DP2012-13

Accru al-Based and Real Earnings
Management: A n Interna tional
Comparison for Investor Protection
Masahiro ENOMOTO
Fumihiko KIMURA
Tomoyasu YAMAGUCHI
Revised May 11, 2015

Accrual-based and Real Earnings Management:
An International Comparison for Investor Protection

Masahiro Enomoto *Research Institute for Economics and Business Administration
Kobe University
2-1 Rokkodaicho, Nada-ku, Kobe 657-8501, JAPANE-mail: menomoto@rieb.kobe-u.ac.jp
Phone: +81-78-803-7031; fax: +81-78-803-7031

Fumihiko Kimura
Graduate School of Economics and Management
Tohoku University
2-2-1 Katahira, Aoba-ku, Sendai 980-8577, JAPANE-mail: fkimura@econ.tohoku.ac.jp
Phone: +81-22-217-6282; fax: +81-22-217-6282

Tomoyasu Yamaguchi
Faculty of Business Administration
Tohoku Gakuin University
1-3-1 Tsuchitoi, Aoba-ku, Sendai 980-8511, JAPANE-mail: yamaguchi@mail.tohoku-gakuin.ac.jp
Phone: +81-22-721-3471; fax: +81-22-721-3471

May, 2015*

Corresponding author.

Accrual-based and Real Earnings Management:
An International Comparison for Investor Protection

Abstract
This paper examines the differences in accrual-based and real earnings management across countries
from the perspective of investor protection. Following prior research (Leuz et al., 2003), we hypothesizethat accrual-based earnings management is more constrained by strict discipline in countries withstronger investor protection. For real earnings management in countries with stronger investor
protection, we have two hypotheses. One is that real earnings management is more often implemented
to substitute for accrual-based earnings management. The other is that real earnings management is lessoften implemented, as with accrual-based earnings management. Our examination uses data from
222,513 firm-year observations drawn from 38 countries covering 1991 to 2010. The results show thatmanagers in countries with stronger investor protection tend to engage in real earnings management
instead of accrual-based earnings management. We also find that real earnings management isconstrained by analyst following. Our results are not affected by the control of audit quality or the
calculation method used for earnings management measures according to country and year.
Highlights
・Earnings management is affected by investor protection in each country.
・Accrual-based earnings management decreases under stronger investor protection.
・Real earnings management increases under stronger investor protection.
・Real earnings management decreases with analyst following.

JEL Classification: G34 K22 M41 P4 G38
Keywords: earnings management, investor protection, corporation law, securities law, regulation

1. Introduction
This paper examines the differences in accrual-based and real earnings management across 38
countries. Healy and Wahlen (1999, 368) state that earnings management occurs when managers use
judgment in financial reporting and in structuring transactions to alter financial reports to either mislead
some stakeholders about the underlying economic performance of the company or to influencecontractual outcomes that depend on reported accounting numbers. Earnings management occurs
through a change in the accrual process or a deviation from normal business activity, or both
simultaneously; the former is called “accrual-based earnings management” (AEM) and the latter “realearnings management” (REM).
Earnings management is affected not only by the factors relating to each manager and firm but
also by institutional factors such as laws, market mechanisms, and regulations (Wysocki, 2004).
Institutional factors vary across countries. International comparative studies on earnings management
have been very important and fruitful (Wysocki, 2011; Gordon et al., 2013). International comparative
studies on earnings management have concentrated on AEM. Leuz et al. (2003) consider investor
protection as an institutional factor influencing managerial behavior and investigate whether investor
protection in various countries was related to AEM. 1 They examine the relationship between outside
investor protection and earnings management in 31 countries from 1990 to 1999, finding that AEM
decreases in countries with stronger investor protection.
Boonlert-U-Thai et al. (2006) also investigate the relationship between investor protection andearnings management using data from 31 countries covering 1996 to 2002, suggesting that earnings
were smoothed in countries where investor protection was advanced. Shen and Chih (2005) show that1

Investor protection is the power to prevent managers from expropriating minority shareholders and creditors within theconstraints imposed by law (La Porta et al., 2002; Leuz et al., 2003).

1

earnings management declines in countries with stronger investor protection and more transparent
accounting disclosure for banks in 48 countries from 1993 to 1999.
Most of the research uses accrual-based measures as key proxies of earnings management.
Besides accrual processes, managers can also change real activities to manage earnings. Recent studies
have examined REM as an important topic in research on earnings management (e.g., Roychowdhury,2006). While REM could have a negative impact on firms’ future value (e.g., Bhojraj et al., 2009; Cohen
and Zarowin, 2010), neither regulators nor auditors can restrain firms from engaging in REM.2 Scrutiny
by regulators and auditors is weaker for REM than for AEM; thus, the costs of REM are lower (Francis
et al., 2011).
Managers might choose cost-effective methods based on the features of AEM and REM; studies
have analyzed the substitutability of the two types of earnings management. For example, Cohen andZarowin (2010) and Zang (2012) present evidence on the tradeoffs between AEM and REM in U.S.
firms. Zang (2012) finds tradeoffs between AEM and REM by focusing on their constraints. The resultsof their studies as well as those in Leuz et al. (2003) raise the questions of whether investor protection
has an effect on REM in the international setting and whether substitution occurs between AEM and
REM. In line with the focus on tradeoffs in REM research, we hypothesize that the level of investor
protection in each country lead to such tradeoffs. Investor protection has developed globally, but
national development levels vary; for example, the Sarbanes–Oxley Act is specific to the United States.
Consequently, we can analyze the substitutability of the two types of earnings management at theinternational level. We predict that REM, rather than AEM, will be implemented in countries withstronger investor protection.
Managers might implement REM in order to bring earnings to a target level and conceal “true”
firm performance and behavior, similar to the use of AEM. Additionally, REM could have a negative
impact on a firm’s future value and be undesirable for investors. For example, Cohen and Zarowin

2

Guuny (2010) suggests that REM makes a positive impact on the future value of firms.

2

(2010) find that firms issuing seasoned equity offerings (SEO) engage in REM and that the decline inpost-SEO performance due to REM is more severe than that caused by AEM. Kim and Sohn (2013)
suggest that REM is positively associated with the implied cost of equity even after controlling for theeffects of AEM. Thus, managers in countries with stronger investor protection might avoid REM as
well as AEM. In this scenario, REM is less prevalent in countries with stronger investor protection.
Following Leuz et al. (2003), we measure AEM using three proxies: (1) the ratio of the standard
deviation of operating income to that of operating cash flow, calculated with time-series data from each
firm; (2) the correlation between changes in accruals and changes in operating cash flow computed fromthe pooled data in each country; and (3) the ratio of the absolute value of accruals to that of operating
cash flow calculated in each firm-year. We measure REM employing two proxies: (1) the correlation
between changes in sales and production costs and (2) the correlation between changes in sales anddiscretionary expenses. 3
As investor protection variables, we use the strength of legal enforcement and the extent ofoutside investor rights under corporate and security law (La Porta et al., 1998, 2006). Furthermore, we
treat disclosure regulations and the analyst’s role as important investor protection factors, including thedisclosure index and analyst following.
Our examination employs 222,513 firm-year observations from 38 countries covering 1991 to2010. The results show that managers in countries with stronger outside investor rights tend to engage
in REM instead of AEM and that REM is constrained by analyst following. This result is not affectedby the control of audit quality or the calculation of earnings management measures according to country
and year.
This paper offers two main contributions. First, most international comparative studies on
earnings management (e.g., Leuz et al., 2003; Shen and Chih, 2005; Boonlert-U-Thai et al., 2006) havelooked only at AEM. We also investigate REM based on the theoretical relationship between REM and3

The correlation between the changes in sales and cash flows from operations, employed in Roychowdury (2006), is notincluded for the reason given in footnote 6.

