The effect of institutional factors on discontinuities inearnings distribution: Public versus private firms in Japan∗

Akinobu Shutoa
Associate Professor
Research Institute for Economics and Business Administration
Kobe University

Takuya Iwasaki
Assistant Professor
Faculty of Commerce
Kansai UniversityaCorresponding author: Research Institute for Economics and Business Administration, Kobe University
2-1, Rokkodaicho, Nadaku, Kobe 657-8501, Japan. E-mail: shuto@rieb.kobe-u.ac.jp∗
Acknowledgements: The authors appreciate the helpful comments and suggestions received from
Kazumi Suzuki, Shota Otomasa, Takashi Yaekura, Takashi Obinata, Masaki Yoneyama, Keiichi Oishi,
and the participants of the workshops conducted at Tokyo University, and Kansai University. All errors
are the responsibility of the author.

1

The effect of institutional factors on discontinuities in earnings
distribution: Public versus private firms in Japan

ABSTRACT: Previous studies have shown that, compared with earnings distributionsin other countries, there are clear discontinuities at zero in the distribution of earnings
levels in Japanese firms (Thomas et al. 2004; Suda and Shuto 2007; Shuto 2009). We
predict that two unique institutional factors in Japan—(1) the alignment between
financial and tax accounting, and (2) the tight relationship between firms and theirbanks—cause the discontinuities in earnings distribution. Consistent with this prediction,
we find that firms with high marginal tax rates and tight relationships with their banks
are more likely to manage earnings to report slightly positive earnings. We also find that
this relationship is more pervasive for private firms than public firms. We contribute to
the literature by examining a significant research setting that has features of both
institutional factors and loss-avoidance behaviors to enable deeper consideration duringhypothesis development.

Keywords Earnings distribution, Discontinuities, Institutional factors, Tax cost, Bank
dependence
JEL Classification M41

2

1. Introduction
Previous studies have indicated that there are clear discontinuities at zero in the
distribution of earnings levels in Japanese firms (Thomas et al. 2004; Suda and Shuto
2007; Shuto 2009). It is no exaggeration to say that the discontinuities at zero in theearnings distribution of Japanese firms are drastic compared to the distributions ofearnings levels in other countries, and that these discontinuities are characteristic of theearnings distribution of Japanese firms. Given the assumptions of recent earnings
management research (Burgstahler and Dichev 1997; Degeorge et al. 1999), theseresults mean that Japanese firms’ managers have strong incentive to avoid earnings
losses.
The purpose of this study is to explore what factors shape these specificdiscontinuities at zero in the earnings distribution. We predict that some uniqueinstitutional features in Japan could cause the peculiar discontinuities in the earnings
distribution of Japanese firms. In particular, we focus on the two institutional factorsthat are often argued as being specific to Japanese firms: (1) the alignment between
financial and tax accounting, and (2) the tight relationship between firms and theirbanks. We expect to find that these factors induce firms’ managers to report slightly
positive earnings that create the discontinuities at zero in the earnings distribution.
Further, we examine the difference between public and private firms in terms ofloss-avoidance behaviors due to these institutional factors, because it is plausible thatthese two factors have greater influence on private firms than public firms.
Previous studies, which have often conducted comparative analysis in aninternational setting, have examined the effect of institutional factors on the propertiesof accounting earnings, usefulness of accounting earnings (Ali and Hwang 2000;
3

Guenther and Young 2000; Bartov et al. 2001; Fan and Wong 2002), accounting
conservatism (Ball et al. 2000; Ball et al. 2003; Bushman and Piotroski 2006; Peek et al.
2008), and earnings management (Leuz et al. 2003; Coppens and Peek 2005;
Burgstahler et al. 2006). Among these studies, Burgstahler et al. (2006) and Coppens
and Peek (2005) have some similarities to our paper since they provide evidence that
institutional features, including the tax environment and a bank-oriented system, canaffect earnings management through comparative analysis between public and private
firms.
Our study is different from these studies in that it focuses on a single country, that
is, Japan, whereas most previous studies have examined the international differences ininstitutional factors. The reason we focus on this research setting is that it has
worthwhile features for inquiry into the relationship between institutional factors andearnings management. First, previous works in the international setting have generallyclassified sample countries into two groups based on their attributes for each
institutional factor using simple dummy variables, and thus have not considered thespecific institutional features of each country. Focusing on Japanese firms provides auseful research setting because Japanese firms have unique features for both
institutional factors and loss-avoidance behaviors, as stated above. Although earnings
management research has identified the presence of extreme discontinuities at zero in
the distribution of earnings levels in Japanese firms (Thomas et al. 2004; Suda andShuto 2007; Shuto 2009), previous comparative studies have not completely explained
why the discontinuities in Japanese earnings distribution are more pervasive anddistinctive, or whether they are caused by Japanese institutional factors, which suggeststhat there is a further research opportunity for examining Japanese firms separately. In
4this study, we conduct a detailed discussion of how the unique institutional factors inJapan induce managers to report slightly positive earnings, and develop hypotheses to
explain this phenomenon. If a significant association between institutional factors andloss avoidance can be observed, then it will provide strong evidence for supporting theconclusion of previous comparative studies that the institutional features affect thereporting incentive of managers and the resulting property of accounting earnings.
Second, previous comparative studies implicitly assume that there is nodispersion of the degree of effect of institutional factors in each country; however, we
can easily understand that this is not a realistic assumption. For example, even incountries wherein financial and tax accounting are strongly aligned, managers cannot
constantly manage earnings downward for tax-management purpose because of accruals
reversal. It is reasonable for us to assume that managers are more likely to manage
earnings when the benefit for tax management is larger. Further, as explained in detailsubsequently, Japanese banks do not always monitor borrower firms constantly; theychange the strength of monitoring and decide to discipline borrower firms depending on
the level of the firms’ performance. Thus, managers are likely to change their earnings
management behaviors based on their bank’s monitoring action. In accordance with thisargument, we measure proxies for the incentive for tax management and bankdependency, and examine the relationship between these proxies in terms of institutional
factors and loss avoidance, which is expected to address the limitations of previous
studies.
Our first research objective is to examine the effect of the incentive for tax
management on loss-avoidance behaviors. The extent of alignment between financial
and tax accounting is expected to have a significant effect on firms’ reporting behaviors
5

(Alford et al. 1993; Ali and Hwang 2000; Ball et al. 2000; Guenther and Young 2000;
Bartov et al. 2001; Burgstahler et al. 2006). It is well known that the degree ofalignment between financial and tax accounting is significantly higher in Japan than inother countries (Guenther and Young 2000; Bartov et al. 2001). Because reported net
income based on Japanese generally accepted accounting principles (GAAP) is strongly
linked to taxable income, Japanese firms’ managers have an incentive to manage
earnings downward to reduce their tax cost. However, reporting extremely low earnings
or losses attract much attention from regulatory agencies and increases the probabilityof being investigated by the tax authorities (Herrmann and Inoue 1996; Coppens andPeek 2005). Thus, managers with incentive for tax management are likely to reduce
earnings to the extent that their earnings are not converted into losses. Consequently, we
predict that managers with a tax-management incentive will manage earnings to reportslightly positive earnings that cause the discontinuities of earnings distribution.
As a proxy for the incentive for tax management, we use marginal tax rate,
following Gramlich et al.’s (2004) method. Consistent with our hypothesis, our result
indicates that the firms with higher marginal tax rate are more likely to engage inearnings management to report slightly positive earnings.
Our second research objective is to investigate the relationship between firms’bank dependence and their loss-avoidance behaviors. A country’s capital market system
can be generally classified as bank-oriented or market-oriented, and the Japanese capital
market is usually classified as a typical bank-oriented system (Ali and Hwang 2000;
Guenther and Young 2000; Bartov et al. 2001). One of the features of the bank-oriented
system in Japan is that a firm’s bank has an important role in monitoring the firm’s
behavior in a financial crisis. The close tie between a firm and a specific bank is often
6referred to as the main bank system (Aoki et al. 1994). The main bank has a strong
incentive and ability to monitor managerial behaviors because it has better access to
inside information about the firms. In contrast to American practice, explicit debt
covenants that require a financial ratio to be maintained at a certain level are not usually
written into bank loan contracts in Japan. We conjecture that this is because banks cantake advantage of their superior and private information in monitoring borrower firms.
In the absence of accounting-based debt covenants, Japanese banks are not likely
to react to slight temporal fluctuations in the accounting numbers of borrower firms.
However, this does not mean that the banks do not consider the accounting-based
performance of borrowers. Previous studies have argued that a main bank may intervenein the management of its borrowing firms, depending on the level of performance of thefirms (Aoki 1994; Aoki et al. 1994). In particular, the reporting of extremely bad
performance leads to various management interventions by firms’ banks, including arecontract, CEO turnover, and installation of directors chosen by the bank (Kaplan and
Minton 1994; Kang and Shivdasani 1995). Therefore, we predict that firm managers
with a close relationship with their banks tend to have an incentive for earnings
management to avoid losses, because reporting losses can be a visible signal of bad
performance in the absence of debt covenants and cause subsequent intervention by
their banks.
To capture the degree of the relationship between firms and their banks, we use
the first principal component from three variables that may affect the relationship withbanks for factor analysis. Our analyses provide evidence suggesting that firms with aclose bank-firm relationship are more likely to manage earnings to report slightly
positive earnings, which is consistent with our prediction.
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Our final research objective is to examine the difference between public andprivate firms in terms of the relationship between institutional features andloss-avoidance incentive. We focus on the differences between public and private firms
in order to further verify the validity of our abovementioned results; the influence ofinstitutional factors that we address in this study is expected to be greater for private
firms (Burgstahler et al. 2006).
We expect that private firms can engage in more income-decreasing earnings
management for tax management than public firms because capital market pressure isabsent for private firms, as are its related earnings-management incentives (Coppens
and Peek 2005; Burgstahler et al. 2006). We also predict that earnings management due
to the bank-firm relationship is more pervasive for private firms. Because private firms
do not generally use the equity market for their financing, bank dependence is stronger
for private firms than for public firms.
First, we find that private firms have a higher incentive to report slightly positive
earnings than public firms. Second, we find that the relationship between the marginal
tax rate and the loss-avoidance tendency is higher for private firms. Finally, our results
reveal that the relationship between the firm-bank relationship and the loss-avoidance
incentive is stronger for private firms. In aggregate, our results are consistent with our
prediction that unique institutional features in Japan give rise to the discontinuities
around zero of earnings distribution, and that the tendency is more pervasive for private
firms than for public firms.
This study contributes to the existing literature and understanding of accounting
practice. First, our study builds on the work of previous studies employing comparative
analysis in an international setting by focusing on a specific situation wherein there are8(1) peculiar discontinuities in the earnings distribution of Japanese firms, and (2)institutional factors often referred to as unique features of Japan. Our study’s
contribution is that it directly connects these two unique features, providing evidence
that the unique institutional factors specific to Japanese firms can shape the property
characteristic of the earnings distribution of Japanese firms. Although comparative
studies have provided useful evidence consistent with institutional factors creating firms’
reporting incentives, these studies have some limitations such as the assumption oftheoretical development in each country and the research method. 1 Our study is
expected to reinforce the findings of previous studies by providing findings without
such limitations, based on deeper consideration during hypothesis development andresearch design.
Further, our study contributes to the debate about whether the discontinuities of
earnings distribution reflect the results of earnings management (Durtschi and Easton
2005, 2009; Jacob and Jorgensen 2007). Our findings that institutional features arestrongly associated with the clear discontinuities of earnings support the assumption ofexisting earnings-management research that the discontinuities of earnings distribution
are due to earnings-management behaviors. Finally, our results present important
implications for the setting of accounting standards, regulation bodies, tax authorities,
and bank loan practice.
The remainder of this paper is organized in the following manner. Section II
1

