Earnings Management and Corporate Governance in...

Earnings Management andCorporate Governance in Asia’sEmerging Markets

Chung-Hua Shen and Hsiang-Lin Chih*

This paper studies the impacts of corporate governance on earnings management. We usefirm-level governance data, taken from Credit Lyonnais Security Asia (CLSA), of nine Asiancountries, in addition to the country-level governance data used in past studies. Our conclu-sion is as follows. First, firms with good corporate governance tend to conduct less earningsmanagement. Second, there is a size effect for earnings smoothing, that is, large size firms areprone to conduct earnings smoothing, but good corporate governance can mitigate the effecton average. Third, there is a turning point for leverage effect, i.e. when the governance index islarge, leverage effect exists, otherwise reverse leverage effect exists. It shows that a highlyleveraged firm with poor governance is prone to be scrutinised closely and thus finds it harderto fool the market by manipulating earnings. Fourth, firms with higher growth (lower earningsyield) are prone to engage in earnings smoothing and earnings aggressiveness, but goodcorporate governance can mitigate the effect. Finally, firms in stronger anti-director rightscountries tend to exhibit stronger earnings smoothing. This counter-intuitive result is dif-ferent from Leuz et al. (2003).

Keywords: Corporate governance, earnings management, investor protection, emergingmarkets

Introduction

S tudies of earnings management (hereafter,EM) have recently attracted a lot of atten-

tion because of the accounting fraud at Enron,WorldCom, Xerox, Royal Ahold, HealthSouth,and so on. The increasing attention to thequality of reported earnings makes the studyof earnings management important again(Levitt, 2000). Schipper (1989) and Healy andWahlen (1999) state that earnings managementis the alternation of firms’ reported economicperformance by insiders to either “misleadsome stakeholders” or to “influence contrac-tual outcomes”. It is premised that insidersengage in EM to dilute their rent-seekingactivities from outsiders. That is, EM is used toreduce outsider interference and protect in-siders’ private control benefits. For instance,insiders can use their discretion in financialreporting to inaccurately reflect firm perfor-

mance and consequently weaken outsiders’ability to govern the firm.

Furthermore, investors who are concernedwith the stock returns examine the earningsfrequently. This heightens capital marketpressure and creates an additional incentivefor firms to engage in earnings manipulation.The regular scrutiny of firms’ financial per-formances by financial analysts in the pastdecades puts further pressure on firms tomaintain earnings momentum to fulfil theexpectation of the market (e.g. Barth et al.,1999; Myers and Skinner, 2002). Thus,knowing the factors that affect earnings man-agement is helpful for investors to foresee thevariation of earnings. In particular, will acompany with good governance have lessincentive to conduct earnings management?

Past studies regarding governance factorsthat affected the earnings management weretypically based on the country-level to account

*Address for correspondence:Department of CooperativeEconomics, National TaipeiUniversity 151, University Rd.,San-Shia, Taipei 273, Taiwan,Republic of China. Tel: (886)2-86746874; Fax: (886)2-86715905; E-mail:[email protected]

EARNINGS MANAGEMENT AND CORPORATE GOVERNANCE IN ASIA’S EMERGING MARKETS 999

Volume 15 Number 5 September 2007© 2007 The AuthorsJournal compilation © 2007 Blackwell Publishing Ltd, 9600 Garsington Road,Oxford, OX4 2DQ, UK and 350 Main St, Malden, MA, 02148, USA

mailto:[email protected]

for the variations (e.g. Leuz et al., 2003). Thesestudies began with the finding that the lawsthat protect investors differ significantly acrosscountries, in part because of differences inlegal origins (La Porta et al., 1998, hereafter,LLSV). Leuz et al. (2003), for example, exam-ined the relation between outside investor pro-tection and earnings management across 31countries using non-financial industries data.They found that strong investor protection at acountry level reduces firms’ EM activities. Incontrast to the above studies using non-financial industries, Shen and Chih (2005)employed data of financial industries tocalculate EM of 48 countries based on themethodologies of Degeorge et al. (1999) andBurgstahler and Dichev (1997). They thenfollowed the anti-director right, legal en-forcement, accounting disclosure and insidetrading analysis technique of LLSV to accountfor the variations of EM across the countries.Their results show that accounting disclosureis the more effective method that explainsvariation of EMs across countries.

Many provisions of investor protection in acountry may not be binding for all firms sincefirms have the flexibility in their corporatecharters and bylaws to either choose to “opt-out” and decline specific provisions or adoptadditional provisions not listed in theirlegal code. Klapper and Love (2004) claimedthat firms could improve investor protectionrights by increasing disclosure, selectingwell-functioning and independent boards,imposing disciplinary mechanism to preventmanagement and controlling shareholdersfrom engaging in expropriation of minorityshareholders, and so forth. Hence, two firmsfrom the same country may perform varyingdegrees of protection to their investors.Namely, the relationship between the country-level legal infrastructure and the firm-levelcorporate governance mechanisms is not aone-to-one correspondence. Though Richard-son et al. (2002) have used the firm-level finan-cial ratios to predict earnings management,they did not consider the role of corporategovernance.

The aim of this paper is to study how corpo-rate governance affects EM of Asian countriesby using firm-level data. In a recent report,Credit Lyonnais Securities Asia (hereafterCLSA) calculated an index with corporategovernance rankings for 495 firms across 25emerging markets and 18 sectors. The descrip-tive statistics presented in the CLSA reportshow that companies that ranked high on thegovernance index have better operating per-formance and higher stock returns. Klapperand Love (2004) applied these governancevariables and found that better corporate

governance is highly correlated with betteroperating performance and market valuation.Also applying CLSA governance measures,Chen et al. (2003) found that both disclosureand non-disclosure corporate governancemechanisms have a significantly negativeeffect on the cost of equity capital. Doidge et al.(2004) also used CLSA data and found thatfirms in less developed countries have lessincentive to improve firm-level governancebecause outside finance is expensive and theadoption of better governance mechanisms isexpensive. Except for these three papers, to thebest knowledge of authors, none have appliedthe corporate governance data directly toinvestigate the relevant issues. This paper usesthe same governance ranking produced byCLSA to further investigate the relationshipbetween firm-level governance and firm-levelearnings management. Then, we studied howthe corporate governance affects the relation-ship between firms’ financial ratios and earn-ings management.

This paper is organised as follows. The nextsection describes the construction of theearnings management measures. We describethe data of CLSA, followed by a section des-cribing the econometric models. Empiricaltests results, and the robustness check arepresented. The final section presents theconclusions.

Earnings management measures

Because insiders can exercise discretion alonga number of different dimensions, and the pre-ferred earnings management method candiffer across countries, a single earnings man-agement measure may not yield a completepicture. Accordingly, we analysed four differ-ent measures of EMs. Our earnings measuresare the same as Leuz et al. (2003), who classi-fied the earnings management into two kindsof manipulations: earnings smoothing andearnings discretion. Bhattacharya et al. (2003)further classified the earnings managementinto three categories: loss avoidance, earningssmoothing and earnings aggressiveness. Inthis paper, we followed Leuz et al.’s (2003)approach in classifying earnings managementinto earnings smoothing (EM1 and EM2 in ournotation below) and earnings aggressiveness(EM3 below).1 The fourth EM measure issimply the average of their rankings (AEM).

Because accounting earnings are the sum ofaccruals and cash flow from operation, we firstintroduce accruals and cash flow. The opera-tional definition of accruals is:

Accruals CA Cash CL STDTP Dep

it it it it it

it it

= −( ) − −(− ) −Δ Δ Δ ΔΔ ;

1000 CORPORATE GOVERNANCE

Volume 15 Number 5 September 2007 © 2007 The AuthorsJournal compilation © Blackwell Publishing Ltd. 2007

DCAit = change in total current asset;DCashit = change in cash/cash equivalents;DCLit = change in total current liabilities;DSTDit = change in short-term debt includedin current liabilities;DTPit = change in income taxes payable;Depit = depreciation and amortisation expense.

Once the accruals are obtained, we calculatecash flow from operation.

Cash flow from operatingoperating earnings

=− Accruals

The first smoothing measure captures thedegree to which insiders use their discretion toalter the accounting component of reportedearnings, that is, accruals, to reduce the vari-ability of operating earnings:

EM SD operating earningsSD cash slow from operation

1 = ( )( ) ;

where SD represents standard deviation, andcash flow from operation is the cash flowdefined above.

A low value of this measure is indicative ofinsiders using their discretion to smoothreported earnings. That is, the higher EM1implies firms are less prone to manage earn-ings.

The second measure of earnings smoothingis based on the contemporaneous correlationbetween the change in accounting and thechange in operating cash flow.

EM Spearman cash flowfrom operation

2 = ()

Δ ΔAccrual,;

where Spearman is the Spearman correlationcoefficient. The concept of this measure is thatthe insiders may also use their discretion toreport accounting accruals that offset eco-nomic shocks to the firm’s operating cash flowthat would otherwise affect reported earnings.A negative correlation implies the use ofdiscretionary accounting accruals to offsetundesirable cash flow shocks, hence, a largeearnings management. Therefore, the higherthe EM2, the less is the tendency to manageearnings.