3

AEM, finding a substitution effect between them regarding outside investor rights at the country level.
However, the results also indicate that analyst following restricts REM. Taken together, the findings
suggest that managers tend to apply REM where the legal environment constrains AEM and that thedevelopment of security markets increases the monitoring of managerial discretionary behaviors.
Second, our results are much more reliable than those of previous studies because of our larger sample
size and longer-term investigation.
The rest of this paper is organized as follows. Section 2 discusses AEM and REM and provides
some institutional background. Section 3 shows our sample selection procedures and the measures of
earnings management and investor protection. Section 4 presents the results on the relationship between
earnings management and investor protection. Section 5 discusses some additional analyses, andSection 6 concludes the paper.

2. Accrual-based and real earnings management and the institutional backgroundManagers can opportunistically manage earnings by changing the accrual process because
managers’ various estimations and judgments are included in the process when they prepare financial
statements. Thus, managers can implement AEM after the end of the fiscal year. However, AEM is
more visible than REM due to the scrutiny of auditors and regulators, among others (Francis et al.,2011) and may lead to reversal in future periods.
In addition to the accrual process, managers can also manage earnings by changing the timing
or structure of operating, investing, or financial decisions, known as REM. This can be more costly interms of future cash flow, as it often cuts funding for research and development (R&D) and marketing,increases price discounts, and reduces capital investments (Graham et al., 2005; Chen et al., 2010).
However, stakeholders have more difficulty detecting REM than AEM, as REM is easier to camouflage
as normal activity (Kothari et al., 2012). Thus, managers will choose AEM and/or REM depending on
the economic context. We focus on both AEM and REM.

4

Regulatory authorities impose various restrictions on managers carrying out AEM because
earnings management can reduce outside investors’ profits. The degree of restriction is believed to varywith the institutional factors of each country. Many studies attach importance to investor protection andinclude corporate law, accounting standards, and security markets as institutional factors. La Porta et al.
(1997, 1998) show that each country’s investor protection is based on its legal origin and that countries
with stronger investor protection have larger and more open capital markets. Leuz et al. (2003) suggestthat AEM decreases in countries with stronger investor protection. In a similar vein, disclosure systems
and analyst following are components of investor protection, thereby reducing AEM (Yu, 2008;
Degeorge et al., 2013). This leads to our first hypothesis:
H1. Ceteris paribus, accrual-based earnings management is more constrained in countries withstronger investor protection.
Though AEM is constrained, the motives for earnings management are not necessarily
controlled. Moreover, investors in countries with developed capital markets should be cautious about
earnings information. Therefore, managers in countries with stronger investor protection will tend tochoose REM over AEM. The research supports this prediction. In a survey of chief financial officers,
Graham et al. (2005) show that managers opt for REM because they fear the risks created byoverzealous regulators. Analyzing mathematical models, Ewert and Wagenhofer (2005) also suggestthat tighter accounting standards lead managers to prefer REM. Cohen et al. (2008) imply that U.S.
managers switched from AEM to REM after the passage of the Sarbanes–Oxley Act, which was seen
as strengthening investor protection. Thus, managers under stricter regulation tend to avoid AEM and
implement REM to achieve income targets. In other words, the tightening of regulations leads torestrained AEM but tends to induce REM.
However, we should also consider another scenario. Investors might consider both AEM andREM to be undesirable, since REM can distort earnings and negatively affect a firm’s future
performance (Cohen and Zarowin, 2010). Similarly, Kim and Sohn (2013) find that the cost of capital5

relates positively to REM. Leuz et al. (2003, 506), focus on AEM, arguing that “strong and wellenforced outsider rights limit insiders’ acquisition of private control benefits, and consequently, mitigate
insiders’ incentives to manage accounting earnings because they have little to conceal from outsiders.”
Their discussion may be considered to be unassociated with earnings management methods (i.e., either
AEM or REM). Thus, managers in countries with stronger investor protection facing the threat of
discipline might be restrained from engaging in both REM and AEM. Based on these competingarguments, we propose the following hypothesis (stated in null form):
H2. There is no association between real earnings management and the strength of investor
protection in a country.

3. Research design
This study tests hypotheses on the relationship between both types of earnings management
and investor protection. The subsections below present a detailed explanation of the two earnings
management and four investor protection variables.

3.1. The variables of accrual-based and real earnings management
Earnings management is classified into AEM and REM. This paper explores the relationshipbetween the two types of earnings management and investor protection. We calculate three measures
for AEM and two measures for REM, then aggregate the results.
Though our country-level earnings management measures may be parsimonious due to thelimited amount of balance sheet and income statement information available for each firm fromStandard & Poor’s Capital IQ database, we can compare between our results and those in Leuz et al.
(2003) by using these measures. We compute and aggregate the measures for each type in order toevaluate each country’s level of earnings management, avoiding the concerns raised by the calculation
methods as much as possible. The measures are ranked from the lowest earnings management level to6the highest (e.g., the country with the least amount of earnings management is ranked first). The
aggregate measure of AEM (REM) is the average rank of the three (two) measures. For aggregation, all
the measures should have somewhat similar tendencies.
3.1.1. The variables of accrual-based earnings management
Three measures of AEM (of the four used by Leuz et al. [2003]) are employed in this study toindicate the extent of earnings management via accruals. Our explanation of the three measures was
drawn from Leuz et al. (2003). As we concentrate on the comparison between AEM and REM, the“small loss avoidance” in Leuz et al. (2003) is not adopted. 4
Following Dechow et al. (1995), accruals are calculated as follows: 5
Accruals (Acc) = (ΔCAit – ΔCashit) – (ΔCLit – ΔSTDit – ΔTPit) – Depit(1)

where ΔCAit is change in total current assets, ΔCashit is change in cash and cash equivalents, ΔCLit ischange in total current liabilities, ΔSTDit is change in short-term debt included in current liabilities,
ΔTPit is change in income taxes payable, and Depit is depreciation and amortization expense. The
subscripts “i” and “t” refer to firm i and year t. We can obtain operating cash flow (CFO) by subtracting
accruals from earnings (CFO = Earnings - Accruals). We do not directly compute accruals from the
cash flow statement because the data are not available in the cash flows of all the countries in the sample.
For reliability, our statistical analysis eliminates outliers, trimming the firm-years within the top andbottom 1% of the accrual and operating cash flow data. All of the variables used to compute themeasures of AEM and REM are divided by total assets at the beginning of the year.
The first measures of AEM are the standard deviation of operating earnings divided by thestandard deviation of operating cash flow (AEM1). This measure assumes that a manager might use
accruals to mitigate the variation of operating cash flow. If this occurs, this measure becomes smaller.