The work of Goncharov and Zimmermann (2006) is a notable exception, as they examined therelationship between ownership structure and earnings management for tax management. The study,
however, has serious limitations for generalization and interpretation: First, their sample is restricted to
Russian companies in 2001 and 2002, and their main purpose is to examine the changes intax-management behaviors in a transition period (Russia modified its tax accounting in 2002). Second,
their tax incentive variable is defined as tax expense for the period divided by earnings before taxes. Thisvariable is usually referred as to effective tax rate, although they called it marginal tax rate. In order tomeasure tax incentive, we have to calculate the proper marginal tax rate, which requires the estimation of
future earnings streams (see section 3.1.1).

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summarizes the institutional features in Japan and develops the hypotheses. Section III
defines the variables used in this study and explains the research design. Section IV
outlines the sample selection procedure and reports descriptive statistics for thevariables used. Section V presents the empirical results for our hypotheses. Section VI
provides a concluding summary.

2. Institutional factors in Japan and development of hypotheses
2.1 The alignment between financial and tax accounting
The close link between financial and tax accounting is expected to have aconsiderable influence on firms’ reporting behaviors (Alford et al. 1993; Ball et al.
2000; Ali and Hwang 2000; Guenther and Young 2000; Bartov et al. 2001; Burgstahler
et al. 2006). One of the characteristic features of institutional factors that may affectmanagerial financial reporting incentive in Japan is the strong alignment between
financial and tax accounting (Ali and Hwang 2000; Guenther and Young 2000). Under
the provisions of the Japanese Corporation Tax Law, Japanese firms are required to
calculate their taxable income on the basis of accounting earnings in accordance withthe standards of Japanese GAAP (Corporation Tax Law, Article 22, item 4).2 Moreover,the financial statements used for the calculation must be finalized pursuant to theCommercial Code through the approval of the general shareholders’ meeting(Corporation Tax Law, Article 74). This system, which strongly links financial
accounting to tax accounting, has traditionally been referred to as “kakutei-kessan-shugi”
2

Although the calculation of taxable income is generally based on financial accounting earnings, some
exceptions exist. Specifically, Corporation Tax Law Article 22 prescribes that with respect to someaccounts that do not match the purpose of Corporation Tax Law, taxable income shall be calculated byadjusting (adding or deducting) financial accounting earnings for taxable purposes.

10in Japanese, representing the unique accounting system in Japan.
In this context, managers have the ability to conduct tax management bymanaging reported financial earnings, which are highly associated with taxable income.
In particular, managers are likely to have an incentive to manage earnings downward asmuch as possible to reduce tax cost, which may lead to less-informative earnings.
Consistent with this argument, previous studies have indicated through comparative
analysis that in the countries wherein taxable income is calculated based on financial
accounting earnings, managers are more likely to engage in earnings management(Burgstahler et al. 2006) and report less-informative earnings (Alford et al. 1993; Ali
and Hwang 2000; Ball et al. 2000; Guenther and Young 2000; Bartov et al. 2001).
However, it must be noted that reporting extremely low earnings through earnings
management might involve high additional costs for firms because it may increase theprobability of being investigated by the tax authorities (Herrmann and Inoue 1996;
Coppens and Peek 2005). Because of the financial and tax accounting alignment
wherein the basic financial statements for taxation purposes are, in general, the audited
and finalized statement, the tax authorities can save the cost of tax investigation. The tax
authorities do not need to inspect the calculation process of taxable income in detail,and thus can concentrate their efforts on possible cases of tax evasion, such as reporting
losses.
Consequently, the best-possible strategy for tax management by Japanesemanagers is to report slightly positive earnings, as reporting losses might increase thepossibility of tax investigation by the tax authorities. Although most Japanese managers
are expected to have an incentive to report slightly positive earnings for tax purposes, as
previous comparative studies assume, we predict that among Japanese firms, the11incentive for earnings management is likely to be greater for firms that gain more
benefit from tax management.
Further, the unique reporting system in Japan creates the possibility of promoting
earnings management for tax-management purpose. A major disclosure difference
between Japan and the United States is that the publicly traded firms in Japan are
required to prepare both consolidated and parent-only financial statements. In this study,
we focus on unconsolidated earnings because the conformity between financial and tax
accounting is generally applicable only to unconsolidated financial statements. In asituation where two types of earnings are publicly disclosed, given that consolidated
earnings are used for investment decisions, managers might be able to manage
unconsolidated earnings for tax-management purposes and consolidated earnings toprovide information to investors simultaneously.

Hypothesis 1: Firms with greater incentive for tax management tend to report slightly
positive earnings.

2.2 The close relationship between firms and their banks
Another institutional factor that we focus on in this study is whether a country’s capital
market can be classified as bank-oriented or market-oriented (Ali and Hwang 2000;
Guenther and Young 2000; Bartov et al. 2001). It is often argued that in a bank-oriented
system, firms have very close relationships with their banks, as most of the capital needs
are supplied by a few banks. In contrast to the market-oriented system, wherein there
are numerous diverse investors, large banks can access private information about firms
and monitor them without needing to monitor public information, such as the12accounting information used in the bank-oriented system (Ali and Hwang 2000;
Guenther and Young 2000). Previous studies provide evidence that firm managers inbank-oriented countries are more likely to report less-informative earnings thanmanagers in market-oriented countries (Ali and Hwang 2000; Guenther and Young
2000; Bartov et al. 2001), consistent with the above argument.
Bank loans have traditionally been one of the major forms of financing in Japan,
and Guenther and Young (2000) showed that the debt/asset ratios of Japanese firms arehigher than that of the US and the UK firms are. Thus, like Germany, Japan has usually
been classified as a typical bank-oriented country in the previous studies (Ali and
Hwang 2000; Guenther and Young 2000; Bartov et al. 2001). Further, it is often said
that the bank-oriented system in Japan has the specific feature of corporate governancethrough monitoring by the main bank. The close tie between a firm and a specific bankis referred to as the main bank system, which is characterized by bank borrowing,
shareholding of client firms, and board members’ exchanges. The main bank has astrong incentive to monitor managerial behaviors as both a creditor and shareholder, andis able to monitor inefficient behaviors of firm managers because it has better access to
inside information about the firms, as stated above.3
In bank loan contracts in Japan, banks do not usually set explicit debt covenants
that require the maintenance of certain financial ratios, which contrasts with the bankloan practice in the United States (Dichev and Skinner 2002).4 In the United States,
3

The main bank can constantly monitor the condition of client firms because borrowers’ checking
accounts provide banks with exclusive access to a continuous stream of borrowers’ data, including their
checking account balances at the bank (Aoki et al., 1994).
4
Recently, debt covenants have sometimes been set in Japanese bank loan contracts, especially in
syndicated loan practice. However, the number of contracts with debt covenants is still small comparedwith the practice in US loan contracts. Okabe and Inamura (2010), who examined the existence of debt
covenants in bank loan contracts in Japan by performing a keyword search in annual reports(yuukashoukenhoukokusho in Japanese) in 2005, reported that there were only 82 contracts with debt

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accounting-based debt covenants can be set to mitigate the conflict between
shareholders and debtholders, which means that in the market-oriented system, debt
contracts depend on public information, such as accounting information, to reduce theagency problem. On the other hand, main banks in the Japanese financial system do notneed accounting-based covenants because of their superior access to private information.
Therefore, Japanese banks are not likely to react to temporal fluctuations in theaccounting numbers of borrower firms.
However, it must be emphasized that this does not mean that Japanese banks do
not have an interest in and react to the accounting-based performance of borrowers.
Previous studies have argued that a main bank may intervene in the management ofborrowing firms, depending on the firm’s level of performance (Aoki 1994; Aoki et al.
1994). Main banks tend to continue providing funds for borrowing firms with good
performance and never intervene in their management. However, when a borrowing
firm’s performance decline significantly, as in the case of losses, the main bank can takethe initiative to restructure the firms by replacing the CEO, dispatching the boards of
directors, etc. Previous studies provide evidence that is consistent with this argument(Kaplan and Minton 1994; Kang and Shivdasani 1995). Therefore, the related banks areunlikely to take immediate action in response to slight fluctuations of financial numbers;
however, various actions for disciplining managers are triggered by clear signals of poor
performance, such as losses. This disciplinary mechanism differs from theAnglo-American (market-oriented) system based on takeovers and bankruptcy
procedures (Arikawa and Miyajima 2007).
We expect that this specific bank-firm relationship in Japan affects earnings
covenants among all listed and unlisted companies.

