
Column (5)
FREQ=1
-0.056**
(-2.430)
0.365***
(11.095)
-0.061***
(-3.585)
0.133***
(4.005)
0.001
(0.079)
-0.008
(-0.753)
0.022*
(1.691)
-0.069***
(-5.239)
0.009
(0.580)
0.026
(1.197)
-0.245***
(-7.421)
-0.060***
(-4.453)
0.058***
(3.446)
0.108***
(4.871)
0.209***
(5.718)
0.075***
(7.435)
-0.018
(-1.412)
0.018
(1.258)
-0.278***
(-9.540)
-0.034***
(-5.079)
-0.028***
(-3.710)
0.037**
(2.343)
0.086***
(6.181)
Yes
31,492
0.259
(1)-(2)
p=0.036**
Column (6)
FREQ=0
-0.050**
(-2.576)
0.058*
(1.830)
-0.038***
(-3.056)
0.008
(0.289)
0.009
(0.752)
-0.011
(-1.538)
0.016*
(1.829)
-0.058***
(-5.993)
-0.011
(-0.911)
0.021
(1.128)
-0.250***
(-8.870)
-0.069***
(-6.873)
0.056***
(4.256)
0.108***
(5.594)
0.196***
(6.317)
0.080***
(11.022)
-0.013
(-1.345)
0.024*
(1.909)
-0.273***
(-11.516)
-0.032***
(-5.757)
-0.029***
(-4.455)
0.037***
(2.686)
0.075***
(7.308)
Yes
45,706
0.248
Column (7)
DISCL=1
-0.051***
(-2.647)
0.359***
(13.111)
-0.042***
(-3.441)
0.104***
(4.220)
0.004
(0.388)
-0.009
(-1.445)
0.014
(1.603)
-0.070***
(-7.524)
-0.007
(-0.591)
0.023
(1.241)
-0.253***
(-9.140)
-0.071***
(-7.330)
0.053***
(4.188)
0.104***
(5.515)
0.196***
(6.445)
0.080***
(11.262)
-0.012
(-1.270)
0.028**
(2.315)
-0.279***
(-11.934)
-0.028***
(-5.277)
-0.029***
(-4.534)
0.043***
(3.219)
0.085***
(8.595)
Yes
47,623
0.246
Column (8)
DISCL=0
0.054***
(3.014)
0.090***
(2.854)
0.006
(0.455)
-0.037
(-1.076)
-0.007
(-0.704)
-0.027***
(-2.952)
-0.014
(-1.168)
-0.026***
(-3.407)
0.005
(0.480)
-0.055***
(-3.292)
-0.037
(-1.055)
-0.057***
(-7.193)
0.027**
(2.441)
0.074***
(4.267)
0.254***
(7.395)
0.004
(0.438)
-0.011
(-0.949)
0.011
(0.741)
-0.275***
(-8.376)
-0.010
(-1.617)
-0.021***
(-2.838)
0.048**
(2.477)
0.043***
(5.548)
Yes
28,947
0.185
(3)-(4)
p=0.000***
Notes: The dependent variable in Panel A of table 5 is NI, which is net income before extraordinary items (#IB),
deflated by beginning of period prices. RET is accumulated market-adjusted stock returns form 9 months before
fiscal year end to three months after fiscal year end. D is a dummy variable which equals one if RET is less than
zero, and zero otherwise. PMC is a measure of product market competition which is calculated as minus one
52
multiplied by the HHIt. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms
competing in each industry-country sample. Industry membership is classified by the four-digit SIC code. To
keep brevity, we only report the coeffients and t-statistics for the key variables.