The third measure is related to earningsaggressiveness, which represents the insidersthat use their reporting discretion to misstatethe firm’s actual economic performance. It isnoted that accrual itself involves judgement ofmanagers; hence, the third measure simplyuses the magnitude of accruals as a proxy forthe amount of discretion insiders use to influ-ence earnings. Thus, the insiders may manipu-late the earnings by adjusting accruals to avoidloss. For firm i, EM3 is equal to the average

value of the magnitude of accruals scaled bythe absolute value of the firm’s cash flow fromoperations to control for firm performance:

EM Accrual cash flow from operating3 = .

The larger EM3 are indicative of large-scaleuse of discretion to manipulate reportedaccounting earnings.

Our fourth EM measure, AEM, is theaverage ranking of the above three earningmanagement measures. Following the conceptof Leuz et al. (2003), we ranked firms’ EM1,EM2 and EM3 and calculated their averages,that is, for firm i,

AEM Rank EM Rank EMRank EM

= ( ) + ( )(+ ( ))

1 23 3.

Note that Rank (EM1) and Rank (EM2) arereversed to be consistent with Rank (EM3).Hence, the higher the AEM, the more is thetendency to earnings management.

Data of CLSA

The CLSA report includes corporate gover-nance (CG) rankings on 495 companies in 25emerging countries in April 2001 and February2002.2 In these reports, firms’ corporate gov-ernance was assessed based on seven key cri-teria. The reports show that good corporategovernance is associated with strong per-formance in several dimensions, includingshare price level, stock returns and accountingprofitability.

The questions in the CLSA report coverseven broad categories: management disci-pline (DISC), transparency (TRAN), inde-pendence (INDP), accountability (ACCT),responsibility (RESP), fairness (FAIR) andsocial awareness (SOCI). The meanings ofthese categories are as follows. “Transpar-ency” refers to the ability of outsiders to assessthe true position of a company. “Discipline”refers to management’s commitment toemphasise shareholder value and financialdiscipline. “Independence” refers to theboard of director’s independence of control-ling shareholders and senior management.“Accountability” refers to the accountabilityof management to the board of directors.“Responsibility” refers to the effectiveness ofthe board to take necessary measures in caseof mismanagement. “Fairness” refers to thetreatment minority shareholders receive frommajority shareholders and management. Thelast category, “social awareness”, refers to thecompany’s emphasis on ethical and sociallyresponsible behaviour.


Volume 15 Number 5 September 2007© 2007 The AuthorsJournal compilation © Blackwell Publishing Ltd. 2007

Furthermore, CLSA included CG6, thesimple average of the first six items and CG7,the weighted average of the seven items, withthe weight being equal to 15 per cent for thefirst six items and 10 per cent for the last one.

In our case, only nine Asian countrieswere adopted, including Hong Kong, India,Indonesia, Korea, Malaysia, the Philippines,Singapore, Taiwan and Thailand. We skippedthe remaining countries since the identifica-tion of the firms in CLSA could not be posi-tively confirmed.3

Earning management andgovernance

Econometric modelThe determinants in our earnings managementequation consider the firm-level corporategovernance variables (CG6 or CG7), as well asthe financial ratios of firms. In addition tothese two types of variables, the interactions ofboth variables are also attempted to investigatethe robustness of the two types of variables.Following the suggestions of Richardson et al.(2002), our model is

EM SizeLeverage ExternalEPS Gr

i i= + ( ) ++ ++

β β ββ ββ

0 1 2

3 4

5

6 7CG or CG

_ oow Earnings YieldPGDP Antidirect LAWDisclosu

++ + ++

ββ β ββ

6

7 8 9

10

_

rre Insider+ β11 (1)

β β βj jj

= + ( )=

0 1 6 72 3 4 5 6

CG or CG ;, , , , ; (2)

where subscript i is the ith firm, EM isproxied by EM1, EM2, EM3 and AEM,respectively; Size is the log of firm’s sales,Leverage is the debt ratio (total debt/totalasset), External is the amount of externalfinancing, EPS_Grow is the number of growthin EPS in the recent three years and Earning-s_Yield is the earnings to stock price. We usedthe Worldscope database (2000), which containsup to 10 years (1991–2000) of historical finan-cial data from home-country annual reportsof publicly-traded companies around theworld, to calculate all EM measures andfinancial variables above. To compute thesevariables we require that each firm haveincome statement and balance sheet informa-tion for at least three consecutive years.PGDP is a 6-year average (1994–1999) of theGDP per capita. Antidirect is the “anti-director rights” index from La Porta et al.(1998), and an aggregate measure of (minor-ity) shareholder rights, ranging from zero to

six. LAW is measured as the mean scoreacross three legal variables used in La Porta etal. (1998): (a) the efficiency of the judicialsystem, (b) an assessment of rule of law and(c) the corruption index. All three variablesrange from zero to 10. Disclosure measuresthe inclusion or omission of 90 items in the1990 annual reports (La Porta et al., 1998).Insider is the 5-year average (1997–2001) ofthe insider trading index taken from theWorld Competitiveness Yearbook (2002). Insiderranges from zero to 10, and the higher thevariable, the less the extent of inside trading.

The relation between firm size (Size) andearnings management is controversial. Oneview is that capital market pressures aregreater for larger firms because they aresubject to closer scrutiny by the investmentbanks and analyst community, leading them toadopt aggressive accounting policies. There-fore, larger firms have higher incentives tomanage earnings (Richardson et al., 2002),which is referred to as the size effect in thispaper. The opposite view is that large firms areoften requested to disclose their informationand hence have less probability to manageearnings. Insiders of small firms, alternatively,are able to retain their private informationmore successfully than their counterparts oflarge companies, suggesting a reverse size effect(Lee and Choi, 2002). Hence, the coefficient onSize is uncertain.

Leverage is used to capture the impact ofdebt contracting on earnings management.Two opposite empirical evidences are foundbetween the leverage and earnings manage-ment. One strand of evidence is that highleverage firms tend to manage earningsaggressively, which is dubbed as the leverageeffect. This is suggested by Sweeney (1994)and Press and Weintrop (1990), who foundthat firms closer to violating debt covenantsmanage earnings more aggressively and thathighly leveraged firms tend to violate debtcovenants. Becker et al. (1998) support thisview and found that managers respond todebt contracting by strategically reportingdiscretionary accruals. Richardson et al. (2002)presented the evidence that debt covenants(as proxied by leverage) are a motivation foraggressive accounting policies of restatementfirms. Opposite to the above view, high lever-age may also imply less earnings manage-ment, which is dubbed as the reverse leverageeffect. Dechow and Skinner (2000) reportedthat firms with high leverage are less likely toreport small increases in earnings. Ke (2001)found that the probability of reporting asmall increase in earnings rather than a smalldecrease in earnings is higher for firms withlow financial leverage. Chung and Kallapur



(2003) did not find evidence of a statisti-cally significant association between abnormalaccruals and leverage. As a result, the relationbetween Leverage and EM is uncertain.

External is the average ratio of the amount ofexternal financing relative to total assets.Firms, which raise external funds from capitalmarket, tend to portray a rosy picture offuture potential. Hence, firms with high exter-nal funds will actively engage in earningsmanagement (Richardson et al., 2002), whichis referred to as the external financing effect. Onthe contrary, firms with higher external fundswill engage in less earnings management,since they are under closer scrutiny by themarket and thus find it harder to fool themarket by manipulating earnings, which isreferred to as reverse external financing effect.

EPS_Grow is the number of years of con-secutive growth of earnings to price (EPS)(Richardson et al., 2002). In particular, it isequal to 1, 2 or 3 if it has consecutive positivegrowth for 1, 2 or 3 years. A higher number ofEPS_Grow has two implications. First, firmsindeed perform well, making its earningsgrow every year. Second, the strong perfor-mance is the result of earnings managementbecause firms face pressure from capitalmarkets to report growing earnings. Barth etal. (1999) found that the market reacts nega-tively to firms that break their string of con-secutive earnings increases. To avoid thisnegative response, a firm with a decline in EPSwill have more incentive for earnings manage-ment, suggesting coefficient on EPS_Grow ispositively related to earnings management.

Earnings_Yield, which denotes the earningyield, or earnings-to-price ratio, is used toexamine the market’s perceptions of futuregrowth. Prior research suggests that growthstock is particularly sensitive to stock price,especially around earnings announcements(Skinner and Sloan, 2002). Richardson et al.(2002) also expect that firms trading at lowearnings yield will be under greater pressureto adopt aggressive accounting policies todeliver the anticipated growth in earnings.Hence, growth stock seems more likely toadopt earnings management. This paper’sintent is not to distinguish growth from valuestocks but rather simply to argue that growthfirms, proxied by low Earnings_Yield, aremore likely to engage in earnings manage-ment. Thus, Earnings_Yield is expected to benegatively related to the extent to which firmsmanage earnings.