4

In this study, we focus on the managers’ choice of AEM and/or REM. However, “small loss avoidance” in Leuz et al. (2003)
does not mean the use of AEM only. Firms might avoid losses by using REM (e.g., Roychowdhury, 2006).
5 We use the balance sheet approach to calculate accruals. This approach provides more data availability than the cash flow
statement approach because our sample period starts in 1991.

7

AEM1 is calculated using the time-series data of each firm, and the median of AEM1 in each country is
its representative value: 6
AEM1 =σ(E)
σ(CFO)(2)

where σ(E) is the standard deviation of the operating income and σ(CFO) is the standard deviation of
the operating cash flows.
The second measure is the correlation between the changes in accruals and changes in operating
cash flow (AEM2). As is well known, the correlation is negative. However, we employ it as an AEM
measure because the large magnitudes of the correlation can be interpreted as income-smoothing
behavior. AEM2 is computed from the pooled data in each country:
AEM2 = ρ(ΔAcc, ΔCFO)(3)

where ΔAcc is change in accruals, and ΔCFO is change in operating cash flows.
The third measure is the ratio of the absolute value of accruals to the absolute value of operating
cash flows (AEM3). Accruals are reflected in various kinds of manager behaviors intended to achievecertain earnings targets. Therefore, we use the absolute value of accruals as an earnings discretionmeasure. We divide by the operating cash flow to control for firm size and performance. AEM3 iscalculated in each firm-year, and we use each country’s median data:
AEM3 =|Acc||CFO |(4)

where |Acc| is the absolute value of accruals and |CFO| is the absolute value of operating cash flows.
AEM1 and AEM2 are employed as smoothing measures, and AEM3 is the measure of discretionused by Leuz et al. (2003). We regard these three measures as the degree of earnings management
through accruals.

The standard deviations of CFO and Operating Income (σ(CFO) and σ(E)) are computed using a minimum of three years ofdata per firm and a maximum of 20 years, which are maximally available in our dataset.

68

3.1.2. The variables of real earnings management
Earnings numbers can be managed not only through AEM but also through REM. Following
Roychowdhury (2006), Cohen et al. (2008), and Cohen and Zarowin (2010), this paper investigates
three types of REM: (1) sales manipulation through price discounts and lenient credit terms, (2) the
reduction of discretionary expenditures such as R&D costs, and (3) overproduction to report lower costof goods sold.
According to Roychowdhury (2006), sales manipulation through price discounts and lenientcredit terms and overproduction lead to abnormally high “production costs” relative to sales. 7 Thus,
sales manipulation and overproduction induce an imbalance in production costs and sales, resulting ina lower correlation between the change in production costs and the change in sales. We therefore use
the contemporaneous correlation between the change in production costs and the change in sales(indicated by REM1) as measures of sales manipulation and overproduction. REM1 is computed fromthe pooled sample in each country; a lower REM1 means that more sales manipulation and/or
overproduction has been carried out: 8
REM1 = ρ(ΔProd, ΔSales)7(5)

We use “production costs” not generally but in the specific context of Roychowdhury (2006), who defines them as the sumof the costs of goods sold and change in inventory. Roychowdhury (2006) also points out that sales manipulation andoverproduction lead to an abnormally low cash flow from operation, while the reduction of discretionary expenses leads to anabnormally high cash flow from operation. Therefore, the net effect on cash flow from operation by REM is ambiguous, as
stated in Roychowdhury (2006). Consequently, consistent with Gunny (2010) and Zang (2012), we do not use the proxy withcash flow from operation in the main analysis. The results including the proxy with the cash flow measure are presented infootnote 23.
8 When a firm engages simultaneously in overproduction and sales manipulation through price discounts, the increase of both
ΔSales and ΔProd leads to a large value of REM1. However, owing to price discounts, the increase in sales is lower than theincrease in production cost. As a result, when a firm engages simultaneously in overproduction and sales manipulation, its
REM1 becomes lower than that in the case of normal sales and production activity. When a firm engages simultaneously inboth manipulations, its REM1 could be higher than that of a firm that engages in either sales manipulation or overproduction.
It may thus be difficult, using our correlation-based index, to detect the simultaneous intentional overproduction and sales
manipulation through discounts. However, a simultaneous increase of ΔSales and ΔProd might result when a firm decreases
unit production cost through mass production and carries out mass sales with lower prices. It might also be interpreted toindicate more normal business activities than when either ΔSales or ΔProd changes abnormally. In our robustness check, we
use the Roychowdhury (2006) model, which allows us to analyze the effect of the simultaneous practice of intentional
overproduction and sales manipulation through discounts.

9where ΔProd is change in production costs, production costs is cost of goods sold plus change ininventory, and ΔSales is change in sales.
The reduction of discretionary expenditures to manage earnings should lead to abnormally low
discretionary expenses (Roychowdhury, 2006). Therefore, such behavior would result in a departurefrom normal levels of discretionary expenses. Based on previous studies’ expression of discretionary
expenses as a linear function of sales (Dechow et al., 1998; Roychowdhury, 2006), we employ thecontemporaneous correlation between the change in discretionary expenses and the change in sales(REM2) as a proxy for a reduction of discretionary expenditures. If discretionary expenditures are
reduced to manage earnings, REM2 should show a lower value. 9 We define discretionary expenses as
selling, general, and administrative expenses (SG&A), which frequently include discretionary expenses
such as R&D, advertising, and employee costs. 10 REM2 is also computed from the pooled sample ineach country:
REM2 = ρ(ΔDE, ΔSales)(6)

where ΔDE is change in selling, general, and administrative expenses.

3.2 The variables of investor protection
3.2.1. Outside investor rights
Following Leuz et al. (2003), we adopt the anti-director right index created by La Porta et al.
(1998) as variables pertaining to outside (minority) investor protection. The index indicates the extent
to which outside investors’ opinions can be heard during shareholders’ meetings; it is calculated byadding 1 when (1) the country allows shareholders to mail their proxy vote to the firm, (2) shareholders

9

REM through sales, discretionary expenditures, and production activities may also be engaged to decrease earnings numbers(Francis et al., 2011). Even then, the change in production cost and discretionary expenses deviate from normal levels relative
to sales, and the values of REM1 and REM2 should be lower.
10 Based on the definition of the data items in Capital IQ, SG&A already includes R&D and advertising costs. It is similar tothe definition used in previous research such as Roychowdhury (2006) and Cohen et al. (2008), who aggregate SG&A, R&D
and advertising costs. Bartov and Cohen (2009) and Gunny (2010) also use the abnormal levels of SG&A as a proxy for REM.