ITL is an indicator variable that is equal to 1 in the years after the first legal case is brought against insider
trading, and zero otherwise (Bhattacharya and Daouk, 2002). Earnings quality is the aggregate earnings
management score from Leuz et al. (2003) multiplied by -1 (so that higher scores indicate higher earnings
quality). The score, based on data over the 1990 to 1999 period, equals the average rank of two earningssmoothing measures and two earnings-discretion measures. EQ is a dummy variable that is equal to 1 if earnings
quality in one country is above the sample country median, 0 otherwise. Interim reporting frequency is the
reporting frequency index that comes from Center for International Financial Analysis and Research. The index
measures the frequency of a country‘s financial reporting, with a score of 4 for quarterly reporting, 2 for semiannual reporting, and so forth. FREQ is a dummy variable that is equal to 1 if the reporting frequency index in
one country is above the sample country median, 0 otherwise. Financial disclosure is the accounting disclosure
index constructed by determining the mean percentage of items, from a pre-specified list of 85 accounting items,
that are included in a sample of fiscal year 1993 annual reports of domestic companies (Center for International
Financial Analysis Research, 1995). DISCL is a dummy variable that is equal to 1 if the accounting disclosure
index in one country is above the sample country median, 0 otherwise.
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level
or better, respectively (two tailed).
53
Panel B: Kahn and Watts (2009) CSCORE Model
Variable
PMC
ROE
MBR
GROWTH
R&D
INVCYCLE
VOL
IFRS
BIG8
SIZE
RET
FOREIGN
GDP
EQMKTCAP
GDPGROWTH
INFLATION
CREDITRIGHTS
FDI
Constant
Column (1)
ITL=1
0.162***
(4.583)
0.013
(1.301)
-0.002***
(-5.529)
-0.001
(-0.136)
0.202
(1.311)
0.331**
(2.189)
-0.062
(-1.560)
-0.037***
(-3.120)
0.034***
(4.047)
-0.054***
(-7.210)
0.022***
(4.953)
-0.000
(-1.081)
0.000***
(6.460)
-0.002***
(-15.284)
0.018***
(11.027)
0.004**
(2.436)
0.106***
(3.440)
-0.003***
(-2.862)
0.127
(1.561)
Column (2)
ITL=0
-0.008
(-0.034)
-0.073*
(-1.793)
-0.007**
(-2.285)
-0.029
(-0.630)
-1.243
(-0.582)
-0.249
(-0.265)
0.850***
(2.842)
0.052
(0.832)
0.055
(0.993)
-0.065**
(-2.147)
-0.038
(-1.115)
0.000
(0.781)
0.000***
(3.846)
0.002***
(3.439)
-0.102***
(-7.781)
0.005
(0.780)
-0.005
(-0.153)
0.016***
(2.604)
-0.684*
(-1.928)
Column (3)
EQ=1
0.243***
(4.308)
0.008
(0.568)
-0.010**
(-2.247)
-0.008
(-0.944)
0.087
(0.430)
0.360*
(1.787)
-0.130**
(-2.304)
-0.082***
(-5.346)
0.071***
(6.575)
-0.004
(-0.457)
-0.001
(-0.077)
-0.000
(-0.710)
0.000***
(9.259)
-0.000***
(-2.858)
0.009***
(5.228)
0.011***
(4.560)
0.229***
(3.309)
-0.003***
(-2.656)
-0.409***
(-4.924)
Column (4)
EQ=0
-0.046
(-0.614)
0.021
(1.506)
-0.002***
(-3.583)
-0.003
(-0.246)
-0.495
(-1.603)
0.797***
(2.882)
0.209***
(3.138)
0.013
(1.007)
0.001
(0.074)
-0.038***
(-3.350)
0.018***
(2.845)
-0.000*
(-1.693)
0.000***
(8.163)
-0.004***
(-11.756)
0.015***
(3.212)
-0.004
(-0.773)
0.004
(0.214)
0.010***
(3.959)
-1.538***
(-5.016)
54
Column (5)
FREQ=1
0.349***
(2.817)
-0.008
(-0.660)
-0.006**
(-2.570)
-0.016*
(-1.750)
-0.330
(-1.414)
0.106
(0.538)
0.035
(0.658)
-0.043**
(-2.474)
0.104***
(7.711)
-0.005
(-0.462)
0.037***
(7.720)
-0.000*
(-1.798)
-0.000
(-0.934)
-0.002***
(-22.438)
0.021***
(5.079)
0.031***
(6.067)
0.091***