The country-level governance variables arealso taken into account. Antidirect is the mea-sure of protection of small shareholders, LAWis the efficiency and integrity of the legal envi-ronment, which also includes corruption and

enforcement, Disclosure is the transparency ofthe financial reports and accounting standard,Insider discusses the severity of insidertrading, and the higher Insider is indicative ofless severity of insider trading.

Equation (2) specifies the coefficient bj as thefunction of corporate governance. The modelsuggests that good governance may lessen theincentive of earnings management triggeredby the above financial ratios.

Empirical evidence

Data description and basic statisticsTable 1 reports the basic statistics of EM1,EM2, EM3 and AEM. In addition to thecountry column, the first column is thenumber of firms used in each country. Indiahas the highest number of firms of 48, andPhilippines has the lowest number of firms of8. The total number of firms used is 203, butthis number varies across the years because ofmissing data in some firms. The highest meanof EM1 falls on Taiwan (0.467), followed byIndia (0.457) and Hong Kong (0.420). Recallthat the higher the EM1, the lesser is theearnings smoothing. Hence, on average, thesethree countries show the lowest tendency toadopt earnings smoothing. In addition, whileTaiwan has the lowest tendency to smoothearnings, its variation is also the largest. Incontrast to the above three countries, Thailandhas the lowest mean of 0.218 with the leastvariation of 0.153.

EM2 shows similar patterns as those of EM1in that the above three countries (Taiwan,Hong Kong and India) still exhibit the highestnumbers. Among these three countries, how-ever, Hong Kong, rather than Taiwan, hasthe highest mean with the highest standarddeviation. Because both EM1 and EM2 aredesignated to detect earnings smoothing, theearnings smoothing is thus less severe. This isprobably because there are many tax deduc-tion mechanisms in these countries to encour-age investment and free trade.4

EM3, which measures earnings aggressive-ness, shows a different result. The highestnumber still falls on Hong Kong (2.291), butthe meaning of earnings management is re-versed. Recall that EM3 is employed to detectthe proportion of accruals to cash flow, andhence a higher EM3 implies a stronger ten-dency to conceal the true earnings by usingthe accruals. This evidence, together with theabove results of EM1 and EM2, suggest thatthe earnings management in Taiwan seems tofocus on adjusting income level (based onEM3), but this adjustment is not related toearnings smoothing (based on EM1 and EM2).



This evidence is in fact consistent with thefinding of Bhattacharya et al. (2003), whofound that firms in Taiwan are actively en-gaged in earnings aggressiveness and lossavoidance but passively in earnings smooth-ing. Thailand and Hong Kong have the secondand the third highest numbers of EM3. By con-trast, the Philippines and Indonesia have thelowest number of 0.600 and 0.650. Thailand,Taiwan and Hong Kong have the highestmarket capitalisation ratios in the region, andthis simple ranking appears to suggest that theEM3 is positively related to the developmentof capital market.

The AEM, adopted from Leuz et al. (2003), isthe ranking of earnings management. HongKong and Taiwan have the lowest numberof 86.54 and 87.46, respectively, whereasThailand and Korea have the highest numberof 131.04 and 120.60, respectively. Hence,in general, the former two countries manipu-late less of the earnings than the latter twocountries.

The corporate governance measures of CG6and CG7 provided by CLSA are also interest-ing. Indonesia has the lowest CG6 (36.84), fol-lowed by the Philippines (51.25) and Korea(57.48), whereas Singapore has the highestCG6 (63.29), followed by Hong Kong (62.81)and Thailand (62.37). Thus, the ranking of CG6seems to not be completely related to the aboveearnings measurement measures. The rankingof CG7 is similar to that of CG6 and hence isskipped here.

Table 2 reports the descriptive statistics ofexplanatory variables used in our sample. Itis surprising to find that the mean of Sizeand External are similar across nine countries.Consistent with our expectation, the highestnumber of debt is in Korea (53.12) sinceKorean firms borrowed substantially duringthe Asian financial crisis; in contrast, the low-est number is in Hong Kong (18.95). Earningsyield (Earnings_Yield) also differs across coun-tries. Hong Kong is 9.29 per cent but Korea isonly -5.01 per cent.

Table 3 presents the correlation coefficientsof all variables used in this paper. Except forthe high correlation of up to 0.747 betweenLAW and Disclosure, the correlation coeffi-cients between other pairwise explanatory va-riables are less than 0.3. Interesting to note isthat Insider has a negative relation with CG6and CG7. That is, the less insider trading of acountry, the lower is the corporate governance.Recall that the higher the Insider, the less theextent of inside trading. Therefore, the nega-tive association between Insider and CG seemsto represent that the demand for sound corpo-rate governance declines when insider tradingof a country is not severe.T

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Regression results

Figure 1 plots the scatter plot between gov-ernance factors (CG6 and CG7) against fourearnings measures. The pattern is unclear forthese two variables because of strong het-eroscedasticity. We adopted the weighted leastsquare (WLS) method by employing depen-dent variables as the weights to remove theheteroscedasticity.5

Tables 4 to 7 report the estimated results ofmodels (1) and (2) using EM1, EM2, EM3 andAEM as dependent variables, respectively.There are four different specifications in eachCG, and the specifications depend on whetherinteraction terms are taken into account or notand hence eight equations are considered ineach table.

Table 4 presents the estimated results usingEM1 as the proxy of earnings management.Coefficients of CG6 and CG7 are found to bepositively significant, indicating that good cor-porate governance increases EM1. Recall that ahigher EM1 denotes less earnings manage-ment, and hence good governance decreasesthe earnings smoothness, which is consistentwith our intuition.

We next discuss the influences of thefinancial variables and how CG6 affects theimpacts of these financial variables. Coeffi-cients of Size are overwhelmingly negative,suggesting that larger firms tend to do moreearnings smoothing. Hence, the size effect issupported, and as noted above, this is con-sistent with Richardson et al. (2002).6 Coeffi-cients of the interaction between Size and

CG6, however, are insignificant, suggestingthat the good governance does not mitigatethe earnings smoothing when the size of firmis large. Coefficients of Leverage are mostlyinsignificant except for specification (C). Inspecification (C), coefficient of Leverage issignificantly positive (which is 0.026), but itsinteraction term with CG6 is significantlynegative (which is -0.0004). Thus, the turningpoint of Leverage is decided by the formula0.026 - 0.0004 ¥ CG6, which is equal to 65.When CG6 is higher than this turning point,leverage effect exists, i.e. highly leveragedfirms conduct more earnings smoothing.When CG6 is lower than this turning point,reverse leverage effect exists, i.e. highly lever-aged firms conduct less earnings smoothing.It represents that a highly leveraged firmwith poor governance is prone to be scruti-nised closely and thus harder to fool themarket by manipulating earnings.

External’s coefficients are significantly posi-tive in the first two specifications, support-ing the reverse external financing effect, i.e.firms with higher external financing engage inless earnings smoothing. In specification (C),however, this effect disappears, as the coef-ficient of the interaction, CG ¥ External, issignificantly positive. EPS_Grow and Earnings_Yield seem not to have a strong effect on EM1,meaning that a firm with high earningsgrowth and a high Earning level is not prone toadopt earnings smoothing. Furthermore, coef-ficients of the interaction terms with CG,CG ¥ Earning_Yield, are insignificant. In sum,CG variables do have an effect on earnings

Table 2: Descriptive statistics of firm characteristics across countries

Country N Size Leverage External EPS_Grow Earnings_Yield

Hong Kong 21 13.64 18.95 0.03 0.38 9.29India 48 12.73 22.16 0.04 0.42 5.94Indonesia 14 13.19 39.67 0.06 0.00 -2.47Korea 15 15.11 53.12 0.12 0.13 -5.01Malaysia 33 13.19 21.56 0.03 0.03 4.46Philippines 8 13.37 31.09 0.07 0.13 3.02Singapore 28 13.17 22.13 0.04 0.11 2.72Taiwan 21 13.27 23.26 0.08 0.29 4.39Thailand 15 12.47 39.02 0.10 0.13 2.28Mean 13.35 30.11 0.06 0.18 2.74Median 13.19 23.26 0.06 0.13 3.02

Size is the log of firm’s sales, Leverage is the debt ratio (total debt/total asset), External is the amount ofexternal financing, EPS_Grow is the number of growth in EPS in the recent three years and Earning_Yield isthe earnings to stock price. We used the Worldscope database (2000), which contains up to ten years (1991–2000) of historical financial data from home-country annual reports of publicly-traded companies around theworld, to calculate the financial variables above. To compute these variables, we require that each firm haveincome statement and balance sheet information for at least three consecutive years.



management. Moreover, it affects the mannerthat financial ratios influence earningsmanagement.