14

management to avoid losses. It is likely that if firm managers can report earnings thatare not extremely low, without managing earnings, they have no incentive to manage
earnings, ceteris paribus. In contrast, managers are likely to have a strong incentive tomanage earnings in those cases in which they will report losses without earnings
management, because reporting losses leads to intervention in management on the partof the banks. Given the absence of explicit debt covenants in the bank loan contracts,
losses can be an important threshold for banks’ decision making, which may also cause
earnings management to avoid losses by borrowing firms. Therefore, we predict thatfirms with strong ties with their banks are likely to conduct earnings management toavoid losses.

Hypothesis 2: Firms with closer relationships with their banks tend to report slightly
positive earnings.

Finally, previous studies argue that there are two more institutional factors that
affect the properties of earnings (La Porta et al. 1997; Ball et al. 2000): (1) the origin of
the legal system, that is, codelaw or common law, and (2) the country’s legal system forexternal shareholder protection. Japan is usually classified as a code-law country, whichis assumed to decrease the quality of earnings (Ali and Hwang 2000; Ball et al. 2000;
Guenther and Young 2000). With respect to external shareholder protection, following
La Porta et al.’s (1997) index of “antidirector rights,” it is often assumed that Japan hasmodest shareholder protection (Ali and Hwang 2000; Guenther and Young 2000; Leuzet al. 2003). In this study, we do not incorporate these institutional factors into ouranalysis for the following reasons. First, it is expected that there is no dispersion of the15degree of effect of these legal institutional factors within a single country. Second, in
contrast to the institutional factors that we address in this study, the above institutional
factors do not provide a rational explanation for why these factors could create
discontinuities at zero in the distribution of earnings levels.

2.3 The incentive for earnings management in public versus private firms
It is predicted that the two unique institutional factors on which we focus in thisstudy affect private firms more than public firms. The most notable difference in theinformation environment between public and private firms is that private firms are notsubject to capital market pressure. Although various theories have put forward
contradicting predictions about the effect of this difference on the earnings managementof private and public firms, previous studies generally show that private firms exhibit
more earnings management than public firms (Burgstahler et al. 2006).
Because private firms do not have an equity-based incentive for earnings
management, which usually encourages income-increasing procedures, it is likely thatprivate firms can engage in more income-decreasing earnings management for tax
management than public firms (Coppens and Peek 2005; Burgstahler et al. 2006). Lossavoidance seems to be a common incentive for both private and public firms; however,
it is unlikely that managers of public firms with a high equity-based incentive have astrong incentive to decrease earnings to target zero earnings, resulting in slightly
positive earnings. Therefore, we expect that the effect of tax incentive on earnings
management is larger for private firms than for public firms.

Hypothesis 3: The relationship between loss-avoidance behavior and tax-management16

incentive is greater for private firms than for public firms.

We also predict that the earnings management due to the bank-firm relationship ismore pervasive for private firms than for public firms. As private firms do not generally
depend on the equity market for their financing, bank dependence is greater for private
firms than for public firms. As primary fund providers, the main banks of private firms
are likely to have great influence on and conduct more severe monitoring of their firms.
This leads to a stronger incentive for private firms to engage in earnings management.

Hypothesis 4: The relationship between loss-avoidance behavior and bank dependenceis greater for private firms than for public firms.

3. Research design
3.1 Variables measurement
3.1.1 Tax management incentive
In this section, we describe the proxies for the incentive for tax management andthe strength of the relationship with the main bank used in our empirical analyses. We
begin by estimating the tax-management incentive variable.
We use the marginal tax rate as the proxy because firms with a high marginal tax
rate can be assumed to have high incentive for tax management (Scholes et al. 2002;
Gramlich et al., 2004). Marginal tax rate is generally defined as the change in thepresent value of cash paid to tax authorities as a result of earning one additional
currency unit (Scholes et al. 2002). To measure a marginal tax rate, it is necessary to17estimate future earnings streams to grasp the future tax cost. Of the potential estimation
methods, we follow the approach employed by Gramlich et al. (2004), who used ataxable income dummy variable in order to model the basic Japanese tax laws
concerning loss carrybacks and carryforwards.
The two reasons for employing the approach of Gramlich et al. (2004) are: (1)previous studies reveal that this dummy variable reasonably captures much of thevariation in firms’ marginal tax rate status (Graham 1996b; Suzuki 2002; Plesko 2003)5
and (2) the approach is especially designed for examining the research setting ofJapanese firms because Gramlich et al. (2004) investigates the effect of keiretsu
affiliation on tax-motivated income shifting among Japanese firms. A detailed definitionof the variable is available in Appendix A.

3.1.2 The relationship between firms and their banks
To measure the strength of firms’ relationships with their banks, we focus on thefollowing three variables.

DEBT = an indicator variable that takes the value of one if the firm has a loan
(short-term or long-term loan), and zero otherwise
LOAN = a sum of short-term and long-term loans, divided by total assets at the end of
the last year
LOAN5 = the average of LOAN for the past five years.

5

Graham (1996b) and Plesko (2003) provide evidence suggesting that the simple dummy variables
approach can reasonably capture corporate marginal tax rate status as well as the simulation approach
adopted by Shevlin (1990) and Graham (1996a), which simulates future earnings streams in order toestimate corporate marginal tax rates. Suzuki (2002) also revealed that for Japanese firms, thisdummy-variables approach generally performs well.

18

These three variables are expected to capture the degree of the relationship between
firms and their banks. DEBT is a dummy variable indicating whether the firm has a loan,
which is expected to reflect whether the firm depends on the bank loan in theirfinancing in general. By using this variable, we can discern firms that do not use bankloan at all and have no connection with banks. LOAN is the sum of a firm’s loans, so it
can directly reflect the degree to which a firm depends on bank loans. LOAN5 is
expected to grasp a firm’s long-term relationship with its banks because it is the averageof their loans for the past five years, reflecting the history of loan financing.
Although each of these variables can be a proxy for the strength of the relationship withthe main bank, focusing on a single variable does not completely capture therelationship, because each of the variables reflect different features of the relationship.
Therefore, we construct a composite measure of the degree of the relationship using
factor analysis to reduce the three financial variables indicated above into a single
index.
Table A1 in Appendix B summarizes the detailed statistics of the principal
component analysis. Panel A indicate the statistics for the whole sample (i.e., public andprivate firms). Factor analysis assumes that attribute measures are intercorrelated, andthat they exert load on a single factor. The results are consistent with such anassumption. First, the panel shows that the correlations among the three variables are all
positive and that all of the correlations are significant, as expected. Second, the panel
reveals that a single factor loaded by these three attribute measures justifies around 74.9
percent of the cumulative variance. Finally, the panel reports the factor loadings, all ofwhich have positive signs, as expected. Therefore, the results suggest that our factor
19analysis provides useful composite measures for the degree of the relationship between
firms and their banks. We can obtain similar results for panel B (public firms) and panel
C (private firms).

3.2 Research models for testing hypotheses
3.2.1 Research models for testing hypothesis 1 and 2
In order to test hypothesis 1, we examine the effect of tax-management incentive
on the discontinuities of the distribution of earnings levels. Specifically, we use thefollowing model to investigate the relationship between marginal tax rate and reporting
small earnings:

LOSSEM = α ＋ β1TAXCOST ＋ β2ASSET ＋ β3∆ASSET ＋ β4∆CFO＋ β5WCA ＋ β6EXT ＋ β7CYCLE ＋ β8AGE ＋ β9ICLAIM＋ β10CEC ＋ Industry dummy,

(1)

where
LOSSEM = an indicator variable that takes the value of one if the firm has scaled
earnings in the interval between 0 (inclusive) and 0.0028 (exclusive), and zero if
the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0(exclusive)
TAXCOST = marginal tax rates based on the method of Gramlich et al. (2004)ASSET = natural log of total assets at the end of the fiscal year
∆ASSET = first difference in total assets, divided by total assets at the end of theprevious year

20∆CFO = first difference in cash flows, divided by total assets at the end of theprevious year
WCA = working capital accruals, divided by total assets at the end of the previous
year
EXT = extraordinary items, divided by total assets at the end of the previous year
CYCLE = the natural log of the length of the operating cycle in days
AGE = the natural log of the firm age
ICLAIM = the reliance on implicit claims, computed using principal component
analysis
Industrial Dummy = industrial dummy variables.

