Effects of earnings management on bank cost of debt

Effects of earnings management on bank cost of debt

Chung-Hua Shena, Yu-Li Huangb

aDepartment of Finance, National Taiwan University, Taipei, TaiwanbDepartment of Insurance and Financial Management, Takming University of Science and

Technology, Taipei, Taiwan

Abstract

This study investigates how earnings management influences credit ratings, andthus the cost of debt, using bank data from 85 countries. Using cross-countrydata also facilitates the investigation of how information asymmetry affects theinfluence of earnings management on ratings. The results indicate that ratersdowngrade ratings when they perceive earnings management, after controllingfor other potential determinants of bank credit ratings, implying that earningsmanagement increases borrowing costs. The negative effect of earnings manage-ment is mitigated for banks in countries with more extensive and effective bank-ing regulations owing to lower information asymmetry, but aggravated incounties with less robust banking regulations.

Key words: Credit rating; Earnings management; Earnings smoothing;Discretionary loan loss provision; Information asymmetry

JEL classification: G15, G21

doi: 10.1111/j.1467-629X.2011.00455.x

1. Introduction

Accountants and financial economists have long recognized that firms exploitflexible accounting rules to manage reported earnings in various contexts.Because Generally Accepted Accounting Principles (GAAP) allow managers,who are privy to more detailed and proprietary information, discretion in select-ing reporting methods, managers have an incentive to cloud the real earnings oftheir firms for personal benefit. Schipper (1989) defined this phenomenon as ‘…apurposeful intervention in the external financial reporting process, with the intentof obtaining some private gain.’ Healy and Wahlen (1999) gave a similar defini-tion. Thus, earnings management (EM) describes a managerial judgment ordecision to alter financial reports to mislead outsiders.

Received 13 April 2010; accepted 20 October 2011 by Robert Faff (Editor).

� 2011 The AuthorsAccounting and Finance � 2011 AFAANZ

Accounting and Finance 53 (2013) 265–300

As firms can accelerate the recognition of accounted earnings through currentaccruals by delaying the recognition of expenses etc., EM is performed by shift-ing income between the current and future periods. Investors who fail to seethrough EM can easily be misled into overpaying for a stock. Thus, firms canachieve short-term benefit by deploying aggressive accounting practices beforepublic offerings. Previous studies have typically focused on the influence of EMon stock returns in the lead up to an initial public offering or seasoned equityoffering (Rangan, 1998; Teoh et al., 1998a,b). However, besides the effect of EMon stock returns,1 this study examines whether EM also affects debt burden.This study examines the impact of EM on borrowing cost through changes in

credit ratings. As banks managing their earnings create asymmetric information,an interesting question is how credit rating agencies (CRAs) evaluate this behav-iour. This question is important because if EM impacts credit ratings, it can alsoinfluence the cost of borrowing (Ogden, 1987; Calomiris et al., 1995; Mintonand Schrand, 1999; Ahmed et al., 2002; Francis et al., 2005).2 While numerousstudies have found that EM affects the cost of capital, the effect is chiefly on thecost of equity.3 This study complements the literature by studying the impacts ofEM on credit rating and thus on cost of debt. The study results show that firmsengaging in EM have lower credit ratings than implied by their profitability andother control variables. In this sense, EM is accompanied by a cost becauseceteris paribus lower credit ratings imply higher borrowing costs.This study uses bank data from 85 countries to explore this issue. This work

focuses on the banking industry because banks have a greater incentive tosmooth earnings than non-financial firms, and their financial stability is para-mount given their critical economic role (Greenawalt and Sinkey, 1988; Beattyet al., 2002; Shen and Chih, 2005). Morgan (2002) also indicated that informa-tion asymmetry is more profound in the banking industry. Additionally, study-ing EM helps regulators and investors understand underlying bankperformance.

1 Studies of how EM affects stock prices are abundant. Collins and Hribar (2000) havedocumented that firms with large accruals have subsequent negative abnormal returns.Rangan (1998) and Teoh et al. (1998a,b) suggested firms manage earnings around initialand seasoned public equity offerings, implying firms want to get more capital by increas-ing abnormal accrual items, but is usually associated with worse subsequent stock priceperformance. Teoh and Wong (2002) used new issue firms as samples and found analystsdo not discount adequately for the high level of discretionary accruals in their forecasts ofsubsequent earnings. Investors, relying on analysts’ forecasts of earnings, overvalue newissue firms and are surprised later when the reported earnings do not meet expectations.

2 They also used firm credit ratings to proxy for firms’ cost of debt.

3 Bhattacharya et al. (2003) compared three measurements of EM, that is, earningsaggressiveness, loss avoidance and earnings smoothing, across 34 stock exchanges. Theyfound EM adversely affects the cost of equity and the trading in the stock market. How-ever, they did not discuss the effect of EM on the cost of borrowing.

266 C.-H. Shen, Y.-L. Huang/Accounting and Finance 53 (2013) 265–300


This study considers earnings smoothing and earnings aggressiveness, where theformer needs to calculate the correlation between changes in loan loss provisions(LLP) and earnings before provisions, and the latter is obtained by fitting aregression of the non-discretionary LLP. As EM in the banking industrydepends heavily on provisions for loan loss (Greenawalt and Sinkey, 1988; Col-lins et al., 1995; Shen and Chih, 2005), the related literature investigates the effectof LLP on credit ratings (Poon et al., 1999; Poon and Firth, 2005). Recent stud-ies have examined the effects of disclosure and corporate governance on ratingsusing U.S. samples. For example, Sengupta (1998) identified a positive associa-tion between quality of corporate disclosure and bond ratings, suggesting thatgovernance mechanisms affect bond ratings by reducing information risk. Bhoj-raj and Sengupta (2003) and Ashbaugh-Skaife et al. (2006) examined the effectof corporate governance on firm credit ratings in industries other than banking.However, corporate governance is directly noted but EM is not, making thesetwo studies different from the present one. Additionally, neither of these twostudies considered information asymmetry across countries. Jiang (2008) usedU.S. data to investigate whether ratings changes were influenced by firms beatingthe following three earnings benchmarks: zero earnings, previous year earningsand analyst forecast earnings. Individual firms beating earnings benchmarksresemble firms engaging in EM for loss avoidance (Burgstahler and Dichev,1997; Bhattacharya et al., 2003),4 but differ in that EM for loss avoidance isdefined at the industry level. That is, EM for loss avoidance is calculated usingall firms in an industry with earnings exceeding the zero threshold over thosewith earnings below the zero threshold. Thus, in the study of Jiang, an individualfirm beating the benchmark may be due to either good performance or EM.This study uses cross-country data to further investigate how asymmetric

information influences the impact of EM on credit ratings at the country level.Recent international studies have investigated the institutional features of coun-tries and financial reporting outcomes, as well as the market effects of the cost ofcapital. This study examines whether the effect of EM on credit ratings is system-atically related to the extent and effectiveness of national banking regulations.The extent and effectiveness of national banking regulation is proxied by a bankbeing in a high-income country,5 in Western Europe or North America, in anenvironment characterized by strong creditor protection. This study hypothesizes

4 Bhattacharya et al. (2003) define three EMs as earnings aggressiveness, loss avoidanceand earnings smoothing.

5 While high-income countries do not always exhibit better governance, they are highlycorrelated. For example, La Porta et al. (1998) found that the high-income countries tendto have a high score of governance. Kaufmann and Kraay (2002) also found that per cap-ita incomes and the quality of governance are strongly positively correlated, where thegovernance is proxied by the weighted index of control of corruption, protection of prop-erty rights or rule of law, voice and accountability, government effectiveness, regulatoryquality and political stability.

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that asymmetric information is mitigated in countries with more extensive andeffective banking regulations. Raters thus view reported earnings as trustworthyand reduce the adverse impact of EM on credit ratings. Alternatively, in an envi-ronment with poor extent and effectiveness of banking regulations, raters recog-nize the existence of severe information asymmetry, and that managers may usetheir judgment to create opportunities for EM. Raters thus do not trust thereported earnings, aggravating the influence of EM on ratings and also increas-ing the cost of borrowing.One notable caveat is as follows. While S&P has identified important determi-

nants of overall risk, they insist that no standard group of financial ratios setsthe minimum requirements for each rating category (Standard and Poor’s Rat-ings Services (S&P), 1999). Thus, as Poon et al. (2009) mentioned, while most ofthe financial variables used in this study are those that S&P uses in determiningratings, S&P claims that its analysts do not use any formula for combining thesescores to determine ratings. Hence, further research to identify the determinantsof the ratings is worthwhile.The remainder of this paper is organized as follows. Section 2 describes the

background of credit ratings and EM. Section 3 presents the extent and effective-ness of banking regulation variables. Section 4 discusses the econometric model.Section 5 summarizes the empirical results. Conclusions are finally drawn inSection 6.

2. Credit ratings and earnings management

2.1. CRAs and credit ratings

Credit ratings have become increasingly important in debt contracts becausethey are considered efficient benchmarks of credit quality (Frost, 2006). CRAsfrequently claim that they evaluate issuers based on public information, includ-ing information in financial statements, prospectuses and auditor reports.(Ghosh and Moon, 2005; Ashbaugh-Skaife et al., 2006). For instance, in state-ments to Congress following the Enron bankruptcy, representatives of the creditrating industry testified that they had based their ratings on information pro-vided by issuers. The Managing Director of S&P Rating Services, R. Barone,stressed ‘Our rating opinions are based on public information provided by theissuer, audited financial information, and qualitative analysis of a company andits sector…, we have no subpoena power to obtain information that a companyis not willing to provide.’ Consequently, S&P Rating Services claimed that theywere victims of the Enron, WorldCom and recent subprime mortgage crisesbecause they had no more information on which to base their ratings thanoutsiders. Essentially, S&P claimed to have been misled, along with others, byissuers concealing information.Although raters stress that they rely solely on public information, we believe

they also gain some non-public information. CRAs frequently meet with



management and enjoy access to confidential information such as financial pro-jections, detailed financial information on particular product lines, new projectplans and board meeting minutes. Thus, raters are the only outsider privy to thisnon-public information. Because the SEC considers this private informationgathering to be part of the rating process that is valuable for investors, ratingagencies are excluded from Regulation FD (which forbids firms from selectivelyrevealing valuable information, except for CRAs).6 However, when ratings arepublicized, accompanying explanations only refer to public information toensure that strict confidence is maintained regarding sensitive issuer-providedinformation.

