Is the quality of accounting information complementary or substitutefor other governance mechanisms?
: Evidence from the real effects of earnings quality in Japan
August, 2017

Abstract: The purpose of this study is to analyze the interrelationship between the effects on investment efficiency of accounting information and other governance structures. How they relate is nottrivial from the theoretical implication. While one predicts that accounting information is the substitutefor other governance tools, the two mechanisms might strengthen each other. I study whether several
governance structures mitigating information problems weakens or strengthens the effects of accounting information on investment. Among the governance mechanisms, this study focuses on institutional
investors, outside directors, and main bank relationship. I show that the alternative information-problem-mitigating mechanisms influences on the firms’ investment efficiency, but the effects are not monotonic. I also find that main bank changes the investment effects of earnings quality, but not institutional ownership and outside directors, by considering their nonlinear effects on investment.

JEL classification:
Keywords: real effects, earnings quality, investment efficiency

11. Introduction
The purpose of this study is to analyze the interrelationship between the effects on investment efficiency of accounting information and other governance structures. How they relate each otheris not trivial from the theoretical implication. While one predicts that accounting information is thesubstitute for other governance tools, the two mechanisms might strengthen each other. I study whether
several governance structures mitigating information problems weakens or strengthens the effects ofaccounting information on investment.
The real effects of accounting information coincides with the effects of “alternative information-problem-mitigating mechanisms”. A bulk of study shows that “high-quality” governancemechanism improves firms’ efficiency. For instance, institutional investors mitigate information problems through their prior ability to monitor managerial behavior (Bushee 1998; Hartzell and Starks
2003; Chen et al. 2007). Other mechanisms I will discuss are main bank relationship and outside directors. These governance forms improve firms’ efficiency by alleviating information and incentive
problems. Disclosing high-quality accounting information, managers are able to resolute the two problems. Lots of studies show that earnings attributes (EAs1) useful for outsiders decrease information
asymmetry in security market and promote monitoring efficiency. Biddle and Hilary (2006) and Biddle
et al. (2009) reveal that the useful EAs improve investment efficiency.
Not trivial is, however, the relationship between the effectiveness of accounting informationand of other governance mechanisms. Accounting information appears to substitute for other governance mechanisms. If managers are able to improve her firm’s efficiency, the effectiveness of accounting would weaken. On the other hand, they might be complementary. Sophisticated investors’ behaviors are the signals to others. If the behaviors are based on accounting information, the effects of accounting strengthens through high-quality governance. Theoretical discussion does not conclude how
they associate.
Empirical evidence is also mixed. Biddle and Hilary (2006) consider that accounting information is the substitute. They show the evidence that useful EAs improve U.S. firms’ investment efficiency, but not Japanese firms’. They infer that Japanese keiretsu and main bank decrease the effectsof the accounting information. Observing investment-cash flow sensitivity (ICS), Baik et al. (2010)
justify the inference. Nevertheless, a few studies show inconsistent evidence with them. Fujitani(2017) examines the effects of accounting information on over- and under-investment, and finds that
useful EAs alleviate them even in recent Japanese firms after controlling the effects of other governance mechanisms including main bank relationship. Beatty et al. (2010) investigates the relationshipbetween the capital structure and the accounting effects. They do not find that bank lending does not
weaken them, which is inconsistent with Biddle and Hilary (2006).
Hence, whether the accounting information complements or substitutes for other governance1

In this study, “earnings attributes (EAs)” is used interchangeably with “earnings quality”.
2structures is interesting research question. This study uses the model identifying under- and over- investment firms, and tests how governance mechanisms change the effects of EAs on under- and overinvestment. Among the governance mechanisms, I focus on institutional investors, outside directors,
and main bank relationship. Using Japanese data, I show that the alternative information-problemmitigating mechanisms influences on the firms’ investment efficiency, but the effects are not monotonic. I also find that main bank changes the investment effects of earnings quality, but not institutional
ownership and outside directors, by considering their nonlinear effects on investment. Specifically,
main bank is sometimes complement, especially on alleviating under-investment, and sometimes substitute for earnings quality.
This study incorporates three contributions. First, I focus on the relationship between thereal effects of accounting information and of the alternative information-problem-mitigating mechanisms. In this sense, this study complements the stream of study on it, such as Baik et al. (2010) andBeatty et al. (2010). However, I sophisticate the tests, for instance I consider the nonlinear effects on
investment. In addition, this is the first study the complementary interrelationship between accounting
information and governance mechanisms with respect to promoting investment efficiency. Second, I
find that non-monotonic effects of governance mechanisms on investment efficiency by the models
developed by Biddle et al. (2009). Using the same methodology, Xiao et al. (2017a) and Xiao et al.
(2017b) analyze the effects of going public on investment efficiency. This study is close to these studies, but I consider the non-linear relationship in the model and find it. Third, I show explicit evidence
on the real effects of accounting information in Japanese economy. Fujitani (2017) is the first study tofind the investment effects of earnings attributes by using investment efficiency. This study complements the literature by more strictly controlling governance mechanisms other than accounting information. This contradicts to the results of Biddle and Hilary (2006).
The remainder of this paper proceeds as follows. The next section introduces the theoreticalbackgrounds, reviews prior literature and constructs hypotheses. Section 3 describes the research design. Section 4 reports the results, and section 5 summarizes the results of robustness tests. Section 6
concludes.

2. Backgrounds and hypotheses
2.1 Theoretical underpinnings
Information and incentive problems worsen the firms’ investment efficiency (Fazzari et al.
1988; Hubbard 1998; Stein 2003). These problems are alleviated by the firms’ disclosure and financial
reporting (Healy and Palepu 2001; Beyer et al. 2010; Bertomeu and Cheynel 2016). Several studies
show that the high-quality financial reporting mitigates asymmetric information, and decreases costof capital (Francis et al. 2004; Francis et al. 2005; Brown and Hillegeist 2007). Thus, the accounting
information quality is likely to affect firms’ investment behaviors. Accounting researchers show the3

evidence of the real effects of earnings around the world (Biddle and Hilary 2006; Biddle et al. 2009;
Fujitani 2017).
Accounting information is not the only one solution to information and incentive problems.
The problems are settled by alternative mechanisms. Managers are able to deal with incentive problems through several governance mechanisms. Jensen and Meckling (1976, p. 323) suggest bonding
mechanisms: auditing by banks or formal controlling systems, such as directors. Banks scrutinize
managers when they start lending or re-lending (Diamond 1984; Krasa and Villamil 1992). Directors
are able to bond managerial incentives to shareholders’ utility by their right to appoint and dismiss
managers and their role in monitoring managers (Shleifer and Vishny 1989). Jensen (1986) proposesthat debt is a solution to the agency problem. Since managers have discretions over payout, they areable to increase her own utility by not exercise payout. On the other hand, managers have to pay
constant interests defined in debt contracts every period. This requirement limits managers’ discretions
over allocation of earnings, and alleviates agency problem between managers and shareholders. Institutional investors also are important monitoring mechanisms of firms (Bushee 1998; Hartzell and
Starks 2003; Chen et al. 2007; Aghion et al. 2013).
Information problems are also alleviated by governance mechanisms other than accounting
information. One of the examples is main bank and keiretsu. Main banks and firms in keiretsu groupare able to communicate firms through private channel. Hoshi et al. (1991) find that firms in keiretsu
groups face less financial constraints than those not in, since keiretsu mitigates information problems.
Relationship banks, coincident with main banks, also seem to function in the same way. Bolton et al.
(2016) find that relationship banks increase costs in ordinary economic condition, but decreases it indepression as relationship banks are able to collect information on the firms even in depression. Sophisticated investors, such as institutional investors, seem to alleviate information problems throughsignaling their advanced evaluation on firms to security market (e.g., Ivashina and Sun 2011) 2 .
Campbell and Kracaw (1980) discuss that security market is confident in the signals of financial intermediaries since they might hardly have the incentives to mislead other market participants3. Overall,managers are able to mitigate information and incentive problems not only by accounting information,
but by internal and external governance mechanisms.