In accordance with many prior studies, we focus on firms that report small profits
or losses in order to grasp earnings management to avoid losses (Beatty et al. 2002;
Luez et al. 2003; Burgstahler et al. 2006). Specifically, this analysis investigates thelevel of scaled earnings within two intervals, one between –0.0028 (inclusive) and 0(exclusive) and the other between 0 (inclusive) and 0.0028 (exclusive). In constructing
the histograms, based on the formula used in previous studies (Degeorge et al. 1999;
Beatty et al. 2002), we use a bin width of twice the interquartile range of the variablemultiplied by the negative cube root of the sample size. The formula indicates that thebin width in our histogram is 0.0014.6 Consequently, following the procedure of Beatty
et al. (2002), we use an interval size twice the bin width used in the histogram.
In the regression model (1), the coefficient of TAXCOST measures the relationship6

The class width calculated according to this rule is rounded to four decimal places. Specifically, thevalue of 0.00137957, which is our correct interval value based on the formula rounded to four decimal
places is 0.0014. Using interval sizes one and three times the bin width does not affect our results.

21

between incentive for tax management and the discontinuity of earnings distribution
around zero. If the relationship is consistent with the prediction of hypothesis 1, thecoefficient of TAXCOST should be positive. Further, we use some control variables to
explain the discontinuity of earnings distribution based on the findings of previous
studies. In particular, following the model of Beatty et al. (2002), we control for firm
size (ASSET), growth (∆ASSET), and profitability (∆CFO). If higher-growth and larger
firms are increasingly more profitable or more likely to manage earnings to avoid
earnings losses, the coefficients of ∆ASSET and ASSET should be positive. We also
expect a firm with greater profits to be more likely to report small earnings rather thansmall losses. Therefore, we expect the coefficients of ∆CFO to be positive.
Working capital accruals (WCA) and extraordinary items (EXT) are included in
the model to control for the effect of discretionary accounting choices. Because working
capital accruals and extraordinary items are likely to be used to manage earnings to
avoid losses, the coefficients of these two variables are expected to be positive.
Burgstahler et al. (2006) contend that the length of the operating cycle in days (CYCLE)
and the number of years since incorporation (AGE) have to be controlled, because thesevariables are likely to be associated with the level of earnings management andvariation between privately held and public firms. We expect that these two variables
are positively associated with the incidence of earnings management.
Finally, we control for a firm’s implicit claims with its stakeholders (ICLAIM)
because prior studies argued that managers’ incentive to beat earnings benchmarks
through earnings management is stronger for firms that rely heavily on implicit claims(Bowen et al. 1995; Matsumoto 2002). The coefficient of ICLAIM is expected to be22

positive.7
In order to test hypothesis 2, we investigate the effect of the strength of therelationship between the firm and its bank on the discontinuity of earnings distribution
using following model.

LOSSEM = α ＋ β1FIN ＋ β2ASSET ＋ β3∆ASSET ＋ β4∆CFO＋ β5WCA ＋ β6EXT ＋ β7CYCLE ＋ β8AGE ＋ β9ICLAIM＋ β10CEC ＋ Industry dummy,

(2)

where
FIN = the strength of the relationship between firm and their bank, computed using
principal component analysis.

We expect that the coefficient of FIN will be positive if the relationship between FIN
and LOSEM is consistent with the prediction of hypothesis 2.

Research Models to Test Hypotheses 3 and 4
Before testing hypotheses 3 and 4, in order to test our basic assumption, we
examine the relationship between loss-avoidance tendency and the difference between
public and private firms using following model.

LOSSEM = α ＋ β1PRIVATE ＋ β2PRIVATE*TAXCOST ＋ β3ASSET
7

Following the method of Matsumoto (2002), we use factor analysis to measure ICLAIM. Specifically, we
focus on three variables (a dummy variable indicating the durable goods industry, research anddevelopment expenses, and labor intensity), and reduce these variables into a single index similar to thecalculation procedure for the FIN variable. Detailed definitions of the variables and the descriptive
statistics are found in Table A2 in Appendix B.

23

＋β4∆ASSET ＋ β5∆CFO ＋ β6WCA ＋ β7EXT ＋ β8CYCLE＋β9AGE ＋ β10ICLAIM ＋ β11CEC ＋ Industry dummy,

(2)

where
PRIVATE = an indicator variable that takes the value of one if the firm is unlisted, andzero otherwise.

We set the PRIVATE dummy variable to take the value of one if the firm is unlisted in
the model. In our hypotheses development, we assume that private firms have greater
incentive to report slightly positive earnings than do public firms. Thus, the coefficientof PRIVATE is expected to be positive based on our prediction.
In order to test hypotheses 3 and 4, we examine the effect of the difference
between public and private firms on the relationship between institutional features (i.e.,
tax-management incentive and the relationship with bank) and loss-avoidance incentive.

LOSSEM = α ＋β1TAXCOST ＋ β2PRIVATE*TAXCOST ＋ β3ASSET＋ β4∆ASSET ＋ β5∆CFO ＋ β6WCA＋β7EXT ＋ β8CYCLE＋ β9AGE ＋ β10ICLAIM ＋ β11CEC ＋ Industry dummy(3)

LOSSEM = α ＋ β1FIN ＋β2PRIVATE*FIN ＋ β3ASSET ＋ β4∆ASSET＋ β5∆CFO ＋ β6WCA ＋ β7EXT ＋ β8CYCLE ＋ β9AGE＋ β10ICLAIM ＋ β11CEC ＋ Industry dummy(4)

We add interaction terms for PRIVATE and TAXCOST (FIN) in regression models (3)and (4) to test the hypotheses. Hypotheses (3) and (4) predict that loss-avoidance24incentives due to these institutional features are greater for private firms. Thus, the signof these interaction variables is predicted to be positive.

4. Sample selection and descriptive statistic
4.1 Sample selection
Our sample selection procedure is summarized in Table 1. The initial sample
included public and private firms for the period 1979–2007 after excluding financial
institutions and other financial institutions. Public firms are defined as firms listed on atleast one of the eight stock exchanges or traded on the over-the-counter market in Japan.
We define private firms as stock companies unlisted on any of the stock exchanges ortraded on the over-the-counter market in Japan. More specifically, to be included in our
sample of private firms, a firm must satisfy any of the following requirements: (1) theamount of stated capital (shihon kin) on the balance sheet as of the end of the mostrecent business year is 500 million yen or more, or (2) the total sum of the amounts inthe liabilities section of the balance sheet as of the end of the most recent business year
is 20 billion yen or more.8［Insert Table 1 about here］
Under the Japanese Company Act, private firms are required to prepare financial
statements in accordance with corporate accounting customs, or GAAP (Company Act,
Article 431). In practice, most private firms that meet the above requirements (i.e., oursample firms) prepare their financial statements following the accounting standards for

8

The Japanese Company Act defines such a firm as a “large company (Daigaisha)” (Company Act, Article
2 (vi)).
25

listed companies.9 Thus, for our sample, we can reasonably compare the financial dataof listed firms and unlisted firms without bias due to differences in the accounting
standards.
We obtained our initial sample of 98,368 observations (60,035 for public firms
observations and 38,333 for private firms) of unconsolidated financial statement datafrom the Nikkei NEEDS Financial QUEST for 1979–2007.10 We use the accounting
data from unconsolidated financial statement because our main hypotheses deal withmanagerial incentives for tax management, and taxable income is generally calculatedon the basis of unconsolidated accounting earnings in Japan.
We deleted firms whose accounting period changed during our analytical period;
this resulted in 56,395 public and 34,799 private firm observations. We also excluded
observations with negative total assets or a negative book value of equity; this resulted
in 56,270 public and 33,345 private firm observations. These observations are used forour earnings distribution analyses. Finally, we restricted our sample to firms reporting
small profits or losses for our regression analysis. The final sample consists of 2,478
public and 4,459 private firm observations.

4.2 Descriptive statistics
Table 2 reports the descriptive statistics of earnings variables for earnings
distribution analysis. The table shows that public firms are more profitable than private
9

The report on the accounting standard for unlisted companies from the Accounting Standards Board ofJapan (2010) argues that this is because these large firms are required to have an accounting auditor suchas a Certified Public Accountant, and that their financial statements must be audited by the accounting
auditor (Company Act, Article 328, Article 337 (1), and Article 396 (1)). Our sample also includes private
firms that satisfy the requirement of the Financial Instruments and Exchange Act, which prescribes theaccounting procedure for listed companies. Therefore, in such a case, there is no gap in financial
statement data between private and public firms.
10
We restrict our sample to observation during the period 1979–2007 because the database we used does
not contain financial data for private firms after January 2008.

26

firms in general. Although the average value of the level of net income for public firms
is 0.022 for our sample period, the average value of private firms is 0.013. Table 3
summarizes the descriptive statistics of the variables for our regression analysis inwhich we focus on firms reporting slight profits or losses to test the hypotheses. The
fact that the LOSSEM dummy variable is 0.880 indicates that 88 percent of observations
in our sample report positive earnings. This clear contrast between profit and loss firms
around zero earnings is consistent with the findings of prior studies examining earnings
distribution for Japanese firms.
［Insert Table 2 about here］
Further, the table shows that the PRIVATE dummy variable is 0.643, which meansthat 64.3 percent of sample firms are private firms. We identify again that the ratio ofprivate firms in our initial sample in Table 1 was only about 38 percent (i.e., the numberof private firms was 38,333, and the total initial sample was 98,688 firms). It should be
noted that the ratio clearly increases from initial sample to subsample with slightearnings, which suggests that observations of private firms are more concentrated
around zero earnings.
［Insert Table 3 about here］
Table 4 reveals the correlation matrix among the variables used in our regression
models. The upper-right-hand portion of the table reports the Spearman rank-ordercorrelations, and the lower-left-hand portion presents the Pearson correlations. In bothcorrelation analyses, both TAXCOST and FIN are significantly and positively associatedwith LOSSEM. The results suggest that earnings management for loss avoidance
increases as tax-management incentive and the bank dependency of firms increase,respectively, as hypothesized.
27

5. Results
5.1 Preliminary analysis
First, we observe the earnings distribution in Japanese firms as a preliminary
analysis. Figure 1 compares the distributions of the scaled net income for public firms(Panel A) and private firms (Panel B). Consistent with the findings of prior studies, bothpanels show drastic discontinuities at zero in the distribution of scaled net income inJapanese firms, which suggests that Japanese firm managers have strong incentive to
avoid earnings losses, given the assumption of general earnings-management research.
［Insert Figure 1 about here］
Table 5 summarizes the standardized differences and the earnings management(EM) ratio in the distributions. The standardized differences are used to test thesignificance of the irregularities near zero earnings through a statistical test based on
Burgstahler and Dichev (1997). 11 The EM ratio is defined as the number ofobservations in the interval to the immediate right of (and including) zero divided by the
number of observations in the interval to the immediate left of zero (Beatty et al. 2002;
Dechow et al. 2003; Brown and Caylor 2005). We use this ratio to test for differences inthe degree of discontinuities around zero between public and private firms’ earnings
distributions.
［Insert Table 5 about here］

11

The standardized difference is the difference between the actual and expected number of observations in
an interval (operationally defined as the average of the number in the two adjacent intervals) divided by
the estimated standard deviation of the difference. Denoting the probability that an observation will fall
into interval i by pi, the variance of the differences between the observed and expected number ofobservations for interval i is approximately Npi(1 – pi) + (1/4)N(p i-1 + pi+1)(1 – pi-1 – pi+1).