2.2. Effects of EM on credit ratings

This study hypothesizes that EM adversely affects credit ratings. As EM cre-ates asymmetric information, this adverse effect suggests that true financialstrength is suspicious and true losses are hidden. This view is frequently associ-ated with former SEC chairman Arthur Levitt (1998), who chastised firms fortheir use of ‘cookie jar’ reserves to manage earnings. Previous research alsofocused on opportunistic EM, suggesting that EM reduces the informativenessof earnings. For example, Bhattacharya et al. (2003) indicated that firm manage-ment of earnings stops reported earnings from being a useful indicator of under-lying performance, reducing their informativeness and increasing theiropaqueness. Raters then realize that severe information asymmetry exists andtake a negative view of the managerial use of EM, resulting in negative percep-tions of firm EM behaviour. The adverse effect implies that raters disagree withearnings manipulation. EM therefore increases bank debt burden because ofadverse credit ratings.Next, this study hypothesizes that asymmetric information is mitigated in

countries with more extensive and effective banking regulations. Such environ-ments motivate raters to view reported earnings as trustworthy, reducing theadverse impact of EM on credit ratings. Previous studies have indicated thatmanagers use accounting judgment to make financial reports more informative(Chaney and Lewis, 1995; Kirschenheiter and Melumad, 2008). This situationcan occur if certain accounting choices or estimates are perceived as credible sig-nals of firm financial performance. For example, Subramanyam (1996) foundthat discretionary accruals do not indicate opportunistic EM and are associatedwith contemporaneous stock prices and future earnings and cash flows and con-cluded that managers choose accruals to increase the informativeness of account-ing earnings. Zarowin (2002) also demonstrated that stock prices are more

6 Jorion et al. (2005) examined the effect of credit rating changes on stock prices andfound that the informational effect of downgrades and upgrades is much greater in thepost-FD period.



informative for firms with greater earnings smoothing, implying that managersuse income smoothing to reveal private information regarding future firm profit-ability.Alternatively, in environments with limited and ineffective banking regula-

tions, raters recognize the existence of severe information asymmetry, and man-agers may use their judgment to create opportunities for EM. Raters thus do nottrust reported earnings, aggravating the adverse impact of EM on ratings andincreasing the cost of borrowing.

2.3. Measures of earnings management

This study considers two forms of EM: earnings smoothing and discretionaryaccrual.7

2.3.1. Earnings smoothing

The motivation for banks to engage in earnings smoothing is evident. BecauseLLP is by far the largest and most important accrual for banks (Ahmed et al.,1999; Kanagaretnam et al., 2004), the use of loan loss allowance to smoothincome has been well studied.8 According to Kwan and O’Toole (1997), if bankmanagement wishes to stabilize bank earnings over time, namely to engage inearnings smoothing, they can achieve this by over-provisioning when bank earn-ings are unusually high and under-provisioning when earnings are unusuallylow. Because LLP is based on judging management on expected losses that havenot yet materialized, earnings smoothing in the banking industry can be mea-sured as follows:9

EM1i;t ¼ qðDLLPi;t

TAi;t�1;DEBPi;t

TAi;t�1Þ ð1Þ

where q denotes the correlation coefficient; TAi represents the total assets ofbank i; and EBPi is earnings before provisions for bank i, defined as net income

7 EM of loss avoidance is an industry measure because it can be calculated only usingobservations of all banks in the banking industry. Namely, only one loss avoidance mea-sure exists for the banking industry and loss avoidance measures do not exist for individ-ual banks. For example, Shen and Chih (2005) used cross-country EM for loss avoidance,and each country can only generate one such type of EM.

8 SeeKwan and O’Toole (1997), Ahmed et al. (1999), Cavallo and Majnoni (2001), Lea-ven and Majnoni (2003), and Kanagaretnam et al. (2004).

9 Zarowin (2002), Bhattacharya et al. (2003), Leuz et al. (2003), and Burgstahler et al.(2006) also used correlation coefficient between changes in accruals and changes in cashflow to measure earnings smoothing.



plus LLP.10 Therefore, to minimize variations in net income, banks adjust LLPto offset its influence on EBP. Specifically, managers increase LLP when facingincreased profits and conversely, decrease LLP when facing decreased profits.Therefore, a higher positive correlation coefficient implies greater earningssmoothing.

2.3.2. Discretionary accrual

Loan loss provisions is separated into non-discretionary and discretionarycomponents. This study defines discretionary LLP (DLLP) by removing compo-nents of LLP that are non-discretionary or beyond managerial control. Employ-ing the same specification as Kanagaretnam et al. (2004), DLLP can be obtainedby first estimating the following regression.11

LLPi;t ¼ a0 þ a1DLOANSi;t þ a2NCOi;t þ a3NPLi;t�1 þ a4DNPLi;t�1

þ a5LLRi;t�1 þ ei;t

ð2Þwhere i and t denote bank i at time t, DLOANS denotes the change in total loansoutstanding; NCO represents net loan charge-offs; NPL is non-performing loans;DNPL denotes the change in non-performing loans; LLR represents the loan lossreserves. Furthermore, ahðh ¼ 0; :::; 5Þ are unknown coefficients. All variablesare deflated by the beginning value of total assets in a year. While coefficient a1is expected to be uncertain, a2, a3, a4, a5 are all expected to be positive. See Be-atty et al. (1995), Ahmed et al. (1999) and Beatty et al. (2002) for the rationaleof their signs.The estimation of Eq. (2) yields the fitted dependent variable and estimated

residuals (ei;t), which are non-discretionary LLP and DLLP, respectively. Thus,the second EM is defined as:

EM2i;t ¼ DLLPi;t ¼ ei;t ð3Þ

Large DLLP values are conventionally interpreted as demonstrating the exis-tence of EM (Cornett et al., 2009). Increasing DLLP indicates overestimation

10 With respect to EM1, we use every 6-year sample to calculate this correlation coeffi-cient. The 6-year sample is overlapped. That is, the correlation coefficient for the period1996–2001 is calculated first and denoted as EM1ij,2001. Next, the correlation coefficientfor the period 1997–2002 is calculated, denoted as EM1ij,2002, and so on. The final correla-tion coefficient to be calculated is for the period 2002–2007 and is denoted as EM1ij,2007.This process yields seven different EM1s for each bank.

11 This study uses cross-country data, and the number of banks surveyed varies amongcountries. For some countries, fewer than 10 banks were surveyed each year, making itimpossible to estimate the equation year by year. This study thus estimates the coefficientsby pooling the banks and years for individual country.



of LLP, consequently reducing reported earnings. Ratings are thus down-graded.

3. Proxies for banking regulation

This study using cross-country data further enables researching how asymmet-ric information influences the impact of EM on credit ratings at the countrylevel. This study uses two conditional variables to proxy the extent and effective-ness of banking regulations at the country level, that is, country developmentlevel and creditor protection.

3.1. Country development level

Previous studies have found that rating agencies apply different standards toissuers depending on national development level. For example, Cantor and Fal-kenstein (2001) pointed out that rating agencies are harsher to non-U.S. firms;that is, they assigned lower ratings to non-U.S. firms even when these firms metthe same standards as U.S. firms. Moreover, Poon (2003) found that rating agen-cies assigned different weights to the same financial ratios for Japanese versusnon-Japanese firms. This phenomenon may occur because information asymme-try makes it difficult for raters to understand the true economic performance of abank, and these asymmetric information problems are more acute in emergingmarkets or developing economies, making financial statements of firms in devel-oping countries more suspicious than those of firms in developed countries(Vives, 2006). Thus, the information asymmetry problem is reduced in high-income or well-developed countries, but aggravated in less-developed countries.This study thus expects that the adverse effect of EM on ratings is mitigated indeveloped countries but enhanced in less-developed countries.

3.2. Creditor protection

Recent international studies have compared institutional features, financialreporting outcomes and market effects of cost of capital among countries. Specif-ically, Ball et al. (2000), Hung (2001) and Leuz et al. (2003) highlighted thatcountry legal and institutional environment can affect firms’ financial reportingincentives and thus the quality of reported financial information. This study usesbanks as samples. Banks can make more risky loans in countries with a strongregulatory environment because their expected loss per loan is reduced by thesuperior creditor protection available in such countries, that is, the protection ofcreditor rights provided by national laws and regulations signals the ease withwhich creditors can repossess collateral and take control of a firm in the event ofdefault. Previous studies suggested that exogenous shocks exerted a larger impacton credit markets in institutional environments characterized by poor creditorprotection (Galindo and Micco, 2007). This study expects banks in countries



with stronger protection for creditor rights to have more extensive and effectivebanking regulation, thus reducing information asymmetry.

4. Econometric model

FollowingAshbaugh-Skaife et al. (2006), this study converts the long-term letterratings of S&P into seven numerical ratings, that is, this study lets AAA = 7,AA+ � AA) = 6, A+ � A) = 5, BBB+ � BBB) = 4, BB+ � BB) = 3,B+ � B) = 2andCCC+andbelow = 1, seeTable 1 for detailedmapping.Theterm Rating denotes S&P’s assessment of the creditworthiness of the issuer during2002–2008,wherea larger number indicates abetter rating.Themodel is as follows:

Ratingij;t ¼ b1EMIij;t�a þ b2Capitalij;t�a þ b3Profitabilityij;t�a

þ b4Liquidityij;t�a þ b5Inefficiencyij;t�a þ b6Qualityij;t�a

þ b7Lnassetij;t�a þ b8SCRi;t þ eij;t:

ð4Þ

Furthermore, this study assumes that:

b1 ¼ b11 þ b12Z ð5Þ

where EMIij,t)a denotes earnings measurement for bank j from country i, whichis EM1ij,t)a and EM2ij,t)a, representing earnings smoothing and discretionaryaccrual, respectively; b1 …, b8 are vectors of coefficients, and e is a random error.The subscript t ) a underscores that each financial ratio is calculated using datataken at 3-year intervals.12 Thus, the sample period for credit rating runs from2002 to 2008, and that for financial ratios runs from 1999 to 2007. Z is the condi-tional variable denoting better extent and effectiveness of banking regulations.The original sample comprises long-term ratings of 3473 bank-year observationsfrom 85 countries.To test the relations between the financial ratios and credit ratings, this study uses

the ordered probitmodel because the seven categories of credit ratings convey ordi-nal risk assessments. This study ranks orderbankpreferences across the rating cate-gories but cannot assumeuniformdifferences in benefits (costs) between categories.Extra bank-specific explanatory variables are included in the rating models

based on a survey of the previous research on the determinants of bank creditratings and bank failure (Poon et al., 1999, 2009; Wheelock and Wilson, 2000;

12 In assigning credit ratings, the agencies adopt a longer-term perspective using a processknown as ‘rating through the cycle’. This is usually implemented by considering 3-yearaverages of relevant financial ratios. This study calculated the average of each financialratio using data taken at 3-year intervals. The 3-year samples were overlapping. Forexample, Capitalij,2001 is the average capital adequacy ratio during 1999–2001, Capi-talij,2002 is the average capital adequacy ratio during 2000–2002, and so on. The last suchvalue is Capitalij,2007.



