How do Normative and Compliant CSR Affect the Earnings Quality of Japanese Firms?

Abstract
This study investigates the empirical relation between the corporate social performance (CSP) and earnings
quality of public firms in Japan. To investigate this query, we adopt several measures including
accruals-based earnings management (AEM) measures and real activities-based earnings management(REM) measures. In general, the comprehensive measurement of the CSP of Japanese firms is positively
associated with earnings quality, suggesting that corporate social responsibility (CSR) activities may
improve the earnings quality of Japanese firms. However, a more detailed investigation using CSP
dimensional indices reveals a number of in-depth findings. First, before controlling for financial
performance, normative CSR is more strongly associated with the AEM measures, whereas compliant CSR
is more strongly associated with the REM measures. Second, after controlling for financial performance inthe regression analysis, the regression slopes of the compliant CSR dimensions are no longer statisticallysignificant, implying the existence of a spurious correlation between compliant CSR and earnings quality.
Finally, the slopes of the normative CSR dimensions remain significant even after controlling for financial
performance. Overall, these results indicate that the managers of firms that show high normative CSR
values tend to avoid adopting AEM and REM measures and that those firms that are conscious of CSR are
more likely to gain investor confidence.

2

1. Introduction
While earnings quality is a financial measure used to report a firm’s financial performance to investors
and other outside stakeholders, corporate social responsibility (CSR) is a business approach that aims todeliver a range of economic, social, and environmental benefits to all stakeholders. However, according to
the current body of research, the relation between earnings quality and the attainment of CSR by firms is
unclear. Given these mixed views, in this study we empirically investigate the relation between theearnings quality and corporate social performance (CSP) of Japanese listed firms.
Earnings quality is considered to be useful for both investors and firm making long-term strategic
decisions (Schipper and Vincent, 2003; Francis et al., 2006). For example, earnings quality rises when
there is less fluctuation in earnings series (Trueman and Titman, 1988). Earnings management, which may
be detrimental to the attainment of earnings quality, is usually conducted by the extensive use ofaccounting accruals (Healy and Wahlen, 1999). The magnitude of accruals, particularly abnormal accruals,
can serve to highlight a lack of earnings quality.1 Earnings management has also been labeled real
activities-based earnings management (REM) by Roychowdhury (2006), Cohen et al. (2008), and Cohen
and Zarowin (2010).2 However, REM behavior may amplify earnings variability (Ronen and Yarri, 2008).
Indeed, Jensen et al. (2004) emphasized that firms ought to seek long-run goals instead of satisfying
short-term objectives (e.g., quarterly profits) in order to satisfy analysts’ expectations. Firms that pursue
long-term goals tend to pay attention to CSR and sustainability, with the aim of building corporate
reputation in the long run.
In this study, we use the five CSP dimensions developed by Suto and Takehara (2016): (1) employee
relations, (2) environmental preservation, (3) social contributions, (4) firm security and product safety, and(5) internal governance and risk management. Suto and Takehara (2016) constructed these CSP indices on
the basis of a stakeholder management approach, with each CSP dimension corresponding to an importantgroup of stakeholders. However, by imposing similar criteria to those proposed by Harjoto and Jo (2015),
we reclassify these five CSP dimensions into two groupings, namely normative CSR and compliant CSR.
We find that normative CSR improves the earnings quality of Japanese firms after controlling for financial
performance, whereas compliant CSR is not significantly associated with earnings quality. Our finding is
crucial because the firm can signal earnings quality to investors only through the normative CSR channel.

1

In Japanese firms, the existence of these so-called accruals effects has been confirmed by Chung et al. (2004) and Kubota
et al. (2010), which reverse within several months of the end of the fiscal year.
2
The effects of real earnings management have been explored by Mande et al. (2000), Hermann et al. (2003), Pan (2009),
and Guo et al. (2012) in different contexts from that in the presented study.

3

The remainder of the paper is organized as follows. We review related studies in Section 2 and develop
the research hypotheses in Section 3. In Section 4, we explain the CSP dimensional indices, several
earnings quality measures, and the other financial variables used in this study. The two-stage portfolio
formation approach and regression models employed are also described. In Section 5, we present theresults of the correlation analyses and of the portfolio formation approach. In Section 6, we report theresults of the regression analyses. Finally, we conclude in Section 7.

2. Earnings Quality and CSR
Suto and Takehara (2016) constructed CSP dimensional indices from the results of a survey conductedby Toyo Keizai Inc. By using these indices, they examined the relation between CSP and financial
performance and found that CSR activities reduce firms’ financial risk. These authors also developed astatistical model to estimate the CSP of firms that did not respond to the survey. Ajward and Takehara(2014) showed that higher CSP alleviates financial constraints in firms, while Aoi et al. (2015) compared
the performance of Japanese public family and non-family firms based on these CSP indices and concluded
that the performance of family firms is worse than that of non-family firms in Japan.
On the relation between CSP and earnings quality, Kim et al. (2012) found that socially responsible
firms are less likely to manage earnings through discretionary accruals, to manipulate real operating
activities, and to be the subject of SEC investigations. Yip et al. (2011) showed that earnings management
and CSR reporting are related, findings that this relation is complementary for the oil and gas industry,
while it is substitutive in the food industry. McDermott (2011) analyzed the relationship between KLD
score and financial reporting quality as well as investment behavior, emphasizing the role of private
benefits for firm managers. Outside the United States, Scholtens and Kang (2012) reported that Asian firms
with relatively high CSR engage in less earnings management.3
In this study, we reclassify the five dimensions of CSR in Suto and Takehara (2016) into normative CSR
and compliant CSR. Harjoto and Jo (2015) defined compliant CSR as CSR actions required (or desired) bylaw, whereas normative CSR actions are voluntary (or discretionary), with firms only taking such actions
to provide the means for human subsistence or enhance quality of life (Ulrich, 2008). Further, Harjoto andJo (2015) showed the different impacts of compliant CSR and normative CSR on analysts’ forecast
dispersion, stock return volatility, and the cost of capital. Hence, these two distinct types of CSR actions
are expected to affect earnings quality differently. Figure 1 illustrates the scope of our research, showing

3

Their sample is limited to only 16 firms in Japan.