28

The tests of standardized differences indicate that the irregularities near zeroearnings are statistically significant in both panels. A chi-square test on EM ratios
indicates that the EM ratio of private firms, 9.023, is significantly higher than the ratioof public firms, 7.190. These results suggest that Japanese managers of both public andprivate firms have strong incentive to avoid losses, and the degree of earnings
management, that is, the degree of discontinuity of earnings distribution, is morepervasive for private firms than public firms.
Finally, we also present the distribution of the changes in net income to comparewith the results for the distribution of the levels of net income. During hypotheses
development, we assumed that the institutional factors on which we focus in this study
induce managers to report slightly positive earnings, and that such incentives arestronger for private firms than for public firms. The reason for expecting salient
earnings management in private firms in particular is that capital market pressure andthe related earnings-management incentive are absent for private firms. Further,
Coppens and Peek (2005) argued and provided evidence suggesting that earnings
management to avoid earnings decreases is due to capital market pressure. Consequently,
given that the two institutional factors we examine here create stronger incentive for
earnings management in private firms, and that the incentive for avoiding earnings
decreases is due to capital market pressure, we expect earnings management to avoid
decreases to be less pervasive for private firms than for public firms.
The results of Figure 2 are consistent with this prediction. The figure shows that
although there are salient irregularities at zero in the distribution of earnings changes for
public firms (Panel A), the irregularities in the earnings distribution for private firms(Panel B) is less clear. The statistical tests of Table 5 also confirm difference in29

irregularities near zero between the two distributions. The table indicates that the EM
ratio of private firms is significantly lower than that of public firms, which suggest thatmanagers of private firms do not have as much incentive to avoid decreases in earnings
as the managers of public firms, and the incentive for earnings management to avoid
decreases is due to capital market pressure.
［Insert Figure 2 about here］
Our overall results on preliminary analyses are consistent with the theoreticalbackground of our hypotheses and the findings of prior studies on the following points.
First, there are drastic irregularities near zero in the distribution of the level of earnings
in Japanese firms, which is the basic assumption behind our primary concern, and isconsistent with the findings of prior studies (Thomas et al. 2004; Suda and Shuto 2007;
Shuto 2009). Second, the irregularities in the distribution of the level of earnings are
more pervasive for private firms than for public firms. Finally, the irregularities in the
distribution of the changes in earnings are less pervasive for private firms than for
public firms. The latter two findings partially support our discussion during hypotheses
development, suggesting that the irregularities in the distribution of the level of earnings
are mainly due to incentives for tax avoidance and maintaining good relationships withbanks.

5.2 Main results
5.2.1 Results for hypotheses 1 and 2
In order to test hypothesis 1, we estimate regression model (1) using pooled
regressions and reported t-statistics based on standard errors clustered at firm and year30

levels following Petersen’s (2009) analyses.12
Table 6 summarizes the regression results for the full sample, public firms, andprivate firms, respectively. In column 3 of Table 6, the regression result for the fullsample shows that the coefficient of TAXCOST, 1.761, is significantly positive at theone percent level, as expected. We also find that the coefficients of TAXCOST in
regression models for public firms (column 4) and private firms (column 5) are alsoboth significantly positive at the one percent level. These results are in accordance withhypothesis 1, suggesting that there are greater irregularities in the distribution of thelevel of earnings as marginal tax rate increases. In other words, firms with a higher
marginal tax rate are more likely to engage in earnings management to report slightly
positive earnings.
［Insert Table 6 about here］
The regression results of model (2) for hypothesis 2 are reported in Table 7.
Column 3 in the table indicates that FIN is significantly and positively associated withLOSSEM at the one percent level for the full sample, which is consistent withhypothesis 1. In columns 4 and 5 of the table, contrasting results for the FIN variables
for public and private firms are presented. For the private firm sample, the coefficient of
FIN is significantly positive at the one percent level, as expected, but the coefficient of
FIN is negative and not significant for the public firm sample. It is likely that the
incentive of maintaining alignment with the bank is stronger for managers of private
firms than for managers of public firms, consistent with our prediction. With respect to12Petersen (2009) indicated that standard errors clustered by firm and time can be useful to control fortime-series correlation and heteroskedasticity simultaneously. Specifically, t-statistics are adjusted forcross-sectional and intertemporal dependence using two-way cluster-robust standard errors. We use thisestimation method for all the following analyses in this paper. If clustering standard errors does not allow
for the inclusion of all our currently included industry dummy variables, we combine at least two industry
dummy variables into one industry dummy variable to estimate the regression.

31

control variables in the regression model for the full sample, we find that larger firms,
higher-growth firms, and firms with greater profits tend to report slightly positive
earnings more often.
［Insert Table 7 about here］

5.2.2 Results for hypotheses 3 and 4
Before testing the hypotheses, Table 8 presents the results for regression model(3), which examines the relationship between the difference between public and private
firms and loss-avoidance incentives. In Table 8, the coefficient of PRIVATE, 0.243, issignificantly positive. Private firms are more likely to report slightly positive earnings
than public firms. The results are in accordance with the findings of preliminary
analysis and again support our assumption.
［Insert Table 8 about here］
Next, in order to test the hypotheses, we examine the effect of the difference
between public and private firms on the relationship between the institutional factor andreporting slightly positive earnings. Table 9 summarizes the results for regression
models (4) and (5) in testing hypotheses 3 and 4. The result of regression model (4) isreported in column 3 of Table 9. Consistent with hypothesis 3, the coefficient of
PRIVATE*TAXCOST is significantly positive at the one percent level. This findingmeans that the marginal tax rate is more associated with the loss-avoidance tendency for
private firms than for public firms. Further, column 4 of the table reveals the results ofregression model (5), showing that PRIVATE*FIN has a significantly positivecoefficient, as hypothesized. The result suggests that the relationship between the degreeof a firm’s bank dependence and the loss avoidance tendency is higher for private firms.
32

Finally, we include the two interaction terms in the model simultaneously; theresult is reported in column 5 of Table 9. The table indicates that although
PRIVATE*TAXCOST has a significantly positive coefficient at the one percent level, thecoefficient of PRIVATE*FIN is positive but not significant. This evidence suggests thatthe incentive for tax avoidance has a higher impact on the loss-avoidance tendency ofprivate firms.

6. Conclusion
The purpose of this study is to explore which factors shape the discontinuities inthe earnings distribution of Japanese firms, because previous studies have revealed that
there are clear discontinuities at zero in the distribution of earnings levels in Japanesefirms. We predict that some institutional factors specific to Japanese firms cause thepeculiar discontinuities at zero in the earnings distribution of Japanese firms. In
particular, we focus on two institutional factors: (1) the alignment between financial andtax accounting, and (2) the close relationship between firms and their banks.
Based on deeper consideration of the effect of institutional factors on financial
reporting incentive, we hypothesize that these factors induce managers to report slightly
positive earnings, creating discontinuities at zero in the distribution of earnings levels.
First, we show that firms with higher marginal tax rates are more likely to conduct
earnings management to report slightly positive earnings. The results suggest thatmanagers with the incentive for tax management tend to manage earnings to reduce
their tax cost.
Second, we provide evidence that firm managers with close relationships with33

their banks tend to engage in earnings management to avoid losses, because reporting
losses can be a visible signal of bad performance that may cause intervention by thebanks. Finally, we reveal that the relationship between these institutional factors and theloss-avoidance behaviors is stronger for private firms than for public firms, which isalso consistent with our prediction. In summary, our results suggest that uniqueinstitutional features in Japan give rise to the discontinuities around zero of earnings
distribution, and the result is more pervasive for private firms than public firms.
We build on the work of previous studies, mainly comparative studies ininternational settings, by providing evidence from a significant research setting in which
there are features of both institutional factors and earnings distribution, based on deeper
consideration during hypothesis development. An important implication of this study isthat institutional factors can affect managers’ reporting incentive and shape thediscontinuities around zero of earnings distribution. It might be beneficial for standard
setters, tax authorities, and bankers to know that Japanese firms’ managers have strong
incentive to report slightly positive earnings due to their institutional factors, and thatthe incentive is greater for managers of private firms.

34

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40

Table 1. Sample selection procedure
Criteria

Firm-years
Publicfirms1

Privatefirms2

Firm-years with financial statements data obtained from the database for 1981 - 20073.