(3.382)
-0.006***
(-4.595)
0.298***
(2.607)
Colum (6)
FREQ=0
-0.066
(-1.331)
0.019
(1.038)
-0.002***
(-3.318)
0.035***
(3.144)
-0.305
(-1.157)
0.562*
(1.937)
-0.101
(-1.411)
0.009
(0.187)
-0.012
(-0.711)
-0.060***
(-6.365)
-0.004
(-0.463)
0.000
(0.084)
0.000***
(9.674)
-0.003***
(-6.900)
0.012***
(5.120)
0.011***
(3.573)
0.006
(0.513)
-0.006**
(-2.311)
-0.067
(-0.514)
Colum (7)
DISCL=1
0.221***
(4.361)
0.011
(0.897)
-0.008**
(-2.217)
0.005
(0.739)
0.190
(1.144)
0.552***
(3.125)
-0.055
(-1.303)
-0.094***
(-6.334)
0.054***
(5.433)
-0.024***
(-2.657)
0.041***
(9.081)
-0.000
(-1.020)
0.000***
(7.310)
-0.002***
(-17.369)
0.020***
(12.504)
0.026***
(5.758)
0.114***
(3.096)
-0.006***
(-5.237)
-0.105
(-1.220)
Colum (8)
DISCL=0
-0.029
(-0.361)
0.014
(0.785)
-0.002***
(-4.828)
-0.010
(-0.606)
0.000
(0.000)
-0.225
(-0.804)
0.036
(0.337)
0.039
(1.516)
-0.007
(-0.400)
-0.063***
(-5.254)
-0.056***
(-4.591)
-0.000
(-0.252)
0.000**
(2.131)
0.000
(0.728)
-0.039***
(-6.705)
0.001
(0.286)
0.005
(0.487)
0.004***
(2.843)
0.390**
(2.063)
Yes
Yes
Firm/Year FE
No. of Obs.
76,686
5,511
Adj. R-squared
0.517
0.477
Difference of coefficients on
(1)-(2)
PMC
p=0.005***
Yes
Yes
Yes
Yes
Yes
Yes
33,421
0.459
48,776
0.431
10,498
0.496
71,699
0.461
35,796
0.494
46,401
0.471
(3)-(4)
p=0.005***
(5)-(6)
p=0.027**
(7)-(8)
p=0.004***
Notes: The dependent variable in Panel B of Table 7 is CSCORE, which is calculated following Kahn and Watts (2009). PMC is a measure of product market competition
which is calculated as minus one multiplied by the HHI t. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms competing in each industrycountry sample. Industry membership is classified by the four-digit SIC code.
ITL is an indicator variable that is equal to 1 in the years after the first legal case is brought against insider trading, and zero otherwise (Bhattacharya and Daouk, 2002).
Earnings quality is the aggregate earnings management score from Leuz et al. (2003) multiplied by -1 (so that higher scores indicate higher earnings quality). The score, based
on data over the 1990 to 1999 period, equals the average rank of two earnings-smoothing measures and two earnings-discretion measures. EQ is a dummy variable that is
equal to 1 if earnings quality in one country is above the sample country median, 0 otherwise. Interim reporting frequency is the reporting frequency index that comes from
Center for International Financial Analysis and Research. The index measures the frequency of a country‘s financial reporting, with a score of 4 for quarterly reporting, 2 for
semi-annual reporting, and so forth. FREQ is a dummy variable that is equal to 1 if the reporting frequency index in one country is above the sample country median, 0
otherwise. Financial disclosure is the accounting disclosure index constructed by determining the mean percentage of items, from a pre-specified list of 85 accounting items,
that are included in a sample of fiscal year 1993 annual reports of domestic companies (Center for International Financial Analysis Research, 1995). DISCL is a dummy
variable that is equal to 1 if the accounting disclosure index in one country is above the sample country median, 0 otherwise.
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. T-statistics are reported in parentheses. ***, **, and * denote
statistically significant at the 1%, 5%, and 10% level or better, respectively (two tailed).