Coefficients of macro and country-levelgovernance variables are also interesting.GDP per capita has a positive effect on EM1regardless of specifications. Accordingly,wealthy countries have less intention to adoptearnings smoothing, representing the effectof better accounting/reporting standards inmore developed countries (LLSV, 1998). Withregard to the country governance, coeffici-ents of Antidirect are significantly positive inspecification (B), suggesting that a firm in acountry with good anti-director rights doesless earnings smoothing, which is consistentwith Leuz et al. (2003). Contrary to thosefound in Leuz et al. (2003), however, strongeranti-director rights show counter-intuitiveresult as it causes stronger earnings smooth-ing. This is because the same coefficientchanges the sign to negative (-0.466) in speci-fication (D), but this effect appears in lowfirm-level governance countries only, and notin high firm-level governance countries,because the coefficient of the interaction term,CG ¥ Antidirect, is positive (0.009). Law andDisclosure do not have strong impact on EM.Insider has a negative effect (-0.466) on EM1in specification (D), while the coefficient ofthe interaction term, CG ¥ Insider, is positive(0.006), representing that good firm-levelgovernance more effectively decreases earn-ings smoothing in countries with less insidetrading.

Similar results are obtained when CG7 isused and hence are not discussed here.

Table 5 presents the estimated results usingEM2 as a dependent variable. Recall that ahigher EM2 implies it is less likely that accrualsto offset economic shocks will be employed,and hence less earnings smoothing. Coeffi-cients of CG6 and CG7 are found to beoverwhelmingly positive regardless of speci-fications, suggesting that firms with weak gov-ernance are prone to use accruals to conductearnings smoothing. These results are thesame as those found in Table 4.

Coefficients of corporate controllable vari-ables in Table 5 also demonstrate similar pat-terns as those reported in Table 4. Namely,Size, which also has a negative sign, implyingsize effect, but this effect is elusive as it is onlysignificant in the first specification. The resultsof Leverage are similar to those reported inTable 4. There is a turning point for the externalfinancing effect on EM2. That is, in the spe-cification (C), the coefficient of External is-12.509, and the coefficient of interaction,CG ¥ External, is 0.199, making the turningpoint of CG6 to be around 63.T

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The coefficients of EPS_Grow are positivein specification (A) and (B) in Table 5, repre-senting firms with higher EPS_Grow, to avoidnegative response by the market, have higherincentives to manage earnings to maintaintheir string of consecutive earnings increases,which is consistent with Barth et al. (1999).However, high EPS_Grow with high CGtends to encourage the earnings smoothingas the coefficient of the interaction term,CG ¥ EPS_Grow, is significantly positive inspecification (C). The coefficient of Earnings ispositive (0.047) in specification (C), while thatof the interaction term, CG ¥ Earning_Yield,is negative (-0.001), suggesting that firmswith higher growth (lower earnings yield) isprone to manage earnings (Skinner and Sloan2002), especially when they have worsegovernance.

The results of Antidirect are similar to thoseshown in Table 4. It is seen that strongerenforcement of laws (LAW) can result in lessearnings smoothing, since the coefficient ispositive (0.616) in specification (D). Interest-ingly, this effect is stronger in countries withworse corporate governance, since the coeffi-cient of the interaction term, CG ¥ LAW, isnegative (-0.009).

Coefficients of determinants in Table 5 arehigher than those in Table 4, suggesting abetter fitting. This is also evidenced by findingmore significant coefficients in Table 5. Espe-cially, when the interaction terms are consid-ered, the adjusted-R2 increases from 0.5 to 0.7roughly.

Table 6 reports the estimation results usingEM3 as a dependent variable. Recall that alarge number of EM3 refers to strong earn-ings aggressiveness. Hence, negative coeffi-cients on CG6 and CG7 imply that bettergovernance lessens the effect of earningsmanagement. Three differences are found inthis table from the previous two tables. First,there is a leverage effect in EM3, i.e. highlyleveraged firms tend to conduct more earn-ings aggressiveness, but there is no reverseleverage effect, which is found in EM1 andEM2. Second, Size and External coefficientsare insignificant for all specifications, sug-gesting that neither large firms, nor firms thatrely on external finances are factors that af-fect the decision of earnings aggressiveness.Hence, there is a size effect for earningssmoothing but there is no size effect for earn-ings aggressiveness. Similarly, there is anexternal financing effect for earnings smoothing

Figure 1: Scatter plot of corporate governance (CG) and EM



Table 4: EM1 prediction function: weighted least square

CG6 CG7

(A) (B) (C) (D) (E) (F) (G) (H)EM1 EM1 EM1 EM1 EM1 EM1 EM1 EM1

Constant 0.515(1.592)

0.089(0.157)

0.829***(2.999)

0.548(1.009)

1.566*(1.674)

0.095(0.166)

0.855***(3.075)

0.562(1.031)

CG 0.007***(2.622)

0.008***(2.951)

0.006***(2.585)

0.008***(3.030)

Size -0.044**(-2.050)

-0.046*(-1.953)

-0.074**(-2.051)

-0.041*(-1.816)

-0.045**(-2.091)

-0.047**(-1.990)

-0.076**(-2.132)

-0.042*(-1.866)

Leverage -0.001(-0.640)

0.0004(0.285)

0.026***(2.450)

0.001(0.658)

-0.001(-0.654)

0.0004(0.277)

0.025***(2.421)

0.001(0.704)

External 0.665*(1.688)

0.958**(2.247)

-3.929(-1.512)

0.619(1.484)

0.671*(1.695)

0.939**(2.200)

-3.260(-1.294)

0.573(1.371)

EPS_Growth 0.095*(1.657)

-0.002(-0.032)

-0.643(-1.577)

-0.076(-1.192)

0.107*(1.882)

0.006(0.096)

-0.729*(-1.811)

-0.072(-1.121)

Earnings_Yield -0.003(-1.218)

-0.001(-0.432)

0.025(1.230)

0.001(0.259)

-0.004(-1.322)

-0.001(-0.482)

0.024(1.223)

0.001(0.260)

PGDP 0.014(1.460)

0.017*(1.903)

0.014(1.462)

0.017*(1.906)

Antidirect 0.155***(3.087)

-0.466***(-3.040)

0.158***(3.087)

-0.460***(-3.037)

LAW -0.111(-1.517)

0.224(1.078)

-0.119(-1.560)

0.200(0.960)

Disclosure 0.005(0.469)

0.031(1.277)

0.006(0.494)

0.036(1.457)

Insider 0.002(0.035)

-0.466**(-2.295)

0.003(0.056)

-0.510***(-2.557)

CG ¥ Size 0.001(1.553)

0.001(1.589)

CG ¥ Leverage -0.0004***(-2.550)

-0.0004***(-2.518)

CG ¥ External 0.064*(1.685)

0.054(1.459)

CG ¥ EPS_Growth 0.010*(1.730)

0.011**(1.995)

CG ¥ Earnings_Yield -0.0004(-1.437)

-0.0004(-1.441)

CG ¥ Antidirect 0.009***(4.220)

0.009***(4.265)

CG ¥ LAW -0.005*(-1.738)

-0.005(-1.639)

CG ¥ Disclosure -0.0003(-1.107)

-0.0004(-1.290)

CG ¥ Insider 0.006**(2.259)

0.007***(2.537)

R2 0.398 0.442 0.456 0.499 0.406 0.451 0.464 0.510Adjusted R2 0.379 0.407 0.427 0.458 0.388 0.417 0.436 0.471N 202 188 202 188 202 188 202 188

***, ** and * represent the level of significance at 0.01, 0.05 and 0.10, respectively.




CG6 CG7


Constant -0.955***(-2.719)

-1.084*(-1.720)

-0.594***(-2.528)

-0.495(-0.894)

-0.945***(-2.684)

-1.040*(-1.649)

-0.568***(-2.392)

-0.401(-0.727)

CG 0.011***(4.011)

0.012***(4.294)

0.011***(4.115)

0.013***(4.502)

Size -0.049**(-2.120)

-0.033(-1.284)

-0.039(-1.255)

-0.021(-0.911)

-0.050**(-2.164)

-0.034(-1.333)

-0.046(-1.512)

-0.024(-1.040)

Leverage -0.003*(-1.826)

-0.002(-1.190)

0.038***(4.124)

-0.001(-0.815)

-0.003*(-1.829)

-0.002(-1.183)

0.038***(4.263)

-0.001(-0.737)

External 1.405***(3.280)

1.781***(3.786)

-12.509***(-5.664)

1.173***(2.758)

1.459***(3.397)

1.800***(3.837)

-11.634***(-5.413)

1.116***(2.636)

EPS_Growth 0.250***(4.035)

0.164***(2.317)

-1.558***(-4.493)

0.033(0.502)

0.264***(4.281)

0.167***(2.365)

-1.579***(-4.597)

0.029(0.450)

Earnings_Yield -0.005(-1.518)

-0.003(-0.977)

0.047***(2.765)

-0.001(-0.329)

-0.005(-1.594)

-0.003(-1.015)

0.044***(2.636)

-0.001(-0.354)

PGDP -0.008(-0.750)

-0.002(-0.233)

-0.007(-0.702)

-0.002(-0.169)

Antidirect 0.071(1.278)

-0.986***(-6.317)

0.075(1.328)

-0.987***(-6.434)

LAW 0.007(0.084)

0.616***(2.912)

0.002(0.022)

0.608***(2.885)

Disclosure -0.012(-0.935)

-0.012(-0.500)

-0.012(-0.956)

-0.012(-0.470)

Insider 0.076(1.456)

-0.092(-0.446)

0.077(1.466)

-0.111(-0.552)