10

are not required to deposit their shares prior to the general shareholders’ meeting, (3) cumulative voting
or proportional representation of minorities in the board of directors is allowed, (4) an oppressedminorities mechanism is in place, (5) the minimum percentage share of capital that entitles a shareholder
to call for an extraordinary shareholders’ meeting is less than or equal to 10% (the sample median), or(6) shareholders have preemptive rights that can be waived only by a shareholders’ vote. In our study,
the index ranges from 0 to 5. Leuz et al. (2003) show that strong and well-enforced outsider rights limit
the acquisition of private control benefits by insiders, thus reducing insiders’ incentives to engage in
AEM. Based on the argument of Leuz et al. (2003) and the discussion in section 2, we predict that therights of outside investors negatively affect AEM and positively or negatively affect REM.
3.2.2. Legal enforcement
The research on investor protection addresses legal enforcement. As La Porta et al. (1998)
point out, “a strong system of legal enforcement could substitute for weak rules.” Employing theframework of La Porta et al (1998), Leuz et al. (2003) suggests legal enforcement as the institutional
factor influencing AEM. Following Leuz et al. (2003), we measure Legal Enforcement as the meanscore across three legal variables used by La Porta et al. (1998): (1) the efficiency of the judicial system,(2) an assessment of rule of law, and (3) the corruption index, all three ranging from 0 to 10. Similar to
the rights of outside investors, legal enforcement is negatively related to AEM and positively or
negatively related to REM.
3.2.3. Disclosure regulations
Disclosure regulations play an important role in investor protection because less information isavailable to minority investors than to insiders, and the former have to depend on the information thatfirms are required to disclose. We predict that, since it is difficult to implement AEM in countries withstricter disclosure regulations (which might lower earnings quality), managers tend to apply REM
instead. Strict disclosure regulations may discipline a manager’s behavior, which would then restrain
REM.

11

Previous studies have often used two indexes relating to national disclosure regulations (e.g.,
Jirasakuldech et al., 2011). One is the index of the Center for International Financial Analysis andResearch (CIFAR) database, and the other comes from the Doing Business project of the World Bank
(the World Bank disclosure index). 11 We use the latter as the variable for disclosure regulation due tothe limited data in the CIFAR index. According to the World Bank, the investor protection index
measures three dimensions of the strength of minority shareholder protections against a director’s
misuse of corporate assets for personal gain. These three dimensions are the (1) transparency of relatedparty transactions (i.e., the extent of disclosure index), (2) liability for self-dealing (the extent of director
liability index), and (3) shareholders’ ability to sue officers and directors for misconduct (the ease ofshareholder suits index). We use the disclosure index from the World Bank as a proxy for the degree of
disclosure regulations. 12 The World Bank disclosure index measures the extent to which investors areprotected through the disclosure of ownership and financial information. The index ranges from 0 to 10,with higher values indicating more disclosure.
3.2.4. Analyst following
Financial analysts also play a key role in investor protection, as they are important informationintermediaries in securities markets (Healy and Palepu, 2001). The extent of analyst following could
affect earnings management in a country. Some studies indicate that monitoring and pressure fromanalysts affect AEM. For example, Yu (2008) and Degeorge et al. (2013) provide evidence that analyst
following negatively affects AEM. Because most analysts are undoubtedly highly skilled monitors of
AEM, they can make it increasingly difficult for managers to engage in AEM; thus, managers arepressed to carry out REM in the presence of the analysts. In this light, we predict that analyst following
has a negative effect on AEM: managers choose REM when the tradeoff between AEM and REM is
more powerful than the effect of discipline on them.

11

These data can be obtained at the World Bank website, http://data.worldbank.org/ (last accessed March 15, 2014).
For example, Ghosh and Revilla (2007) use the disclosure index from the World Bank as one proxy for the quality ofinvestor protection.

1212

In this regard, however, Cohen and Zarowin (2010) find a negative association between analyst
following and total earnings management, a metric that includes both AEM and REM. 13 In addition,
Hong et al. (2012) indicate that both AEM and REM are significantly reduced by analyst following.
Their results indicate that AEM and REM are restrained because analysts scrutinize and monitor firm
activities. 14 Thus, analysts may pay attention to both accounting and real activity. In this case, both
AEM and REM could be reduced.

3.3. Regression models
To test the hypotheses on the relationship between the two types of earnings management andinvestor protection, we constructed the following regression models based on Leuz et al. (2003).
Equations (7) to (10) are single regression models, and equation (11) is a multiple regression model that
includes the four independent variables used in the single regression models. Following Leuz et al.
(2003), we estimate these models by rank regression: 15
AEMj (or REMj) = β0 + β1 Outside Investor Rightsj + εj

(7)

AEMj (or REMj) = β0 + β1 Legal Enforcementj + εj

(8)

AEMj (or REMj) = β0 + β1 Disclosure Indexj + εj

(9)AEMj (or REMj) = β0 + β1 Analyst Followingj + εj

(10)

AEMj (or REMj) = β0 + β1 Outside Investor Rightsj + β2 Legal Enforcementj+ β3 Disclosure Indexj + β4 Analyst Followingj + εj

(11)where AEM is the aggregate accrual-based earnings management score, REM is the aggregate realearnings management score, Outside Investor Rights is the anti-director rights index from La Porta etal. (1998), Legal Enforcement is the average score across three legal variables used by La Porta et al.

13

However, they do not compare the relationships of AEM and REM with analyst following.
According to Cohen and Zarowin (2010), there are two possible views on analyst following: (1) AEM and REM are
restrained because analysts scrutinize and monitor firm activities, or (2) AEM and REM are accelerated because analyst
forecasts serve as incentives for managers, who strive to meet or beat them.
15 OLS regressions with unranked variables produce similar results.
14

13

(1998), Disclosure Index is the disclosure index from the World Bank ranging from 1 to 10, Analyst
Following is the average number of analyst following for all firms in each country, j is country, and ε isan error term. Under AEM (REM), the predicted signs of the coefficients of Outside Investor Rights,
Legal Enforcement, Disclosure Index, and Analyst Following are negative (positive or negative).

3.4. Sample selection procedure
Our sample is based on the 49 countries studied by La Porta et al. (1998) and constructed by
the listed firms in nonfinancial sectors, covering 1991 to 2010, that were selected from Capital IQ. The
former criterion is used to perform a comparison with the many studies that refer to La Porta et al.
(1998). To be included in our analysis, the firms had to have available balance sheets and income
statements for at least three consecutive years and for the countries (at least 300 firm-years) to allow us
to obtain the data required for calculating the representative data from each country. Finally, we
excluded the countries that experienced hyperinflation over the sample period, as hyperinflation could
seriously affect our earnings management measures. 16 Argentina, Brazil, Peru, Turkey, Venezuela, andZimbabwe were thus cut from our sample, leaving a final sample of 222,513 firm-year observations
from 38 countries. 17
Table 1 provides the descriptive statistics for each country. It shows the number of firm-years
in each country and year. Our total sample size (222,513) is much larger than that of Leuz et al. (2003)
due to the extended sample periods and the use of Capital IQ. The rightmost column shows the numberof firm-years in each country: the United States has the largest at 53,072, followed by Japan, India, andCanada. Nigeria has the smallest at 300. The bottom row provides the number of firm-years in eachyear, the smallest of which was in 1991 and the highest in 2010. In general, the available observations

16 We define hyperinflation as inflation over 100% per year. Since hyperinflation heavily influences our measures over thelong term, we exclude this data.
17 Our sample is limited by the coverage in Capital IQ. The data we obtained may be biased toward larger firms and/or higher
developed countries. As shown in Table 1, for some countries, data availability is low before year 2000. There is room forargument on the database used by the study. See Garcia Lara et al. (2006) for further discussion on global databases.