4

that in addition to examining the overall relation between CSP and earnings quality (H1), we investigate
the differential impacts of normative and compliant CSR actions on accruals-based earnings management(AEM) measures and REM measures (H2 and H3).

[Figure 1 around here]

3. Hypothesis Development
Those firms/managers more conscious about social responsibility tend to be more honest to all types of
stakeholders and avoid unethical conduct. As a result, they sustain the credibility of stakeholders bycontinuing CSR actions. In this case, a comprehensive CSP score, which is an imperfect signal of thedegree of social consciousness of managers, is positively correlated with earnings quality measures. In
other words, those firms that endeavor to maintain integrity do not manage their earnings in order todeceive their stakeholders. Thus, H1 is proposed as follows:

H1: A composite measure of CSP is positively associated with earnings quality.

Next, we consider the interrelations among normative CSR, compliant CSR, AEM, and REM. Typically,
stakeholders find normative CSR to be vaguer than compliant CSR. Because stakeholders are lessinformed about normative CSR actions, the degree of information asymmetry is larger in normative CSR
dimensions. By contrast, stakeholders find it relatively easy to access the private information owned bymanagers because the evaluation items in compliant CSR actions are binding (or nonbinding) targets
required by law. Therefore, the managers of firms with high normative CSR scores are honest and ethical(i.e., they do not manage earnings), although their social consciousness is unknown to stakeholders. H2 isthus proposed as follows:

H2: The accounting earnings of firms with higher normative CSR are less managed by both AEM andREM strategies.

However, the relation between compliant CSR and earnings quality is more complicated. Since bothbinding and nonbinding actions are included in compliant CSR activities, there exists a large dispersion ofcompliant CSR among firms. For example, many Japanese firms have ISO9001 certification, while others
do not. Although ISO standards including ISO9000s are voluntary, the implementation of ISO standards
5shifts costs to firms. For this reason, firms with inferior financial performance might be unable to afford to
implement ISO9001. Hence, although we can observe a positive correlation between compliant CSR
actions and earnings quality measures, compliant CSR may not always enhance earnings quality. Therefore,
in the empirical analyses, we control for the financial performance of firms, using return on assets (ROA),
to investigate this relation. H3 is proposed as follows:

H3: After controlling for the financial performance of firms, compliant CSR is not associated withearnings quality.

4. Data and Methodology
4.1. Earnings Quality Measures
Our primary observation period is for the nine fiscal years from 2007 to 2015 for which the CSP
dimensional indices constructed by Suto and Takehara (2016) are available. However, to be able tocompute the necessary statistics from accounting data, we go back an additional five years. Financial
statement and stock price data were extracted from the Nikkei NEEDS Database published by Nikkei
Digital Media, Inc.
In this study, we define total accruals (ACC) as the difference between earnings before extraordinary
items (EBEI) and cash-flow from operations (CFO). First, we compute EBEI from the corresponding itemson the income statement and obtain CFO directly from the cash-flow statement. Because EBEI is equal to(CFO + ACC) by definition, accruals in this study are computed as EBEI – CFO.
We decompose total accruals into normal and abnormal components by using the modified Jonesmodel (1) proposed by Dechow et al. (1995). We then estimate the following cross-sectional regression
equation separately by each industry for each sample year:

ACC j ,t
TA j ,t 1

  0  1

ADJREV j ,t
PPE j ,t
1
 1
 2
  j ,t .
TA j ,t 1
TA j ,t 1
TA j ,t 1(1)where ΔADJREV is the difference between changes in sales and accounts receivables, PPE is property,
plant, and equipment measured at net book value, and νj,t is a residual term.
The fitted values from the OLS estimation are used to construct the normal accruals (NAC) components,
and their residual terms are used as abnormal accruals (ABNAC). These abnormal accruals components
represent firm-specific accrual components in excess of industry averages.
In addition to the absolute value of abnormal accruals (|ABNAC|), we use two another measures ofaccounting figures-based earnings quality measures: variability and smoothness. The variability of6earnings (EBEISD) in this study is defined as the standard deviation of earnings before extraordinary items(EBEI) over the past five years. The “smoothness” measure is a ratio of the standard deviation of EBEI to
the standard deviation of CFO.
As for our REM measures, we follow the method employed by Roychowdhury (2006); moreover, thedata construction method is almost identical. First, by conducting cross-sectional regressions for every
industry and year, we compute abnormal cash-flows from operations (ABNCFO), which are defined as aresidual term from the following regression model:

CFO j ,t
TA j ,t 1

  0  1

SLS j ,t
SLS j ,t
1
 1
 2
  j ,t .
TA j ,t 1
TA j ,t 1
TA j ,t 1(2)

We also compute two additional REM measures, namely abnormal production (ABNPROD) andabnormal R&D expenditures (ABNEXP), by employing regression models (3) and (4) proposed byRoychowdhury (2006).4 Roychowdhury (2006) hypothesized that ABNPROD is higher and ABNEXP islower for a firm sample with unusually low profit.