CG ¥ Size 0.0003(0.910)

0.0004(1.165)

CG ¥ Leverage -0.001***(-4.450)

-0.001***(-4.589)

CG ¥ External 0.199***(6.104)

0.187***(5.864)

CG ¥ EPS_Growth 0.024***(4.991)

0.025***(5.122)

CG ¥ Earnings_Yield -0.001***(-3.152)

-0.001***(-3.028)

CG ¥ Antidirect 0.016***(7.109)

0.016***(7.292)

CG ¥ LAW -0.009***(-3.129)

-0.009***(-3.119)

CG ¥ Disclosure 0.0001(0.229)

0.0001(0.169)

CG ¥ Insider 0.002(0.647)

0.002(0.771)

R2 0.513 0.511 0.730 0.625 0.529 0.532 0.737 0.646Adjusted R2 0.498 0.480 0.716 0.595 0.514 0.503 0.723 0.617N 202 188 202 188 202 188 202 188





CG6 CG7


Constant 0.937***(2.688)

1.754***(2.741)

0.493(1.634)

1.320**(2.050)

0.993***(2.795)

1.800***(2.757)

0.515*(1.673)

1.320**(2.008)

CG -0.006**(-2.119)

-0.006*(-1.945)

-0.006***(-2.333)

-0.006**(-2.199)

Size 0.002(0.088)

-0.005(-0.200)

0.057(1.426)

-0.006(-0.209)

0.001(0.038)

-0.005(-0.190)

0.054(1.364)

-0.005(-0.201)

Leverage 0.003*(1.951)

0.003*(1.822)

-0.015(-1.301)

0.003*(1.866)

0.003*(1.842)

0.003*(1.709)

-0.014(-1.192)

0.003*(1.739)

External 0.122(0.288)

0.242(0.516)

-1.507(-0.531)

0.191(0.395)

0.152(0.349)

0.290(0.608)

-1.623(-0.582)

0.233(0.473)

EPS_Growth 0.058(0.947)

0.067(0.951)

-0.195(-0.437)

0.060(0.813)

0.058(0.928)

0.071(0.990)

-0.114(-0.254)

0.064(0.853)

Earnings_Yield -0.010***(-3.183)

-0.008***(-2.467)

0.053***(2.411)

-0.008**(-2.295)

-0.010***(-3.276)

-0.008***(-2.548)

0.054***(2.496)

-0.008***(-2.410)

PGDP -0.020**(-1.995)

-0.021**(-1.988)

-0.021**(-2.007)

-0.022**(-2.020)

Antidirect -0.094*(-1.692)

-0.152(-0.847)

-0.098*(-1.710)

-0.146(-0.807)

LAW 0.128(1.590)

0.018(0.073)

0.137(1.609)

0.033(0.133)

Disclosure -0.027**(-2.151)

-0.015(-0.514)

-0.028**(-2.089)

-0.018(-0.613)

Insider 0.164***(3.108)

0.295(1.254)

0.164***(3.047)

0.330(1.400)

CG ¥ Size -0.001*(-1.696)

-0.001*(-1.690)

CG ¥ Leverage 0.0003(1.531)

0.0002(1.405)

CG ¥ External 0.028(0.662)

0.030(0.734)

CG ¥ EPS_Growth 0.003(0.551)

0.002(0.356)

CG ¥ Earnings_Yield -0.001***(-2.893)

-0.001***(-3.001)

CG ¥ Antidirect 0.001(0.355)

0.001(0.290)

CG ¥ LAW 0.002(0.483)

0.002(0.446)

CG ¥ Disclosure -0.0001(-0.490)

-0.0002(-0.371)

CG ¥ Insider -0.002(-0.582)

-0.002(-0.730)

R2 0.357 0.353 0.412 0.357 0.358 0.356 0.414 0.361Adjusted R2 0.337 0.311 0.380 0.303 0.338 0.314 0.383 0.307N 196 182 196 182 196 182 196 182




but no such effect for earnings aggressive-ness. Third, consistent with our intuition, theresults of Earnings_Yield suggest that a firmwith higher growth (lower earnings yield) ismore active in managing earnings, but goodgovernance can mitigate the effect. Thisfinding is similar to that in earningssmoothing (EM2).

Third, most of the country-level indices,including PGDP, Antidirect and Disclosure, aresignificant. Hence, firms in countries withgood economic development and countrygovernance engage in less earnings aggres-siveness, which is consistent with Leuz et al.(2003).

Coefficients of determinants are smallerthan those of the previous two tables, beingconsistent with the finding that most coeffi-cients are insignificant.

Table 7 shows the estimated results of usingAEM. Recall that AEM is the average of therankings of EM1, EM2 and EM3 for each firm.The higher AEM implies firms have higherextent of earnings management. The coeffi-cients of CG6 and CG7 are both significantlynegative, implying that better governance isconsistent with lower earnings management,on average. It is also worth to noting thatcoefficients of Size are positive in specification(C) and (G), suggesting that larger firms tendto do more earnings management. Coefficientsof the interaction between Size and CG6,however, are significantly negative, suggest-ing that the good governance can mitigate theearnings management, on average. As otherresults do not change qualitatively, we skip thediscussion.

Robustness checkWe examine whether a firm belonging to abusiness group affects our results or not. This isbecause a firm belonging to a business groupcould have more channels to hide or increase itscosts or revenues.7 Thus, it gets more chance tomanage earnings. We obtain the information ofa firm belonging (or not belonging to) businessgroups by first visiting the websites of oursample firms. It is easy if the websites explicitlyand clearly record which group(s) a firmbelongs to. When the statement is ambiguous,however, we key in the names of firms inGoogle and Yahoo! to examine any relevantnews to obtain the information of belonging (ornot belonging).8 Table A2 shows our classifica-tion of belonging to a business group.

Tables 8 and 9 report the descriptive statis-tics for earnings management measures andcorporate governance indices of firms belong-ing to (and not belonging to) a business group.It is shown that all paired mean differences in

Tables 8 and 9 are statistically insignificant,showing that whether a firm belongs to a busi-ness group does not greatly affect earn-ings management or corporate governance.Table 10 shows that firms have higher earningsgrowth and higher earnings yield if they donot belong to business groups.

Tables 11 reports the estimated results byadding a group dummy variable, which isequal to 1 if a firm belongs to a business groupand 0 otherwise, when the earnings manage-ment is proxied by AEM. Table 12 addressesthe issue of endogenous earnings yield (Earn-ings_Yield) where the instruments are all otherexplanatory variables (except for earningsyield), including group dummy variable andcountry-level governance indices as instru-ments. Again, the earnings management isproxied by AEM.9 Results of Tables 11 and 12are similar as those obtained from Table 7. Ourfindings are, therefore, robust to the consider-ation of business groups and the endogeneityof the earnings yield. More importantly, thecoefficients of group dummy variables areoverwhelmingly insignificant, indicating thatwhether firms belong to a business group doesnot affect the extent of earnings management,at least in our sample firms.

Conclusion

This paper studies the impacts of corporategovernance on earnings management. Theindex of corporate governance was taken fromCredit Lyonnais Security Asia, who studyfirms in nine Asian countries. The measuresof earnings management include earningssmoothing and earnings aggressiveness de-fined by Leuz et al. (2003) and Bhattacharya etal. (2003). Our conclusion is as follows.

First, corporate governance indices arefound to be negatively related to the extent towhich firms manage earnings. That is, firmswith good corporate governance tend toconduct less earnings management. Second,there is a size effect for earnings smoothing,that is, large firms are prone to conduct earn-ings smoothing. It is worth noting that thiseffect or other types of size effect do not existfor the management of earnings aggressive-ness, but good corporate governance indicesseem to mitigate the size effect, on average.

Third, there is a turning point for leverageeffect, i.e. when corporate governance index ishigher than 60–70, leverage effect exists, other-wise, reverse leverage effect exists. Namely,higher leveraged firms conduct earningssmoothing more in a good governance regimebut less in weak governance regime. It repre-sents that a highly leveraged firm with poor



Table 7: AEM prediction function: weighted least square

CG6 CG7

(A) (B) (C) (D) (E) (F) (G) (H)AEM AEM AEM AEM AEM AEM AEM AEM

Constant 160.249***(3.851)

344.404***(4.624)

88.259***(2.416)

274.296***(3.763)

163.465***(3.885)

352.474***(4.675)

84.785**(2.292)

277.629***(3.765)

CG -1.073***(-3.324)

-1.048***(-3.126)

-1.130***(-3.544)

-1.121***(-3.367)

Size 0.362(0.132)

-0.012(-0.004)

11.190***(2.343)

-0.588(-0.192)

0.420(0.152)

0.003(0.001)

11.759***(2.497)

-0.518(-0.169)

Leverage 0.291(1.588)

0.139(0.700)

-3.470***(-2.452)

0.101(0.514)

0.272(1.492)

0.125(0.632)

-3.416***(-2.454)

0.076(0.388)

External 10.325(0.204)

-35.203(-0.633)

-74.108(-0.216)

-8.522(-0.152)

14.915(0.291)

-28.736(-0.512)