55
Table 6 Additional evidence on how country development and debt market affect the association
between product market competition and accounting conservatism
Panel A: Basu (1997) Model
Column (1)
Column (2)
Column (3)
Column (4)
DEVELOP=1
DEVELOP=0 DEBT=1
DEBT=0
-0.017
0.018
-0.016
-0.013
(-1.158)
(0.587)
(-1.070)
(-0.342)
D*RET
0.287***
0.068
0.287***
0.270***
(13.262)
(1.310)
(13.002)
(4.563)
PMC*RET
-0.021**
-0.009
-0.022**
-0.007
(-2.335)
(-0.467)
(-2.145)
(-0.357)
PMC*D*RET
0.039*
-0.003
0.043**
-0.014
(1.914)
(-0.076)
(2.297)
(-0.695)
D
0.008
-0.049***
0.004
-0.024
(1.117)
(-2.804)
(0.516)
(-0.984)
PMC
-0.015***
0.000
-0.016***
-0.020*
(-2.975)
(0.015)
(-2.916)
(-1.719)
PMC*D
0.004
-0.023
-0.001
-0.008
(0.542)
(-1.378)
(-0.085)
(-0.530)
MBR
-0.042***
-0.120***
-0.052***
-0.148***
(-7.139)
(-7.336)
(-8.372)
(-8.033)
MBR*D
-0.005
0.047**
-0.005
0.066***
(-0.680)
(2.312)
(-0.594)
(2.747)
MBR*RET
-0.005
-0.012
-0.010
0.029
(-0.336)
(-0.447)
(-0.677)
(0.856)
MBR*D*RET
-0.175***
-0.016
-0.188***
-0.059
(-7.930)
(-0.314)
(-8.308)
(-1.031)
LEV
-0.057***
-0.095***
-0.064***
-0.086***
(-9.288)
(-5.006)
(-9.787)
(-4.241)
LEV*D
0.033***
0.035
0.036***
0.014
(3.835)
(1.533)
(4.063)
(0.544)
LEV*RET
0.086***
0.113***
0.102***
0.112***
(6.459)
(3.607)
(7.508)
(3.230)
LEV*D*RET
0.195***
0.239***
0.176***
0.146**
(8.442)
(3.948)
(7.434)
(2.203)
SIZE
0.057***
0.076***
0.052***
0.073***
(10.305)
(4.710)
(8.953)
(4.331)
SIZE*D
-0.013*
-0.008
-0.011
-0.035*
(-1.827)
(-0.423)
(-1.440)
(-1.698)
SIZE*RET
0.037***
0.002
0.033***
0.004
(3.765)
(0.077)
(3.303)
(0.157)
SIZE*D*RET
-0.314***
-0.223***
-0.303***
-0.345***
(-15.946)
(-4.521)
(-14.939)
(-6.639)
LIT*D
-0.023***
0.014
-0.025***
-0.007
(-5.905)
(1.001)
(-5.779)
(-0.602)
LIT*RET
-0.031***
-0.005
-0.033***
-0.012
(-6.363)
(-0.251)
(-6.058)
(-0.671)
LIT*D*RET
0.053***
0.080**
0.060***
0.012
(4.770)
(2.177)
(5.069)
(0.377)
Constant
0.039***
0.115***
0.049***
0.179***
(6.167)
(4.712)
(7.555)
(7.272)
Firm/Year fixed effects
Yes
Yes
Yes
Yes
No. of Obs.
73,278
11,480
68,678
9,997
Adj. R-squared
0.224
0.215
0.220
0.230
Difference of coefficients on(1)-(2)
(3)-(4)
PMC*D*RET
p=0.077*
p=0.039**
Notes: The dependent variable in Panel A of table 7 is NI, which is net income before extraordinary items (#IB),
deflated by beginning of period prices. RET is accumulated market-adjusted stock returns form 9 months before
Variable
RET
56
fiscal year end to three months after fiscal year end. D is a dummy variable which equals one if RET is less than
zero, and zero otherwise. PMC is a measure of product market competition which is calculated as minus one
multiplied by the HHIt. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms
competing in each industry-country sample. Industry membership is classified by the four-digit SIC code. To
keep brevity, we only report the coeffients and t-statistics for the key variables.