PRODj ,tTAj ,t 1

  0  1 

SLS j ,tSLSt 1
SLSt
1
 1
 2
 3
  j ,t .
TAj ,t 1
TAj ,t 1
TAj ,t 1
TAj ,t 1

DISEXPj ,tTA j ,t 1

  0  1 

SLS t 11
 1
  j ,t .
TA j ,t 1
TA j ,t 1

(3)

(4)

4.2 CSP Dimensional Indices
A stakeholder-focused approach to corporate governance emphasizes that CSR activities can be linked todifferent stakeholders (e.g., employees, the environment, communities, customers, suppliers) and that firms
are required to choose the appropriate architecture of internal governance and adopt strategies under the
existing regulatory framework. Given these stakeholder relationships, Suto and Takehara (2016) defined
the following five CSP dimensions: 1) employee relations, 2) environmental preservation, 3) social
contributions, 4) firm security and product safety, and 5) internal governance and risk management.5
The first dimension, employee relations (EMP), which includes workplace conditions, can enhance
employee quality and motivation (Turban and Greening, 1997). Appropriate working hours and wages,
minority employment, job stability, safe working conditions, and skills development are all related to the4

By using equations (2) and (3) of Roychowdhury (2006), we computed two additional REM measures: abnormal cost of
goods sold and abnormal inventory growth. However, the Pearson correlation between abnormal production (ABNPROD)
and abnormal cost of goods sold in our pooled sample was high at 0.916. Thus, we decided not to use these two measuresand focus instead on ABNPROD.
5
These CSP dimensional indices have been used in several empirical studies of Japanese data (e.g., Ajward and Takehara,
2014; Aoi et al., 2015).

7

employee relations variable. The second dimension, environmental preservation (ENV), is a pillar of CSP
in a society that is increasingly concerned with climate change. The third dimension, social contributions(SC), is related to a firm’s policy for and response to social demands. Good relationships and coordination
with the surrounding community can reduce the costs associated with local conflicts, attract humanresources, and enhance corporate reputation. On the contrary, negative relationships with the surrounding
community can narrow business perspectives and increase the costs and risks associated with business
operations. These three dimensions, namely employee relations (EMP), environmental preservation (ENV),
and social contributions (SC), are classified as normative CSR. Hence, they serve as a signal to
stakeholders about the social consciousness of firms/managers.
The fourth dimension, firm security and product safety (SS), is related to the quality of a firm’s products
and the sustainability of its business. Therefore, it provides competitive advantage in the long-term byaffecting a firm’s relationships with its customers. The fifth dimension, internal governance and risk
management (IG), is related to the quality of disclosure, compliance, internal auditing, andself-disciplining that a firm demonstrates. These last two dimensions are classified as compliant CSR.
Finally, Suto and Takehara (2016) also defined a composite CSP (CSP) measure as a comprehensive index
of the above five dimensions (EMP, ENV, SC, SS, IG). We use this comprehensive measure of CSP to test
H1.

4.3. Portfolio Formation Method and Regression Model
Suto and Takehara (2016) pointed out the statistically significant relation between social performanceand financial performance in Japan. Hence, we expect that firms with lower profitability and/or firms thatface tighter financially constraints have an incentive to increase their cash-flows. Thus, they manage theirearnings by using REM strategies. To avoid misleading interpretations from the results of the univariate
correlation analysis, we adopt the two-stage sequential portfolio formation method in this study.
To control for the correlation between financial performance, measured by ROA, and earnings quality,
we first rank sample firms based on their ROA and construct ROA-ranked quintile portfolios at the end of
September for each year of the study period. Second, each of these five ROA-ranked quintile portfolios are
subdivided into five groups based on their dimensional CSP scores. As a result, we obtain 25(=5×5) ROAand CSP-ranked portfolios. Since the five CSP-ranked quintile portfolios in the same ROA quintile arealmost at the same ROA level, we are able to investigate the CSP–earnings quality relation after
eliminating the effects of ROA on earnings quality.
8In addition to the two-stage portfolio formation, we also conduct multivariate cross-sectional regression
analysis, using the following control and instrumental variables:Natural logarithm of total assets (in million of Japanese yen) (lnTA);



ROA, which we must control for when testing H3;

Leverage ratios, computed as non-current liabilities over total assets (LEV);

Sales growth rates of the past five years (SLSG) (Lakonishok et al., 1994)



Firm age, defined as number of years from foundation date (AGE);

Dummy variable that equals 1 if the firm is not listed on the Tokyo Stock Exchange First
Section (NOTSE1); and

Lagged one-year value of composite CSP (CSPB).

Note that lnTA, LEV, and SLSG are measures of financial constraints, financial stability, and future
growth, respectively, whereas NOTSE1, AGE, and CSPB are used as instrumental variables in the
two-stage regression analysis. The two-stage least squares regression model is given as follows:

EQ j ,t     1CSPj ,t   2 ROA j ,t   3 LEV j ,t   4 SLSG j ,t
4

8

i 1

i 1

   i SizeDummyi , j ,t    i YearDummyi  2006, j ,t   j ,t .

(5)

In model (5), the dependent variable EQj,t is one of six earnings quality measures, while CSPj,t is thecomposite CSP measure or one of the CSP dimensional indices.6

4.4. Sample and Descriptive Statistics

Table 1 reports the number of sample firms for which the CSP dimensional indices and accounting
figures between 2007 and 2015 were available. We report the number of observations at the aggregatedsector level, where the sector classification schemes are based on Kubota and Takehara (2007).
The first to ninth rows report the number of sample firms in each sector for each year. On average, wehave more than 500 firm observations every year, with the largest number in the investment goods andconsumption goods and services industries. The 10th to 12th rows list the number of firms belonging toeach section of the Tokyo Stock Exchange and the other stock exchanges in Japan. The final row denotes
the total number of firms. The majority of our sample comes from the Tokyo Stock Exchange First Section.
By adding the number of firms in 2007–2015 at the bottom of the table, we have 4,742 firm-year

6

We conducted Hausman’s test and Sargan’s over-identification test before conducting the two-stage least squares
regression. In addition, the standard errors were corrected by applying the one-way (Year) cluster-robust method.