2

Institutional investors are able to use private information their own trading. This causes anotherserious agency problem, discussed by Bebchuk et al. (2017). I will discuss this point later in myanalysis.
3 However, they point out that information production of financial intermediaries is not able to totally solve information problems: it does not lead to the first best equilibrium (pp. 876-880).
4

2.2 Prior literature and hypotheses
Governance mechanisms alleviating information asymmetry other than accounting information4, alternative information-problem-mitigating mechanisms hereafter5, might relate to the effectsof accounting information on firm’s investment decision. One possibility is that the alternative information-problem-mitigating mechanisms shrink the importance of accounting information. For instance, Biddle and Hilary (2006) find that earnings quality mitigates firm’s financial constraints in US,
but it does not in Japan. They infer that:(A) ccounting quality should have a smaller effect on investment efficiency in countries
where bank financing and keiretsu are important sources of capital than in countries where
equity is a dominant source of capital, because capital suppliers in credit-based economies
have alternative avenues for reducing information asymmetry. (Biddle and Hilary 2006, p.
976)

Beatty et al. (2010) test whether how a firm funds changes the strength of the effects ofearnings quality on investment or not. According to Biddle and Hilary's (2006) discussion, firms
mainly funding from banks are able to restrict asymmetric information through the private communication with the banks or banks’ monitoring. For those firms, accounting information does not seem tobe important. On the other hand, accounting information plays incremental role in firms mainly funding from arm’s length capital resource, since they do not have the private channel. Thus, I expect that
accounting information and alternative information-problem-mitigating mechanisms are substitute.
The empirical evidence is mixed. Baik et al. (2010) find that
The other possibility is that the alternative information-problem-mitigating mechanisms increase the importance of accounting information. Chen et al. (2011) find the evidence consistent withthis expectation. When banks evaluate firm’s repayment capacity, they might use accounting information. Thus, the earnings quality, the proxy of accounting information quality, might affect investment in firms funding from banks than in those not doing. Using private firms in emerging countries,
they find that the effects of earnings quality on investment efficiency are stronger as firms depend
more on banks. Prior studies do not provide the conclusive evidence on how accounting informationand other governance mechanisms associates each other.
Among the alternative information-problem-mitigating mechanisms, I focus on three governance mechanisms. First, main bank is one of the solutions to alleviate information and incentive
problems. Those two problems are mitigated by the private communication channel which main banks

4

Becht et al. (2003) is worth referring for comprehensive survey on corporate governance andagency problems (asymmetric information).
5 I follow Beatty et al. (2010) when using this terminology.
5

have (e.g., Hoshi and Kashyap 1990; Hoshi et al. 1990; Hoshi et al. 1991; Kang and Shivdasani 1995).
Based on these studies, Biddle and Hilary (2006) infer that main banks substitute for accounting information.

H1: The strength of firm-main bank relationship changes the investment effects of earnings quality.

Second, institutional investors are likely to alleviate asymmetric information. They are able
to monitor managerial decision from long-term perspective. Bushee (1998) discussed that institutional
investors decrease the managerial incentives on myopic behavior. Then, he hypothesizes and finds that
institutional investors prevent managers from cutting R&D expenses in order to beat and meet benchmark earnings. Hartzell and Starks (2003) find that institutional investors mitigate agency problem bychanging manager's compensation scheme. Aghion et al. (2013) also show that institutional ownershippromotes R&D investment and patent. They hypothesize institutional investors are able to discipline``lazy managers’’ by their forces to influence on managerial careers. Such effects of institutional investors monitoring on investment might weaken the effects of accounting information.
On the other hand, considering that institutional investors utilize accounting information,
institutional ownership appears to strengthen its effects. Since institutional investors focus on longterm performance, they pay much attention to signals from managers. Thus the behavior of institutional investors reflect accounting information. Then, the behavior becomes the reliable signals toother investors (Campbell and Kracaw 1980). The effects of institutional investors help accounting
information spread out over the security market. While the two predictions on institutional investors
are opposite signs, I summarize them as follows:

H2: The power of institutional investors changes the investment effects of earnings quality.

Third, internal governance also alleviates asymmetric information. Outside directors are therepresentative of investors to monitor the managerial behavior. Outside directors do not have the interests to build the manager’s empire, so they seem to monitor managers more effective than insiders.
In particular, Japanese government recently promotes firms to introduce outside directors in order toincrease transparency by revising Japanese Corporate Acts and announcing Japanese version of Corporate Governance Code. However, outside directors also face asymmetric information. They do nothave the same information as the insider does. Thus, when they monitor managers, they might collectinformation from accounting information. If so, the information quality determines their monitoring
quality, thus accounting information and outside directors are complementary. I summarize the discussion as:6H3: The power of outside directors changes the investment effects of earnings quality.

In the previous discussion, I assume the governance effects on investment are monotonic.
However, several previous studies imply that they are nonlinear (He and Tian 2013; Sapra et al. 2014;
Bolton et al. 2016; Bebchuk et al. 2017). For instance, Bebchuk et al. (2017) discuss that the agency
problems of institutional ownership. They point out that the concentration of ownership of institutional
ownership raises the agency problems. Bolton et al. (2016) analyze the costs and benefits of relationship banking. They show that while relationship banking is costly in stable economic condition, is lesscostly in economic crisis. On the other hand, several studies provide the evidence on the advantagesof governance systems. Overall, I expect that the effectiveness of the each governance mechanism
increases with their power, and then shrinks after a threshold. Thus, this study tests the nonlinearity of
governance effects as preliminary analysis. In addition, I consider the non-monotonic effects wheninvestigating the relationship between the investment effects of earnings quality and the governancemechanisms.

H4a: The effects of governance mechanisms on investment are not monotonic.

H4b: The interrelationship between the effects of accounting information and of the alternative information-problem-mitigating mechanisms is not monotonic.

3. Research design
3.1 Over- and under-investment
I follow Biddle et al.'s (2009) model when identifying over- and under-investment (over andunder) firms (model (1)). They define over- and underinvestment with unexpected components ofinvestment. They regress investment (𝑖𝑛𝑣) at period 𝑡 on lagged sales growth (𝑠𝑔), then define as“unexpected investment (𝑢𝑒_𝑖𝑛𝑣)” the deference between the estimated level of investment and therealized value. The lagged sales growth is the proxy of investment opportunity.
𝑖𝑛𝑣𝑖,𝑡 = 𝛼0 + 𝛼1 𝑠𝑔𝑖,𝑡−1 + 𝜀1,𝑖,𝑡(1)

I also define the absolute value of the unexpected components of investment (𝑎𝑏[𝑢𝑒_𝑖𝑛𝑣])
as investment efficiency. This increases with the multitude of the unexpected components of investment, which indicate inefficient levels. To test the robustness of the main analysis, I also use Goodman
et al.'s (2014) model and Chen et al.'s (2011) model (their definitions are described in Appendix).