DEVELOP is a dummy variable that is equal to 1 if the Logarithmic of per capita GDP in one country is above
the sample country median, 0 otherwise (La Porta et al. 2006). DEBT is an indicator variable that is equal to 1 if
debt/GNP is above the sample country median, 0 otherwise (Ball et al. 2008b).
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level
or better, respectively (two tailed).
57
Panel B: Kahn and Watts (2009) CSCORE Model – DEVELOP AND DEBT
Variable
Column (1)
Column (2)
DEVELOP=1
DEVELOP=0
PMC
0.242***
-0.13
(4.930)
(-1.457)
ROE
-0.004
0.012
(-0.351)
(0.662)
MBR
-0.009**
-0.002***
(-2.360)
(-4.282)
GROWTH
-0.003
-0.002
(-0.464)
(-0.138)
R&D
0.177
0.105
(1.117)
(0.122)
INVCYCLE
0.349**
0.340
(2.233)
(0.677)
VOL
-0.119***
0.223**
(-2.884)
(2.261)
BIG8
-0.021*
0.057
(-1.666)
(1.112)
SIZE
-0.001
0.112***
(-0.054)
(6.261)
RET
-0.015*
-0.070***
(-1.726)
(-5.999)
FOREIGN
0.036***
-0.012
(8.386)
(-0.985)
AGE
-0.000*
0.000
(-1.905)
(0.528)
GDP
-0.000
0.000***
(-0.909)
(5.237)
EQMKTCAP
-0.002***
-0.002***
(-14.050)
(-4.524)
GDPGROWTH
0.021***
0.006***
(7.701)
(2.579)
INFLATION
0.024***
-0.002
(7.226)
(-0.720)
CREDITRIGHTS
0.086***
-0.004
(4.431)
(-0.613)
FDI
-0.002**
0.004
(-2.029)
(1.503)
Constant
0.357***
0.328**
(3.683)
(2.009)
Firm/Year fixed effects
Yes
Yes
No. of Obs.
69,345
21,659
Adj. R-squared
0.487
0.466
Difference of coefficients on
(1)-(2)
PMC
p=0.004***
Column (3)
DEBT=1
0.218***
(5.190)
-0.013
(-1.326)
-0.007**
(-2.432)
-0.004
(-0.634)
0.057
(0.421)
0.126
(0.860)
-0.177***
(-4.744)
-0.049***
(-3.712)
0.074***
(8.323)
-0.026***
(-3.946)
0.051***
(12.954)
-0.000
(-1.115)
0.000**
(2.511)
-0.002***
(-15.536)
0.019***
(10.650)
0.005***
(2.585)
0.102***
(4.600)
-0.005***
(-4.860)
0.220***
(2.986)
Yes
71,209
0.465
Column (4)
DEBT=0
-0.101
(-1.002)
0.063**
(2.015)
-0.002***
(-5.004)
0.015
(0.792)
-1.122**
(-2.017)
0.856*
(1.807)
0.329***
(2.651)
0.068**
(2.217)
-0.030
(-1.304)
-0.082***
(-5.403)
-0.022
(-1.600)
0.000
(0.232)
0.000***
(7.811)
-0.002***
(-11.151)
-0.010**
(-2.573)
0.010**
(2.515)
-0.005
(-0.661)
0.005***
(3.305)
0.304*
(1.868)
Yes
12,959
0.448
(3)-(4)
p=0.003**
Notes: The dependent variable in Panel B of Table 7 is CSCORE, which is calculated following Kahn and Watts
(2009). PMC is a measure of product market competition which is calculated as minus one multiplied by the HHI t.
HHI (Herfindahl-Hirschman index) is the sum of the squared market shares of the firms competing in each
industry-country sample. Industry membership is classified by the four-digit SIC code.