9

observations and 784 firm samples.

[Table 1 around here]

Table 2 reports the descriptive statistics of the variables used in the subsequent analyses. The medianand mean of the absolute value of abnormal accruals (|ABNAC|) are 1.979% and 2.776% with a standard
deviation of 2.648%. Variability (EBEISD) has a median value of 1.493% and a mean value of 1.991%.
Smoothness has a median value of 0.531 and a mean value of 0.648.
For the REM measures, the median and mean of abnormal cash-flows from operations (ABNCFO) are
0.168% and 0.130%, respectively. The median of abnormal production (ABNPROD) is positive at 0.771%,
while the mean is -1.045%, suggesting a negative skew. Finally, in the case of abnormal R&D expenditures(ABNEXP), the median is -0.909% and the mean is 1.050%.
Next, the means of the CSR scores of CSP, EMP, ENV, SC, SS, and IG are 0.529, 0.326, 0.305, 0.212,
0.226, and 0.145, respectively. Note that in Suto and Takehara (2016) all the CSP dimensional indices arenormalized so that they approximately obey a standard normal distribution, and thus we find that oursample belongs to the group of firms with somewhat above-average CSP scores.7 Finally, for the control
variables, the means are 11.720 (log in million Japanese yen) for firm size, 2.499% for ROA, 17.510% forleverage, and 1.174% for sales growth. The mean firm age is 66 years, suggesting that relatively
long-living firms responded to the CSR questionnaire survey.

[Table 2 around here]

5. Results from the Portfolio Formation Method
5.1. Correlation Analysis Results

Before interpreting the results of the two-stage portfolio formation analysis, we check the simple
correlation among the variables. Panel A of Table 3 presents the Spearman rank correlations among themeasures of earnings quality, while Panel B shows the correlations between the measures of earnings
quality and CSP indices. The lower numbers in each cell are the p-values. Panel A shows that thecorrelation between the absolute value of abnormal accruals and earnings variability (EBEISD) is 0.129(p<0.01), while that between the variability and smoothness measures is 0.694 (p<0.01). This finding
7

Because we need to compute the time series of accounting measures by using five years of data before our testing period,
new firms are not included in our sample, which may create some bias.

10

suggests that Japanese firms tend to smooth their earnings by using accounting accruals. Of the REM
measures, ABNCFO is negatively (positively) correlated with ABNPROD (ABNEXP), while the Spearman
correlation between ABNPROD and ABNEXP is very low at -0.849.
Next, we detect the relationships between the REM and AEM measures. The third column from the rightshows that ABNCFO is positively correlated with |ABNAC| with an estimated correlation of 0.007 as wellas with EBEISD with an estimated correlation of 0.069, which seems to be reasonable. ABNPROD is
negatively correlated with EBEISD and smoothness, and ABNEXP is positively related with EBEISD andsmoothness. These results indicate that these two types of earnings quality measures, AEM and REM,
affect the attainment of CSR in different ways.
Panel B reports the correlations of these earnings quality measures with the other variables. |ABNAC|
is negatively correlated with CSP with a coefficient of -0.046 and the variability measure (EBEISD) is
negatively correlated with CSP with a coefficient of -0.049, which implies that both lower abnormal
accruals and lower variability enhance composite CSP, suggesting that earnings quality is positively related
to CSP.
We also find that the correlations of |ABNAC| with employee relations (EMP) and with environmental
preservation (ENV) are significant (-0.071 and -0.063, respectively), which suggests that firms with lower
abnormal accruals pay more attention to these CSP dimensions. As for earnings variability, its correlation
with ENV is negative and significant at -0.093. In other words, the more firms are concerned aboutprotecting the environment, the lower are their earnings variations.
For the REM measures, we find that ABNCFO and ABNEXP are positively related to CSP and that
ABNPROD is negatively correlated to CSP (p<0.01). These results show that real cash-flow effects
influence the level of CSP (Kanodia, 2006). Specifically, lower abnormal expenditures and higher
abnormal cash-flows can enhance CSP, as can lower abnormal production costs.
For each CSP dimension, all REM measures are significantly related to SS (firm security and productsafety) and IG (internal governance and risk management) as shown in the fifth and sixth columns from theleft. Compliant CSR and ROA are more strongly associated with the REM measures. The Spearman
correlation between ROA and ABNCFO is high at 0.359 and that between ROA and ABNPROD is -0.261.
Although the correlation analysis reported in Table 3 is just a preliminary one, the findings demonstrate theneed for an alternative analysis that controls for ROA.