7

3.2 Earnings attributes
I use a proxy of earnings quality (𝑒𝑞), which is defined as the weighted average
indicator for the principal components of 4 earnings attributes. The 4 attribute are persistence, predictability, smoothness, and accruals quality of earnings, which prior studies discuss that are useful characteristics of earnings to investors (Francis et al. 2004; Francis et al. 2008).
Earnings persistence (𝑝𝑒𝑟) equals coefficient in AR1. Using 10 year data from t-9 throught, I estimate AR1 and define 𝑝𝑒𝑟 as the coefficient on previous year earnings. Predictability (𝑝𝑟𝑒) is
standard deviation of residuals calculated from AR1. Using 10 year data from t-9 through t, I estimate
AR1 and define 𝑝𝑟𝑒 as the standard deviation of the residuals in each year. Earnings smoothness(𝑠𝑚𝑜) equals 𝑣𝑜𝑙 divided by cash flow volatility (𝑣𝑐𝑓). Earnings smoothed through accruals are disperse less than cash flow. Thus, I define 𝑠𝑚𝑜 as 10 year earnings standard deviation (𝑣𝑜𝑙) scaled by
10 year cash flow standard deviation ( 𝑣𝑐𝑓 ). Accruals quality ( 𝑚𝑎𝑞 ) equals one developed by
McNichols (2002). Dechow and Dichev (2002) model regresses accruals (𝑎𝑐𝑐) on previous and former
cash flow (𝑐𝑓), and define the 5 year standard deviation of residuals as accruals quality. McNichols(2002) add several control variables in order to avoid omitted variable problem. I estimate McNichols'(2002) model by running separated industry-year regressions. Then, I take standard deviation of residuals in each firm.
Before estimating the principal components of the 4 earnings attributes, I take the signs of
all the earnings attributes except for persistence and smoothness. By this operation, the 4 variables
increase with the usefulness of the earnings. The definitions of each earnings attribute are describedin Appendix.
3.3 The alternative information-problem-mitigating mechanisms
I use three variables representing governance mechanisms. One is firms’ relationship to theirmain bank. Japanese Banking Acts permits banks to hold firms’ stock. Thus, I aggregate into one index
both the percentage debt lending from the main bank and its shareholdings. The aggregated main bankvariable (main) is calculated from the weights estimated by principal components analysis. This indicator increases with the strength of firm-main bank relationship. Institutional ownership (inst) equals
the percent of their shareholdings to whole issued stocks, which represents the power of institutional
investors’ monitoring. inst increases with their monitoring. Outside directors (outd) represents thepower of outside directors in a firm. This equals the percent of outside directors to whole directors.
This increases with the strength of their monitoring.
3.4 Models
To test the effects of earnings quality and the alternative information-problem-mitigating8

mechanisms, I use the models developed by Biddle et al. (2009). If these mechanisms mitigate investment efficiency, they decrease investment levels in over-investment firms and increase them in underinvestment firms. Considering opposite relationship between the two groups, Biddle et al. (2009) develop the model:

𝑖𝑛𝑣𝑖,𝑡 = 𝜂0 + 𝜂1 𝑒𝑞𝑖,𝑡−1 + 𝜂2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 +𝜂3 𝒈𝒐𝒗𝑖,𝑡−1+𝜂4 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜂5 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡(2)This model regresses investment on earnings quality (𝑒𝑞𝑖,𝑡−1), governance variables (𝒈𝒐𝒗),
and the interactions between them and indicator (𝑜𝑣𝑒𝑟𝑖,𝑡 ), which equals one if a firm includes overinvestment group. Independent variables also include control variables (𝒁) and industry (𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 )and year fixed (𝒚𝒆𝒂𝒓𝑡 ) effects. The subscripts i, t, and I depicts firm i, period t, and industry I, respectively. The hypothesis indicates that 𝜂1 and 𝜂3 are positive, and 𝜂1 + 𝜂2 and 𝜂3 + 𝜂4 are negative.
The former prediction corresponds with the hypothesis regarding under-investment, and the latter doeswith that on over-investment.
As the preliminary analysis, I investigate how the alternative information-problem-mitigating mechanisms affects investment efficiency, monotonic or non-linear. I decompose the effects of themechanisms into two: the firms with greater strength of them than the median or the mean values.
Then I add the indicators to model (2) and observe whether the coefficients change across the groups.
If the difference is significant, H4 is supported: the effects are not monotonic.

𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋3 𝑒𝑞𝑖,𝑡−1 ∗ 𝒈𝒐𝒗𝑖,𝑡−1+𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1+𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡+𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡(3)

To test the relationship between the investment effects of earnings quality and the governance mechanisms, I add the interaction term between governance variables and earnings quality(model (3)). However, if the governance effects are nonlinear, I am not able to isolate it by model (3).
Thus I also run the model by entangling the sample into two: the firms with greater strength of themthan the median or the mean values. The coefficients in interest are 𝜋3 and 𝜋4 . They are positive if
the relationship is complementary, and negative if substitute.

4. Results
4.1 Sample and descriptive statistics
The sample of the models to estimate 𝑢𝑒_𝑖𝑛𝑣 and to calculate EAs consists of firms reporting financial statements in March under J GAAP. Since several variables need 9 prior and one former
9periods’ data, the initial sample covers the period from 2001 through 2015. I exclude financial andutility firms (Nikkei Medium Classification Industry Code [NKILM] 47-52 and 65-69), I winsorize
each variable at the 1% and 99% levels.
In main analysis, I winsorize each variable at the 1% and 99% levels. The final sample usedin the main analysis consists of 11,515 firm-year observations. All the data are obtained from Nikkei
NEEDS Financial Quest 2.0 and Nikkei NEEDS Cges.

[Table 1]

Table 1 reports sample distribution with respect to year and industry. Industry classifications
are Nikkei Medium Classification Industry Code (2-digit code, [NKILM]). In table 2, I show descriptive statistics of variables used in the analyses. No variable takes extraordinary level, because of sampling requirement. All variables are defined in Appendix. Table 3 shows a Spearman/Pearson correlation matrix of the variables. I also check that VIF of every variable in each model is less than 4.0.
Multicollinearity does not seem to be serious issue.

[Table 2][Table 3]
4.2 Preliminary analyses
Before examining the interrelationship between earnings quality and other governancemechanisms, I investigate how the governance mechanisms influence on investment efficiency. In
particular, I focus on whether their relationship is monotonic or not (H4a). To test the non-monotonicity, I decompose the effects of the governance mechanisms into two, the effects in firms with themmore than median and with them less than it.

[Table 4]

Table 4 shows the results of the analysis. The first column reports the results of the baselinemodel testing the direct effects of three governance mechanisms. I just report partial derivatives withrespect to those variables in the table. The coefficient on main bank is significantly positive in underinvestment firms, and is significantly negative in over-investment firms. The coefficient on institutional ownership is significantly positive in under-investment firms, and is not statistically significant
in over-investment firms. Outside directors negatively relate to investment in under-investment firms,
and positively relate in under-investment firms. Overall, main bank and institutional ownership alle-10

viate under-investment, and main bank main bank mitigate over-investment. Outside directors, however, adversely impact investment inefficiency.
The second to forth columns show the results of the models testing nonlinearity of the effectsof the governance mechanisms. In the second column, I decompose the effects of main bank into two:the firms with greater strength of their relationship to their main bank than the median and otherwise.
The coefficients on main in firms with the less strength are significantly consistent with the expectation,
not otherwise. The results in the first column seem to derive from those firms. In the third column, I
decompose the effects of institutional investors into two: the firms with the larger institutional ownership than the median and otherwise. Only in firms with stronger institutional investors’ power than the
median, the coefficient on institutional ownership is significantly positive in under-investment firms.
The forth column shows the results when entangling the effects of outside directors into two: the firms
with the stronger outside directors’ power than the mean and otherwise. Since the median value ofoutd is zero, I separate firms based on its mean value (Table 3). From this results, I find that thecoefficients derived in the first column are from the firms with the stronger outside directors’ power.

[Table 5]

To observe the comprehensive results, I include all the decomposed operationalization into
one model. Table 5 reports the result. According to VIFs of the model, multicollinearity does not seem
serious problem (untabulated). All the results are consistent with the results in Table 4. Overall, mainbank promotes investment efficiency in firms with the greater strength of relationship to the firms, andso do institutional investors in firms with larger ownership. Outside directors do not promote investment efficiency, but they worsen it in firms with greater power. Those results support H4a: the relationship between investment efficiency and governance mechanisms is nonlinear.
4.3 Main analysis
Table 6 reports the results of the model adding earnings quality. This model coincides with
Biddle et al. (2009) and its subsequent studies. Earnings quality positively relates to investment inunder-investment firms, and negatively relates in over-investment. The coefficients on main bank,institutional ownership, and outside directors do not change from the previous models. Consistent with
Fujitani (2017), I confirm that earnings quality promotes firm’s investment efficiency by using Japanese data. In addition, this results indicate that earnings quality and the other governance mechanisms
have incremental effects on investment. However, we are not able to observe the interrelationshipbetween the investment effects of earnings quality and of the other governance mechanisms.