DEVELOP is a dummy variable that is equal to 1 if the Logarithmic of per capita GDP in one country is above the
sample country median, 0 otherwise (La Porta et al. 2006). DEBT is an indicator variable that is equal to 1 if
debt/GNP is above the sample country median, 0 otherwise (Ball et al. 2008b).
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. Tstatistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level or
better, respectively (two tailed).
58
Table 7 Sensitivity Test – Re-estimation using Ball & Shivakumar (2006) Model
Variable
CFO
DCFO*CFO
PMC*CFO
PMC*DCFO*CFO
Other Control Variables
Firm/Year fixed effects
No. of Obs.
Adj. R-squared
Difference of coefficients
on PMC*DCFO*CFO
(1)
All firms
-0.543***
(-46.132)
0.921***
(12.351)
-0.038**
(-2.213)
0.333*
(1.851)
Yes
Yes
74313
0.638
Investor Protection
(2)
(3)
High
Low
-0.457***
-0.543***
(-28.253)
(-32.216)
0.867***
0.964***
(5.721)
(3.848)
-0.031*
-0.052**
(-1.781)
(-2.237)
0.891**
-0.134
(1.972)
(-0.523)
Yes
Yes
Yes
Yes
54065
20248
0.547
0.703
(2)-(3)
0.038**
Security Regulation
(4)
(5)
High
Low
-0.463***
-0.552***
(-30.143)
(-33.151)
0.923***
1.012***
(6.007)
(4.045)
-0.027
-0.054**
(-1.534)
(-2.341)
1.031**
-0.217
(2.372)
(-0.767)
Yes
Yes
Yes
Yes
56461
17852
0.547
0.703
(4)-(5)
0.016**
Public Enforcement
(6)
(7)
High
Low
-0.447***
-0.511***
(-29.147)
(-31.017)
0.748***
1.210***
(4.641)
(4.351)
-0.039**
-0.048**
(-2.132)
(-1.981)
1.147**
-0.143
(2.514)
(-0.614)
Yes
Yes
Yes
Yes
53186
21127
0.547
0.703
(6)-(7)
0.025**
Tax Compliance
(8)
(9)
High
Low
-0.417***
-0.464***
(-29.178)
(-31.174)
0.647***
0.776***
(3.267)
(3.143)
-0.022
-0.068***
(-1.332)
(-2.781)
1.311***
-0.097
(2.914)
(-0.123)
Yes
Yes
Yes
Yes
47291
20248
0.547
0.703
(8)-(9)
0.019**
Notes: The dependent variable in this table is firm‘s total accruals (ACC). CFO is firm‘s operating cash flow. DCFO equals one if CFO is negative, zero otherwise. PMC is a
measure of product market competition which is calculated as minus one multiplied by the HHI t. HHI (Herfindahl-Hirschman index) is the sum of the squared market shares
of the firms competing in each industry-country sample. Industry membership is classified by the four-digit SIC code.
INVPRO is the index of investor protection constructed as the principal component of disclosure, liability standards, and anti-director rights (La Porta et al. 2006). Investor
protection is identified as ‗high‘ if INVPRO is above the sample median, ‗low‘ otherwise. SECREG is the index of enforcement of securities laws and is measured as the sum
of the index of public enforcement of securities laws and the index of private enforcement of securities laws. Security regulation is identified as ‗high‘ if SECREG is above
the sample median, ‗low‘ otherwise. PUBENF is the index of public enforcement of securities laws, measured as the arithmetic mean of four underlying indices: Supervisor
Characteristics index, Investigative Powers index, Orders index and Criminal index. The variable is ranked between 0 (weak public enforcement to 1 (strong public
enforcement). Public enforcement is identified as ‗high‘ if PUBENF is above the sample median, ‗low‘ otherwise. TAXCOM measures the time to prepare, file and pay (or
withhold) three major types of taxes: the corporate income tax, value added or sales tax, and labor taxes, including payroll taxes and social security contributions. Tax
compliance is identified as ‗high‘ if TAXCOM is below the sample median, ‗low‘ otherwise. For brevity, we only report the coefficients for the items important to our
research questions, and the coefficients of other variables are omitted in the tables.