-167.983(-0.501)

1.228(0.022)

EPS_Growth -8.393(-1.143)

-6.672(-0.798)

53.561(0.994)

-0.319(-0.037)

-9.522(-1.291)

-7.362(-0.871)

65.326(1.220)

-0.576(-0.067)

Earnings_Yield 0.243(0.677)

0.135(0.357)

-2.682(-1.013)

0.011(0.029)

0.231(0.639)

0.115(0.300)

-2.315(-0.882)

-0.033(-0.086)

PGDP -2.820***(-2.332)

-3.126***(-2.622)

-2.943***(-2.355)

-3.278***(-2.665)

Antidirect -23.392***(-3.582)

26.574(1.294)

-24.242***(-3.610)

27.375(1.335)

LAW 22.044**(2.302)

-22.780(-0.818)

23.478***(2.343)

-17.794(-0.631)

Disclosure -3.930***(-2.617)

-4.172(-1.275)

-4.081***(-2.613)

-4.919(-1.474)

Insider 12.366**(2.004)

58.975**(2.164)

12.679**(2.024)

62.425**(2.312)

CG ¥ Size -0.158***(-3.108)

-0.162***(-3.296)

CG ¥ Leverage 0.054***(2.667)

0.053***(2.650)

CG ¥ External 1.629(0.322)

3.182(0.641)

CG ¥ EPS_Growth -0.843(-1.124)

-1.033(-1.379)

CG ¥ Earnings_Yield 0.043(1.108)

0.037(0.964)

CG ¥ Antidirect -0.725***(-2.520)

-0.757***(-2.624)

CG ¥ LAW 0.655*(1.724)

0.609(1.566)


0.010(0.232)

CG ¥ Insider -0.633*(-1.694)

-0.676*(-1.831)

R2 0.463 0.455 0.488 0.483 0.470 0.466 0.497 0.495Adjusted R2 0.446 0.421 0.461 0.441 0.454 0.432 0.470 0.454N 202 188 202 188 202 188 202 188




Tab

le8:

Des

crip

tive

stat

isti

csof

earn

ings

mea

sure

s(E

M1,

EM

2,E

M3,

and

AE

M)o

ffirm

sno

tbel

ongi

ngto

busi

ness

grou

p(g

roup

=0)

and

firm

sbe

long

ing

tobu

sine

ssgr

oup

(gro

up=

1)

NE

M1

EM

2E

M3

AE

M

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Gro

up=

049

0.35

90.

262

0.04

91.

376

-0.8

810.

199

-1.0

000.

000

1.03

01.

800

0.12

612

.577

108.

136

40.0

8022

.667

188.

000

Gro

up=

113

60.

382

0.31

00.

009

2.02

7-0

.826

0.32

3-1

.000

1.00

00.

687

0.68

20.

066

5.42

298

.549

44.6

3415

.000

191.

667

t–te

stof

the

dif

f.G

roup

(0m

inus

1)

-0.4

98-1

.377

1.30

01.

392

Gro

up=

0m

eans

firm

sd

ono

tbe

long

toce

rtai

nbu

sine

ssgr

oup;

Gro

up=

1m

eans

firm

sbe

long

toce

rtai

nbu

sine

ssgr

oup.

EM

1,E

M2,

EM

3an

dA

EM

are

the

earn

ing

man

agem

ent

mea

sure

sd

efine

din

this

pape

r.Fo

llow

ing

Leu

zet

al.(

2003

),E

M1

and

EM

2ar

eto

mea

sure

earn

ings

smoo

thin

g,an

dE

M3

isto

mea

sure

earn

ings

aggr

essi

vene

ss.T

hehi

gher

EM

1(a

ndE

M2)

impl

ies

firm

sar

ele

sspr

one

toco

nduc

tear

ning

ssm

ooth

ing,

and

the

high

erE

M3

impl

ies

firm

sar

em

ore

pron

eto

cond

ucte

arni

ngs

aggr

essi

vene

ss.A

EM

issi

mpl

yth

eav

erag

eof

the

rank

ings

ofE

M1,

EM

2an

dE

M3

for

each

firm

.The

high

erA

EM

impl

ies

firm

sha

vehi

gher

exte

ntof

earn

ings

man

agem

ent.

Tab

le9:

Des

crip

tive

Stat

isti

csof

corp

orat

ego

vern

ance

mea

sure

s(C

G6

and

CG

7)of

firm

sno

tbel

ongi

ngto

busi

ness

grou

p(g

roup

=0)

and

firm

sbe

long

ing

tobu

sine

ssgr

oup

(gro

up=

1)

NC

G6

CG

7

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Gro

up=

049

60.7

019.

188

32.4

2979

.743

60.0

249.

367

32.4

0079

.600

Gro

up=

113

659

.093

12.8

8117

.771

85.8

2958

.561

13.0

0816

.995

86.8

00t–

test

ofth

ed

iff.

Gro

up(0

min

us1)

0.93

80.

840

Gro

up=

0m

eans

firm

sd

ono

tbel

ong

toce

rtai

nbu

sine

ssgr

oup;

Gro

up=

1m

eans

firm

sbe

long

toce

rtai

nbu

sine

ssgr

oup.

CG

6an

dC

G7

are

the

aver

age

ofco

rpor

ate

gove

rnan

cein

dex

inC

LSA

(200

2).C

G6

and

CG

7ar

eth

eav

erag

eof

corp

orat

ego

vern

ance

ind

exin

CL

SA(2

002)

.The

high

erC

G6

(and

CG

7)re

pres

ents

bett

erco

rpor

ate

gove

rnan

ce.

Tab

le10

:Des

crip

tive

stat

isti

csof

firm

char

acte

rist

ics

offir

ms

not

belo

ngin

gto

busi

ness

grou

p(g

roup

=0)

and

firm

sbe

long

ing

tobu

sine

ssgr

oup

(gro

up=

1)

NSi

zeLe

vera

geE

xter

nal

EP

S_G

row

Ear

ning

s_Y

ield

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Mea

nSD

Min

Max

Gro

up=

049

13.0

541.

454

9.26

316

.269

25.6

1119

.318

0.00

070

.801

0.04

20.

075

-0.1

370.

262

1.93

91.

088

0.00

03.

000

6.21

17.

968

-32.

887

32.2

00G

roup

=1

136

13.3

091.

264

10.8

8516

.447

27.2

3020

.377

0.00

073

.350

0.05

20.

070

-0.0

590.

571

1.31

61.

191

0.00

03.

000

2.63

611

.810

-62.

618

21.8

52t–

test

ofth

ed

iff.

Gro

up(0

min

us1)

-1.0

89-0

.496

-0.8

653.

348*

**2.

333*

**

Gro

up=

0m

eans

firm

sd

ono

tbel

ong

toce

rtai

nbu

sine

ssgr

oup;

Gro

up=

1m

eans

firm

sbe

long

toce

rtai

nbu

sine

ssgr

oup.

Size

isth

elo

gof

firm

’ssa

les,

Leve

rage

isth

ed

ebtr

atio

(tot

ald

ebt/

tota

las

set)

,Ext

erna

lis

the

amou

ntof

exte

rnal

finan

cing

,EP

S_G

row

isth

enu

mbe

rof

grow

thin

EPS

inth

ere

cent

thre

eye

ars

and

Ear

ning

s_Y

ield

isth

eea

rnin

gsto

stoc

kpr

ice.

We

used

the

Wor

ldsc

ope

data

base

(200

0),w

hich

cont

ains

upto

ten

year

s(1

991–

2000

)of

hist

oric

alfin

anci

alda

tafr

omho

me-

coun

try

annu

alre

port

sof

publ

icly

-tra

ded

com

pani

esar

ound

the

wor

ld,t

oca

lcul

ate

the

finan

cial

vari

able

sab

ove.

Toco

mpu

teth

ese

vari

able

s,w

ere

quir

eth

atea

chfir

mha

vein

com

est

atem

ent

and

bala

nce

shee

tin

form

atio

nfo

rat

leas

tth

ree

cons

ecut

ive

year

s.