[Table 6]11

[Table 7]

To test the interrelationship, I add the interaction term between earnings quality and thealternative information-problem-mitigating mechanisms to the model. Since the preliminary tests suggest that the investment effects of governance mechanisms are not monotonic, I also decompose thesample into two groups as do in the preliminary analyses. Table 7 reports the results of the tests investigating the effects of main bank on the investment effects of earnings quality. The first column shows
the results of the analysis using pooled sample. The interactions are not statistically significant. Considering the nonlinear effects of main bank, I decompose the sample into two based on median valuesof main. In the second and third columns, I report the results when using firms with less than andgreater than the median, respectively. In the second column, the effects of earnings quality is significantly consistent with my expectation in under-investment firms, the interaction is significantly positive in under-investment firms. In the third column, the coefficient on earnings quality in over-investment firms is significantly negative, and those on the interaction are not significant. The difference ofthe coefficient on earning quality between the second and the third columns is significant in underinvestment firms, but insignificant in over-investment. Overall the results support H1. However, since
the findings are complicated, I discuss the interpretation in 4.4.

[Table 8]

In Table 8, I report the results of the investigation the effects of institutional investors on theinvestment effects of earnings quality. The first column shows the results of the analysis using wholesample. The interactions are not statistically significant. Considering the nonlinear effects of institutional ownership on investment, I decompose the sample into two based on median values of inst. The
second and third columns report the results when using firms with less than and greater than the median,respectively. The direct effects of institutional ownership on investment are consistent with the previous analyses. In the second column, the effects of earnings quality is significantly consistent with myexpectation in under-investment firms, and the interaction is not statistically significant. In the thirdcolumn, neither the coefficients on earnings quality nor on the interaction are statistically significant.
The difference of the coefficient on earning quality between the second and the third columns is not
significant. Thus, institutional investors do not change the effects of earnings quality on investment
efficiency: H2 is not supported.

[Table 9]

Table 9 shows the results of the tests on the effects of outside directors on the investment
12

effects of earnings quality. The first column shows the results of the analysis using whole sample. Theinteractions are not statistically significant. Considering the nonlinear effects of outside directors on
investment, I decompose the sample into two based on mean values of outd. The second and thirdcolumns report the results when using firms with less than and greater than the median, respectively.
The investment effects of outside directors are consistent with the previous analyses. In the second
column, the effects of earnings quality is significantly consistent with my expectation, and the interaction is not significant. In the third column, the coefficients on earnings quality are consistent withmy expectation in over-investment firms, and those on the interaction are insignificant. The differenceof the coefficient on earning quality between the second and the third columns is not significant. Thisevidence does not support H3.
4.4 More discussion on main bank and earnings quality
In order to interpret the results more deeply, I plot the interrelationship between the effects
of main bank and earnings quality in the second column of Table 7. Figure 1 plots how the effects ofearnings quality change with the strength of firm-main bank relationship. In under-investment firms,
the relationship increases the effects of earnings quality. This is consistent with complementary hypothesis. On the other hand, the relationship increases in over-investment firms: which is consistent
with the substitute hypothesis.

[Figure 1][Figure 2]

Figure 2 plots how the effects of main bank change with earnings quality. In under-investment firms, earnings quality increases the coefficients of main banks. This is consistent with complementary hypothesis. On the other hand, the coefficients on main bank are significantly negative in the
middle of the levels of earnings quality in over-investment firms. Overall, the interrelationship between earnings quality and main bank is mixed: sometimes complementary and sometimes substitute.

5. Robustness tests
To confirm the robustness of main tests, I change the definitions of the variables and reexamine the models. I change the specification of the model estimating expected investment levels. In
main analysis, 𝑢𝑒_𝑖𝑛𝑣 is calculated by model (1). However, this modified model can make differences in the results of main analysis. To check the robustness in terms of the first stage, I define
𝑢𝑒_𝑖𝑛𝑣 by using models developed by Chen et al. (2011) and by Fujitani (2017). Almost all the results
are consistent with main analysis. In both results, the coefficients on earnings quality are significant
and consistent with my expectation.
13

6. Conclusion
I analyze the interrelationship between the effects of accounting information and of othergovernance mechanisms on investment efficiency by using Japanese data, where banking governanceis thought to be dominant. This study focuses on main bank, institutional ownership, and outside directors as the alternative information-problem-mitigating mechanisms. I find that those governancemechanisms affect investment efficiency, but the effects are not monotonic. Considering this nonlinear
effects, I find that main bank changes the effects of earnings quality on investment. The relationshipis sometimes complementary, especially on alleviating under-investment, and sometimes substitutional.
This study is not able to answer why Biddle et al. (2009) do not observe the significant
effects of earnings quality on investment efficiency in Japan. However, I show that their discussion is
too simple to describe the complicated picture of accounting information and governance mechanisms.
For complete explanation, future research should more focus on the reason of the results this study
derives.

14

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17

Table 1. Sample distribution
2010
2011
2012
2013
2014
2015 Total
Foods
81
78
79
76
80
76
470
Textile Products
37
35
39
38
36
37
222
Pulp & Paper
18
18
0
18
18
18
90
Chemicals
157
160
160
158
156
153
944
Drugs
35
36
35
36
37
32
211
Stone, Clay & Glass Products
48
47
46
45
45
44
275
Iron & Steel
45
47
48
45
43
43
271
Nonferrous Metal & Metal Products
95
92
93
88
87
87
542
Machinery
185
184
177
174
177
177
1,074
Electric & Electronic Equipment
208
212
202
198
194
195
1,209
Motor Vehicles & Auto Parts
76
76
73
73
70
70
438
Precision Equipment
35
35
34
36
33
33
206
Other Manufacturing
70
67
70
66
69
67
409
Construction
132
128
127
123
127
121
758
Wholesale Trade
209
207
212
211
215
208
1,262
Retail Trade
79
83
82
80
79
79
482
Real Estate
30
34
40
36
37
37
214
Railroad Transportation
27
27
28
27
27
27
163
Trucking
31
31
31
31
30
31
185
Warehousing & Harbor Transportation
32
33
33
34
34
32
198
Services
311
315
311
316
322
317
1,892
Total
1,941
1,945
1,920
1,909
1,916
1,884
11,515
Each year depicts each fiscal year ending in March. For instance, the column “2010” shows the number of observations
reporting financial statement ending in March 2010. Industry classifications are Nikkei Medium Classification Industry Code(2-digit code, [NKILM])

Table 2.descriptive
statistics
variables
mean
median
sd
min
1st Q
3rd Q
max
inv
0.0566
0.04340.053700.01930.07660.6074
ue_inv-0.0015-0.00960.0425-0.0758-0.0273
0.01510.1788per_ni
0.41730.44320.3222-0.5634
0.2222
0.63251.2358pre_ni-0.0244-0.0203
0.0166-0.0841-0.0319-0.0124-0.0028
smo_ni-0.5986-0.55620.3090-1.5771-0.7890-0.3620-0.0992
maccq_ni-0.0214-0.0168
0.0165-0.0988-0.0267-0.0106-0.0031eq
0.00250.26381.2848-5.9350-0.6809
0.93382.4327inst
0.14210.08570.154400.01040.23470.6001outd
0.104900.1352000.16670.5714mbnk
0.22240.1972
0.2340000.35941
mbnk_sh0.02080.01990.0180000.03820.0497main
0.0000-0.06191.0851-1.4890-0.8657
0.81883.4535𝑐𝑓𝑖,𝑡0.07310.07040.0648-0.1127
0.03580.10790.2693
𝑐𝑎𝑠ℎ𝑖,𝑡−1
2.29610.51497.06320.01830.24191.215253.6364𝑞𝑖,𝑡−1
0.71150.54680.63850.01590.39030.7643
4.1081
𝑣𝑠𝑔𝑖,𝑡−10.12190.08840.11800.00680.04640.1531
0.6913𝑣𝑖𝑛𝑣𝑖,𝑡−1
0.03280.02260.03270.00180.01250.04070.1879𝑛𝑒𝑔𝑖,𝑡−10.117800.3224000
1
All observations falling in the top or bottom 0.5 % with respect to each variable are winsorized. All variables are definedin Appendix.