14

increase annually. Canada and India, for example, had scant data in the early 1990s; after the second
half of the decade, however, a large amount of data was included in Capital IQ.
[Insert Table 1 here]

4. Empirical results
4.1. Descriptive statistics
Table 2 shows the scores for AEM and REM. Panel A provides three AEM measures and theiraggregated score—the average rank of three measures (rightmost column). Lower AEM1 and AEM2scores imply that accrual management is used to reduce the variations in earnings and conceal economic
shocks to the firm’s CFO. For AEM1, Australia has the highest score. Portugal has the lowest score,followed by Italy, Spain, France, and Austria. This tendency is maintained in AEM2. Both measures arehigher in Anglo-American countries than in Asia and Continental Europe, indicating that AngloAmerican countries employ the lower extent of AEM. AEM3 (accruals divided by CFO) is reflected invarious manager behaviors as their attempt to achieve specific earnings targets. All three variables—
AEM1, AEM2, and AEM3—have similar tendencies. 18 Therefore, we may use the aggregated AEM
score (AEM), which is the average rank of the three measures.
[Insert Table 2 here]
REM1 is the extent to which overproduction for earnings management is pervasive in a country.
As shown in Panel B, Japan posts the lowest level of earnings management (0.928), followed by South
Korea, Taiwan, Austria, and Greece. The mean is 0.787.
REM2 is the correlation between the change in sales and the change in discretionary expenses.
If discretionary expenses are rarely curtailed, REM2 is expected to rise, as discretionary expenses

18 In our research, the calculation of AEM1, AEM2, and AEM3 is related to that of EM1, EM2, and EM3, as in Leuz et al.
(2003). The country scores seem to be similar to those in Leuz et al. (2003) overall. The calculated means of AEM1 and AEM2
are close to those of EM1 and EM2. However, AEM3 and EM3 have a comparatively large difference of 0.25, possibly due tothe extended sample periods.

15

typically increase in proportion to sales. The balance between sales and discretionary expenses isnegatively affected when the former is managed. The results of REM2 are similar to those of REM1.
REM2 shows that Oceanic countries tend to employ REM more than AEM. 19
The rightmost column of Panel B shows the aggregated score of the REM measures. The resultof REM2 is also similar to that of REM1. Hence, we may calculate the aggregated REM score (REM)
the same way we calculated AEM.
Table 3 contains the institutional variables for the regression analysis of the 38 countries.
Outside Investor Rights and Legal Enforcement are similar to those in Leuz et al. (2003). A characteristicof Disclosure Index is that not all the developed countries do have high scores (e.g., Germany’s index
is five). Overall, the Analyst Following of European countries is relatively high. The two variables areadded to the Leuz et al. (2003) model and tested for association with AEM and REM.
[Insert Table 3 here]
Table 4 provides the correlation matrix for the AEM, REM, and investor protection variables.
AEM is negatively correlated to REM, and the p-value is under 5% (two-tailed). These results imply thesubstitutability between AEM and REM. The correlation between AEM (REM) and Outside Investors
Rights is significantly negative (positive). These results are consistent with our predictive analysis.20[Insert Table 4 here]
4.2. Regression results
Panels A and B of Table 5 show the regression results. In Section 2, we hypothesized that AEM
is constrained in countries with better investor protection but that REM implementation may increaseor decrease. Higher values for all of the independent variables (Outside Investor Rights, Legal
Enforcement, Disclosure Index, and Analyst Following) lead to better investor protection. Thus, the19Australia’s lower AEM and higher REM is a typical case that supports our prediction. The results might be affected by thecharacteristics of Australia’s data, such as industrial composition. However, as countries with different industry compositions(e.g., Hong Kong) show similar results, the effects of industrial composition do not dominate our results.
20 AEM1, AEM2, and AEM3 are highly correlated with each other. The correlation between REM1 and REM2 is also high.
Therefore, the use of the aggregated AEM score (AEM) and REM (REM) are justified.

16

predicted signs for the coefficient of these variables are negative when the dependent variable is AEM.
When REM is the dependent variable, no signs for the coefficients of these variables are predicted under
the null hypothesis (H2).
Since the results of the single and multiple regressions are similar in Panels A and B, we
describe the results only of the multiple regressions. Panel A contains the results for AEM. As predicted
and consistent with Leuz et al. (2003), the coefficients of Outside Investor Rights and Legal
Enforcement are significantly negative. These results support H1 and indicate that AEM is less prevalent
in countries with better investor protection. However, the coefficients of Disclosure Index and Analyst
Following are insignificant. 21
Panel B shows the results for REM. As expected, they are contrary to the results for AEM. The
coefficient of Outside Investor Rights is significantly positive, indicating that REM is more prevalent
in countries with better outside investor rights. The coefficient of Legal Enforcement is not significant.
As with the results for AEM, the coefficient of Disclosure Index is insignificant. In contrast to the resultsfor AEM, the coefficient of Analyst Following is significantly negative. This result is consistent with
Cohen and Zarowin (2010), who find a negative association between analyst following and earnings
management. 22 This finding is consistent with the notion that analyst monitoring influences managers’
behavior and limits REM. 23
The results in this section indicate that AEM is more constrained in countries with better investor
protection. Managers in countries with better outside investor rights tend to engage in REM instead of
AEM. The coefficient of Analyst Following is insignificant for AEM, while REM is more constrained21

The relationship between analyst following and AEM is not similar to that in Yu (2008). However, the analysis of Yu (2008)
is based on firm-year data.
22 The result of Legal Enforcement is not consistent with that seen in Table 4. We infer that the result is controlled by othervariables.
23 Following Roychowdhury (2006), we add the third measure based on abnormal cash flow from operations to REM1 and
REM2. It is computed as the correlation between the change in CFO and the change in sales, ρ(ΔCFO, ΔSALES). When werecalculate the REM measure, the coefficient of Analyst Following remains negatively significant, but that of Outside Investor
Rights is not significant, possibly because the effects of overproduction and of the reduction of discretionary expenses are
mixed, as noted in footnote 7.