Table 11: AEM prediction function: business group effect

CG6 CG7


Constant 187.231***(4.228)

321.683***(4.202)

133.556***(3.453)

278.885***(3.526)

192.962***(4.306)

332.732***(4.289)

122.276***(3.032)

282.274***(3.513)

CG -0.773**(-1.981)

-0.788*(-1.848)

-0.864**(-2.246)

-0.912**(-2.139)

Group -9.231(-1.178)

-7.277(-0.863)

-11.998(-1.536)

-6.678(-0.790)

-9.136(-1.155)

-7.315(-0.859)

-67.914(-1.060)

-6.724(-0.788)

Size -2.814(-0.996)

-1.573(-0.500)

9.959**(2.070)

-1.811(-0.571)

-2.778(-0.971)

-1.571(-0.498)

14.662**(2.133)

-1.729(-0.544)

Leverage 0.239(1.294)

0.123(0.608)

-2.424(-1.529)

0.117(0.578)

0.225(1.217)

0.110(0.546)

-2.494(-1.595)

0.098(0.485)

External 38.111(0.644)

14.526(0.230)

-685.482(-1.510)

3.342(0.052)

43.184(0.717)

21.914(0.343)

-756.742*(-1.728)

11.570(0.179)

EPS_Growth -2.651(-0.341)

-1.059(-0.120)

-68.191(-1.051)

-1.091(-0.123)

-3.661(-0.464)

-1.765(-0.198)

-68.345(-1.034)

-1.528(-0.170)


0.119(0.314)

-2.325(-0.864)

0.032(0.081)

0.140(0.390)

0.109(0.286)

-2.089(-0.782)

0.002(0.005)

PGDP -0.004***(-2.828)

-0.003***(-2.613)

-0.004***(-2.854)

-0.003***(-2.678)

Antidirect -19.055***(-2.833)

-0.657(-0.021)

-19.742***(-2.851)

0.208(0.007)

LAW 20.844**(2.205)

-18.040(-0.511)

22.115**(2.231)

-10.861(-0.302)

Disclosure -4.003***(-2.636)

-4.222(-1.272)

-4.156***(-2.627)

-4.862(-1.439)

Insider 18.988**(2.542)

72.549**(2.171)

19.637**(2.597)

72.004**(2.182)

CG ¥ Size -0.180***(-3.469)

-0.237***(-2.952)

CG ¥ Leverage 0.038*(1.655)

0.038*(1.692)

CG ¥ External 11.048(1.594)

12.397*(1.846)

CG ¥ EPS_Growth 0.948(1.028)

0.947(1.001)


0.030(0.777)

CG ¥ Antidirect -0.278(-0.608)

0.306(0.509)

CG ¥ LAW 0.568(1.142)

0.487(0.950)


0.009(0.200)

CG ¥ Insider -0.789(-1.642)

-0.769(-1.624)

R2 0.475 0.462 0.501 0.474 0.480 0.471 0.511 0.484Adjusted R2 0.454 0.420 0.469 0.423 0.460 0.431 0.477 0.433N 185 170 185 170 185 170 185 170




Table 12: AEM prediction function: instrumental variable methods

CG6 CG7


Constant 161.592***(3.890)

363.269***(3.751)

87.691**(2.402)

273.359***(3.580)

185.888***(4.213)

377.709***(3.670)

112.967***(2.991)

307.679***(3.696)

CG -1.291***(-2.651)

-1.209**(-2.210)

-1.297**(-2.529)

-1.272*(-1.818)

Size 0.355(0.129)

-0.120(-0.039)

11.551**(2.420)

-0.470(-0.147)

-2.332(-0.828)

-1.661(-0.513)

10.873**(2.288)

-2.502(-0.728)

Leverage 0.406(1.605)

0.137(0.652)

-3.501**(-2.488)

0.093(0.464)

0.385(1.551)

0.081(0.362)

-2.184(-1.409)

0.112(0.548)

External 77.555(0.605)

-12.066(-0.082)

-102.477(-0.297)

-23.690(-0.207)

164.825(1.162)

118.605(0.771)

-758.361*(-1.727)

103.922(0.682)

EPS_Growth -10.069(-1.367)

-6.257(-0.692)

50.848(0.943)

-0.846(-0.101)

-5.181(-0.667)

-0.942(-0.089)

46.177(0.717)

-2.084(-0.238)


1.204(0.401)

-3.068(-1.161)

-0.315(-0.137)

2.923(1.037)

2.354(0.768)

-1.738(-0.643)

2.142(-0.719)

PGDP -0.003*(-1.838)

-0.003**(-2.187)

-0.005***(-2.584)

-0.005**(-2.382)

Antidirect -26.266**(-2.449)

-22.681(-0.416)

-25.756**(-2.304)

-48.980(-0.656)

LAW 23.417**(1.987)

-22.681(-0.814)

25.096**(2.318)

2.664(0.065)

Disclosure -4.271**(-2.112)

-4.727(-1.027)

-5.039***(-2.592)

-1.873(-0.345)

Insider 15.458(1.351)

62.042*(1.801)

25.871**(2.083)

48.652(1.043)

CG ¥ Size -0.162***(-3.197)

-0.181***(-3.600)

CG ¥ Leverage 0.054***(2.705)

0.034(1.504)

CG ¥ External 1.998(0.393)

12.324*(1.832)

CG ¥ EPS_Growth -0.800(-1.065)

0.645(0.699)


0.026(0.669)

CG ¥ Antidirect -0.805(-1.229)

0.409(0.371)

CG ¥ LAW 0.651*(1.722)

0.326(0.576)


-0.056(-0.575)

CG ¥ Insider -0.695(-1.188)

-0.265(-0.312)

R2 0.461 0.455 0.489 0.482 0.477 0.470 0.503 0.483Adjusted R2 0.444 0.421 0.463 0.441 0.459 0.433 0.474 0.436N 185 170 185 170 185 170 185 170




governance is scrutinised closely by themarket and thus finds it harder to fool themarket by manipulating earnings.

Fourth, firms with higher growth (lowerearnings yield) tend more actively to engage inearnings smoothing and earnings aggressive-ness, but good corporate governance can miti-gate the effect.

Finally, being consistent with the findings ofLeuz et al. (2003), a firm in a country with goodanti-director rights engages in less earningssmoothing and earnings aggressiveness. Con-trary to those found in Leuz et al. (2003), stron-ger anti-director rights may result in strongerearnings smoothing. This effect, however,appears in low firm-level governance coun-tries only, and not in high firm-level gov-ernance countries. Stronger enforcement oflaws can result in less earnings smoothing,which is also consistent with Leuz et al. (2003),but this effect is stronger in countries withworse corporate governance. Therefore, onpredicting earnings management across coun-tries, in addition to country-level governance(such as Leuz et al., 2003), the role of corporategovernance should have to be taken intoaccount.

Notes

1. Following Leuz et al. (2003), we did not considerthe effect of discretionary and non-discretionaryearnings management.

2. The report in April 2001 entitled “Saints andSinners: Who’s Got Religion?” and “Make meHoly . . . but not yet!” in February 2002.

3. The names of the firms in CLSA were based on aliteral translation, which may not have matchedthe actual names of the firms.

4. Taiwan provides tax deductions for high technol-ogy firms and other deemed crucial industries.Hong Kong is a free trade harbour. India alsoprovides many tax deductions to encourageinvestments. Hence, this may be the reason oflow smoothing management in the threecountries.

5. More specifically, we attempted differentweights, i.e. CG1, CG1.5 and CG2 in WLS.Reported results are based on CG1.

6. If firm size can be used as a proxy for informa-tion asymmetry and earnings management ismeasured by earnings smoothing, managersmay choose to smooth earnings to lower earn-ings volatility and, in so doing, convey morevaluable, more relevant information to unin-formed investors (Fukui, 2000; Goel andThakor, 2003). Larger firms have, therefore,higher incentives to engage in earningssmoothing.

7. For example, Claessens and Fan (2002) interpretthat, in Asia, agency problems have been exacer-bated by low corporate transparency, associatedwith rent-seeking and relationship-based trans-actions, extensive group structures and diversi-fications, and risky financial structures.

8. We key in the names of firms as well as the keywords of subsidiary, consolidation and group inGoogle and Yahoo! to examine any relevantnews.

9. Since the results are qualitatively the same if theearnings management is proxied by EM1, EM2or EM3, we present the results of AEM only.

Appendix

Table A1: CLSA corporate governance attributes

Governance attribute CLSA explanation

Disclosure attribute

Transparency(TRAN)

The ability of outsiders to assess the true position of a company(1) Disclosure of financial targets (e.g. three- and five-year ROA/ROE)(2) Timely release of Annual Report(3) Timely release of semi–annual financial announcements(4) Timely release of quarterly results(5) Prompt disclosure of results with no leakage ahead of announcement(6) Clear and informative results disclosure(7) Accounts presented according to IGAAP(8) Prompt disclosure of market-sensitive information(9) Accessibility of investors to senior management(10) Website where announcements are updated promptly



Table A1: Continued


Non-disclosure attribute

Discipline(DISC)

Public commitment to CG and financial discipline(1) Explicit public statement placing priority on CG(2) Management incentivised toward a higher share price(3) Sticking to clearly defined core business(4) Having an appropriate estimate of cost of equity(5) Having an appropriate estimate of cost of capital(6) Conservation in issuance of equity or dilative instruments(7) Ensuring debt is manageable, used only for projects with adequate

returns(8) Returning excess cash to shareholders(9) Discussion in Annual Report on CG

Independence(INDP)

Board is independent of controlling shareholders and is separate fromsenior management(1) Board and senior management treatment of shareholders(2) Chairman who is independent from management(3) Executive management committee comprised differently from the board(4) Audit committee chaired by independent director(5) Remuneration committee chaired by independent director(6) Nominating committee chaired by independent director(7) External auditors unrelated to the company(8) No representatives of banks or other large creditors on the board

Accountability(ACCT)