18

Table 3.
correlation coefficient
(1)
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)

inv
ue_inv
per_nipre_nismo_ni
maccq_ni
eq
inst
outd
mbnk
mbnk_shmain
𝑐𝑓𝑖,𝑡
𝑐𝑎𝑠ℎ𝑖,𝑡−1𝑞𝑖,𝑡−1
𝑣𝑠𝑔𝑖,𝑡−1
𝑣𝑖𝑛𝑣𝑖,𝑡−1
𝑛𝑒𝑔𝑖,𝑡−10.8479-0.0370-0.1658-0.1058-0.0409-0.1485
0.18230.1171-0.0544-0.1118-0.10830.2446-0.0398
0.09180.02780.2525-0.0483(2)0.7360-0.0291-0.0892-0.0208-0.0324-0.07170.12530.0797-0.0374-0.0690-0.06930.1861-0.0627
0.05350.01110.1844-0.0299(3)-0.0213-0.0247
0.0953-0.1855
0.13180.09780.0812-0.0409-0.03730.0000-0.02430.0339-0.0838
0.0192-0.1492-0.0472
0.0062(4)-0.1830-0.04360.0856
0.46570.49180.92530.0108-0.0793-0.0084
0.22290.1398-0.0387-0.2043-0.0555-0.4265-0.2117-0.2389(5)-0.1821-0.0289-0.2098
0.5564-0.03440.5907-0.11840.04270.00680.01660.0153-0.05200.0663-0.0309-0.1384
0.0211-0.0367(6)
0.04450.03400.11410.47800.01060.67030.1070-0.1191-0.02080.20460.1198-0.0051-0.2770-0.0637-0.3898-0.2663-0.1937(7)-0.1479-0.0204
0.07450.91750.64910.62150.0115-0.0791-0.01270.21210.1299-0.0401-0.2061-0.0663-0.4517-0.2199-0.2234(8)0.24480.17550.09580.0199-0.0968
0.11810.01980.1547-0.1872-0.1131-0.1957
0.1253-0.0900
0.13020.0221-0.0232-0.0902(9)0.10880.0527-0.0340-0.0559
0.0388-0.0640-0.0453
0.1490-0.0862-0.1951-0.1833
0.06280.1477
0.09280.0217
0.0982-0.0081

(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
(1)
inv
-0.0677 -0.0850 -0.0982
0.2812 -0.1906
0.22750.00480.3194 -0.0656(2)
ue_inv-0.0416 -0.0320 -0.0513
0.1639 -0.1482
0.1003 -0.0011
0.1715 -0.0394(3)
per_ni-0.0445 -0.0089 -0.0359
0.0430 -0.0323
0.0342 -0.1470 -0.0327
0.0066(4)
pre_ni0.02320.20070.1344 -0.0642 -0.2279 -0.1125 -0.4260 -0.2307 -0.2257(5)smo_ni0.00680.01770.0158 -0.0760 -0.0111 -0.0945 -0.1574 -0.0651 -0.0543(6)
maccq_ni
0.02160.17730.11910.0142 -0.2856 -0.0308 -0.3481 -0.1542 -0.1872(7)eq0.02440.19790.1320 -0.0587 -0.2483 -0.1184 -0.4322 -0.2174 -0.2163(8)
inst-0.1932 -0.0930 -0.1800
0.1443 -0.0523
0.22520.0414 -0.0091 -0.1133(9)
outd-0.1053 -0.1876 -0.1854
0.05440.03650.15100.0006
0.0660 -0.0233
(10)
mbnk
0.22140.7547 -0.0403 -0.1513
0.01700.00490.05550.0270(11)mbnk_sh0.17750.7821 -0.1241 -0.1419 -0.0785 -0.0580 -0.1368 -0.0034
(12)
main
0.76730.7673-0.1060 -0.1640 -0.0476 -0.0316 -0.0556
0.0156𝑐𝑓𝑖,𝑡(13)-0.0392 -0.1143 -0.1000-0.06100.1880 -0.0764
0.1895 -0.2106
𝑐𝑎𝑠ℎ𝑖,𝑡−1(14)-0.0924 -0.1906 -0.1844
0.0095-0.1471
0.1256 -0.2407
0.0329𝑞𝑖,𝑡−1(15)-0.0057 -0.0186 -0.0158
0.08710.01600.01050.2139 -0.0309
𝑣𝑠𝑔𝑖,𝑡−1(16)0.0200 -0.0800 -0.0391 -0.0608
0.10390.02650.10440.1748𝑣𝑖𝑛𝑣𝑖,𝑡−1(17)0.0483 -0.1679 -0.0779
0.1117 -0.0245
0.11820.17830.0541
𝑛𝑒𝑔𝑖,𝑡−1(18)0.0330 -0.0030
0.0196 -0.2119
0.0374 -0.0164
0.15000.0752
Pearson correlation coefficients are reported at the lower left diagonal matrix, and Spearman correlation coefficients are reported at thelower right diagonal matrix. All observations falling in the top or bottom 0.5 % with respect to each variable are winsorized. All variables
are defined in Appendix.

19

Table 4. The effects ofGovernance mechanisms(Obs=11,515)
variables
under-investment
dinv/dmain
full<median>medianpredbaseline
COEFFsS.E.

+

0.002a0.0004main
COEFFsS.E.

0.004a0.000

0.00080.00060.012a0.0033

inst
COEFFsS.E.

outd
COEFFsS.E.

0.002a0.00040.002a0.00040.011a0.00330.024
0.013a0.01710.0034-0.014a0.00310.036-0.014a0.01940.0031-0.003a0.0008-0.0010.0059-0.0190.042a0.02690.0129dinv/dinst
full<median>median+0.012a0.0033

dinv/doutd
full<median>median+-0.014a0.0031-0.013a0.0031over-investment
dinv/dmain
full<median>median--0.003a0.0008-0.008a0.000

0.00230.0014-0.0010.0058-0.003a0.0008

0.006
0.007

0.04830.00810.033a0.0076

dinv/dinst
full<median>median--0.0020.0058

dinv/doutd
full<median>median-0.034a0.0076

0.033a0.0076

controls
𝑜𝑣𝑒𝑟𝑖,𝑡0.054a0.00150.048a0.0026
0.055a0.00200.055a0.0015𝑐𝑓𝑖,𝑡0.073a0.01010.072a0.01000.073a0.01010.073a0.0100𝑐𝑎𝑠ℎ𝑖,𝑡−1
0.000
0.00010.000
0.00010.000
0.00010.000
0.0001𝑞𝑖,𝑡−1-0.0010.0007-0.0010.0007-0.0010.0007-0.0010.0007
𝑣𝑠𝑔𝑖,𝑡−1
0.002
0.00520.002
0.00520.002
0.00520.002
0.0052
𝑣𝑖𝑛𝑣𝑖,𝑡−10.197a0.02350.196a0.0236
0.196a0.02340.196a0.0236
𝑛𝑒𝑔𝑖,𝑡−1-0.003b0.0012-0.003a0.0012-0.003a0.0012-0.003b0.0012
year/industry
yes
yes
yes
yes
C.R.S.E.
yes
yes
yes
yes
other interactions
yes
yes
yes
yes
AdjR
0.5202
0.52110.52030.5205b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficients on eq and gov variables are partial derivatives
with respect to them in each group. “year/industry” indicates whether each model includes year and industry fixed effects or not. “C.R.S.E”
indicates whether standard errors are clustered by firm (Petersen 2009; Cameron and Miller 2015).