60,03538,333

Less:
Change in accounting period during our analysis period
Firm-years with negative total assets or book value of equity(3,640)(125)(3,534)(1,454)

Sample for earnings distribution analysis

56,27033,345

Less:
Missing data to calculate variables for regression analysis(53,792)(28,886)

2,478

4,459

Sample for regression analysis

Note:
Financial statements data, managerial ownership data, and share price data necessary for the study are available from the Nikkei
NEEDS Financial QUEST. The industry is based on the Nikkei industry classification code (Nikkei gyousyu chu-bunrui). The
financial statements data is based on unconsolidated financial statements.
1
Public firms are defined as firms listed on at least one of the eight stock exchanges in Japan or traded on the over-the-countermarket. The eight stock exchanges are Tokyo, Osaka, Nagoya, Sapporo, Niigata, Kyoto, Hiroshima, and Fukuoka.
2
Private firms are defined as stock companies unlisted on at least one of the eight stock exchanges in Japan or traded on theover-the-counter market, and are required to satisfy any of the following requirements: (1) the amount of stated capital (shihonkin) on the balance sheet as of the end of the most recent business year is 500 million yen or more, or (2) the total sum of theamounts in the liabilities section of the balance sheet as of the end of the most recent business year is 20 billion yen or more.
3
Excluding financial institutions (banks, securities companies, and insurance companies) and other financial institutions (credit
and leasing).

41

Table 2. Descriptive statistics on earnings
Panel A. Public firms’ net income
firm-year
Mean
1981
0.033
1982
0.032
1983
0.026
1984
0.029
1985
0.030
1986
0.025
1987
0.025
1988
0.031
1989
0.036
1990
0.035
1991
0.034
1992
0.025
1993
0.018
1994
0.015
1995
0.016
1996
0.020
1997
0.020
1998
0.016
1999
0.009
2000
0.012
2001
0.013
2002
0.007
2003
0.013
2004
0.026
2005
0.028
2006
0.030
2007
0.028
Total
0.022
Panel B. Private firms’ net income
firm-year
Mean
1981
0.021
1982
0.018
1983
0.017
1984
0.020
1985
0.018
1986
0.017
1987
0.017
1988
0.020
1989
0.025
1990
0.026
1991
0.027
1992
0.018
1993
0.011
1994
0.009
1995
0.008
1996
0.011
1997
0.013
1998
0.007
1999
0.002
2000
0.004
2001
0.002
2002
0.003
2003
0.004
2004
0.012
2005
0.010
2006
0.007
2007
0.024
Total
0.013

SD
0.035
0.040
0.034
0.041
0.039
0.036
0.034
0.033
0.036
0.034
0.049
0.034
0.032
0.033
0.033
0.033
0.043
0.040
0.049
0.096
0.094
0.064
0.065
0.073
0.138
0.087
0.091
0.066

p25
0.012
0.010
0.009
0.010
0.011
0.009
0.009
0.013
0.017
0.017
0.015
0.010
0.006
0.005
0.005
0.008
0.008
0.005
0.001
0.003
0.000
-0.008
0.002
0.008
0.010
0.010
0.011
0.007

Median
0.027
0.024
0.021
0.023
0.024
0.020
0.021
0.026
0.029
0.029
0.028
0.021
0.015
0.014
0.015
0.017
0.018
0.015
0.011
0.013
0.013
0.009
0.013
0.021
0.025
0.028
0.027
0.020

p75
0.049
0.046
0.040
0.042
0.043
0.039
0.037
0.043
0.046
0.046
0.045
0.036
0.031
0.029
0.030
0.033
0.033
0.030
0.026
0.032
0.033
0.027
0.032
0.044
0.049
0.055
0.055
0.039

N

SD
0.043
0.042
0.044
0.040
0.038
0.046
0.041
0.038
0.038
0.045
0.141
0.045
0.041
0.046
0.042
0.033
0.090
0.057
0.043
0.053
0.050
0.070
0.066
0.064
0.095
0.088
0.375
0.089

p25
0.003
0.002
0.002
0.002
0.003
0.002
0.002
0.004
0.005
0.004
0.003
0.002
0.001
0.000
0.001
0.001
0.002
0.001
0.000
0.000
-0.007-0.003-0.001
0.002
0.002
0.001
0.003
0.001

Median
0.011
0.010
0.011
0.011
0.012
0.010
0.011
0.014
0.016
0.015
0.014
0.012
0.007
0.006
0.007
0.007
0.008
0.006
0.004
0.005
0.005
0.005
0.006
0.010
0.012
0.013
0.013
0.009

p75
0.032
0.029
0.028
0.029
0.031
0.028
0.028
0.032
0.036
0.037
0.036
0.029
0.023
0.021
0.020
0.021
0.021
0.018
0.015
0.019
0.019
0.019
0.020
0.025
0.031
0.032
0.031
0.026

N

Note:
Net income (annual net income) is scaled by total assets at the end of the previous year.

42

1,472
1,464
1,419
1,481
1,524
1,563
1,548
1,484
1,430
1,464
1,619
1,801
1,912
2,021
2,096
2,159
2,235
2,307
2,355
2,423
2,557
2,658
2,777
2,905
3,045
3,200
3,351
56,270

949
987
1,007
1,041
1,063
1,081
1,084
1,088
1,130
1,195
1,343
1,432
1,466
1,467
1,441
1,423
1,412
1,390
1,387
1,345
1,331
1,343
1,310
1,264
1,201
1,145
1,020
33,345

Table 3. Descriptive statistics on the variables for our regression analysis
LOSSEM
PRIVATETAXCOSTFIN
ASSET∆ASSET
∆CFO
WCA
EXT
CYCLE
AGE
ICLAIM
CEC

Mean
0.880
0.643
0.724
1.004
10.419
0.010
0.000
0.006
-0.0034.690
3.665-0.231
0.019

Min
0.000
0.000
0.000
-2.9746.449-0.364-0.459-0.236-0.1620.841
1.946-2.835-3.500Median
1.000
1.000
1.000
0.944
10.336-0.0070.000
0.001-0.001
4.892
3.761-0.180
0.000

Max
1.000
1.000
1.000
3.995
14.740
0.985
0.399
0.355
0.099
6.987
4.663
3.358
3.833

SD
0.325
0.479
0.447
1.606
1.494
0.121
0.089
0.062
0.013
1.147
0.513
1.085
0.519

Skewness-2.333-0.596-1.005-0.1300.288
2.125-0.0920.832-0.506-0.928-0.783-0.191
0.138

Kurtosis
6.445
1.355
2.010
2.553
3.451
12.2255.850
7.707
22.992
4.291
3.355
2.835
20.828

N
6,937
6,937
6,889
6,550
6,937
6,937
6,937
6,937
6,937
6,937
6,937
6,937
6,937

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between zero (inclusive)and 0.0028 (exclusive), and zero if the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0 (exclusive);
PRIVATE = an indicator variable that takes the values of one if the firm is un-listed, and zero otherwise;
TAXCOST = an indicator variable that takes the values of one if the firm has positive marginal tax rates based on the assumptions
described Gramlich et al. (2004), and zero otherwise. See also Appendix;
FIN = the strength of the relationship between firm and their bank, computed using principal component analysis. See also
Appendix;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) ,divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as(yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). If the costof goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current
year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net
income.
All variables are winsorized by year at the extreme 1 percent and 99 percent.

43

LOSSEM
1.00
0.04***
0.30***
0.06***
0.09***
0.06***
0.01
0.02*
0.01
0.04***-0.01
0.03**
0.02

PRIVATE
0.04***
1.00
0.16***
0.27***-0.13***
0.09***
0.00
0.05***
0.12***-0.15***-0.32***-0.34***
0.02

TAXCOST
0.30***
0.16***
1.00
0.05***
0.05***
0.12***
0.03**
0.09***
0.03**-0.02-0.13***-0.06***
0.04***

FIN
0.06***
0.29***
0.05***
1.00
0.26***
0.19***
0.00
0.10***
0.09***
0.15***-0.17***-0.29***
0.00

ASSET
0.09***-0.09***
0.06***
0.23***
1.00
0.04***
0.00
0.02-0.06***
0.28***
0.06***
0.11***
0.00∆ASSET
0.06***
0.07***
0.12***
0.13***
0.02*
1.00-0.13***
0.39***
0.02*
0.03**-0.11***-0.04***
0.01

∆CFO
0.01
0.01
0.02*
0.01
0.01
-0.05***
1.00-0.63***-0.06***
0.01
-0.01
-0.02
0.00

WCA
0.01
0.03**
0.09***
0.04***-0.02*
0.27***-0.59***
1.00
0.04***
0.04***-0.09***
0.00
0.01

EXT
0.03**
0.17***
0.09***
0.09***-0.07***
0.06***-0.02*
0.07***
1.00-0.03**-0.04***-0.08***
0.01

CYCLE
0.03**-0.15***-0.03**
0.13***
0.27***
0.00
0.02**
0.00-0.03**
1.00
0.12***
0.48***
0.04***

AGE-0.01-0.34***-0.14***-0.19***
0.06***-0.08***-0.02-0.05***-0.13***
0.07***
1.00
0.28***
0.02

ICLAIM
0.02*-0.33***-0.06***-0.31***
0.11***-0.03**-0.01
0.01-0.11***
0.49***
0.25***
1.00
0.02*

CEC
0.01
-0.01
0.14***-0.03**
0.03**-0.01
0.01
0.03**
0.01
0.04***
0.03**
0.04***
1.00

44

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between 0 (inclusive) and 0.0028 (exclusive), and zero if the firm has scaled earnings in theinterval between -0.0028 (inclusive) and 0 (exclusive);
PRIVATE = an indicator variable that takes the values of one if the firm is un-listed, and zero otherwise;
TAXCOST = an indicator variable that takes the values of one if the firm has positive marginal tax rates based on the assumptions described Gramlich et al. (2004), and zero otherwise. See also Appendix;
FIN = the strength of the relationship between firm and their bank, computed using principal component analysis. See also Appendix;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) , divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as (yearly average accounts receivable) / (total revenue / 360) + (yearly
average inventory) / (cost of goods sold / 360). If the cost of goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net income.
All independent variables are winsorized at one percent by year*** Statistically significant at the 0.01 level of significance using a two-tailed t-test** Statistically significant at the 0.05 level of significance using a two-tailed t-test* Statistically significant at the 0.1 level of significance using a two-tailed t-test