[Table 3 around here]115.2. Results from the Two-Stage Portfolio Formation

Table 4 shows the results obtained by employing the two-stage portfolio formation method. In the firststage, we constructed ROA-ranked quintile portfolios, each of which was subdivided into five CSP-ranked
groups in the second stage. Table 4 shows the averages of the ROA, CSP, and earnings quality measures of
the highest and lowest CSP portfolios. We also conducted Student t-tests under the null hypothesis of nodifference between the highest and lowest CSP portfolios. Panels A to F of Table 4 highlight the U-shaped
pattern between the ROA and AEM measures, while the REM measures are a monotonically decreasing
function of ROA. We interpret this finding is evidence that firms use AEM strategies to smooth earnings,
whereas they use REM strategies to increase earnings.
Panel A shows the relations among the ROA, composite CSP, and earnings quality measures. In mostcases, the AEM measures of the lowest CSP portfolios are higher than those of the highest CSP portfolios.
For the REM measures, ABNCFO and ABNEXP (ABNPROD) are higher in the higher (lower) CSP
portfolios. Overall, the results in Panel A support H1, confirming that comprehensive CSP is positively
associated with earnings quality.
Panels B to D show the relation between normative CSR and earnings quality. For EMP (employee
relations, Panel B) and ENV (environmental preservation, Panel C), the differences in the AEM measures
between the high EMP (or ENV) portfolios and low EMP (or ENV) portfolios are statistically significant inmany cases. By contrast, the differences in the REM measures are significant only in ROA3 to ROA5. Thisfinding suggests that when ROA is sufficiently high as in the ROA1 and ROA2 portfolios, firms haveinsufficient incentives to increase their cash-flows. As a result, EMP and ENV are not associated with REM
in these high ROA portfolios. This finding lends support to H2, namely that the earnings of firms withsuperior normative CSR are less managed.
However, the tendency we find in EMP and ENV is not observed in SC (social contributions). The
correlations between SC and the AEM measures are unclear, while those between SC and the REM
measures are very strong. These trends between SC and the earnings quality measures are thus similar to
those between compliant CSR and earnings quality. In Panel E for SS (firm security and product safety)
and Panel F for IG (internal governance and risk management), the differences in the REM measures
between high SS/IG and low SS/IG portfolios are statistically significant in most cases. This finding
implies that although firms are not coerced to invest in philanthropic activities, many large Japanesecompanies may feel pressured to do so. Furthermore, investors understand firms’ social contributions andexpenditure because they are simple metrics. In that sense, social contributions are close to compliant CSR
in Japan. Hence, we find a strong association between compliant CSR and REM measures, thereby
12rejecting H3. However, in the two-stage portfolio formation method, we only controlled for the ROA of thefirms, which means that we should not form a conclusion on H3 at this stage.

[Table 4 around here]

6. Regression Analysis Results

CSP, as the independent variable in the regression equations, may be correlated with the error terms
because of the presence of endogeneity (Wooldridge, 2010). If this were the case, the management
decisions revealed in their earnings management behavior may actually determine the level of the CSR of
their firms instead.8 Because we do not know a priori whether firms with higher earnings quality are moresocially conscious, firms with higher CSP may pay attention to earnings quality or firms with stronger
internal control, higher reliability of accounting numbers, and higher accountability may attain a higher
level of CSP emanating from their management decisions. To address these concerns, we employ a
two-stage least squares regression, with the following three instrumental variables introduced into thefirst-stage regression: one-year lagged CSP, firm age, and NOTSE1. Table 5 reports the results from thecross-sectional regressions for several measures of earnings quality regressed on the alternative CSP
measures and the control variables.
In the composite CSP result shown in Panel A, we find that the estimated regression coefficient for|ABNAC| is -0.044 (n.s.), suggesting that the higher the level of composite CSP, the lower is |ABNAC|. The
coefficient for EBEISD is -0.052 (p<0.05). As for the REM measures, ABNPROD has negative andsignificant coefficients (p<0.01), whereas ABNCFO and ABNEXP have positive and significant
coefficients (p<0.1 and p<0.01, respectively). These results reconfirm the previous results for the various
earnings quality measures, thereby supporting H1.
Panel B shows the results for employee relations (EMP). The coefficient for |ABNAC| has an estimate of-0.160 (p<0.01). Further, the slopes for EBEISD, ABNPROD, and ABNEXP are -0.143, -0.842, and 0.878,
respectively (p<0.01).
For environmental preservation (ENV) in Panel C, the coefficients for |ABNAC| and EBEISD have slopesof -0.245 (p<0.05) and -0.128 (p<0.01), respectively, implying that the lower the ENV score, the lower isearnings quality. On the contrary, the REM measures offer weaker explanatory power for this CSP
dimension, perhaps because they are more related to firms’ disclosure decisions to outside stakeholders
8

Francis et al. (2006) discussed two causality channels, namely governance to earnings quality and earnings quality to
governance.

13

including current stockholders. Overall, these findings support H2, namely that the earnings of firms withsuperior normative CSR are less managed by pursuing AEM and REM strategies.
For the compliant CSR dimensions (SC, SS, IG), none of the coefficients reported in Panels D to F isstatistically significant. Indeed, although we find a significant relation between compliant CSR and the
AEM measures in Table 4, when we control for the firm size effects (lnMV), financial credibility (LEV),
future growth (SLSG), and year effects in addition to financial performance (ROA), we cannot find any
clear relation between compliant CSR and earnings quality. In sum, these observations support H3.

[Table 5 around here]

7. Conclusion

In this study, we investigated the empirical relation between CSP and the earnings quality of public
firms in Japan, using data for the nine fiscal years from 2007 to 2015. Overall, we found that CSP is
negatively associated with both the AEM and the REM measures, suggesting that CSR activities may
improve the earnings quality of Japanese firms. However, a more detailed investigation using five CSP
dimensional indices reveals the different effects of normative and compliant CSR on earnings quality.
Before controlling for financial performance, we showed that normative CSR is more strongly associatedwith the AEM measures, whereas compliant CSR is more strongly associated with the REM measures.
However, after controlling for financial performance in the pooled regression analysis, the regression
slopes for the compliant CSR dimensions are no longer statistically significant. This finding suggests that alarge part of the positive association between compliant CSR and earnings quality can be explained byfinancial performance. By contrast, the slopes of the normative CSR scores remain statistically significant
even after controlling for financial performance. Overall, the presented results indicate that the managers
of firms that have normative CSR values tend to avoid adopting AEM and REM measures and that thosefirms that are conscious about social responsibility are more likely to gain investor confidence throughtheir CSR actions. Thus, the normative CSR scores analyzed in this study provide supplementary signals toinvestors when they are evaluating the earnings quality of Japanese firms.