20

Table 5. The full-model
full-model
COEFFsS.E.

0.004a0.000

0.00090.0006

dinv/dinst<median>median

0.025
0.010a0.01710.0035

dinv/doutd<median>median

0.037-0.015a0.01910.0032over-investment
dinv/dmain<median>median-0.007a-0.0010.00230.0014

dinv/dinst<median>median

0.016
0.008

0.04840.0081

dinv/doutd<median>median-0.0140.039a0.02710.0128variables
under-investment
dinv/dmain<median>mediancontrols
𝑜𝑣𝑒𝑟𝑖,𝑡0.050a0.0031𝑐𝑓𝑖,𝑡0.073a0.0100𝑐𝑎𝑠ℎ𝑖,𝑡−1
0.000
0.0001𝑞𝑖,𝑡−10.000
0.0007
𝑣𝑠𝑔𝑖,𝑡−1
0.002
0.0052
𝑣𝑖𝑛𝑣𝑖,𝑡−10.195a0.0236
𝑛𝑒𝑔𝑖,𝑡−1-0.003a0.0012
year/industry
yes
C.R.S.E.
yesother interactions
yes
AdjR
0.5214
obs
11,515b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficientson eq and gov variables are partial derivatives with respect to them in each group. “year/industry”
indicates whether each model includes year and industry fixed effects or not. “C.R.S.E” indicates
whether standard errors are clustered by firm (Petersen 2009; Cameron and Miller 2015).

21

Table 6. Model (2)predeq-baseline model
COEFFsS.E.

under-investment
over-investment+-

0.002a-0.005a0.00040.0009𝜕𝑖𝑛𝑣/𝜕𝑚𝑎𝑖𝑛
under-investment
over-investment+-

0.001a-0.002a0.00040.0008

𝜕𝑖𝑛𝑣/𝜕𝑖𝑛𝑠𝑡
under-investment
over-investment+-

0.012a0.000

0.00320.0057

𝜕𝑖𝑛𝑣/𝜕𝑜𝑢𝑡𝑑
under-investment
over-investment+--0.012a0.031a0.00300.0076

𝜕𝑖𝑛𝑣/𝜕𝑒𝑞

controls
𝑜𝑣𝑒𝑟𝑖,𝑡0.054a0.0015𝑐𝑓𝑖,𝑡0.070a0.0100𝑐𝑎𝑠ℎ𝑖,𝑡−1
0.000
0.0001𝑞𝑖,𝑡−1-0.0010.0007
𝑣𝑠𝑔𝑖,𝑡−1-0.0050.0054
𝑣𝑖𝑛𝑣𝑖,𝑡−10.188a0.0229𝑛𝑒𝑔𝑖,𝑡−1-0.004a0.0012
year/industry
yes
C.R.S.E.
yes
AdjR
0.528
obs
11,515This table reports the results obtained by: 𝑖𝑛𝑣𝑖,𝑡 = 𝜂0 + 𝜂1 𝑒𝑞𝑖,𝑡−1 + 𝜂2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜂3 𝒈𝒐𝒗𝑖,𝑡−1 +𝜂4 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜂5 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .
b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficients on
eq and gov variables are partial derivatives with respect to them in each group. “year/industry” indicates
whether each model includes year and industry fixed effects or not. “C.R.S.E” indicates whether standard
errors are clustered by firm (Petersen 2009; Cameron and Miller 2015). All the other variables are defined in Appendix.

22

Table 7. Model (3):Main banks and earnings
qualitypredpooledCOEFFsS.E.

main<med
COEFFsS.E.

main>med
COEFFsS.E.

under-investment
over-investment+-

0.002a-0.005a0.00040.00090.004a-0.0030.0011
0.002

0.001-0.003b0.00080.0015(𝜕𝑖𝑛𝑣/𝜕𝑒𝑞)*( 𝜕𝑖𝑛𝑣/𝜕𝑚𝑎𝑖𝑛)under-investment
over-investment

?
?

0.000
0.001

0.00030.00060.002b0.002

0.00090.00180.000-0.0010.00060.00120.001a-0.002a0.00040.00080.003b-0.005b0.00120.0022

0.000
0.000

0.00070.0014

under-investment
over-investment

0.012a0.001

0.00330.00580.010b0.004

0.00440.00760.015a-0.0020.0044
0.008

under-investment
over-investment-0.012a0.031a0.00290.0076-0.013a0.022b

0.00390.0096-0.010b0.045a0.00410.0118

𝜕𝑖𝑛𝑣/𝜕𝑒𝑞𝜕𝑖𝑛𝑣/𝜕𝑚𝑎𝑖𝑛
under-investment
over-investment
𝜕𝑖𝑛𝑣/𝜕𝑖𝑛𝑠𝑡

𝜕𝑖𝑛𝑣/𝜕𝑜𝑢𝑡𝑑

controls
𝑜𝑣𝑒𝑟𝑖,𝑡0.053a0.00150.050a0.003
0.049a0.0024𝑐𝑓𝑖,𝑡0.071a0.01
0.080a0.01310.057a0.0143
𝑐𝑎𝑠ℎ𝑖,𝑡−1
0.000
0.00010.000
0.0001-0.000a0.0001𝑞𝑖,𝑡−1-0.0010.0007-0.0010.0011-0.0010.0007
𝑣𝑠𝑔𝑖,𝑡−1-0.0050.0053-0.0050.0084-0.0060.0055𝑣𝑖𝑛𝑣𝑖,𝑡−10.187a0.02280.207a0.02740.145a0.0355𝑛𝑒𝑔𝑖,𝑡−1-0.004a0.0012-0.0030.002-0.005a0.0013
year/industry
yes
yes
yes
C.R.S.E.
yes
yes
yes
AdjR
0.52830.52500.5255
obs
11,5155,758
5,757This table reports the results obtained by: 𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋3 𝑒𝑞𝑖,𝑡−1 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 +𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .
b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficients on eq and gov variables are partial
derivatives with respect to them in each group. “year/industry” indicates whether each model includes year and industry fixed effects ornot. “C.R.S.E” indicates whether standard errors are clustered by firm (Petersen 2009; Cameron and Miller 2015).

23

Table 8. Model (3):Institutional investors andearnings quality
pooledCOEFFsS.E.

inst<med
COEFFsS.E.

inst>med
COEFFsS.E.

𝜕𝑖𝑛𝑣/𝜕𝑒𝑞
under-investment
over-investment+-

0.002a-0.005a0.00050.00120.002b-0.005a0.00070.00160.000-0.0040.00110.002(𝜕𝑖𝑛𝑣/𝜕𝑒𝑞)*( 𝜕𝑖𝑛𝑣/𝜕𝑖𝑛𝑠𝑡)
under-investment
over-investment?
?