LOSSEM
PRIVATETAXCOSTFIN
ASSET∆ASSET
∆CFO
WCA
EXT
CYCLE
AGE
ICLAIM
CEC

Table 4. Correlations matrix among the variables for our regression analysis

Table 5. Standardized differences
Panel A: Standardized differences
Values for standardized differences ofremaining 77 intervals4

Values for test intervals
Standardizes difference
to the left of 02

Standardizes difference
to the right of 03

Mean

MedianMinimum

Maximum

Fig.1 Panel A-23.206***

11.523***0.03-0.019-2.4952.529

Fig.1 Panel B
Fig.2 Panel A-39.270***-15.343***

27.792***17.816***-0.114-0.076-0.212-0.221-3.19-4.741

2.235
3.790

Fig.2 Panel B-3.782***18.655***-0.273-0.097-10.647

1.935

EM ratio

χ2-value5

Fig.1 Panel A
Fig.1 Panel B

7.19
9.023

4.778**(Fig 1 panel A vs Fig 1 Panel B)

Fig.2 Panel A
Fig.2 Panel B

2.009
1.517

36.126***(Fig 2 panel A vs Fig 2 Panel B)

Panel B: The EM ratio

Notes:
1
The standardized difference is the difference between the observed and expected number of firm-years in an interval, standardized
by estimated standard deviation of the difference.
2
The standardized difference for the interval immediately to the left of zero is expected to provide a more powerful test for earnings
management to avoid decreases (loss) in earnings, and it should be considered the primary test for earnings management. Negativevalues represent the evidence of earnings management to avoid decreases (loss) in earnings.
3
The standardized the difference for the interval immediately to the right of zero provides an alternative, and probably lesspowerful, test for earnings management to avoid decreases (loss) in earnings. Positive values represent evidence of earnings
management aimed at avoiding decreases (loss) in earnings.
4This includes standardized differences belonging to 77 of 81 intervals shown in each of the figures, where the four omitted
standardized differences correspond to the most extreme intervals adjacent to zero and the most extreme negative and the mostextreme positive intervals. The standardized differences for the most extreme interval are undefined because an adjacent interval
exists on only one side.
5
The chi-square statistics for the EM ratio differences are computed using the usual 2×2 contingency table.
** Statistically significant at the 0.5 level.
*** Statistically significant at the 0.01 level.

45

Table 6. Regression results on the relationship between marginal tax rate and firms
with reporting slightly earnings
Independent
Variable

Expected
Sign

Constant
TAXCOST

+

ASSET

-

∆ASSET

+

∆CFO

+

WCA

+

EXT

+

CYCLE

+

AGE

-

ICLAIM

+

CEC

+

Full
LOSSEM
Coefficient(z-value)

PrivateLOSSEM
Coefficient(z-value)-0.954*(-1.783)
1.761***(13.559)
0.159***(4.135)0.961*(1.842)0.008(0.015)-0.573(-0.755)
4.220(0.947)0.035(0.699)0.139(1.513)-0.042(-0.573)-0.011(-0.155)-1.069(-1.218)
1.270***(8.104)0.176***(3.598)1.450*(1.746)0.347(0.465)0.777(0.507)
11.242**(2.224)0.087(0.779)0.074(0.396)0.109(0.832)0.068(0.812)-0.375(-0.354)
2.122***(14.298)
0.163***(2.658)0.682(1.254)0.003(0.004)-1.202(-1.077)-4.525(-0.958)
0.033(0.662)0.222*(1.804)-0.147(-1.023)-0.090(-0.933)

Yes

Yes

Yes

-2017.108
0.127
6,630

-855.959
0.093
2,419

-1133.261
0.167
4,211

Industry dummy
Log likelihood
McFadden R2
N

PublicLOSSEM
Coefficient(z-value)

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between 0 (inclusive)and 0.0028 (exclusive), and zero if the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0 (exclusive);
TAXCOST = an indicator variable that takes the values of one if the firm has positive marginal tax rates based on the assumptions
described Gramlich et al. (2004), and zero otherwise. See also Appendix;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) ,divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as(yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). If the costof goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current
year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net
income.
All independent variables are winsorized at one percent by year.
z-statistics are adjusted for cross-sectional and intertemporal dependence using two-way cluster-robust standard errors proposedby Petersen (2009).
*** Statistically significant at the 0.01 level of significance using a two-tailed z-test** Statistically significant at the 0.05 level of significance using a two-tailed z-test* Statistically significant at the 0.1 level. of significance using a two-tailed z-test

46

Table 7. Regression results on the relationship between the alignment with banks
and firms with reporting slightly earnings
Independent
Variable

Expected
Sign

Constant
FIN

+

ASSET

-

∆ASSET

+

∆CFO

+

WCA

+

EXT

+

CYCLE

+

AGE

-

ICLAIM

+

CEC

+

Full
LOSSEM
Coefficient(z-value)
0.962*(1.693)0.109***(3.666)0.151***(5.462)
1.409**(2.510)0.981*(1.784)1.164(1.358)2.639(0.599)-0.026(-0.512)-0.018(-0.172)
0.121(1.540)0.077(1.207)

Industry dummy
Log likelihood
McFadden R2
N

PublicLOSSEM
Coefficient(z-value)

PrivateLOSSEM
Coefficient(z-value)

0.992(1.072)-0.012(-0.221)0.165***(3.336)
1.788**(2.158)1.642**(2.100)2.558*(1.672)
11.084**(2.246)-0.013(-0.142)-0.104(-0.543)
0.166(1.251)0.100(1.288)1.463(1.392)0.147***(3.527)0.170***(3.476)
1.280**(2.159)0.772(1.097)0.517(0.503)-4.619(-1.050)-0.027(-0.482)0.044(0.304)0.064(0.452)0.052(0.563)

Yes

Yes

Yes

-2322.658
0.038
6,550

-904.121
0.041
2,404

-1397.133
0.049
4,146

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between 0 (inclusive)and 0.0028 (exclusive), and zero if the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0 (exclusive);
FIN = the strength of the relationship between firm and their bank, computed using principal component analysis. See also
Appendix;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) ,divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as(yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). If the costof goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current
year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net
income.
All independent variables are winsorized at one percent by year.
z-statistics are adjusted for cross-sectional and intertemporal dependence using two-way cluster-robust standard errors proposedby Petersen (2009).
*** Statistically significant at the 0.01 level of significance using a two-tailed z-test** Statistically significant at the 0.05 level of significance using a two-tailed z-test* Statistically significant at the 0.1 level. of significance using a two-tailed z-test

47

Table 8. Regression results on the effect of the difference public and private firms
on the reporting slightly earnings
Independent
Variable

Expected
Sign

Constant
PRIVATE

+

ASSET

-

∆ASSET

+

∆CFO

+

WCA

+

EXT

+

CYCLE

+

AGE

-

ICLAIM+

CEC+

LOSSEM
Coefficient(z-value)
0.066(0.117)0.243**(2.114)0.195***(6.441)1.405***(2.735)1.074**(1.994)
1.466*(1.752)2.531(0.629)0.020(0.438)0.050(0.450)0.045(0.603)0.072(1.201)

Industry dummy

Yes

Log likelihood
McFadden R2
N-2455.037
0.037
6,937

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between 0 (inclusive)and 0.0028 (exclusive), and zero if the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0 (exclusive);
PRIVATE = an indicator variable that takes the values of one if the firm is un-listed, and zero otherwise;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) ,divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as(yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). If the costof goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current
year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net
income.
All independent variables are winsorized at one percent by year.
z-statistics are adjusted for cross-sectional and intertemporal dependence using two-way cluster-robust standard errors proposedby Petersen (2009).
*** Statistically significant at the 0.01 level of significance using a two-tailed z-test** Statistically significant at the 0.05 level of significance using a two-tailed z-test* Statistically significant at the 0.1 level. of significance using a two-tailed z-test48

Table 9. Regression results on the effect of the difference public and private firms
on the relationship between institution features and reporting slightly earnings
Independent
Variable

Expected
Sign

LOSSEM
Coefficient(z-value)

Constant
TAXCOST+PRIVATE*TAXCOST

+

FIN

+

PRIVATE*FIN

+

ASSET

-

∆ASSET

+

∆CFO

+

WCA

+

EXT

+

CYCLE

+

AGE

-

ICLAIM+

CEC+-1.053*(-1.890)
1.405***(9.271)0.603***(4.356)

0.155***(3.925)1.018*(1.776)-0.075(-0.137)-0.821(-1.092)
4.288(0.975)0.046(0.836)0.166(1.629)0.004(0.052)-0.018(-0.245)

Industry dummy
Log likelihood
McFadden R2
N

LOSSEM
Coefficient(z-value)
1.366**(2.434)-0.014(-0.235)0.151***(2.594)0.128***(4.041)
1.524***(2.680)0.991*(1.779)0.967(1.184)5.392(1.195)-0.018(-0.306)-0.069(-0.600)
0.125(1.555)0.051(0.812)LOSSEM
Coefficient(z-value)-0.675(-1.152)
1.462***(9.488)0.531***(3.724)0.088(1.592)0.038(0.709)0.128***(3.611)0.866(1.499)-0.171(-0.311)-1.028(-1.307)
3.482(0.798)-0.003(-0.049)
0.180*(1.908)0.080(1.018)-0.019(-0.255)