Rojas-Suarez, 2001; Bongini et al., 2002; Poon and Firth, 2005). The five finan-cial ratios employed in this study are averaged over a 3-year period to minimizebusiness cycle effects. Capital denotes the capital adequacy ratio, defined by theBank of International Settlement; Profitability represents the average ratio of netincome to total assets; Liquidity is the average ratio of liquid assets to customerand short-term funding; Inefficiency denotes the average ratio of cost to income,and Quality represents the average ratio of LLP to net interest revenues. Capital,Profitability and Liquidity should positively affect ratings, whereas Inefficiencyand Quality should exert a negative impact.Bank size and sovereign credit ratings (SCR) are also included as control vari-

ables.13 Lnasset is defined as the natural logarithm of the average of total assetsover the past 3 years. UBS Investment Bank (2004) indicated that larger compa-nies tend to have higher credit ratings, and that size metrics offer the strongest

Table 1

Matching the letter ratings with numerical ratings

S&P’s long-term credit ratings Numerical Number of bank-year observations

AAA 7 33

AA+ 6 37

AA 6 217

AA) 6 443

A+ 5 473

A 5 476

A) 5 431

BBB+ 4 325

BBB 4 194

BBB) 4 163

BB+ 3 100

BB 3 126

BB) 3 115

B+ 2 112

B 2 79

B) 2 91

CCC+, CCC, CCC), D, SD 1 58

Credit ratings are the long-term issuer credit ratings compiled by Standard & Poor’s and reported on

BankScope database. The ratings range from AAA (highest rating) to D (lowest rating). From rat-

ings AA to CCC, S&P rating agency adds a plus (+) and a minus ()) to represent the strength and

weakness in a grade of rating for every issuer. These ratings reflect S&P’s assessment of the credit-

worthiness of the obligor with respect to its senior debt obligations.

13 This paper does not include main effect of HIC. First, the correlation between sovereigncredit rating and HIC, MIC is high, suggesting multicollinearity. Next, based on the liter-ature (see Poon, 2003; Poon and Firth, 2005; Poon et al., 2009) and the suggestion of rat-ing agencies that sovereign credit ratings can control for country-specific effects andmacroeconomic conditions, this study only considers sovereign credit ratings.



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Highlight

statistical correlation with credit ratings. They indicated that size metrics alsoreflect important qualitative factors, such as geographic and product marketdiversification, competitive positions, bargaining power, market share and brandstature.14 The sovereign rating of a country is important in determining individ-ual bank ratings because it captures important macroeconomic and institutionalcharacteristics of the countries where the banks are located. Sovereign credit rat-ings, in terms of SCR, are similarly transformed from letter ratings into sevennumerical ratings. Borensztein et al. (2006) found ‘the sovereign ceiling effect’was statistically highly significant, especially in the banking industry. The yeardummies are also included to control the effects of time on ratings and are repre-sented by Year03, Year04, Year05, Year06, Year07 and Year08.Equation (5) further assumes that the coefficient of EM, b1, is affected by Z,

which is the vector of our conditional variables, that is,Z = (HIC, MIC, EEUROPE, EASIA, LATIN, CREDITOR)The first set conditional variables comprise the development dummies, includ-

ing the dummies for high-income countries (HIC) and middle-income countries(MIC) because this study considers three groups of countries. The second setconditional variables are the regional dummies, comprising the dummies forEastern Europe and Central Asia (EEUROPE), East Asia and the Pacific(EASIA), and Latin America and the Caribbean (LATIN). Rich countries, iden-tified based on the definitions of World Development Indicators published by theWorld Bank, in these three regions are excluded. Thus, only middle- and low-income countries from each region are included.15 Next, CREDITOR indicatesthe degree of protection a country affords to creditor rights, which ranges from 0to 4, with a higher score representing stronger creditor protection.

5. Empirical results

5.1. Data sources and descriptive statistics

S&P long-term credit ratings, bank financial information and SCR areobtained from FitchIBCA BankScope. Meanwhile, country income level (HIC

14 S&P indicates they evaluate the overall business risk and overall financial risk of eachissuer. Economic risk, industry risk, market position, diversification and management/strategy are the five important factors in determining the overall business risk rating,while credit risk, earnings, liquidity and funding, market risk, capitalization and financialflexibility are the six key factors used to assess the overall financial risk of a bank. A preli-minary overall bank rating is derived from both the overall business risk rating and theoverall financial risk rating. S&P claims that its analysts do not use any formula forcombining these scores to determine ratings.

15 For example, East Asia and the Pacific excludes Japan, Hong Kong, Korea and Singa-pore; Eastern Europe and Central Asia excludes Slovenia; and Latin American, and theCaribbean excludes the Bahamas, Bermuda and Puerto Rico.



and MIC) and the regional dummies (EEUROPE, EASIA and LATIN) areobtained from World Development Indicators. The term ‘CREDITOR’ is takenfrom La Porta et al. (1998). See Table 2 for details.Table 3 lists the number of bank-year observations per country, descriptive

statistics for two EM indicators and conditional variables for the sample coun-tries. Column 1 lists the number of bank-years assigned S&P long-term issuerratings across regions and countries. The sample comprises bank-issued ratings.Western Europe has the largest number of banks (1056) in the sample whileAfrica has the smallest (53). Column 2 lists the measure of earnings smoothing,EM1, which is highest in Monaco (0.98), followed by Vietnam (0.97), Lithuania(0.94), Jamaica (0.87) and Colombia (0.86). Banks in less-developed countriesthus are more likely to smooth their earnings. Particularly, banks in Africascored highest in earnings smoothing (EM1), followed by those in Eastern Eur-ope and Central and South America. Column 3 lists the average level of DLLP,EM2, where the largest five numbers are for Russia (0.0189), Kazakhstan(0.0140), Liechtenstein (0.0102), Lebanon (0.0099) and Poland (0.0087). Onceagain, the ranking suggests EM (EM2) is more prevalent in developing than inadvanced countries. The highest score occurs in Eastern Europe. Banks in Wes-tern Europe exhibit lower earnings smoothing and discretionary accrual thanthose from less-developed regions. Columns 4–5 list whether the banks are fromHIC or MIC, while columns 6–8 list their regions. The next column lists thebasic statistics of CREDITOR.Table 4 lists the correlation coefficients among the variables. Three interesting

results are noted as follows. First, the correlation coefficient between credit ratingand EM1 is )0.072, implying a negative correlation between credit ratings andearnings smoothing, even though the coefficient is small. Second, a negative cor-relation coefficient exists between credit rating and EM2 ()0.144), suggesting anassociation between high DLLP and low rating. Finally, the remaining correla-tion coefficients of each pair are also small, suggesting no problem of multicollin-earity.Table 5 lists the descriptive statistics of bank credit ratings against EM and

financial ratios, sovereign ratings and conditional variables. Panel A comparesbank credit ratings against two EMs and financial ratios. Notably, the averageEM1 increases with deteriorating ratings. That is, a bank with an AAA ratinghas the lowest EM1 (0.2190), while one with a B rating has the highest (0.4773),implying that lower-rated banks are more likely to smooth their earnings. Thepattern of EM2 against ratings resembles that of EM1. Except for AAA, EM2values increase with deteriorating ratings. That is, banks with higher DLLP tendto receive worse ratings. Overall, lower-rated banks are associated with activeEM, indicating that raters consider EM a negative factor when assigning creditratings.The relationships between financial ratios and ratings generally meet expecta-

tions, except for Profitability. Higher profitability does not always ensure betterratings. For example, ROA is 0.76 for AAA-rated banks, but 1.18 for B-rated



banks. The increase in Inefficiency closely matches the decline in ratings. How-ever, Capital ratio exhibits a U-shaped pattern in relation to ratings, with lowerratios coinciding with BBB and higher ratios coinciding with AAA, B and CCC.

Table 2

Mnemonics, definitions and sources of variables

Variables Descriptions Sources

Dependent variable

Rating S&P’s long-term issuer ratings of commercial banks. We convert

S&P long-term letter ratings into seven numerical ratings,

that is, we let AAA= 7, AA+ � AA– = 6, A+ � A–

= 5, BBB+ � BBB– = 4, BB+ � BB– = 3, B+ � B–

= 2 and CCC+ and below = 1

BankScope

Independent variables

EM1 The correlation coefficient of changes in loan loss provisions

(LLP) and earnings before LLP. We use every 6-year data

to calculate this correlation coefficient

Author’s

calculation

EM2 Discretionary LLP, refer to Kanagaretnam et al.’s (2004)

specification, where the estimated error term is discretionary

LLP. We use the average of DLLP over the past 3 years

Capital The average of the ratio of required capital to risky

assets over the past 3 years

BankScope

Profitability The average of the ratio of net income to total assets

over the past 3 years

BankScope

Liquidity The average of the ratio of liquid assets to customer

and short-term funding over the past 3 years

Bank Scope

Inefficiency The average of the ratio of cost to income


BankScope

Quality The average of the ratio of LLP to net interest

revenues over the past 3 years

Lnasset Natural logarithm of the average of total assets


BankScope

SCR All sovereign credit ratings are coded as seven

ordinal values, where AAA=7, AA=6, A = 5,

BBB=4, BB=3, B = 2 and CCC or CCC below=1

BankScope

Conditional variables

HIC Dummy variable, 1 if the country is located in

high-income countries, 0, otherwise

WDI

MIC Dummy variable, 1 if the country is located in

middle-income countries, 0, otherwise

WDI

EEUROPE Dummy variable, 1 if East Europe and Central Asia countries, 0,

otherwise. Only middle- and low-income countries are included

WDI

EASIA Dummy variable, 1 if East Asia and Pacific countries, 0, otherwise.

Only middle- and low-income countries are included

WDI

LATIN Dummy variable, 1 if Latin American and Caribbean countries, 0,

otherwise. Only middle- and low-income countries are included

WDI

CREDITOR Aggregating different creditor rights. The index ranges from 0 to 4,

with higher score representing higher creditor protection

LLSV

WDI: World Development Indicators Database (2006). LLSV: La Porta et al. (1998).