17

by analyst following. The former result occurs because analyst following may have restraining andaccelerating effects on AEM. Analyst following not only restrains AEM through manager monitoring
but also accelerates it by inducing earnings management incentives for managers to meet an analyst’s
earnings forecast. Because these effects offset each other, the coefficient of Analyst Following has
insignificant value for AEM. Analysts are sophisticated and very likely to closely monitor REM activity
that could destroy a firm’s value. Analysts could induce managers’ earnings management incentives toachieve an analyst’s earnings forecast, but managers tend not to engage in value-destroying REM whenmany analysts are following.
Thus, managers tend to engage in REM where the legal environment curbs AEM, and thedevelopment of security markets increases the monitoring of managerial discretionary behaviors.
However, there is no evidence of a relationship between AEM (REM) and the disclosure index level.
[Insert Table 5 here]

5. Robustness checks
We conduct additional analyses to check the robustness of our findings. Leuz et al. (2003)
pointed out the endogeneity bias between outside investor protection and earnings management. We
conducted further analyses using a two-stage least squares estimation (2SLS) to address endogeneity.
Following Leuz et al. (2003), legal origins and per capita GDP are employed as instrumental variables
for Outside Investor Rights and Legal Enforcement. Legal origin refers to the basis of the legal systemof each country. Legal origin is determined involuntarily in many countries through processes such as
occupation and colonization. Per capita GDP is employed because the construction and maintenance ofvarious legal systems require national wealth. We therefore use legal origin and per capita GDP as
instrumental variables. Legal Origin Code (English, French, German, or Scandinavian legal origins) isbased on the classifications in La Porta et al. (1998) and is transformed into three dummy variables. Per
Capita GDP is computed as the average per capita GDP from 1981 to 1990 in each country and18

converted to a ranked variable. Table 6 presents the results from the 2SLS. When the dependent variable
is AEM, the coefficient of Outside Investor Rights is significantly negative but that of Legal
Enforcement is not significant. For REM, the coefficient of Outside Investor Rights is significantly
positive, but the coefficient of Analyst Following is not significant. This shows that the potential
endogeneity issue is not completely resolved. 24 However, the results for Outside Investor Rights do notchange qualitatively.
[Insert Table 6 here]
Previous research considers Big 4 audits (Francis and Wang, 2008) as factors that could affectearnings management. We re-estimate equation (11), including the ratio of companies audited by Big 4
firms in each country as an additional independent variable (indicated by Big4 Ratio). 25 The rank
regression results, including the Big4 Ratio, are presented in Table 7. Although the coefficients of the
Big4 Ratio are not significant, the coefficients of the other variables are essentially similar to those in
the previous section. Hence, the results in the previous section are robust when we add the regression
models to the Big4 Ratio.
[Insert Table 7 here]
The third robustness check uses the abnormal discretionary expenses and production costs
developed by Roychowdhury (2006). The standard deviations are employed as proxies of the level of
earnings management in each country. These measures are based on the methods developed by Dechow
and Dichev (2002).

24

The results of the first-stage regressions show that almost all of the coefficients of Legal Origin Code are significant in bothof the equations of the first stage. Per Capita GDP is significantly positive in the equation for Legal Enforcement but notsignificant in that for Outside Investors Rights. Moreover, we carried out a Durbin–Wu–Hausman test to check theendogeneity of Outside Investor Rights and Legal Enforcement. The test rejected the null of the instruments being exogenousat the 5% (regression for AEM) and 10% (regression for REM) levels.
25 We exclude the Big4 Ratio from the main regression models to ensure the sample size as much as possible in the main
analysis.

19

To obtain the abnormal level of production costs (A_Prod) and discretionary expenses (A_DE),
our estimations use the following regression models estimated for each industry-year combination ineach country, where industry is identified by a two-digit SIC code:

Prodijt / Assetijt-1 = β0 + β1 (1 / Assetijt-1) + β2 (Salesijt / Assetijt-1) + β3 (ΔSalesijt / Assetijt-1)+ β4 (ΔSalesijt-1 / Assetijt-1) + εijt

(12)

DEijt / Assetijt-1 = β0 + β1 (1 / Assetijt-1) + β2 (Salesijt-1 / Assetijt-1) + εijt

(13)Standard deviations of A_Prod and A_DE are computed from the pooled samples from eachcountry (σ(A_Prod) and σ(A_DE)), used as the country-level representative value. A higher value ofstandard deviation indicates more REM. The aggregated REM score (REM_Roy) is computed as theaverage rank of σ(A_DE) and σ(A_DE).
Table 8 shows the rank regression results using REM_Roy. While Analyst Following has an
insignificant coefficient, the coefficients of Outside Investor Rights and Legal Enforcement are
significantly positive, indicating that REM is more implemented in countries with stronger investor
protection. Roychowdhury’s (2006) model leads to smaller sample sizes in countries. For example,
Jordan has the fewest observations, at only 33, owing to the requirements for the estimation of A_Prod
and A_DE. New Zealand, Ireland, and Austria have also fewer than 100 observations. The A_Prod and
A_DE requirement for at least 300 observations in each country excludes 10 countries from our sample.
The results of reduced sample remain similar, as seen in Table 8.
[Insert Table 8 here]
In Section 4.2, we follow Leuz et al. (2003) and compute the legal enforcement score as theaverage across three variables used in La Porta et al. (1998): (1) the efficiency of the judicial system,(2) an assessment of rule of law, and (3) the corruption index.26 In this section, we create a new legal

26

The first variable ranges from 0 to 10 and the second and third variables from -2.5 to +2.5.

20

enforcement variable using data available for the sample period and re-estimate equation (5). In
particular, to re-compute the value of Legal Enforcement, we replace an assessment of the rule of law
and the corruption index with their counterparts from the year 2000, based on Kaufmann et al. (2004)or Kaufmann et al (2009). Table 9 shows the rank regression results using the new proxy for legal
enforcement by Kaufmann et al. (2004). 27 The results are the same as before and are thus robust whensubstituted with the variable of Legal Enforcement. 28[Insert Table 9 here]
Next, based on the aggregation of earnings management by country shown in Table 5, weanalyze the relationship between investor protection and earnings management through 38 observations,which is much smaller than the 222,513 firm-years from the 38 countries in Table 1. To address thisdecreased sample size, we use measures of earnings management calculated by country and year andre-estimate equation (11). 29
The recalculation increases the sample size from 38 to 515. To run the regression equation (11),two types of earnings management measures are ranked in the pooled data and converted from 1 to 38.
In addition, we add year indicator variables to equation (11) to control for year effects. The results inTable 10 are similar to those in Table 5 for AEM; for REM, however, the coefficient of Legal
Enforcement is significantly positive at a marginal level, and the coefficient of Disclosure Index issignificantly positive. Thus, the results of this additional analysis corroborate the results of the previous
analyses.
[Insert Table 10 here]

27

The results in Kaufmann et al. (2009) are also similar as before.
Furthermore, when the disclosure index is replaced by Disclosure Requirement in La Porta et al. (2006), the results are alsosimilar to those shown in Table 5.
29 Because each country-year requires at least 30 observations to obtain two earnings management measures, the amount of
the data for each country is unbalanced (for example, Japan has data for 19 years, whereas Nigeria has data only for three
years). Hence, we do not adopt country-year-based analysis as the primary analysis.
28