-0.0010.001

0.00250.00450.016-0.0190.01530.0431

0.003-0.0040.004
0.0064

𝜕𝑖𝑛𝑣/𝜕𝑖𝑛𝑠𝑡
under-investment
over-investment

0.012a0.000

0.00320.00560.040b0.033

0.018
0.0485

0.005
0.005

0.00520.0079𝜕𝑖𝑛𝑣/𝜕𝑚𝑎𝑖𝑛
under-investment
over-investment

0.001a-0.002a0.00040.00080.001b-0.0020.00040.00120.002b-0.003a0.00070.001

𝜕𝑖𝑛𝑣/𝜕𝑜𝑢𝑡𝑑
under-investment
over-investment-0.012a0.031a0.003
0.0077-0.011a0.042a0.00360.0136-0.014a0.024a0.00490.0083

controls
𝑜𝑣𝑒𝑟𝑖,𝑡0.054a0.00150.053a0.00230.050a0.0027𝑐𝑓𝑖,𝑡0.070a0.01
0.064a0.01390.076a0.0137
𝑐𝑎𝑠ℎ𝑖,𝑡−1
0.000
0.00010.000
0.00010.000
0.0001𝑞𝑖,𝑡−1-0.0010.0007-0.0010.00110.000
0.0008
𝑣𝑠𝑔𝑖,𝑡−1-0.0050.0053-0.0020.0072-0.0090.0077
𝑣𝑖𝑛𝑣𝑖,𝑡−10.187a0.02290.198a0.03170.167a0.0267𝑛𝑒𝑔𝑖,𝑡−1-0.004a0.0012-0.004a0.0015-0.0030.002
year/industry
yes
yes
yes
C.R.S.E.
yes
yes
yes
AdjR
0.52790.51350.5267
obs
11,5155,759
5,756This table reports the results obtained by: 𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋3 𝑒𝑞𝑖,𝑡−1 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 +𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .
b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficients on eq and gov variables are partial
derivatives with respect to them in each group. “year/industry” indicates whether each model includes year and industry fixed effects ornot. “C.R.S.E” indicates whether standard errors are clustered by firm (Petersen 2009; Cameron and Miller 2015).

24

Table 9. Model (3):Outside directors and earnings quality
pooledCOEFFsS.E.

outd<mean
COEFFsS.E.

outd>mean
COEFFsS.E.

𝜕𝑖𝑛𝑣/𝜕𝑒𝑞
under-investment
over-investment+-

0.001a-0.005a0.00050.001

0.001b-0.005a0.00060.0011

0.002-0.006b0.00110.0026(𝜕𝑖𝑛𝑣/𝜕𝑒𝑞)*( 𝜕𝑖𝑛𝑣/𝜕𝑜𝑢𝑡𝑑)
under-investment
over-investment

?
?

0.002
-0.001

0.002
0.0059-0.0120.026

0.01550.0259-0.001
0.002

0.00330.0106

under-investment
over-investment-0.012a0.031a0.00290.00770.033-0.0220.02030.0296-0.015a0.039a0.00520.0134

𝜕𝑖𝑛𝑣/𝜕𝑚𝑎𝑖𝑛
under-investment
over-investment

0.001a-0.002a0.00040.00080.001a-0.003a0.00050.00090.001b-0.0020.00060.00140.011a00.00330.00580.014a0.005

0.00460.00720.008-0.0020.00440.0089𝜕𝑖𝑛𝑣/𝜕𝑜𝑢𝑡𝑑

𝜕𝑖𝑛𝑣/𝜕𝑖𝑛𝑠𝑡
under-investment
over-investment

controls
𝑜𝑣𝑒𝑟𝑖,𝑡0.054a0.00150.054a0.00170.051a0.0035𝑐𝑓𝑖,𝑡0.070a0.0101
0.068a0.01180.071a0.0162
𝑐𝑎𝑠ℎ𝑖,𝑡−100.000100.000100.0001𝑞𝑖,𝑡−1-0.0010.000700.0009-0.0010.001
𝑣𝑠𝑔𝑖,𝑡−1-0.0050.0054-0.0030.007-0.0070.0079𝑣𝑖𝑛𝑣𝑖,𝑡−10.188a0.02290.204a0.02930.173a0.0333𝑛𝑒𝑔𝑖,𝑡−1-0.004a0.0012-0.004a0.0013-0.004b0.0021
year/industry
yes
yes
yes
C.R.S.E.
yes
yes
yes
AdjR
0.52790.53360.5147
obs
11,515
6,605
4,910This table reports the results obtained by: 𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋3 𝑒𝑞𝑖,𝑡−1 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 +𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .
b and a indicate significance at the 5 and 1% levels, respectively, using a two-tailed test. Coefficients on eq and gov variables are partial
derivatives with respect to them in each group. “year/industry” indicates whether each model includes year and industry fixed effects ornot. “C.R.S.E” indicates whether standard errors are clustered by firm (Petersen 2009; Cameron and Miller 2015).

25

Over-investment

-.01

0

.01

.02

Under-investment

-2

0

2

4

-2

0

2

4

Figure 1. The change in the earnings quality effects on investment. This figure presents how themarginal effects of earnings quality (eq) estimated model (3) vary with the strength of main bankrelationship in firms with less than the median. The x axis presents the values of main, and the yaxis presents the marginal effects of earnings quality on investment. The left panel reports thechanges in under-investment firms, and the right panel does in over-investment firms. The plotted
region describes the confidence at 95% levels. The line in the region presents the estimated coefficients. The estimated model is: 𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋3 𝑒𝑞𝑖,𝑡−1 ∗𝒈𝒐𝒗𝑖,𝑡−1+𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡 +𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .

26

Over-investment

-.04

-.02

0

.02

Under-investment

-6

-4

-2

0

2

-6

-4

-2

0

2

Figure 2. The change in the main bank effects on investment. This figure presents how the marginal
effects of the strength of main bank relationship (main) estimated model (3) vary with the strengthof main bank relationship in firms with less than the median. The x axis presents the values of eq,
and the y axis presents the marginal effects of the main bank relationship on investment. The left
panel reports the changes in under-investment firms, and the right panel does in over-investment
firms. The plotted region describes the confidence at 95% levels. The line in the region presents theestimated coefficients. The estimated model is: 𝑖𝑛𝑣𝑖,𝑡 = 𝜋0 + 𝜋1 𝑒𝑞𝑖,𝑡−1 + 𝜋2 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 +𝜋3 𝑒𝑞𝑖,𝑡−1 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋4 𝑒𝑞𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 ∗ 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋5 𝒈𝒐𝒗𝑖,𝑡−1 + 𝜋6 𝒈𝒐𝒗𝑖,𝑡−1 ∗ 𝑜𝑣𝑒𝑟𝑖,𝑡 +𝜋7 𝑜𝑣𝑒𝑟𝑖,𝑡 + 𝛿 ∙ 𝒁 + 𝒚𝒆𝒂𝒓𝑡 + 𝒊𝒏𝒅𝒖𝒔𝒕𝒓𝒚𝐼 + 𝜀5,𝑖,𝑡 .