Table 3

Basic statistics of earnings management and conditional variables

Region and Country N EM1 EM2 HIC MIC EEUROPE EASIA LATIN CREDITOR

North America 814 0.4162 )0.0005Canada 67 0.3347 )0.0010 1 0 0 0 0 1

USA 747 0.4235 )0.0005 1 0 0 0 0 1

Western Europe 1056 0.3389 0.0001

Austria 14 0.7239 )0.0019 1 0 0 0 0 3

Belgium 24 )0.0824 )0.0004 1 0 0 0 0 2

Denmark 27 0.1375 0.0010 1 0 0 0 0 3

Finland 20 )0.0317 )0.0008 1 0 0 0 0 1

France 219 0.4501 )0.0011 1 0 0 0 0 0

Germany 79 0.4202 )0.0016 1 0 0 0 0 3

Greece 39 0.4117 )0.0032 1 0 0 0 0 1

Iceland 3 0.7300 0.0052 1 0 0 0 0 na

Ireland 87 0.2942 )0.0006 1 0 0 0 0 1

Italy 136 0.4935 0.0046 1 0 0 0 0 2

Liechtenstein 9 0.6695 0.0102 1 0 0 0 0 na

Luxembourg 55 0.2692 )0.0005 1 0 0 0 0 na

Monaco 3 0.9807 na 1 0 0 0 0 na

Netherlands 55 0.3372 )0.0008 1 0 0 0 0 2

Norway 17 0.0696 0.0013 1 0 0 0 0 2

Portugal 22 0.5485 )0.0007 1 0 0 0 0 1

Spain 55 0.5622 0.0004 1 0 0 0 0 2

Sweden 22 0.5267 0.0047 1 0 0 0 0 2

Switzerland 31 )0.1753 )0.0029 1 0 0 0 0 1

Turkey 33 0.0883 0.0051 0 1 1 0 0 2

United

Kingdom

106 0.0081 )0.0003 1 0 0 0 0 4

Oceania 169 0.1663 )0.0003Australia 128 0.0975 )0.0004 1 0 0 0 0 1

New Zealand 38 0.3815 0.0001 1 0 0 0 0 3

Papua New Guinea 3 0.5971 na 0 0 0 1 0 na

Far East and

Central Asia

666 0.3947 0.0003

China 30 0.6383 )0.0040 0 1 0 1 0 na

Hong Kong 49 0.0408 0.00004 1 0 0 0 0 4

India 27 0.7619 )0.0009 0 0 0 0 0 4

Indonesia 24 )0.1474 )0.0016 0 1 0 1 0 4

Japan 240 0.4022 0.0006 1 0 0 0 0 2

Kazakhstan 47 0.7844 0.0140 0 1 0 0 0 na

Korea 62 0.2650 )0.0030 1 0 0 0 0 3

Malaysia 28 0.6216 )0.0008 0 1 0 1 0 4

Philippines 24 0.8500 0.0048 0 1 0 1 0 0

Singapore 21 0.3151 )0.0025 1 0 0 0 0 4

Taiwan 69 0.5140 )0.0013 na na 0 1 0 2

Thailand 43 )0.0975 )0.0020 0 1 0 1 0 3

Vietnam 2 0.9673 0.0027 0 0 0 1 0 na

Africa 53 0.5857 0.0002



Table 3 (continued)


Egypt 14 0.8144 0.0012 0 1 0 0 0 4

Morocco 9 0.4655 )0.0002 0 1 0 0 0 na

Nigeria 6 0.2819 0.0026 0 0 0 0 0 4

South Africa 14 0.3975 )0.0009 0 1 0 0 0 3

Tunisia 10 0.7836 0.0007 0 1 0 0 0 na

Eastern Europe 264 0.4680 0.0102

Bulgaria 30 0.4045 )0.0012 0 1 1 0 0 na

Croatia 7 0.1215 )0.0016 0 1 1 0 0 na

Cyprus 2 )0.0473 0.0024 1 0 0 0 0 na

Czech Republic 25 0.5553 0.0009 0 1 1 0 0 na

Georgia 2 )0.1541 )0.0004 0 1 1 0 0 na

Hungary 4 na na 0 1 1 0 0 na

Latvia 3 0.0931 na 0 1 1 0 0 na

Lithuania 3 0.9421 na 0 1 1 0 0 na

Poland 5 0.7191 0.0087 0 1 1 0 0 na

Romania 9 0.7030 0.0055 0 1 1 0 0 na

Russian Federation 151 0.4529 0.0189 0 1 1 0 0 na

Slovakia 11 0.8343 )0.0115 0 1 1 0 0 na

Slovenia 3 0.8188 0.0025 1 0 0 0 0 na

Ukraine 9 0.1431 0.0028 0 1 1 0 0 na

South and Central

America

325 0.4340 )0.0010

Argentina 11 )0.0571 )0.0004 0 1 0 0 1 1

Bahamas 8 0.1532 0.0001 1 0 0 0 0 na

Bermuda 12 )0.1860 0.0010 1 0 0 0 0 na

Bolivia 8 0.3076 0.0055 0 1 0 0 1 na

Brazil 98 0.5064 )0.0015 0 1 0 0 1 1

Chile 33 0.4554 )0.0019 0 1 0 0 1 2

Colombia 5 0.8626 0.0021 0 1 0 0 1 0

Costa Rico 5 0.7179 na 0 1 0 0 1 na

El Salvador 19 0.6000 0.0037 0 1 0 0 1 na

Dominican Republic 1 0.6554 na 0 1 0 0 1 na

Guatemala 2 na 0.0031 0 1 0 0 1 na

Jamaica 7 0.8655 na 0 1 0 0 1 na

Mexico 53 0.4897 )0.0037 0 1 0 0 1 0

Panama 19 0.0295 )0.0015 0 1 0 0 1 na

Peru 8 0.7719 )0.0014 0 1 0 0 1 0

Puerto Rico 3 0.5683 na 1 0 0 0 0 na

Trinidad and

Tobago

12 0.5018 0.0007 0 1 0 0 1 na

Uruguay 21 0.3346 0.0028 0 1 0 0 1 2

Middle East 128 0.3422 )0.0004Bahrain 20 0.1831 )0.0037 1 0 0 0 0 na

Israel 15 0.2791 )0.0008 1 0 0 0 0 4

Jordan 2 )0.0057 0.0050 0 1 0 0 0 na

Kuwait 27 0.5245 )0.0000 1 0 0 0 0 na

Lebanon 21 0.5500 0.0099 0 1 0 0 0 na



Capital ratio is highest for CCC-rated banks, probably because governmentawareness of their poor ratings leads to partial suspension of their lending activi-ties (decreasing their assets), or requests for funding (increasing their capital).Liquidity ratios exhibit a pattern similar to that of Capital, namely a U-shapedpattern, with lower ratios falling on the ratings in the middle and higher ratioson ratings at the two extremes. Additionally, liquidity ratios are highest forCCC-rated banks, probably because the threat of bank runs leads these banks toincrease their liquidity. Thus, while CCC-rated banks display the weakest Profit-ability, Inefficiency and Quality, they are strongest in Capital and Liquidity. Thisphenomenon probably results from regulators request to avoid bank runs.Finally, ratings increase with bank size.Panel B illustrates the positive relationship between sovereign and bank rat-

ings. With few exceptions, the national sovereign rating acts as the ceiling forlocal bank rating.16 Panel C lists the conditional variables across different ratinggrades. AAA-rated banks exist only in high-income countries, and most bankselsewhere have lower ratings. Furthermore, creditor protection and ratingsexhibit no significant trend.

Table 3 (continued)


Oman 4 )0.0313 0.0056 0 1 0 0 0 na

Qatar 7 0.0715 )0.0043 1 0 0 0 0 na

Saudi Arabia 18 0.2311 )0.0008 1 0 0 0 0 na

United Arab

Emirates

14 0.3520 )0.0041 1 0 0 0 0 na

The sample year is from 2002 to 2008 across 85 countries. N is the number of banks in the region

and country. EM1 and EM2 are the EM indicators; earnings smoothing (EM1) is the correlation

between changes in loan loss provisions (LLP) and changes in earnings before LLP. Discretionary

accrual (EM2) is discretionary LLP, which are the residuals from the estimation of LLP. Two set

conditional variables are included to examine whether asymmetric information affects the relation-

ship between EM and credit ratings. The first set is the development level of a country, including

HIC, MIC, EEUROPE, EASIA and LATIN. HIC is an indicator variable taking on the value of 1 if

the country stems from high-income countries and 0 otherwise. MIC is an indicator variable taking

on the value of 1 if the country stems from middle-income countries. EEUROPE is an indicator vari-

able taking on the value of 1 if the country stems from East Europe and Central Asia region. EASIA

is an indicator variable taking on the value of 1 if the country stems from East Asia and Pacific

region. LATIN is an indicator variable taking on the value of 1 if the country stems from Latin

America and the Caribbean region. The second conditional variable, CREDITOR, is an indication

of the degree of a country’s creditors’ rights protection, ranging from 0 to 4, with higher scores repre-

senting higher creditor protection.

16 Banks ratings are rarely higher than their sovereign ratings. In our samples, there are53 bank-year observations that exhibit this phenomenon. These cases are found in Brazil,Costa Rica, Czech Republic, Greece, Indonesia, Italy, Jordon, Panama, Peru, Poland andTurkey.



5.2. Effects of earnings smoothing on ratings

Table 6 lists the estimated results for an ordered probit model using EM1 as aproxy for EM. The table contains seven specifications. When EM1 is consideredalone (in specification A), namely without considering the interaction terms, thecoefficient ofEM1 is significantly negative, indicating that banks engaging in moreearnings smoothing are likely to receive lower ratings. This phenomenon alsoimplies that raters know which banks are smoothing their earnings and view suchbehaviour negatively. Restated, in the view of raters, earnings smoothing cloudsearnings information and stops reported earnings from describing underlying per-formance, reducing their informativeness and increasing their opacity. This adverseeffect implies that raters disagree with the manipulation of earnings. Earningssmoothing therefore increases bank debt burden because of adverse credit ratings.Next, when the specifications include the interaction terms, the coeffi-

cients of EM1 remain overwhelmingly negative and are significant in three of six

Table 4

Correlation coefficients

Rating SCR EM1 EM2 HIC MIC EEUROPE EASIA LATIN

SCR 0.800

EM1 )0.072 )0.054EM2 )0.144 )0.093 0.010

HIC 0.747 0.872 )0.083 )0.103MIC )0.734 )0.842 0.069 0.104 )0.971EEUROPE )0.466 )0.393 0.065 0.197 )0.523 0.538

EASIA )0.198 )0.225 0.007 )0.028 )0.304 0.299 )0.081LATIN )0.345 )0.495 0.043 )0.043 )0.514 0.529 )0.095 )0.080CREDITOR )0.070 )0.175 )0.094 0.005 )0.085 0.036 0.000 0.193 )0.162

Rating is S&P long-term issuer ratings of commercial banks. We convert S&P long-term alphanu-

meric ratings into 7 numerical ratings, that is, we let AAA = 7, AA+ � AA– = 6, A+ � A– = 5,

BBB+ � BBB– = 4, BB+ � BB– = 3, B+ � B– = 2 and CCC+ and below = 1. SCR are sover-

eign credit ratings coded as 7 ordinal values, where AAA=7, AA+ � AA– = 6, A+ � A– = 5,

BBB+ � BBB) = 4, BB+ � BB– = 3, B+ � B– = 2 and CCC+ and below = 1. EM1 and

EM2 are the EM indicators; earnings smoothing (EM1) is the correlation between changes in loan

loss provisions (LLP) and changes in earnings before LLP. Discretionary accrual (EM2) is discretion-

ary LLP, which are the residuals from the estimation of LLP. Two set conditional variables are

included to examine whether asymmetric information can affect the relationship between EM and

credit ratings. The first set is the development level of a country, including HIC, MIC, EEUROPE,

EASIA and LATIN. HIC is an indicator variable taking on the value of 1 if the country stems from

high-income countries and 0 otherwise. MIC is an indicator variable taking on the value of 1 if the

country stems from middle-income countries. EEUROPE is an indicator variable taking on the value

of 1 if the country stems from East Europe and Central Asia region. EASIA is an indicator variable

taking on the value of 1 if the country stems from East Asia and Pacific region. LATIN is an indica-

tor variable taking on the value of 1 if the country stems from Latin America and the Caribbean

region. CREDITOR is an indication of the degree of a country’s creditors’ rights protection, ranging

from 0 to 4, with higher scores representing higher creditor protection.