Proper accountability of management to the Board(1) Board plays a supervisory rather than executive role(2) Non-executive directors demonstrably independent(3) Independent, non-executive directors constitute at least half of the

board(4) Foreign nationals presence on the board(5) Full board meetings at least every quarter(6) Board members able to exercise effective scrutiny(7) Audit committee that nominates and reviews work of external auditors(8) Audit committee that supervises internal audit and accounting

procedures

Responsibility(RESP)

Record of taking measures in case of mismanagement:(1) Acting effectively against individuals who have transgressed(2) Record of taking measures in cases of mismanagement(3) Measures to protect minority interests(4) Mechanisms to allow punishment of executive/management committee(5) Share trading by board members fair and fully transparent(6) Board small enough to be efficient and effective

Fairness(FAIR)

Treatment of minorities:(1) Majority shareholders treatment of minority shareholders(2) All equity holders having right to call general meeting(3) Voting methods easily accessible (e.g. through proxy voting Quality of

information provided for general meetings)(4) Guiding market expectations on fundamentals(5) Issuance of ADRs or placement of shares fair to all shareholders(6) Controlling shareholder group owning less than 40% of company(7) Portfolio investors owning at least 20% of voting shares(8) Priority given to investor relations(9) Total board remuneration rising no faster than net profits



Table A1: Continued


Other attribute

Social Awareness(SOCL)

Labour and environmental issues:(1) Explicit policy emphasising strict ethical behaviour(2) Not employing the under-aged(3) Explicit equal employment policy(4) Adherence to specified industry guidelines on sourcing of materials(5) Explicit policy on environmental responsibility(6) Abstaining from countries where leaders lack legitimacy (e.g.

Myanmar)

Table A2: Classifications of belonging to a business group

No. Country Firm name Group No. Country Firm name Group

1 Hong Kong Li & Fung 1 39 India Pfizer India 12 Hong Kong Cathay Pacific 1 40 Indonesia Unilever 13 Hong Kong Swire 1 41 Indonesia Ramayana 14 Hong Kong HK Electric 1 42 Indonesia Telkom 15 Hong Kong Kingboard Chemical 1 43 Indonesia Sari Husada 16 Hong Kong Esprit Holdings 1 44 Indonesia Bimantara Citra 17 Hong Kong SmarTone 1 45 Indonesia Astra Int’l 18 Hong Kong New World Dev 1 46 Indonesia Semen Gresik 19 Hong Kong Hutchison Whamoa 1 47 Indonesia Gudang Garam 1

10 Hong Kong Texwinca Holdings 1 48 Indonesia HM Sampoerna 111 Hong Kong ASM Pacific 1 49 Indonesia Tempo Scan 112 Hong Kong Yue Yuen 1 50 Indonesia Indofood 113 Hong Kong South China Morning Post 1 51 Indonesia Matahari 114 India Dr 1 52 Indonesia Indah Kiat 115 India Castrol 1 53 Indonesia Indocement 116 India Cipla 1 54 Korea Daelim Ind 117 India Hero 1 55 Korea LG Chenical 118 India BPCL 1 56 Korea Hyundai Motor 119 India Asian 1 57 Korea SK Telecom 120 India Hindalco 1 58 Korea Kia Motors 121 India ITC 1 59 Korea SK Corp 122 India Colgate-Palmolive 1 60 Korea LG Electronics 123 India ABB 1 61 Korea Samsung Heavy Industry 124 India Nestle 1 62 Malaysia BAT 125 India Tata 1 63 Malaysia Tanjong 126 India Cummins 1 64 Malaysia Road Builder 127 India Ranbaxy 1 65 Malaysia Carlsberg 128 India NIIT 1 66 Malaysia UMW 129 India Siemens India 1 67 Malaysia IOI Corp 130 India Grasim Industries 1 68 Malaysia EON 131 India VSNL 1 69 Malaysia Gamuda 132 India Reliance Indystries 1 70 Malaysia Malakoff 133 India Bajaj Auto 1 71 Malaysia IJM 134 India Tata Tea 1 72 Malaysia MISC 135 India Britannia Industries 1 73 Malaysia Tan Chong 136 India IOC 1 74 Malaysia Sime Darby 137 India ONGC 1 75 Malaysia Pgas 138 India Tata Infotech 1 76 Malaysia JTI 1



Table A2: Continued

No. Country Firm name Group No. Country Firm name Group

77 Malaysia Guinness 1 132 Thailand Siam Makro 178 Malaysia Resorts 1 133 Thailand Elect’y Geneat’g 179 Malaysia Telekom Malaysia 1 134 Thailand PTTEP 180 Malaysia Genting 1 135 Thailand United Broadcast 181 Indonesia Matahari 1 136 Thailand Telecom Asia 182 Indonesia Indah Kiat 1 137 Hong Kong CLP 083 Indonesia Indocement 1 138 Hong Kong MTRC 084 Malaysia YTL Power 1 139 Hong Kong Giordano 085 Malaysia MPI 1 140 Hong Kong HK&China Gas 086 Malaysia Golden Hope 1 141 Hong Kong Techtronic Inds 087 Malaysia K. Guthrie 1 142 Hong Kong Johnson Electric 088 Malaysia Tenaga 1 143 Hong Kong First Pacific Company 089 Malaysia TRI 1 144 Hong Kong Television Broadcasts 090 Malaysia MAS 1 145 India Hindustan 091 Malaysia Magnum 1 146 India BSES 092 Malaysia B Toto 1 147 India Cadbury 093 Philippines PLDT 1 148 India Punjab 094 Philippines Globe 1 149 India Glaxo 095 Philippines ABS-CBN 1 150 India TISCO 096 Philippines Manila Electric Co 1 151 India HPCL 097 Philippines San Miguel Corp 1 152 India L & T 098 Singapore SPH 1 153 India BHEL 099 Singapore ST Egg 1 154 India IPCL 0

100 Singapore SC Logistics 1 155 India Nalco 0101 Singapore SC Marine 1 156 India Zee Telefilms 0102 Singapore NOL 1 157 India Satyam Computers 0103 Singapore Keppel Land 1 158 India MTNL 0104 Singapore City Dev 1 159 India Novartis India 0105 Singapore Cerebos 1 160 India Wockhardt 0106 Singapore Venture 1 161 India M & M 0107 Singapore Natsteel Ltd 1 162 Korea Kepco 0108 Singapore Keppel Corp 1 163 Korea Hite Brewery 0109 Singapore Creative 1 164 Korea Cheil Jedang 0110 Singapore A-P Breweries 1 165 Korea Posco 0111 Singapore SingTel 1 166 Korea Shinsegae Dept Store 0112 Singapore Delgro 1 167 Malaysia Nestle 0113 Singapore Haw Par Corp 1 168 Malaysia Star 0114 Singapore Wing Tai 1 169 Philippines Jollibee Foods Corp 0115 Singapore Cycle & Carriage 1 170 Philippines Petron Corp 0116 Singapore Robinson 1 171 Philippines Ayala Corp 0117 Singapore Singland 1 172 Singapore Sing Airlines 0118 Singapore Parkway 1 173 Singapore Marco Polo 0119 Singapore Datacraft 1 174 Singapore FCC 0120 Singapore F&N 1 175 Singapore Want Wanr 0121 Singapore UOL 1 176 Taiwan SPIL 0122 Taiwan China Steel 1 177 Taiwan Compeq 0123 Taiwan Delta 1 178 Taiwan Giant 0124 Taiwan China Airlines 1 179 Taiwan Nien Hsing 0125 Taiwan Nan Ya Plastics 1 180 Thailand Adv Info Service 0126 Taiwan Formosa Plastics 1 181 Thailand Tot’l Access Com 0127 Taiwan Hon Hai 1 182 Thailand Delta Elec Thai 0128 Thailand Thai Union Frozen 1 183 Thailand Bangkok Bank 0129 Thailand Siam City Cement 1 184 Thailand Land&Houses (F) 0130 Thailand Siam Cement 1 185 Thailand Bangkok Exp’way 0131 Thailand BEC World 1



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Chung-Hua Shen is currently a professor inthe Department of Money and Banking inNational ChengChi University, Taiwan. Hereceived his PhD from Washington Universityin the USA. He teaches financial market andfinancial institutions, global financial systemsand corporate governance. He was the Ful-bright scholar in 2000 and Eisenhower Fel-lower in 2006. He has published papers inJournal of Banking and Finance, Journal of Money,Banking and Credit, Journal of Econometrics,Journal of International Money and Finance, Inter-national Journal of Economics and Finance,Review of Quantitative Finance and Accounting,Southern Economic Journal, Journal Policy Mod-eling, Eastern Economic Journal, Journal of Mac-roeconomics, Pacific Basin Finance Journal,Journal of Business and Economics, InternationalEconomic Journal, Applied Economics, AppliedFinancial Economics and International Journal forForecasting.



http://ssrn.com

http://www.sec.gov/news

Hsiang-Lin Chih is currently an associate pro-fessor and the director in Department of Coop-erative Economics at the College of Business,National Taipei University, Taiwan. He teachescorporate finance, financial institutions man-

agement, cooperative economics, nonprofitfinancial management, and corporate socialresponsibility. He has published papers inAcademia Economic Papers, Journal of BusinessEthics and Journal of Banking and Finance.



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