Yes

Yes

Yes

-1914.912
0.131
6,282

-2118.633
0.039
6,282

-1908.445
0.134
6,282

Note:
LOSSEM = an indicator variable that takes the value of one if the firm has scaled earnings in the interval between 0 (inclusive)and 0.0028 (exclusive), and zero if the firm has scaled earnings in the interval between -0.0028 (inclusive) and 0 (exclusive);
PRIVATE = an indicator variable that takes the values of one if the firm is un-listed, and zero otherwise;
TAXCOST = an indicator variable that takes the values of one if the firm has positive marginal tax rates based on the assumptions
described Gramlich et al. (2004), and zero otherwise. See also Appendix;
FIN = the strength of the relationship between firm and their bank, computed using principal component analysis. See also
Appendix;
ASSET = the natural log of total assets at the end of the fiscal year;∆ASSET = first difference in total assets, divided by total assets at the end of the previous year;∆CFO = fitst difference in cash flows, divided by total assets at the end of the previous year;
WCA = working capital accruals (∆current assets − ∆cash and cash equivalents) − (∆current liabilities − ∆financing item) ,divided by total assets at the end of the previous year;
EXT = extraordinary items (extrordinary gains – extraordinary losses), divided by total assets at the end of the previous year;
CYCLE = the natural log of the length of the operating cycle in days. We compute the length of the operating cycle in days, as(yearly average accounts receivable) / (total revenue / 360) + (yearly average inventory) / (cost of goods sold / 360). If the costof goods sold number is not reported, we use total revenue minus operating income instead;
AGE = the natural log of the firm age. The firm age is the difference the year when the firm actually incorporated and the current
year;
ICLAIM = the reliance on implicit claims, computed using principal component analysis. See also Appendix;
CEC = the change in executive compensations (total cash salary and total bonus paid to all directors) divided by the change in net
income.
All independent variables are winsorized at one percent by year.
z-statistics are adjusted for cross-sectional and intertemporal dependence using two-way cluster-robust standard errors proposedby Petersen (2009).
*** Statistically significant at the 0.01 level of significance using a two-tailed z-test** Statistically significant at the 0.05 level of significance using a two-tailed z-test* Statistically significant at the 0.1 level. of significance using a two-tailed z-test

49

Figure 1. The distribution of scaled the annual net income
Panel A：Public firms
2000
1800
1600
1400
1200
1000
800
600
400
200
0
-0.0495-0.033-0.016500.01650.033

0.0495

0.066

0.033

0.0495

0.066

Panel B: Private firms
3000

2500

2000

1500

1000

500

0
-0.0495-0.033-0.016500.0165

Notes: The distribution interval widths are 0.0014, and the location of zero on the horizontal axis is indicated by the dashed line.
The first interval to the right of zero contains observations in the [0.0000, 0.0014), the second interval contains [0.0014, 0.0028),
and so forth.

50

Figure 2. The distribution of scaled changes in the annual net income
Panel A：Public firms
3000

2500

2000

1500

1000

500

0
-0.028-0.0224-0.0168-0.0112-0.005600.00560.01120.01680.02240.028

0.01120.01680.02240.028

Panel B: Private firms
3000

2500

2000

1500

1000

500

0
-0.028-0.0224-0.0168-0.0112-0.005600.0056

Notes: The distribution interval widths are 0.0007, and the location of zero on the horizontal axis is indicated by the dashed line. The
first interval to the right of zero contains observations in the [0.000, 0.0007), the second interval contains [0.0007, 0.0014), and soforth.

51

Appendix A. Variable definition of marginal tax rate
In order to measure marginal tax rate, we use a taxable income dummy variable
following the method of Gramlich et al. (2004). Under Japanese tax laws, in general,
Japanese firms are allowed to carry losses back one year and forward five to offset
positive pre-tax income in these other years (Ishi 1993; Kuboi 1991). However, loss
carrybacks were not permitted in Japan between 1984 and 1988, or between 1992 and2007. Further, with respect to carryforwards, losses from the immediate preceding years
could not be used to offset taxable income in 1986 and 1987 (Kuboi 1991). In otherwords, taxpayers were required to wait one year before using the net operating loss
carry forwards. Consequently, in accordance with the above prescription of Japanese tax
laws, the taxable income dummy variable (i.e., marginal tax rate) is defined based on
the conditions of the following three periods.
1. The period from the beginning of January 1981 to the end of March 1984, and theperiod from the beginning of April 1988 to the end of March 1992.
The marginal tax rates variable takes the value of one in following two case, and zerootherwise: (1) if current pre-tax income is both greater than zero and exceeds the sum of
net losses for the two preceding years (i.e., loss carryforward), and (2) if the pre-tax loss
is smaller than the prior-year’s pre-tax income (i.e., loss carryback).
2. The period from the beginning of April 1984 to the end of March 1986, and theperiod from the beginning of April 1992 to the end of March 2007.
The marginal tax rates variable takes the value of one in the following case, and zerootherwise: if current pre-tax income is both greater than zero and exceeds the sum of net
losses for the two preceding years (i.e., loss carryforward).
3. The period from the beginning of April 1986 to the end of March 1988.
The marginal tax rates variable takes the value of one in the following case, and zerootherwise: if positive pre-tax income exceeds the sum of net losses for 1983 and 1984
(1984, 1985).

52

Appendix B. Table A1
Principal component analysis results on measuring financial institutions’relationships
Panel A: Principal component analysis for public and private firm
Correlation matrix
DEBT
LOAN
LOAN5
DEBT
1.00
0.61***
0.56***
LOAN
0.44***
1.00
0.94***
LOAN5
0.44***
0.94***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
2.248
74.9%
74.9%
Component 2
0.690
23.0%
97.9%
Component 3
0.062
2.1%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DEBT
0.446
0.895
0.006
LOAN
0.634
-0.311-0.708LOAN5
0.632-0.3200.706
Panel B: Principal component analysis for public firm
Correlation matrix
DEBT
LOAN
LOAN5
DEBT
1.00
0.62***
0.56***
LOAN
0.45***
1.00
0.93***
LOAN5
0.44***
0.92***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
2.241
74.7%
74.7%
Component 2
0.680
22.7%
97.4%
Component 3
0.079
2.7%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DEBT
0.453
0.891
0.012
LOAN
0.632
-0.312-0.710LOAN5
0.629-0.3290.704
Panel C: Principal component analysis for private firm
Correlation matrix
DEBT
LOAN
LEV
DEBT
1.00
0.59***
0.57***
LOAN
0.48***
1.00
0.95***
LOAN5
0.48***
0.94***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
2.297
76.6%
76.6%
Component 2
0.647
21.6%
98.1%
Component 3
0.056
1.9%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DEBT
0.462
0.887
0.001
LOAN
0.627
-0.326-0.708LOAN5
0.627-0.3280.707
Note:
Table reports the results from computing FIN measure using principal component analysis based on following three variables.
DEBT = an indicator variable that takes the values of one1 if the firm has a loan (short-term orand long-term loan), and zerootherwise.
LOAN = a sum of short-term and long-term loans, divided by total assets at the end of the last year.
LOAN5 = the average of LOAN for the past five years;

Although we conduct a principal components factor analysis of the three variables (DEBT, LOAN, and LOAN5)
using annual cross-sectional regressions, in the interest of brevity, we present the results of a polled cross-section,
time-series estimation.

53

Appendix B. Table A2
Principal component analysis results on measuring reliance on implicit claims
Panel A: Principal component analysis for public and private firm
Correlation matrix
DUR
R&D
LABOR
DUR
1.00
0.34***
0.13***
R&D
0.14***
1.00
0.08***
LABOR
0.20***
0.1***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
1.293
43.1%
43.1%
Component 2
0.912
30.4%
73.5%
Component 3
0.795
26.5%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DUR
0.636
-0.174-0.752
R&D
0.497
0.838
0.226
LABOR
0.591
-0.5170.619
Panel B: Principal component analysis for public firm
Correlation matrix
DUR
R&D
LABOR
DUR
1.00
0.29***
0.05***
R&D
0.10***
1.00-0.02***
LABOR
0.10***
0.04***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
1.161
38.7%
38.7%
Component 2
0.958
31.9%
70.6%
Component 3
0.882
29.4%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DUR
0.651
-0.018-0.759
R&D
0.529
0.727
0.438
LABOR
0.544
-0.687
0.483
Panel C: Principal component analysis for private firm
Correlation matrix
DUR
R&D
LABOR
DUR
1.00
0.14***
0.21***
R&D
0.09***
1.00
0.03***
LABOR
0.23***
0.06***
1.00
Total variance explained
Eigenvalue% of Variance
Cumulative %Component 1
1.275
42.5%
42.5%
Component 2
0.962
32.1%
74.6%
Component 3
0.763
25.4%
100.0%
Principal components (eigenvectors)
Component 1
Component 2
Component 3
DUR
0.673
-0.165-0.721
R&D
0.362
0.924
0.126
LABOR
0.646
-0.3460.681
Note:
Table reports the results from computing ICLAIM measure using principal component analysis based on following three variables.
DUR = an indicator variable that takes the values of one if membership in a durable goods industry (Nikkei gyousyu chu-bunrui13-33, and 41), and zero otherwise;
R&D = research and development expenses divided by total assets at the fiscal year.
LABOR = labor intensity ( 1 – (fixed assets subject to depreciation divided by total assets at the fiscal year)).

Although we conduct a principal components factor analysis of the three variables (DUR, R&D and LABOR) using
annual cross-sectional regressions, in the interest of brevity, we present the results of a polled cross-section,
time-series estimation.

54