Table 5

Relation of ratings with EM, financial ratios, sovereign ratings and conditional variables

Variables

Bank ratings

AAA AA A BBB BB B CCC

Panel A: EM and financial ratios

EM1 0.2190 0.2757 0.3815 0.4363 0.4346 0.4773 0.4510

EM2 0.0005 )0.0005 )0.0006 )0.0009 0.0012 0.0069 0.0134

Capital 18.58 12.42 14.14 13.17 15.02 19.29 23.21

Profitability 0.76 1.00 0.96 0.96 1.05 1.18 0.32

Liquidity 29.23 24.04 23.04 22.21 28.87 39.05 44.22

Inefficiency 43.24 58.42 58.53 58.50 61.52 69.79 70.39

Quality 15.60 13.36 14.62 23.87 35.45 20.70 10.67

Lnasset 7.64 7.73 7.34 7.13 6.80 6.26 5.74

Panel B: sovereign credit ratings (SCR)

AAA 1.00 0.85 0.63 0.28 0.03 0.03 0.00

AA 0.00 0.13 0.25 0.23 0.05 0.00 0.00

A 0.00 0.01 0.12 0.24 0.05 0.00 0.00

BBB 0.00 0.00 0.00 0.22 0.35 0.33 0.44

BB 0.00 0.00 0.00 0.03 0.51 0.29 0.33

B 0.00 0.00 0.00 0.00 0.01 0.35 0.16

CCC 0.00 0.00 0.00 0.00 0.00 0.00 0.07

Panel C: conditional variables

HIC 1.00 1.00 0.96 0.68 0.11 0.03 0.00

MIC 0.00 0.00 0.04 0.29 0.83 0.96 1.00

EEUROPE 0.00 0.00 0.01 0.07 0.20 0.40 0.86

EASIA 0.00 0.00 0.03 0.12 0.16 0.18 0.00

LATIN 0.00 0.00 0.02 0.12 0.28 0.29 0.03

CREDITOR 1.63 1.48 1.60 1.82 2.21 1.51 1.5

Number

of banks

33 697 1380 682 341 282 58

Panel A describes EM indicators and financial ratios across different bank credit ratings. Earnings

smoothing (EM1) is the correlation between changes in loan loss provisions (LLP) and changes in earn-

ings before LLP. Discretionary accrual (EM2) is discretionary LLP, which are the residuals from the

estimation of LLP. The financial ratios employed here are the average of the past 3 years to minimize

the business cycle effect. The term Capital is the average ratio of required capital to risky assets. Profit-

ability is the average ratio of net income to total assets, Liquidity stands for the average ratio of liquid

assets to customer and short-term funding, Inefficiency denotes the average ratio of cost to income, and

Quality is the average ratio of LLP to net interest revenues.Lnasset is defined as the natural logarithmof

total assets. In Panel B, we present sovereign credit ratings (SCR) across different bank credit ratings.

The SCRare categorized intoAAA,AA,A,BBB,BB,B, andCCC. In PanelC, two set conditional vari-

ables across different bank credit ratings. The first set is the development level of a country, including

HIC,MIC, EEUROPE,EASIA andLATIN.HIC is an indicator variable taking on the value of 1 if the

country stems from high-income countries and 0 otherwise. MIC is an indicator variable taking on the

value of 1 if the country stems frommiddle-income countries. EEUROPE is an indicator variable taking

on the value of 1 if the country stems fromEast Europe andCentral Asia region. EASIA is an indicator

variable taking on the value of 1 if the country stems from East Asia and Pacific region. LATIN is an

indicator variable taking on the value of 1 if the country stems from Latin America and the Caribbean

region.The second conditional variable,CREDITOR, is an indicationof the degree of a country’s credi-

tors’ rights protection, ranging from0 to 4,with higher scores representing higher creditor protection.



Table 6

Relation between EM1 and ratings

Explanatory

variables

Ordered probit model

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

EM1 )0.107**()2.36)

)0.249***()2.95)

)0.049()0.95)

)0.073()1.52)

)0.066()1.41)

)0.134***()2.92)

)0.190***()2.41)

EM1 · HIC 0.198**

(2.09)

EM1 · MIC )0.230**()2.44)

EM1 · EEUROPE )0.406***()3.64)

EM1 · EASIA )0.443***()2.86)

EM1 · LATIN 0.673***

(2.77)

EM1 · CREDITOR 0.071*

(1.66)

Capital 0.011***

(3.56)

0.011***

(3.40)

0.011***

(3.44)

0.012***

(3.91)

0.011***

(3.41)

0.011***

(3.56)

0.013***

(3.81)

Profitability 0.150***

(14.93)

0.146***

(14.34)

0.147***

(14.52)

0.158***

(13.35)

0.144***

(14.04)

0.146***

(14.29)

0.145***

(14.18)

Liquidity 0.004***

(5.22)

0.004***

(5.10)

0.004***

(4.97)

0.004***

(5.03)

0.004***

(5.34)

0.004***

(5.24)

0.003***

(4.20)

Inefficiency )0.004***()5.14)

)0.004***()5.19)

)0.005***()5.19)

)0.005***()5.25)

)0.005***()5.34)

)0.005***()5.61)

)0.005***()5.14)

Quality )0.005***()7.42)

)0.005***()7.34)

)0.005***()7.29)

)0.005***()7.22)

)0.005***()7.20)

)0.004***()7.09)

)0.005***()7.46)

Lnasset 0.831***

(22.05)

0.815***

(21.44)

0.814***

(21.37)

0.818***

(21.59)

0.838***

(22.13)

0.832***

(22.09)

0.826***

(21.76)

SCR 0.898***

(33.05)

0.873***

(31.18)

0.874***

(31.35)

0.893***

(33.17)

0.892***

(32.88)

0.917***

(33.58)

0.900***

(32.26)

Year03 )0.194**()2.19)

)0.193**()2.15)

)0.192**()2.14)

)0.195**()2.21)

)0.192**()2.15)

)0.201**()2.25)

)0.195**()2.17)

Year04 )0.243***()2.84)

)0.248***()2.87)

)0.247***(2.86)

)0.239***()2.79)

)0.239***()2.79)

)0.239***()2.79)

)0.246***()2.84)

Year05 )0.195**()2.20)

)0.201**()2.23)

)0.200**()2.22)

)0.183**()2.05)

)0.194**()2.18)

)0.191**()2.14)

)0.193**()2.15)

Year06 )0.004()0.05)

)0.014()0.16)

)0.015()0.17)

0.006

(0.07)

0.009

(0.10)

0.005

(0.06)

)0.005()0.06)

Year07 0.104

(1.23)

0.102

(1.18)

0.101

(1.170

0.116

(1.37)

0.112

(1.32)

0.116

(1.37)

0.098

(1.15)

Year08 0.106

(1.25)

0.109

(1.27)

0.105

(1.23)

0.122

(1.44)

0.112

(1.32)

0.117

(1.39)

0.109

(1.28)

Log likelihood )2271.21 )2220.30 )2219.51 )2266.04 )2264.77 )2264.50 )2222.27Observations 2428 2366 2366 2428 2428 2428 2365



specifications. While the remaining three coefficients are insignificant (specifica-tions C, D and E), their coefficients of interaction terms of EM1 with MIC,EEUROPE and EASIA are significant. These results again indicate that banksengaging in earnings smoothing tend to receive lower ratings.This study then discusses the coefficients of the interaction terms, namely

EM1 · Z (specifications B to G). The coefficients of the interaction termsbetween EM1 and HIC and EM1 and MIC are significantly positive and nega-tive, respectively. This result supports the study hypothesis that the negativeimpact of EM1 on ratings is reduced in countries with strong banking regula-tions and increased in those with weak banking regulations. Moreover, whenHIC = 1, the net coefficient of (EM1 + EM1 · HIC) becomes )0.051(= )0.249 + 0.198), whereas it becomes )0.279 (= )0.049 ) 0.230) whenMIC=1. The former is statistically insignificantly different from zero (t-statis-tic = )1.02), while the latter is significantly negative (t-statistic = )3.40), sug-gesting that the influence of EM1 on ratings is near neutral in HIC but furtheraggravated in MIC. Namely, when assigning ratings to banks in high-incomecountries, the existence of low information symmetry indicates that raters arenot concerned with bank earnings smoothing regardless of their awareness ofsuch behaviour or that they simply view enhancing the informativeness ofaccounting earnings as a managerial decision.The next three columns list the coefficients of interaction terms between EM1

and three regional dummies, namely EEUROPE, EASIA and LATIN. The coef-ficients of the interaction terms of EM1 with EEUROPE and EASIA are signifi-cantly negative, indicating that in these two regions, raters downgrade theratings of banks that smooth their earnings. Unexpectedly, the coefficient of theinteraction term between EM1 and LATIN is significantly positive. One reason

Table 6 (continued)

t-statistics are in parenthesis and are based on the standard errors adjusted for clustering on each

country. *, ** and *** denote the significance at the 10 per cent, 5 per cent and 1 per cent level, respec-

tively. Earnings smoothing (EM1) is the correlation between changes in loan loss provisions (LLP)

and changes in earnings before LLP. The financial ratios employed here are the average of the past

3 years to minimize the business cycle effect. The term Capital is the average ratio of required capital

to risky assets. Profitability is the average ratio of net income to total assets, Liquidity stands for the

average ratio of liquid assets to customer and short-term funding, Inefficiency denotes the average

ratio of cost to income, and Quality is the average ratio of LLP to net interest revenues. Lnasset is

defined as the natural logarithm of total assets. We add year dummies, including Year03, Year04,

Year05, Year06, Year07 and Year08. Two set conditional variables are included to examine whether

asymmetric information can affect the relationship between EM and credit ratings. The first set is the

development level of a country. HIC is taking on the value of 1 if the country stems from high-income

countries and 0 otherwise. MIC is taking on the value of 1 if the country stems from middle-income

countries. EEUROPE is taking on the value of 1 if the country stems from East Europe and Central

Asia region. EASIA is taking on the value of 1 if the country stems from East Asia and Pacific region.