14

References:

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16

Figure 1. Relation between CSP and Earnings Quality
CSR Actions

Earnings Quality

H1

Normative CSR

AEM

Employee Relations

Absolute Value ofAbnormal Accruals

H2

Environment
Preservation

Variability ofEarnings

SocialContribution

Income Smoothing

Abnormal CFO

Security of the Firm
and Products Safety

Abnormal Production

Internal Governanceand Risk Management

H3

Abnormal R&D
Expenditure

Compliant CSR

REM

Table 1. Number of Sample Firms
Number of sample firms at the end of September of each year (2007–2015) and number of firms listed on theTokyo Stock Exchange’s First Section (TSE1), Second Section (TSE2), and all other stock exchanges in Japan(Others) are reported. Number of firms in the most upper right four columns is with non-duplicated counting,and a single firm appears a maximum of four times in our sample period. The total firm-years are 4,742.
Transportation

Consumption Goods

Investment
Goods

2007

117

192

79

17

2008

121

208

89

2009

122

218

2010

128

2011

Real Estate

All Sectors

10

10

425

16

11

15

460

90

15

11

12

468

223

96

16

13

14

490

130

226

117

19

11

15

518

2012

135

235

123

20

11

15

539

2013

149

254

135

22

11

16

587

2014

154

279

136

23

11

16

619

2015

153

290

142

24

10

17

636

TSE1

167

291

140

26

13

20

657

TSE2

31

49

31

2

0

0

113

Others

7

21

27

2

0

1

58

Total

199

348

174

29

13

21

784

Services

17

UtilityTable 2. Descriptive Statistics[Earnings Quality Measures]|ABNAC|: Absolute value of abnormal accruals, EBEISD (earnings variability): Standard deviation of earnings before
extraordinary items, Smoothness: EBEISD/[standard deviation of cash-flows from operations] (in %), ABNCFO: Abnormal
cash-flows (in %), ABNPROD: Abnormal production costs (in %), and ABNEXP: Abnormal expenditure (in %).
[CSP Measures]
CSP: Comprehensive measure of CSP, EMP: Employee relations, ENV: Environmental preservation, SC: Social
contributions, SS: Firm security and product safety, IG: Internal governance and risk management[Control/Instrumental Variables]
lnTA: Natural logarithm of total assets (in million Japanese yen), ROA: Return on assets, LEV: Non-current liabilities to total
assets, SLSG: Growth rate of sales revenue (in %), Age: Number of years from foundation.
1st Qu.

MedianMean

3rd Qu.

S.D.

|ABNAC|

0.935

1.979

2.776

3.750

2.648

EBEISD

0.880

1.493

1.991

2.572

1.629

Smoothness

0.319

0.531

0.648

0.836

0.479

ABNCFO-2.471

0.168

0.130

2.539

4.578

ABNPROD-5.1560.711-1.0455.751

11.935ABNEXP-4.641-0.909

1.050

3.934

10.706CSP-0.576

0.751

0.529

1.829

1.592

EMP

-0.455

0.517

0.326

1.273

1.250

ENV

-0.342

0.342

0.305

0.954

0.863

SC

-0.496

0.292

0.212

0.962

1.053

SS

-0.223

0.638

0.226

1.050

1.234

IG

-0.337

0.254

0.145

1.037

1.313

lnTA

10.67011.60011.72012.730

1.469

ROA

0.884

2.206

2.499

4.309

4.228

LEV

6.702

14.48017.51025.08013.767SLSG-5.165

1.498

1.174

7.415

15.593AGE

56.49065.25066.74078.10021.40018

Table 3. Spearman Correlation Matrix
The definitions of the variables are the same as in Table 2.
Panel A. Correlation among Earnings Quality Measures|ABNAC||ABNAC|EBEISD

1.000

Smoothness

ABNCFOABNPRODABNEXP

0.129-0.1060.007-0.0150.018

p -value

0.000

0.000

0.644

0.312

0.219

EBEISD

1.000

0.694

0.069

-0.0870.075

p -value

0.000

0.000

0.000

0.000

Smoothness

1.000

0.046

-0.0700.084

p -value

0.002

0.000

0.000

ABNCFO

1.000-0.3080.089

p -value

0.000

0.000

ABNPROD

1.000-0.849p -value

0.000

Panel B. Correlation between Earnings Management Measures and CSP/Control Variables
CSP

EMP

ENV

SC

SS

IG

lnTA

ROA

LEV

AGE

SLSG

|ABNAC|-0.046-0.071-0.063-0.054-0.026-0.003-0.121-0.006-0.108-0.133-0.050(p -value)

0.001

0.000

0.000

0.000

0.072

0.839

0.000

0.695

0.000

0.000

0.001

EBEISD-0.049-0.063-0.093-0.060-0.003-0.029-0.1360.084-0.030-0.1150.017(p -value)

0.001

0.000

0.000

0.000

0.848

0.046

0.000

0.000

0.042

0.000

0.252

Smoothness

0.052

0.056

0.028

0.032

0.039

-0.0340.049-0.001

0.118

0.071

0.003

(p -value)

0.000

0.000

0.052

0.028

0.008

0.018

0.001

0.963

0.000

0.000

0.855

ABNCFO

0.094

0.054

0.032

0.078

0.094

0.043

0.068

0.359

-0.124-0.1130.055(p -value)

0.000

0.000

0.027

0.000

0.000

0.003

0.000

0.000

0.000

0.000

0.000

ABNPROD-0.123-0.065-0.040-0.093-0.135-0.058-0.032-0.261

0.119

0.054

-0.012(p -value)

0.000

0.000

0.006

0.000

0.000

0.000

0.027

0.000

0.000

0.000

0.412

ABNEXP

0.117

0.064

0.053

0.083

0.138

0.045

0.016

0.055

-0.022-0.019-0.027(p -value)