21

6. Conclusion
This paper examined the relationships between earnings management and investor protection
across 38 countries. Our tests were based on data of 222,513 firm-year observations covering 1991 to2010. Leuz et al. (2003) show that strong investor protection limits earnings management. While theirmeasures of earnings management are mainly accrual-based, we focused on both AEM and REM.
First, following Leuz et al. (2003), Yu (2008), and Degeorge et al. (2013), we hypothesized that
AEM is more constrained in countries with stronger investor protection. Next, we formulated two
hypotheses for REM: the first is that REM is more often implemented in countries with stronger investor
protection because of the substitution between AEM and REM; the second is that, since REM is likely
to negatively affect a firm’s future operating performance and to be restrained through discipline on
managers in the same way as AEM, REM is more constrained in countries with stronger investor
protection.
Our results are consistent with the substitution effect between AEM and REM and indicate thatoutside investor rights are negatively correlated to AEM and positively correlated to REM. However,
we also provide evidence that the more analysts investigate firms, the less REM is carried out, consistent
with the argument that REM is restrained through the discipline by analyst following. The results do
not change under the control of audit quality, alternative definitions of the investor protection variables,or the use of AEM and REM measures calculated by country and year.
REM is a “departure from normal operational practices” (Roychowdhury, 2006, 337) and will
likely impact future performance negatively. For example, Bhojraj et al. (2009) suggest that the effort
to meet or beat analyst forecasts by using AEM and REM has a negative effect on a firm’s future ROA
and stock price.
Our results show that strong investor protection restricts AEM and imply that a strong legal
environment, which is outside investor rights, heightens the risk of firm value reduction via REM, while22the presence of analysts as investor protection is effective in monitoring REM. We show the substitution
effect between AEM and REM in terms of outside investor rights at the country level.
As with prior research, we use a parsimonious model to calculate the proxies for AEM and
REM due to the limited data available from Capital IQ. Future research is required to improve themodel and estimate AEM and REM using detailed international accounting data. Furthermore, weshould attempt to overcome the endogeneity problem by incorporating more appropriate models in
future research because some of our results are not supported in the 2SLS test for robustness.

Acknowledgments
We appreciate the helpful comments from Bin Srinidhi (editor), an anonymous referee, Steven
Dellaportas, Makoto Nakano, Masumi Nakashima, Akinobu Shuto, Alfred Wagenhofer, Tatsushi
Yamamoto, and workshop participants at the 13th Annual Conference of the Asian Academic
Accounting Association (AAAA), the 6th International Workshop on Accounting and Regulation, Kobe
University, and Osaka University. We gratefully acknowledge the financial support of the Japan
Securities Scholarship Foundation and the Ishii Memorial Securities Research Promotion Foundation.

23

References
Bartov, E., Cohen, D. A., 2009. The “numbers game” in the pre-and post-Sarbanes–Oxley eras. J.
Account. Audit. Financ. 24, 505–534.
Bhojraj, S., Hribar, P., Picconi, M., Mcinnis, J., 2009. Making sense of cents: An examination of firmsthat marginally miss or beat analyst forecasts. J. Financ. 64, 2361–2388.
Boonlert-U-Thai, K., Meek, G. K., Nabar, S., 2006. Earnings attributes and investor-protection:
International evidence. Int. J. Account. 41, 327–357.
Chen, J. Z., Rees, L. L., Sivaramakrishnan, S., 2010. On the use of accounting vs. real earnings
management to meet earnings expectations—A market analysis. Working paper. Available at
SSRN: http://ssrn.com/abstract=1070122.
Cohen, D., Dey, A., Lys, T., 2008. Real and accrual based earnings management in the pre and post
sarbanes oxley periods. Account. Rev. 83, 757–787.
Cohen, D. A., Zarowin, P., 2010. Accrual-based and real earnings management activities aroundseasoned equity offerings. J. Account. Econ. 50, 2–19.
Dechow, P. M., Dichev, I. D., 2002. The quality of accruals and earnings: The role of accrual estimation
errors. Account. Rev. 77, 35–59.
Dechow, P. M., Kothari, S. P., Watts, R. L., 1998. The relation between earnings and cash flows. J.
Account. Econ. 25, 133–168.
Dechow, P. M., Sloan, R. G., Sweeney, A. P., 1995. Detecting earnings management. Account. Rev. 70,
193–225.
Degeorge F., Ding Y, Jeanjean T., Stolowy H. 2013. Analyst coverage, earnings management andfinancial development: An international study. J. Account. Public. Policy. 32, 1–25.
Ewert, R., Wagenhofer, A., 2005. Economic effect of tightening accounting standards to restrict
earnings management. Account. Rev. 80, 1101–1124.

24

Francis, J., Wang, D., 2008. The joint effect of investor protection and Big 4 audits on earnings
quality around the world. Contemporary. Account. Res. 25, 157–191.
Francis, B., Hasan, I., Li, L., 2011. A cross-country study of legal environment and real earnings
management. Working paper. Available at SSRN: http://ssrn.com/abstract= 1740036.
García Lara, J. N., García Osma, B., Gill de Albornoz, B., 2006. Effects of database choice oninternational accounting research. Abacus. 42, 426–454.
Ghosh, S., Revilla, E., 2007. Enhancing the efficiency of securities markets in East Asia. World Bank
Policy Research Working Paper 4129, 1–27. Available at the World Bank e-library.
http://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-4129.
Gordon, E. A., Greiner, A., Kohlbeck, M., Lin, S., Skaife, H., 2013. Challenges and opportunities incross-country accounting research. Account. Horiz. 27, 141–154.
Graham, J. R., Harvey, C. R., Rajgopal, S., 2005. The economic implications of corporate financial
reporting. J. Account. Econ. 40, 3–73.
Gunny, K. 2010. The relation between earnings management using real activities manipulation andfuture performance: Evidence from meeting earnings benchmarks. Contemporary. Account. Res. 27,
855–888.
Healy, P. M., Wahlen, J. M., 1999. A review of the earnings management literature and its implications
for standard setting. Account. Horiz. 13, 365–384.
Healy, P. M., Palepu, K., 2001. A review of the empirical disclosure literature. J. Account. Econ. 31,
405–440.
Hong, Y., Huseynov, F., Zhang, W., 2012. Earnings management and analyst following: A simultaneous
equations analysis. Working Paper. Available at SSRN: http://ssrn.com/abstract=2139559.
Jirasakuldech, B., Zorn, T., Geppert, J., Dudney, D., 2011. Financial disclosure, investor protection, andstock market behavior: an international comparison. Rev. Quantitative Fin. Account. 37, 181–205.