LATIN is taking on the value of 1 if the country stems from Latin America and the Caribbean region.

The second conditional variable, CREDITOR, is an indication of the degree of a country’s creditors’

rights protection, ranging from 0 to 4, with higher scores representing higher creditor protection.



for this phenomenon is that bank ratings that meet or exceed sovereign ceilingsare popular in LATIN. Ratings for 66 bank-year observations meet or exceedSCR in Latin America, accounting for 46 per cent of the sample in that region.These numbers and percentages are higher than those of other regions. Thisstudy hypothesizes that firm motivation to manage earnings is reduced whenbank ratings meet or exceed the sovereign ceiling. This possibility is verified byremoving bank ratings that meet or exceed the sovereign ceiling from the samplewhen conducting robust testing (see Section 5.4 and Tables 8 and 9), in whichcase the coefficient becomes insignificant.Finally, the coefficient of interaction terms EM1 · CREDITOR is significantly

positive, indicating that the negative effect of earnings smoothing on ratings isreduced for banks located in countries with strong creditor protection. Further-more, the net coefficient of (EM1 + EM1 · CREDITOR) is )0.119, )0.048,0.023 and 0.094 when CREDITOR is 1, 2, 3 and 4, respectively, with the middletwo coefficients being insignificant. Earnings smoothing thus exerts negative, neu-tral or positive effects on ratings for banks located in countries with weak, strongand very strong protection of creditor rights, respectively. Consequently, thehypothesis presented in this study is supportedwhenusingCREDITORas aproxy.The coefficients of the control variables used in this study, Capital, Profitabil-

ity, Liquidity, Inefficiency, Quality, Lnasset and SCR, meet expectations and thusare not discussed.

5.3. Effects of discretionary accrual on ratings

Table 7 resembles Table 6 except that EM1 is replaced by EM2. When onlyEM2 is considered (in specification A), its coefficient is significantly negative.Furthermore, when the interaction terms are included in the specifications, thecoefficients of EM2 remain overwhelmingly negative, and three of the six are sig-nificant. These results indicate that banks with higher DLLP tend to receivelower ratings, and that CRAs are aware of bank EM and consider it an adverseinfluence on ratings.Regarding conditional variables, the results of EM2 · HIC and EM2 · MIC

closely resemble those of using EM1; that is, their coefficients are significantlypositive and negative, respectively. Moreover, the net coefficient of the former isnear zero ()26.580 + 27.373 = 0.793, t-statistic = 0.15), and the net coefficientof the latter becomes )28.444 (= )0.841 ) 27.603) when MIC = 1. Thus, EM2exerts neutral and negative effects on ratings in HIC and MIC, respectively, sup-porting the study hypothesis.The coefficient of EM2 · EEUROPE is significantly negative, and the coeffi-

cients of EM2 with the remaining two less-developed regions, namely EASIAand LATIN, are insignificant. Furthermore, the coefficient of EM2 · CREDI-TOR is insignificant. The negative influence of earnings aggressiveness thus isaggravated in EEUROPE but remains unchanged when the conditional variablesare EASIA, LATIN and CREDITOR.



Tab

le7

RelationbetweenEM2andratings

Exp

lanatory

variables

Ordered

ProbitModel

(A)

(B)

(C)

(D)

(E)

(F)

(G)

EM2

)17.026***

()3.09)

)26.580***

()3.42)

)0.841

()0.09)

)1.347

()0.17)

)19.541***

()3.21)

)16.741***

()3.01)

)18.952

()1.10)

EM2

·HIC

27.373**

(2.21)

EM2

·MIC

)27.603**

()2.24)

EM2

·EEUROPE

)29.937***

()2.58)

EM2

·EASIA

23.389

(1.23)

EM2

·LATIN

)7.526

()0.18)

EM2

·CREDIT

OR

0.949

(0.11)

Capital

)0.00004

()0.01)

)0.001

(0.16)

)0.001

()0.17)

)0.001

()0.07)

)0.0001

()0.03)

)0.0001

()0.00)

0.001

(0.08)

Profitability

0.224***

(12.22)

0.216***

(11.66)

0.215***

(11.63)

0.224***

(12.22)

0.225***

(12.23)

0.223***

(12.18)

0.217***

(11.65)

Liquidity

0.004***

(5.95)

0.004***

(5.50)

0.004***

(5.49)

0.004***

(5.95)

0.004***

(5.97)

0.004***

(5.87)

0.004***

(5.45)

Ineffi

ciency

)0.005***

()3.04)

)0.005***

()3.10)

)0.005***

()3.10)

)0.005***

()3.09)

)0.005***

()3.05)

)0.005***

()3.05)

)0.006***

()3.15)

Quality

)0.007***

()10.13)

)0.007***

()10.82)

)0.007***

()10.83)

)0.007***

()10.54)

)0.007***

()10.05)

)0.007***

()10.12)

)0.006***

()9.56)

Lnasset

0.802***

(17.05)

0.784***

(16.52)

0.784***

(16.53)

0.801***

(17.03)

0.802***

(17.06)

0.802***

(17.04)

0.804***

(17.03)



Tab

le7(continued)

Exp

lanatory

variables

Ordered

ProbitModel

(A)

(B)

(C)

(D)

(E)

(F)

(G)

SCR

0.850***

(24.57)

0.844***

(24.29)

0.844***

(24.29)

0.850***

(24.68)

0.849***

(24.49)

0.851***

(24.51)

0.855***

(24.42)

Year03

)0.120

()1.09)

)0.130

()1.16)

)0.130

()1.16)

)0.138

()1.25)

)0.118

()1.08)

)0.120

()1.09)

)0.124

()1.12)

Year04

)0.198*

()1.88)

)0.213**

()2.00)

)0.213**

()2.00)

)0.203*

()1.93)

)0.197*

()1.87)

)0.197*

()1.88)

)0.203*

()1.92)

Year05

)0.058

()0.54)

)0.086

()0.79)

)0.086

()0.79)

)0.069

()0.65)

)0.055

()0.52)

)0.059

()0.55)

)0.060

()0.56)

Year06

0.162

(1.51)

0.143

(1.31)

0.143

(1.31)

0.166

(1.55)

0.164

(1.53)

0.161

(1.50)

0.155

(1.43)

Year07

0.147

(1.43)

0.139

(1.34)

0.139

(1.34)

0.154

(1.49)

0.152

(1.48)

0.146

(1.42)

0.125

(1.20)

Year08

0.121

(1.13)

0.122

(1.12)

0.121

(1.11)

0.129

(1.20)

0.127

(1.18)

0.121

(1.13)

1.121

(1.12)

Loglikelihood

)1498.58

)1456.65

)1456.57

)1493.50

)1497.30

)1498.54

)1480.13

Observations

1584

1537

1537

1584

1584

1584

1561

t-statistics

are

inparenthesisandarebased

onthestan

darderrors

adjusted

forclusteringoneach

country.

*,**and***denote

thesign

ificance

atthe10per

cent,5per

centand1per

centlevel,respectively.Discretionaryaccrual

(EM2)

isdiscretionaryloan

loss

provisions(LLP),whicharetheresidualsfrom

the

estimationofLLP.Thefinan

cial

ratiosem

ployedherearetheaverageofthepast3years

tominim

izethebusinesscycleeff

ect.Theterm

Capitalistheaverage

ratioofrequired

capital

toriskyassets.Profitability

istheaverageratioofnet

incometo

totalassets,Liquidity

stan

dsfortheaverageratioofliquid

assetsto

customer

andshort-term

funding,

Ineffi

ciency

denotestheaverageratioofcost

toincome,

andQuality

istheaverage

ratioofLLPto

net

interest

revenues.

Lnassetisdefined

asthenaturallogarithm

oftotalassets.Wead

dyear

dummies,includingYear03,Year04,Year05,Year06,Year07andYear08.Twosetcon-

ditionalvariablesareincluded

toexam

inewhether

asym

metricinform

ationcanaff

ecttherelationship

betweenEM

andcreditratings.Thefirstsetisthedevel-

opmentlevelofacountry.

HIC

istakingonthevalueof1ifthecountrystem

sfrom

high-incomecountriesan

d0otherwise.MIC

istakingonthevalueof1if

thecountrystem

sfrom

middle-incomecountries.EEUROPE


sfrom

EastEuropean

dCentral

Asiaregion.

EASIA


sfrom

EastAsiaandPacificregion.LATIN


sfrom

Latin

AmericaandtheCaribbeanregion.Thesecondconditional

variable,CREDIT

OR,isan

indicationofthedegreeofacountry’screditors’rights

protection,

rangingfrom

0to

4,withhigher

scoresrepresentinghigher

creditorprotection.



5.4. Effects of sovereign ceiling

While this study considered SCR as one of the explanatory variables for con-trolling the third variable effect (Borensztein et al., 2007), influences from otherunknown routes may persist and bias the hypothesis. To overcome this problem,the sample banks are divided into two groups: the first group comprising bankswhose ratings meet or exceed sovereign ratings (501 observations) and the secondgroup comprising all other banks (2974 observations).17

Table 8 lists the estimated results of EM1 using the second group only (bankratings below sovereign ratings). First, the sign and significance of the coefficientsremain the same as those listed in Table 6, which uses data for the whole sampleexcept the coefficient of EM1 · LATIN. The coefficient of EM1 · LATIN isunexpectedly significantly positive in Table 6 and becomes insignificant inTable 8, indicating that the effect of EM on ratings in Latin regions is insignifi-cant after removing banks with ratings that meet or exceed the sovereign ceiling.This result is consistent with the bank incentive to manage earnings being weak-ened when bank ratings meet or exceed the sovereign ceiling. Next, the net coeffi-cient of EM1 + EM1 · HIC is )0.019 (t-statistic = )0.41) and thus is stillinsignificantly different from zero. Consequently, EM1 exerts neutral and nega-tive effects in countries with better and worse extent and effectiveness of bankingregulations, respectively, even after removing data involving bank ratings thatmeet or exceed the sovereign ceiling.Table 9 resembles Table 8, but uses EM2 as the EM indicator. The size, sign

and significance of the coefficients are little changed, and thus their influences arenot discussed.

5.5. Effects of accounting standards

Recent studies have investigated the influence of different accounting stan-dards on accounting quality.18 In contrast to local accounting rules (domestic,GAAP), which differ across markets and countries, IFRS are a set of uniform

17 The number of observations in the first group accounts for 14.4 per cent of total num-ber, where 448 observations hit the sovereign ceiling and 53 observations exceed the sover-eign ceiling.