0.000

0.000

0.000

0.000

0.000

0.002

0.280

0.000

0.122

0.183

0.065

19

High

A. Composite CSP

0.767-9.074

5.593

ABNPRODABNEXP

2.360

EBEISD

2.797

2.905

|ABNAC|

Smoothness

1.310

ENV

ABNCFO

7.529

High

ROA

C. Environment

5.780

ABNEXP

0.766

Smoothness

2.558

2.151

EBEISD-8.841

2.616|ABNAC|ABNPROD

1.853

EMP

ABNCFO

7.431

ROA

High

5.992

B. Employee Relations

ABNEXP

3.299

3.141

-9.774

ABNCFO

0.743

Smoothness

ABNPROD

0.697

2.366

EBEISD

2.368
1.720

***

0.675

**

0.640

5.453

-10.336

3.288
4.771

-5.271

1.379

1.857

3.089

2.420

**

1.456

3.467 **

-0.994

***

3.628

High

2.302

-2.707

0.848

0.609

1.873

*

3.760

High

4.836

-5.869

1.740

0.669

1.903

2.510

***

*

*

8.511 *

Low

3.860

-7.633

2.720

0.708

3.146

3.170

-1.641

7.979

Low

4.067

-8.469

2.930

3.206

3.151

2.501

|ABNAC|

3.729

7.933

2.491

CSP

High

-1.895 ***

7.572

ROA

Low

ROA1 (Highest ROA)ROA Quintile

***

*

***

***

2.709

-3.133

0.698

0.579

1.898
*

3.235 ***

-0.8493.774

Low-0.052-0.280

0.171

0.523

1.831

2.910

-1.4303.705

Low-0.697

0.326

20

2.714

-2.035

0.178

0.603

1.554

2.528

1.483

2.196

High

1.879

-1.345-0.097

0.671

1.471

2.253

1.810

2.153

High

2.521

0.344
-2.163-0.007 ***
***

0.619

***

0.542

**

1.652

2.871

3.002 *
1.938

2.434

2.211

High

*

-2.180

0.041
0.918

2.536 ******

-1.1410.530-0.284

0.612

1.326

2.306

1.512

1.059

-0.799 *1.919 *

3.232 **

-0.824

2.174

Low
***

-0.072

**

High

1.022

-1.654-0.782

0.611

1.270

1.992

1.816

1.002

2.893 **

***

**

***

***

High

1.085

-0.403-0.384

0.609

1.488

2.054

2.453

0.999

High

-0.676

0.534

1.896

3.195

-1.594

2.202

Low

***

3.451 ***

-0.619

0.604

1.908

3.178

-1.891 ***

2.180

Low

ROA3 (Midium ROA)

-1.711 ***

3.762

Low

ROA2

**

-1.487

***

**

***

***

-0.689 *2.161 **

-1.289 **0.610

1.717 **

2.579

-0.8961.048

Low-1.1042.886-1.552

0.584

1.648

2.886

-1.536

1.005

Low

**

3.035 ***

-1.5870.605

1.707 *

2.708 **

-1.851 ***

0.988

Low

ROA4

Table 4. Differences in Earnings Quality between High and Low CSP Firms

2.726-0.974-1.001

0.786

2.127

2.989

1.582

-2.392

High

1.554

0.790

-1.853

0.709

2.120

2.829

1.796

-2.043

High

2.947

-1.118-1.015

0.789

2.223

2.818

2.539

-2.313

High

2.702

0.216

-2.191 **

0.724

2.655

3.643

-0.922 ***-2.449

Low

1.419

1.402

-2.2040.778

2.937 *

3.445

-1.650 ***-2.797

Low

1.801

0.708

-1.814

0.711

2.653

3.358

-1.998 ***-2.406

Low

ROA5 (Lowest ROA)

3.091

2.253

0.711

3.122

-9.574

5.449|ABNAC|EBEISD

Smoothness

ABNCFOABNPRODABNEXP

5.785

ABNEXP

5.341

ABNEXP

1.033-2.5743.069-7.014 **3.177-10.097

ABNCFO

0.622

2.712
0.734 **

2.182

0.586

EBEISD

Smoothness

ABNPROD

1.736

**

3.159

3.046

3.868

1.576

3.074

1.464

|ABNAC|-1.710

3.697

1.527

***

ENV

7.762

5.710
High

7.348

Low

3.583

-6.380-7.840 **
*

1.507

3.393

0.627

1.998

0.692

2.797

1.282

3.686

High

2.810

-3.550

1.283

0.649

1.737

2.645

1.550

3.659

2.964 **

***

*

**

***

3.202

-1.761

8.336

Low

3.167

-7.805

3.231

0.739

2.788

3.094

-1.329

7.876

High

ROA

High

-10.338ABNPROD

F. Int. Gov. and Risk Mng.

0.701

2.544

EBEISD

3.539

3.006

|ABNAC|

Smoothness

1.305

EMP

ABNCFO

7.495

ROA

High

1.602

CSP

E. Security and Safety

7.841

ROA

High

D. Social Contributions

Low

ROA1 (Highest ROA)ROA Quintile

0.422

**

**

***

-1.2880.140-0.755

0.505

0.544

1.696

2.602

-1.483

3.666

Low
***

***

0.607 ***

0.203 ***

0.520

1.820

2.789

-1.6043.670

Low-0.974***0.476 ***

21

0.823

-0.266

0.442

0.559

1.589

3.062

1.485

2.202

High

1.415

-0.908-0.292

0.599

1.704

2.937

1.235

2.183

High

1.691

-1.127

0.623

0.300

1.537

2.708

1.535

2.181

0.525 **

***

High

***

2.120

***

-2.780*****-1.727**3.255 ***

-0.635

0.549

1.630

2.881

-1.876

2.170

Low

***

4.125 ***

-0.982

0.551

1.645

3.083

-1.8632.200

Low-2.017

***

0.183

0.570

-0.456

0.547

1.528

2.392

1.498

0.958

High

1.194

0.233

-0.272

0.596

1.704

2.386

1.248

1.071

High

0.201

0.418

-0.258

0.620

1.507

1.584

**

1.024

High
***

3.508 ***

-0.860

0.615

1.841

3.248

-1.319

2.189

Low

ROA3 (Midium ROA)

1.782

3.119

-1.275

3.747

Low

ROA2

Table 4. (Contd.)