See Appendix A for the definitions of other variables. The standard errors are adjusted for clustering by firm. T-statistics are reported in parentheses. ***, **, and * denote
statistically significant at the 1%, 5%, and 10% level or better, respectively (two tailed).
59
Table 8 Sensitivity Test – Including other measures of product market competition
Variable
DIFF*D*RET
MKTSIZE*D*RET
ENTCOST*D*RET
PMC*D*RET
Other Control Variables
Firm/Year FE
No. of Obs.
Adj. R-squared
Difference of coefficients
on PMC*D*RET
(1)
All firms
-0.004
(-0.168)
0.013**
(2.234)
-0.018***
(-3.131)
0.142***
(2.972)
Yes
Yes
84,835
0.433
Investor Protection
(2)
(3)
High
Low
0.068
-0.023
(1.324)
(-0.678)
0.023**
0.004
(2.345)
(0.521)
-0.016**
0.005
(-2.031)
(0.978)
0.098***
-0.113***
(2.781)
(-2.846)
Yes
Yes
Yes
Yes
51,109
33,426
0.512
0.480
(2)-(3)
p=0.001***
Security Regulation
(4)
(5)
High
Low
0.052
-0.012
(1.138)
(-0.391)
0.027**
0.006
(2.438)
(0.647)
-0.013**
0.004
(-1.994)
(0.938)
0.107***
-0.097***
(2.847)
(-2.779)
Yes
Yes
Yes
Yes
53,085
31,750
0.509
0.483
(4)-(5)
p=0.001***
Public Enforcement
(6)
(7)
High
Low
0.047
-0.019
(1.114)
(-0.614)
0.016**
0.007
(2.114)
(0.704)
-0.019**
0.013
(-2.224)
(1.224)
0.117***
-0.092***
(2.904)
(-2.745)
Yes
Yes
Yes
Yes
49,047
35,788
0.507
0.479
(6)-(7)
p=0.001***
Tax Compliance
(8)
(9)
High
Low
0.088
-0.061
(1.554)
(-1.178)
0.029**
0.008
(2.517)
(0.742)
-0.024**
0.017
(-2.561)
(1.327)
0.127***
-0.124***
(3.014)
(-3.142)
Yes
Yes
Yes
Yes
45,601
35,718
0.486
0.487
(8)-(9)
p<0.000***
Notes: Table 6 presents the regression coefficients and the corresponding t-statistics from replicating the empirical tests in Table 3 and Table 4 by including three dimensions
of product market competition proposed by Karuna (2007) in the regressions. Variable definitions are detailed in Appendix A.
INVPRO is the index of investor protection constructed as the principal component of disclosure, liability standards, and anti-director rights (La Porta et al. 2006). Investor
protection is identified as ‗high‘ if INVPRO is above the sample median, ‗low‘ otherwise. SECREG is the index of enforcement of securities laws and is measured as the sum
of the index of public enforcement of securities laws and the index of private enforcement of securities laws. Security regulation is identified as ‗high‘ if SECREG is above
the sample median, ‗low‘ otherwise. PUBENF is the index of public enforcement of securities laws, measured as the arithmetic mean of four underlying indices: Supervisor
Characteristics index, Investigative Powers index, Orders index and Criminal index. The variable is ranked between 0 (weak public enforcement to 1 (strong public
enforcement). Public enforcement is identified as ‗high‘ if PUBENF is above the sample median, ‗low‘ otherwise. TAXCOM measures the time to prepare, file and pay (or
withhold) three major types of taxes: the corporate income tax, value added or sales tax, and labor taxes, including payroll taxes and social security contributions. Tax
compliance is identified as ‗high‘ if TAXCOM is below the sample median, ‗low‘ otherwise.
For brevity, we only report the coefficients for the items important to our research questions, and the coefficients of other variables are omitted in the tables. The standard
errors are adjusted for clustering by firm. T-statistics are reported in parentheses. ***, **, and * denote statistically significant at the 1%, 5%, and 10% level or better,
respectively (two tailed).
60