18 The aim of International Financial Reporting Standards (IFRS) is to achieve the globalharmonization and convergence of financial reporting rules and regulations. Most studiesexamine firms’ voluntary decisions to provide financial reports that confirm with ‘highquality’ international accounting standards. However, the results are mixed. For example,Leuz and Verrecchia (2000) examined German firms that adopt IAS or U.S. GAAP andfound that those firms adopting IAS exhibit lower bid-ask spreads, higher turnover anddecrease in spreads. Daske (2006) examined voluntary IAS adoption by German firmsand found that IFRS firms even exhibit a higher cost of equity capital than local GAAPfirms. Hung and Subramanyam (2007) and Cuijpers and Buijink (2005) found no differ-ence for those firms that adopt the two accounting systems.



Table 8

Relation between EM1 and ratings considering sovereign ceiling effect

Explanatory

variables


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

EM1 )0.126***()2.56)

)0.632***()6.14)

)0.019()0.35)

)0.081()1.58)

)0.078()1.56)

)0.129***()2.62)

)0.168**()1.98)

EM1 · HIC 0.613***

(5.36)

EM1 · MIC )0.613***()5.36)

EM1 · EEUROPE )0.531***()4.01)

EM1 · EASIA )0.536***()2.97)

EM1 · LATIN 0.294

(0.58)

EM1 · CREDITOR 0.077*

(1.72)

Capital )0.004()0.75)

)0.005()0.98)

)0.005()0.98)

)0.003()0.61)

)0.004()0.87)

)0.003()0.74)

)0.002()0.36)


(15.41)

0.165***

(16.39)

0.165***

(16.39)

0.171***

(17.39)

0.152***

(14.36)

0.158***

(15.31)

0.149***

(14.01)

Liquidity 0.005***

(9.65)

0.005***

(9.43)

0.005***

(9.43)

0.005***

(9.61)

0.005***

(9.88)

0.005***

(9.65)

0.005***

(7.95)

Inefficiency )0.004***()4.25)

)0.004***()4.28)

)0.004***()4.28)

)0.004***()4.31)

)0.004***()4.45)

)0.004***()4.31)

)0.004**()4.29)

Quality )0.005***()7.24)

)0.005***()7.55)

)0.005***()7.55)

)0.005***()7.22)

)0.005***()7.09)

)0.005***()7.17)

)0.005***()7.18)

Lnasset 0.825***

(20.26)

0.809***

(19.51)

0.809***

(19.51)

0.813***

(19.88)

0.840***

(20.48)

0.826***

(20.29)

0.821***

(20.04)

SCR 0.955***

(29.07)

0.894***

(26.62)

0.894***

(26.62)

0.938***

(28.77)

0.938***

(28.15)

0.958***

(29.15)

0.958***

(27.89)

Year03 )0.191**()2.07)

)0.192**()2.05)

)0.192()2.05)

)0.194**()2.10)

)0.187**()2.01)

)0.190**()2.05)

)0.192**()2.05)

Year04 )0.225**()2.54)

)0.226**()2.52)

)0.226**()2.52)

)0.220**()2.49)

)0.218**()2.45)

)0.222**()2.50)

)0.228**()2.54)

Year05 )0.178**()1.90)

)0.169*()1.77)

)0.169*()1.77)

)0.163*()1.72)

)0.174*()1.85)

)0.177*()1.89)

)0.170*()1.79)

Year06 0.014

(0.15)

0.017

(0.17)

0.017

(0.17)

0.026

(0.28)

0.027

(0.29)

0.017

(0.18)

0.016

(0.17)

Year07 0.071

(0.78)

0.081

(0.87)

0.081

(0.87)

0.084

(0.92)

0.076

(0.83)

0.073

(0.80)

0.059

(0.64)

Year08 0.092

(1.01)

0.106

(1.14)

0.106

(1.14)

0.110

(1.21)

0.091

(1.00)

0.092

(1.01)

0.094

(1.02)




rules that apply to all public companies in the markets where they are enforced.19

Barth et al. (2008) found that firms from 21 countries applying InternationalAccounting Standards (IAS) generally exhibit less EM, more timely loss recogni-tion and more value relevance of accounting amounts than do firms applyingdomestic standards. IAS 30.43 requires banks to provide much more detailedinformation regarding loan losses. IFRS 7 requires similar disclosure. Theso-called detailed information includes the manner in which the provisions andlosses on uncollectible loans are determined, mutations in the course of provisionduring the period covered by the financial statement (additions, write-offs ofuncollectible loans and collections on write-offs) and the aggregate provision atthe balance date (Moison, 2004). That is, the information regarding requiredloan losses that must be released is much more detailed under IFRS, and thusthe accounting principle differs from that used previously.Thus, this study creates a dummy variable, IFRS, to control for the possible

effect of different accounting standards on credit ratings. The dummy variableequals unity if the banks adopt the IFRS, and zero otherwise. The accountingstandards come from BankScope, which has provided accounting informationsince 2005. Once this dummy variable is included as the additional control var-iable, the sample period begins from 2005, reducing the number ofobservations.

Table 8 (continued)


country. *, ** and *** denote the significance at the 10 per cent, 5 per cent and 1 per cent level,

respectively. The total number is 2,974, which accounts for 85.6 per cent of total observations. We

exclude those banks’ rating meeting or exceeding sovereign credit ratings (SCR). Earnings smoothing

(EM1) is the correlation between changes in loan loss provisions (LLP) and earnings before LLP. The

term Capital is the average ratio of required capital to risky assets. Profitability is the average ratio of

net income to total assets, Liquidity is the average ratio of liquid assets to customer and short-term

funding, Inefficiency denotes the average ratio of cost to income, and Quality is the average ratio of

LLP to net interest revenues. Lnasset is defined as the natural logarithm of total assets. We add year

dummies, including Year03, Year04, Year05, Year06, Year07 and Year08. Two set conditional vari-

ables are included to examine whether asymmetric information affects the relationship between EM

and ratings. The first set is the development level of a country. HIC is taking on the value of 1 if the

country is from high-income countries and 0 otherwise. MIC is taking on the value of 1 if the country

is from middle-income countries. EEUROPE is taking on the value of 1 if the country is from East

Europe and Central Asia region. EASIA is taking on the value of 1 if the country is from East Asia

and Pacific region. LATIN is taking on the value of 1 if the country is from Latin America and the

Caribbean region. The second conditional variable, CREDITOR, is an indication of the degree of a

country’s creditors’ rights protection, ranging from 0 to 4, with higher scores representing higher cred-

itor protection.

19 Banks also need to follow the rules of IFRS. Particularly, IFRS has introduced somenew standards that are particularly important to banks: for example, IAS 30, IAS 32 andIAS 39 (and currently IFRS 7).



Table 9

Relation between EM2 and ratings considering sovereign ceiling effect

Explanatory

variables


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

EM2 )15.670**()2.52)

)24.466***()2.75)

0.772

(0.08)

1.528

(0.17)

)18.453***()2.68)

)15.680**()2.51)

)29.007()1.44)

EM2 · HIC 25.238*

(1.90)

EM2 · MIC )25.238*()1.90)

EM2 · EEUROPE )30.051**()2.36)

EM2 · EASIA 23.207

(1.10)

EM2 · LATIN 2.034

(0.03)

EM2 · CREDITOR 6.521

(0.63)

Capital 0.001

(0.18)

0.0003

(0.04)

0.0003

(0.04)

0.001

(0.12)

0.001

(0.15)

0.001

(0.18)

0.001

(0.21)


(6.67)

0.259***

(6.31)

0.259***

(6.31)

0.268***

(6.58)

0.273***

(6.70)

0.270***

(6.67)

0.265***

(6.33)

Liquidity 0.005***

(8.66)

0.005***

(8.41)

0.005***

(8.41)

0.005***

(8.73)

0.005***

(8.71)

0.005***

(8.66)

0.005***

(7.90)

Inefficiency )0.003**()1.99)

)0.004**()2.00)

)0.004**()2.00)

)0.004**()2.05)

)0.004**()1.99)

)0.003**()1.99)

)0.004**()2.15)

Quality )0.007***()8.52)

)0.008***()9.32)

)0.008***()9.32)

)0.007***()8.88)

)0.007***()8.44)

)0.007***()8.52)

)0.007***()7.89)

Lnasset 0.855***

(16.20)

0.837***

(15.61)

0.837***

(15.61)

0.864***

(16.14)

0.856***

(16.19)

0.855***

(16.20)

0.859***

(16.10)

SCR 0.892***

(21.62)

0.881***

(21.24)

0.881***

(21.24)

0.889***

(21.61)

0.890***

(21.41)

0.892***

(21.59)

0.902***

(21.35)

Year03 )0.126()1.12)

)0.137()1.19)

)0.137()1.19)

)0.142()1.26)

)0.125()1.11)

)0.126()1.12)

)0.129()1.14)

Year04 )0.214**()1.98)

)0.234**()2.14)

)0.234**()2.14)

)0.217**()2.01)

)0.213**()1.97)

)0.214**()1.98)

)0.218**()2.01)

Year05 )0.58()0.53)

)0.090()0.81)

)0.090()0.81)

)0.072()0.66)

)0.055()0.50)

)0.057()0.53)

)0.059()0.54)

Year06 0.132

(1.18)

0.106

(0.93)

0.106

(0.93)

0.133

(1.19)

0.134

(1.19)

0.132

(1.18)

0.130

(1.15)

Year07 0.053

(0.48)

0.034

(0.30)

0.034

(0.30)

0.056

(0.50)

0.060

(0.54)

0.053

(0.48)

0.025

(0.23)

Year08 0.086

(0.78)

0.080

(0.70)

0.080

(0.70)

0.102

(0.91)

0.093

(0.83)

0.086

(0.77)

0.086

(0.76)




Panels A and B of Table 10 list the estimated results of using EM1 and EM2,respectively, as the EM proxies, by including IFRS as the control variable. Theconcerned coefficients of EM1 and EM2 remain significantly negative. Addition-ally, the coefficients of the interaction terms resemble the results without consid-ering this additional control variable, except that the coefficient ofEM1 · CREDITOR becomes insignificant. Thus, the hypothesis presented hereis robust to different accounting standards. The coefficients of IFRS are all insig-nificant, suggesting that different accounting standards do not affect credit rat-ings. This result is consistent with Cuijpers and Buijink (2005), who used impliedcost of capital estimates and found no significant differences across local GAAPand IFRS firms in the European Union.