*

***

***

-2.135***-1.539*2.503 **

-0.793

0.619

1.558

2.332

-1.702

1.015

Low

***

3.578 ***

-1.583 ***

0.593

1.562

2.775

-1.6111.030

Low-2.325***3.986 ***

-1.087

0.638

1.681

2.688

-1.292

0.983

Low

ROA4

1.578

0.395

-1.664

0.739

2.268

2.962

1.477

-2.145

High

0.782

1.352

-1.467

0.768

2.372

2.832

1.256

-2.220

High

2.177

-0.310-1.026

0.739

2.142

2.887

1.645

-2.084

1.760

0.509

-1.978

0.755

2.398

3.066

-1.827 ***-2.435

Low

0.861

2.023

-2.054

0.742

2.582

2.889

-1.699 ***-2.175Low

1.362

1.368 *

-2.136 **

0.758

2.362

3.104

-1.310 ***-2.189Low

ROA5 (Lowest ROA)High

Table 5. Regressing Earnings Quality on CSP
The dependent variables are the various measures of accounting quality and the definitions of all the variables
are the same as in Table 2. *, **, and *** correspond to significance levels of 10%, 5%, and 1%, respectively,
where standard errors are corrected by using the two-way cluster error correction method.
|ABNAC|

Panel A.

EBEISD

Smoothness

Intercept

2.981

CSP

-0.044-0.052 **ROA-0.040 *LEV
SLSG
Adjusted R

2

Panel B.

Intercept****

ABNEXP-2.098 *0.186 *

-1.183 ***1.260 ***

-0.001-0.006

0.413 ***

-0.773 ***0.226 ***

-0.012 ***0.002

0.004 ***

-0.014 **-0.0030.041 **

-0.002

0.007

0.001

-0.008 **0.067 ***

-0.032

0.044

0.073

0.058

0.155

0.104

0.050

|ABNAC|EBEISD

Smoothness

ABNCFOABNPRODABNEXP-0.006-0.198-0.191

3.063 ***

1.907 ***

***

***

-0.039 *LEV
SLSG-0.1650.651 ******

0.878 ***

-0.0070.135-0.8420.000-0.006

0.413 ***

-0.768 ***0.220 **

-0.011 ***0.003

0.004 ***

-0.015 **0.006

0.031 *

-0.003

0.007

0.001

-0.008 ***0.068 ***

-0.034

0.045

0.081

0.056

0.152

0.092

0.037

ABNCFOABNPRODABNEXP-0.035-0.456-0.500|ABNAC|Intercept

ABNPROD

0.003

ROA

Panel C.

ABNCFO

1.573-0.160Adjusted R

0.637

***

-0.282

EMP

2

1.837

***

EBEISD

Smoothness

3.179

***

1.873

***

ENV

-0.245**-0.128***ROA-0.043 *-0.002-0.006

0.415 ***

-0.780 ***0.239 ***

LEV

-0.013 ***0.002

0.004 ***

-0.015 **0.002

0.037 *

SLSG

-0.002

0.008

0.001

-0.009 ***0.073 ***

-0.039 *

0.047

0.073

0.058

0.150

0.086

0.031

Adjusted R

2

***

0.640
0.003

0.151

-0.584

1.077 *

22

Table 5 (Continued.)|ABNAC|

Panel D.

Intercept

3.011

SC

-0.104

ROA
LEV
SLSG
Adjusted R
Intercept-0.040-0.012

***

LEV
SLSG
2

Adjusted R-1.1340.413***-0.774******-0.015**0.000-0.009

**

0.001

0.068

1.345
0.227
0.037

***

-0.033

0.055

0.151

0.096

0.042

ABNCFOABNPRODABNEXP

0.674 ***

-0.084-0.332-0.216-0.0290.220

1.788 ***
-0.068-0.040*-0.012

***

**

0.000

-0.006

0.002

0.004

***

0.412

-0.768-0.014

**

0.000

*

0.007

0.000

-0.008

0.044

0.072

0.053

0.156

EBEISD***

1.730-0.018-0.040*-0.012

***

0.000
0.003

Smoothness
***

0.668

***

-0.031ABNCFO

0.239***-1.165-0.771-0.016

**

0.004

***

0.008

0.000

-0.010

0.043

0.071

0.053

0.148

0.073
0.085

-0.0330.042*-0.014

0.413

0.220
0.038

***

ABNPROD****-0.002

23

0.068

0.980
***

0.096

-0.108-0.0060.004-0.792***-0.002-0.0372-0.910*

Smoothness

IG

SLSG

0.290

0.071

2.907

LEV

0.047

EBEISDIntercept

ROA

0.004

ABNEXP

0.081-0.0060.003

ABNPROD

0.043|ABNAC|

Panel F.

0.000

ABNCFO|ABNAC|

0.013

ROA

0.669

***

-0.0200.008

2.864 ***

SS

1.732

Smoothness***-0.013*-0.003
2

Panel E.

Adjusted R

EBEISD***ABNEXP

0.757
0.089

***

0.223
0.034

***

-0.0390.028