27

Appendix1. Variable definitions
A1.1 Investment
Investment level (𝑖𝑛𝑣𝑖,𝑡 ) is net investment in tangibles, intangibles and R&D projects. Tangibles investment equals the change in property, plant and equipment [B01063] from t-1 through t
pluses its depreciation and its impairment. Intangibles investment equals the change in intangibles[B01076] pluses its depreciation and its impairment. R&D investment is cash outflow on research anddevelopment [H01033]. I use sum of depreciation, amortization, and impairment with regard to tangibles and intangibles ([H01005] and [H01009]).
A1.2 Earnings attributes
Earnings used in the following definitions is earnings before taxes and special items[D01066] (Data source: FQ).
Earnings volatility (𝑣𝑜𝑙𝑖,𝑡 ) is the standard deviation of 10 years earnings from period t-9 toperiod t, all scaled by the average of total assets (computed as the average of total assets [B01110] at
the beginning and the end of t). To represent the ease to predict future earnings, I add negative sign
(Data source: FQ).
Earnings persistence (𝑝𝑒𝑟𝑖,𝑡 ) is the AR1 coefficient 𝜙1𝑖,𝑡 in regression model: 𝑒𝑎𝑟𝑖,𝑡 =𝜙0𝑖,𝑡 + 𝜙1𝑖,𝑡 𝑒𝑎𝑟𝑖,𝑡−1 + 𝜏𝑖,𝑡 , using data from t-9 through t. Ear is scaled by the average of total assets(computed as the average of total assets [B01110] at the beginning and the end of t) (Data source: FQ).
Earnings predictability (𝑝𝑟𝑒𝑖,𝑡 ) is the 9-year standard deviation of residuals from AR (1)model: 𝑒𝑎𝑟𝑖,𝑡 = 𝜙0𝑖,𝑡 + 𝜙1𝑖,𝑡 𝑒𝑎𝑟𝑖,𝑡−1 + 𝜏𝑖,𝑡 , using data from t-9 through t. Ear is scaled by the averageof total assets (computed as the average of total assets [B01110] at the beginning and the end of t)(Data source: FQ).
Smoothness (𝑠𝑚𝑜𝑖,𝑡 ) is earnings volatility (𝑣𝑜𝑙𝑖,𝑡 ) divided by cash flow volatility (𝑣𝑐𝑓𝑖,𝑡 ), alldivided by the average of total assets (computed as the average of total assets [B01110] at the beginning and the end of t). To represent the ease to predict future earnings, I add negative sign (Data source:
FQ).
Dechow and Dichev's (2002) accruals quality (𝑎𝑐𝑞𝑖,𝑡 ) is the 5-year standard deviation ofresiduals from the following regression model: 𝑎𝑐𝑐𝑖,𝑡 = 𝜓0 + 𝜓1 𝑐𝑓𝑜𝑖,𝑡−1 + 𝜓2 𝑐𝑓𝑜𝑖,𝑡 + 𝜓3 𝑐𝑓𝑜𝑖,𝑡+1 +
𝜐𝑖,𝑡 , from t-4 through t. 𝑎𝑐𝑐𝑖,𝑡 and 𝑐𝑓𝑜𝑖,𝑡 are respectively accruals and cash flow components ofearnings, defined at section A1.3. All variables are scaled by the average of total assets (computed as
the average of total assets [B01110] at the beginning and the end of t). This regression model is estimated by running separated industry-year regressions. To represent the ease to predict future earnings,
I add negative sign.
McNichols' (2002) accruals quality (𝑚𝑎𝑞𝑖,𝑡 ) is the 5-year standard deviation of residuals
from the following regression model: 𝑎𝑐𝑐𝑖,𝑡 = 𝜒0 + 𝜒1 𝑐𝑓𝑜𝑖,𝑡−1 + 𝜒2 𝑐𝑓𝑜𝑖,𝑡 + 𝜒3 𝑐𝑓𝑜𝑖,𝑡+1 + 𝜒4 𝑠𝑔𝑖,𝑡 +28

𝜒5 𝑝𝑝𝑒𝑖,𝑡 + 𝜔𝑖,𝑡 , from t-4 through t. 𝑎𝑐𝑐𝑖,𝑡 and 𝑐𝑓𝑜𝑖,𝑡 are respectively accruals and cash flow components of earnings. 𝑠𝑔𝑖,𝑡 is sales growth from t-1 through t, and 𝑝𝑝𝑒𝑖,𝑡 is capital intensity at t. Allvariables are defined at section A1.3, and scaled by the average of total assets (computed as the average of total assets [B01110] at the beginning and the end of t). This regression model is estimated byrunning separated industry-year regressions. To represent the ease to predict future earnings, I add
negative sign.
Aggregate indicator of EAs (𝑎𝑔𝑔2𝑖,𝑡 ) is the estimated principal components of 4 EAs including 𝑝𝑒𝑟𝑖,𝑡 , 𝑝𝑟𝑒𝑖,𝑡 , 𝑠𝑚𝑜𝑖,𝑡 , and 𝑚𝑎𝑞𝑖,𝑡 . The eigenvalues of the indicators are 0.0748, 0.7171,
0.4576, 0.5203, respectively.
A1.3 Other variables
Accruals ( 𝑎𝑐𝑐𝑖,𝑡 ) is computed by the items on balance sheet: (∆𝑐𝑎𝑖,𝑡 − ∆𝑐𝑎𝑠ℎ𝑖,𝑡 ) −(∆𝑐𝑙𝑖,𝑡 − ∆𝑠𝑡𝑑𝑖,𝑡 − ∆𝑡𝑝𝑖,𝑡 ) − 𝑑𝑒𝑝𝑖,𝑡 . ∆𝑐𝑎𝑖,𝑡 is the change in current asset [B01021] from t-1 throught. ∆𝑐𝑎𝑠ℎ𝑖,𝑡 is the change in cash and cash equivalents [B01022]. ∆𝑐𝑙𝑖,𝑡 is the change in current liabilities [C01021]. ∆𝑠𝑡𝑑𝑖,𝑡 is the change in debt included in current liabilities [C01026]. ∆𝑡𝑝𝑖,𝑡 is thechange in total taxes payable, which is sum of income taxes payable [C01040], accrued business officetaxes [C01041], and accrued consumption taxes [C01042]. 𝑑𝑒𝑝𝑖,𝑡 is depreciation [H01005]. All variables are divided by the average of total assets (computed as the average of total assets [B01110] at
the beginning and the end of t) (Data source: FQ).
Assets growth (𝑎𝑔𝑖,𝑡 ) is the change in total assets [B01110] from t-1 through t, all scaled bytotal assets at t-1 (Data source: FQ).
Capital intensity (𝑝𝑝𝑒𝑖,𝑡 ) equals property, plant and equipment [B01063] at t scaled by total
assets [B01110] at t (Data source: FQ).
Cash flow from operation (𝑐𝑓𝑜𝑖,𝑡 ) equals the difference between earnings and accruals(𝑎𝑐𝑐𝑖,𝑡 ). Earnings equals operating income [D01029] on profit and loss statement. All variables are
divided by the average of total assets (computed as the average of total assets [B01110] at the beginning and the end of t) (Data source: FQ).
Cash flow volatility (𝑐𝑓𝑣𝑖,𝑡 ) equals the standard deviation of cash flow from operation(𝑐𝑓𝑜𝑖,𝑡 ) over 10 years, from t-9 through t (Data source: FQ).
Cash holdings (𝑐𝑎𝑠ℎ𝑖,𝑡 ) equals cash and cash equivalents [B01022] divided by the averageof total assets (computed as the average of total assets [B01110] at the beginning and the end of t)(Data source: FQ).
Firm’s age (𝑎𝑔𝑒𝑖,𝑡 ) is the difference between the year at t and the actual year of foundation[PRMTD1] (Data source: FQ).
The ratio of institutional investor (𝑖𝑛𝑠𝑡𝑖,𝑡 ) equals the sum of stock held by foreign investors,
trust accounts and special life insurance accounts, all divided by total stock traded in equity market
29

(Data source: Cges).
Main banks (𝑚𝑎𝑖𝑛𝑖,𝑡 ) equals the the estimated principal components of two variables withrespect to firm-bank relationship, including debt dependence of the main bank and the percent of mainbank’s shareholding. The eigenvalues of the indicators are 0.7071 and 0.7071, respectively (Datasource: Cges).
Return on assets (𝑟𝑜𝑎𝑖,𝑡 ) equals operating income [D01029] scaled by the average of total
assets (computed as the average of total assets [B01110] at the beginning and the end of t) (Datasource: FQ).
Sales growth (𝑠𝑔𝑖,𝑡 ) equals the change in sales [D01021] from t-1 through t, all scaled bysales at t-1 (Data source: FQ).
Sales growth volatility (𝑠𝑔𝑣𝑖,𝑡 ) equals the standard deviation of sales growth (𝑠𝑔𝑖,𝑡 ) over 10
years, from period t-9 through period t. (Data source: FQ).
The proxy of tobin’s q (𝑞𝑖,𝑡 ) is the sum of market value of equity and book value of liabilities,
scaled by book value of equity [B01110]. Market value of equity equals firm’s stock price [MXCLOSE]
times its number traded in equity market. The number of traded stocks equals the sum of the numberof stock issued [A01057] at the end of period t, and the number of treasury stocks [A01058]. Book
value of liabilities is the sum of long-term loans and bonds [C01058], and short-term loans and bonds[C01026] (Data source: FQ).

Appendix 2. Other specifications of the model estimating efficient investment30