5.6. Considering absolute value of DLLP

This study also uses the absolute value of DLLP rather than DLLP in EM2 tocalculate earnings aggressiveness [Eqn (3)]. Previous studies indicated that bankmanagers may use DLLP to smooth earnings by increasing DLLP when earn-ings are too high or decreasing DLLP when they are too low. For example, Cor-nett et al. (2009) examined signed and absolute discretionary accruals and foundthat the impacts on the signed and absolute values of DLLP were effectively thesame, suggesting that firm EM is largely one-sided. Furthermore, Kanagaretnamet al. (2007) also used signed and unsigned DLLP to investigate the relationshipbetween audit fees and DLLP and found that absolute DLLP can properlymeasure firm EM.

Table 9 (continued)



respectively. The total number is 2,974, which accounts for 85.6 per cent of total observations. We

exclude those banks’ ratings meeting or exceeding sovereign credit ratings (SCR). Discretionary

accrual (EM2) is discretionary loan loss provisions (LLP), which are the residuals from the estima-

tion of LLP. The financial ratios are the average of the past 3 years. The term Capital is the average

ratio of required capital to risky assets. Profitability is the average ratio of net income to total assets,

Liquidity is the average ratio of liquid assets to customer and short-term funding, Inefficiency denotes

the average ratio of cost to income, and Quality is the average of LLP to net interest revenues. Lnas-

set is defined as the natural logarithm of total assets. We add year dummies, including Year03,

Year04, Year05, Year06, Year07 and Year08. Two set conditional variables are included to examine

whether asymmetric information affects the relationship between EM and ratings. The first set is the

development level of a country. HIC is taking on the value of 1 if the country is from high-income

countries and 0 otherwise. MIC is taking on the value of 1 if the country is from middle-income

countries. EEUROPE is taking on the value of 1 if the country is from East Europe and Central Asia

region. EASIA is taking on the value of 1 if the country is from East Asia and Pacific region. LATIN

is taking on the value of 1 if the country is from Latin America and the Caribbean region. The sec-

ond conditional variable, CREDITOR, is an indication of the degree of a country’s creditors’ rights

protection, ranging from 0 to 4, with higher scores representing higher creditor protection.



Table 10

Relation between EM and ratings considering accounting standards

Panel A Relation between EM1 and ratings considering accounting standards

Explanatory

variables


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

EM1 )0.207**()2.02)

)0.215*()1.85)

0.073

(1.12)

0.045

(0.72)

0.032

(0.54)

)0.018()0.31)

0.019

(0.19)

EM1 · HIC 0.299**

(2.34)

EM1 · MIC )0.291**()2.28)

EM1 · EEUROPE )0.438***()2.98)

EM1 · EASIA )0.322()1.56)

EM1 · LATIN 0.496

(1.53)

EM1 · CREDITOR )0.005()0.11)

IFRS )0.013()0.18)

)0.021()0.27)

)0.009()0.11)

0.049

(0.61)

)0.029()0.38)

0.000

(0.00)

)0.049()0.63)


Panel B Relation between EM2 and ratings considering accounting standards

Explanatory

variables


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

EM2 )16.185**()2.56)

)23.934***()2.69)

)0.409()0.05

)3.617()0.44)

)17.603***()2.59)

)15.940***()2.50)

)11.841()0.58)

EM2 · HIC 23.276*

(1.84)

EM2 · MIC )23.777*()1.88)

EM2 · EEUROPE )23.656*()1.90)

EM2 · EASIA 17.100

(0.67)

EM2 · LATIN )7.968()0.19)


(0.24)

IFRS )0.044()0.41)

)0.043()0.39)

)0.042()0.39)

)0.039()0.36)

)0.043()0.39)

)0.042()0.39)

)0.067()0.60)




Panel A of Table 11 lists the results of using |EM2| as a proxy of EM. Theresults are similar when using EM2 as a proxy of EM. The coefficient of |EM2|is significantly negative, suggesting that banks with larger absolute DLLP tendto receive lower ratings. The interaction term of HIC · |EM2| is significantlypositive and those of MIC · |EM2| and EEUROPE · |EM2| are significantlynegative. These results are consistent with using EM2 as the proxy of EM.This study also uses DDLLP rather than DLLP to calculate earnings smooth-

ing [Eq. (1)], labelled EM3. DLLP is the LLP that removes write-off and otherfactors in the LLP equation. While LLP is correlated with subsequent write-off,indicating it is not used for EM, DLLP does not suffer this issue. Thus, thisstudy also uses DDLLP in the EM1 formula (earnings smoothing).Panel B of Table 11 lists the results of using DDLLP rather than DLLP to cal-

culate earnings smoothing. Because none of the coefficients related to EM3 aresignificant, regardless of their interaction with other conditional variables, thisstudy posits that rating agencies do not see EM3 as earnings smoothing andhence it does not negatively affect ratings.

6. Conclusions

This study investigates the effect of EM on credit ratings and thus on the costof debt using 3473 banks from 85 countries. This study considers two EMs, thatis, earnings smoothing (EM1) and earnings aggressiveness (EM2). Moreover,this study hypothesizes that information asymmetry is lower in countries with

Table 10 (continued)



respectively. We create a dummy variable, International Financial Reporting Standards (IFRS), to

control for the effect of different accounting standard. The dummy variable is equal to unity if the

banks adopt the IFRS and zero otherwise. In Panel A, EM1 is the correlation between changes in

loan loss provisions (LLP) and earnings before LLP. In Panel B, EM2 is discretionary LLP, which

are the residuals from the estimation of LLP. The financial ratios are the average of the past 3 years.

The term Capital is the average ratio of required capital to risky assets. Profitability is the average

ratio of net income to total assets, Liquidity is the average ratio of liquid assets to customer and

short-term funding, Inefficiency denotes the average ratio of cost to income, and Quality is the aver-

age ratio of LLP to net interest revenues. Lnasset is defined as the natural logarithm of total assets.

We add year dummies, including Year06, Year07 and Year08. Two set conditional variables are

included to examine whether asymmetric information affects the relationship between EM and rat-

ings. The first set is the development level of a country. HIC is taking on the value of 1 if the country

is from high-income countries and 0 otherwise. MIC is taking on the value of 1 if the country is from

middle-income countries. EEUROPE is taking on the value of 1 if the country is from East Europe

and Central Asia region. EASIA is taking on the value of 1 if the country is from East Asia and Paci-

fic region. LATIN is taking on the value of 1 if the country is from Latin America and the Caribbean

region. The second conditional variable, CREDITOR, is an indication of the degree of a country’s

creditors’ rights protection, ranging from 0 to 4, with higher scores representing higher creditor pro-

tection. We skip the reports of coefficients of control variables, including financial ratios, sovereign

ratings and year dummies, for brevity.



Table 11

Other EM proxies and ratings

Panel A Using the absolute value of DLLP as EM proxy

Explanatory

variables


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

|EM2| )21.473***()3.30)

)35.976***()4.83)

14.916

(1.36)

0.986

(0.10)

)19.214***()3.01)

)23.080***()3.51)

)28.569**()1.99)

|EM2|·HIC 51.641***

(4.00)

|EM2|·MIC )50.510***()3.89)

|EM2|·EEUROPE )37.779***()3.29)

|EM2|·EASIA )32.919()1.46)

|EM2|·LATIN 59.002

(1.38)

|EM2|·CREDITOR 3.649

(0.49)


Panel B Using correlation between the changes in DLLP and EBP as EM proxy

Explanatory

variables


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

EM3 0.033

(0.58)

)0.190()1.20)

0.056

(0.91)

0.035

(0.60)

0.052

(0.89)

0.028

(0.47)

0.035

(0.30)

EM3 · HIC 0.249

(1.47)

EM3 · MIC )0.247()1.41)

EM3 · EEUROPE )0.087()0.22)

EM3 · EASIA )0.342()1.37)

EM3 · LATIN 0.139

(0.47)


(0.01)




more extensive and effective banking regulations but higher in those with worseextent and effectiveness of banking regulations. In a country that lacks robustbanking regulations, raters know that severe information asymmetries exist, andthat managers may use their judgment to create opportunities for EM. Ratersthus tend to downgrade ratings when they identify instances of EM, increasingthe cost of debt. Alternatively, in countries with more robust banking regulation,raters view management use of their knowledge of the business and associatedopportunities to select reporting methods, estimates and disclosures that matchthe economics of the relevant businesses, potentially increasing the usefulness ofaccounting in communicating information. Thus, raters believe reported earningsare trustworthy, reducing the negative impact of EM on credit ratings. Thisstudy defines countries with robust banking regulation as those with high income(HIC) and strong creditor protection (CREDITOR).The study results demonstrate that first, the negative influence of EM on rat-

ings exists and is robust after controlling for other potential determinants ofbank credit ratings. The negative effect holds for both earnings smoothing andearnings aggressiveness. In this sense, EM has an associated cost because ceterisparibus lower credit ratings imply higher borrowing costs.Second, the negative effect of EM is mitigated in counties with more extensive

and effective banking regulations but aggravated in countries with less robustbanking regulations. Furthermore, the neutral effect exists in countries with exten-sive and effective banking regulations. Thus, raters trust reported earnings incountries with more robust banking regulations and are little concerned with EM,causing EM to have little effect on ratings and the associated cost of borrowing.

Table 11 (continued)



respectively. In Panel A, |EM2| is the absolute value of discretionary LLP (DLLP), which are the

residuals from the estimation of loan loss provisions (LLP). In Panel B, EM3 is the correlation

between the changes in DLLP and earnings before LLP. The financial ratios employed here are the

average of the past 3 years to minimize the business cycle effect. The term Capital is the average ratio

of required capital to risky assets. Profitability is the average ratio of net income to total assets,

Liquidity stands for the average ratio of liquid assets to customer and short-term funding, Ineffi-

ciency denotes the average ratio of cost to income, and Quality is the average ratio of LLP to net

interest revenues. Lnasset is defined as the natural logarithm of total assets. We add year dummies,

including Year03, Year04, Year05, Year06, Year07 and Year08. Two set conditional variables are

included to examine whether asymmetric information can affect the relationship between EM and

credit ratings. The first set is the development level of a country. HIC is taking on the value of 1 if

the country stems from high-income countries and 0 otherwise. MIC is taking on the value of 1 if

the country stems from middle-income countries. EEUROPE is taking on the value of 1 if the coun-

try stems from East Europe and Central Asia region. EASIA is taking on the value of 1 if the coun-

try stems from East Asia and Pacific region. LATIN is taking on the value of 1 if the country stems

from Latin America and the Caribbean region. The second conditional variable, CREDITOR, is an

indication of the degree of a country’s creditors’ rights protection, ranging from 0 to 4, with higher

scores representing higher creditor protection. We skip the reports of coefficients of control vari-

ables, including financial ratios, sovereign ratings and year dummies, for brevity.



Robust testing reveals that the effect of EM is usually supported even afterremoving sample data that meet or exceed the sovereign ceiling. This result isalso robust after considering different accounting standards.

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