25

Kaufmann, D., Kraay, A., Mastruzzi, M., 2004. Governance matters III: Governance indicators for 1996,
1998, 2000, and 2002. World Bank Econ Rev. 18, 253–287.
Kaufmann, D., Kraay, A., Mastruzzi, M., 2009. Governance matters VIII: Aggregate and individual
governance indicators 1996–2008. World Bank Policy Research Working Paper No. 4978.
Available at SSRN: http://ssrn.com/abstract=1424591
Kim, J., Sohn, B. C., 2013. Real earnings management and cost of capital. J. Account. Public. Policy.
32, 518–543.
Kothari, S. P., Mizik, N., Roychowdhury, S., 2012. Managing for the moment: The role of real activity
versus accruals earnings management in SEO valuation. Working Paper. Available at SSRN:
http://ssrn.com/abstract=1982826.
La Porta, R., Lopez-de-Silanes F., Shleifer, A., Vishny R., 1997. Legal determinants of external finance.
J. Financ. 52, 1131–1150.
La Porta, R., Lopez-de-Silanes F., Shleifer, A., Vishny R., 1998. Law and finance. J. Polit. Econ. 106,1113–1155.
La Porta, R., Lopez-de-Silanes F., Shleifer, A., Vishny R., 2002. Investor protection and corporate
Valuation. J. Financ. 75, 1147–1170.
La Porta, R., Lopez-de-Silanes F., Shleifer A., 2006. What works in securities laws? J. Financ. 61, 1–
32.
Leuz, C., Nanda, D., Wysocki, P., 2003. Earnings management and investor protection: an international
comparison. J. Financ. Econ. 69, 505–527.
Roychowdhury, S., 2006. Earnings management through real activities manipulation. J. Account. Econ.
42, 335–370.
Shen, C. H., Chih, H. L., 2005. Investor protection, prospect theory, and earnings management: Aninternational comparison of the banking industry. J. Bank. Financ. 29, 2675–2697.
Yu, F., 2008. Analyst coverage and earnings management. J. Financ. Econ. 88, 245–271.

26

Wysocki, P., 2004. Discussion of ultimate ownership, income management, and legal and extra-legal
institutions. J. Account. Res. 42, 463–474.
Wysocki, P., 2011. New institutional accounting and IFRS. Account. Bus. Res. 41, 309–328.
Zang, A. Y. 2012. Evidence on the trade-off between real activities manipulation and accrual-based
earnings management. Account. Rev. 87, 675–703.

27

Table 1
Firm-year observations in each country.
Country
Australia
Austria
Belgium
CanadaChile
Denmark
FinlandFranceGermany
GreeceHong Kong
India
Indonesia
IrelandIsraelItaly
Japan
Jordan
Malaysia
MexicoNetherlands
New ZealandNigeria
NorwayPakistan
Philippines
Portugal
Singapore
South Africa
South Korea
Spain
Sri Lanka
SwedenSwitzerland
TaiwanThailand
UnitedKi dUnitedStates

Total

1991
45
10
16
2
13
28
25
95
113
0
30
1
0
18
0
16
856
0
70
0
35
5
0
18
0
1
1
38
18
0
7
0
24
55
0
2
272
246
2,060

1992
49
14
16
2
14
28
25
99
115
0
32
1
2
18
0
16
910
0
73
0
37
5
0
17
0
1
1
38
18
0
8
0
25
56
0
2
279
311
2,212

1993
50
14
16
3
24
28
27
100
115
0
48
4
25
18
1
15
954
0
76
6
37
5
0
19
0
5
2
38
18
0
28
0
25
57
0
36
281
749
2,824

1994
52
15
17
6
26
26
26
100
111
0
49
4
28
18
2
18
979
0
76
23
37
5
0
19
0
5
2
40
18
0
41
0
27
57
1
39
280
1,018
3,165

1995
53
15
15
10
29
26
28
100
111
0
57
5
31
19
4
20
1,017
0
80
15
35
7
0
23
0
2
3
49
18
104
44
0
26
59
1
43
281
1,415
3,745

1996
52
13
22
27
37
28
27
122
100
4
86
7
52
18
4
33
1,038
0
114
15
39
9
0
25
11
3
9
96
22
113
47
0
30
63
1
68
260
1,932
4,527

1997
65
14
17
201
35
33
30
122
110
6
113
5
63
21
5
30
1,005
0
164
26
46
11
1
21
40
10
7
99
24
136
46
0
38
60
1
59
241
2,498
5,403

1998
107
25
27
463
40
44
43
176
184
26
150
25
85
23
33
56
1,091
0
200
41
63
15
1
32
76
22
12
115
40
149
57
1
77
88
2
72
346
2,747
6,754

1999
244
31
36
555
70
54
50
223
260
37
246
37
117
27
44
67
1,156
1
261
50
71
22
1
36
97
37
12
153
63
161
64
2
101
102
16
112
420
2,888
7,924

2000
278
42
61
656
84
70
74
303
343
51
344
650
125
28
65
91
1,564
3
351
56
89
40
0
51
86
44
16
206
80
197
74
5
138
128
180
176
497
2,974
10,220

2001
368
49
66
716
89
80
88
341
414
58
439
770
132
35
77
121
1,728
3
429
63
94
47
0
62
97
60
23
258
109
298
78
5
157
135
240
209
542
3,172
11,652

2002
491
49
66
736
101
83
91
360
419
66
579
875
122
33
78
132
1,980
7
458
68
91
53
4
69
74
63
27
292
127
355
83
5
174
141
296
227
580
3,106
12,561

2003
626
54
66
840
110
86
97
402
462
79
678
1,098
126
40
102
144
2,128
45
500
72
99
64
13
81
70
89
35
334
146
442
88
6
205
152
367
247
673
3,572
14,438

2004
686
57
69
864
114
86
98
430
470
83
716
1,585
125
40
116
153
2,216
53
542
77
101
78
32
88
92
93
37
360
149
534
92
15
225
157
871
271
697
3,613
16,085

2005
706
57
78
914
115
88
100
460
499
93
760
1,770
128
43
123
163
2,315
58
594
79
103
83
42
96
110
96
38
394
162
599
99
18
238
167
985
312
739
3,730
17,154

2006
748
57
91
952
119
92
101
495
541
123
805
1,889
143
44
142
176
2,435
67
656
79
104
83
56
109
127
104
38
414
176
699
99
24
262
171
1,033
338
796
3,875
18,263

2007
816
58
94
1,043
123
95
102
509
555
138
819
1,978
157
49
148
191
2,663
77
707
81
111
87
54
123
151
106
39
425
187
1,323
100
30
301
176
1,192
353
865
3,979
20,005

2008
811
61
88
1,012
127
95
104
508
548
164
836
2,050
182
53
143
198
3,141
37
727
84
113
85
38
133
160
103
40
454
198
1,317
103
71
319
178
1,323
365
889
3,611
20,469

2009
915
60
89
1,083
129
95
105
513
537
176
850
2,125
196
54
165
205
3,233
34
759
87
115
91
31
144
175
108
40
471
214
1,307
105
140
344
180
1,399
377
924
3,698
21,273

2010
1,003
59
88
1,146
130
92
107
518
524
183
881
2,306
217
54
212
207
3,241
5
788
88
116
93
27
150
189
112
41
491
222
936
103
158
366
183
1,461
390
954
3,938
21,779

Total
8,165
754
1,038
11,231
1,529
1,257
1,348
5,976
6,531
1,287
8,518
17,185
2,056
653
1,464
2,052
35,650
390
7,625
1,010
1,536
888
300
1,316
1,555
1,064
423
4,765
2,009
8,670
1,366
480
3,102
2,365
9,369
3,698
10,81653,072
222,51328

Table 2
Score for earnings management.
Panel A: Score for AEM
Country
Australia
South Africa
SwedenUnited States
CanadaUnited Kingdom
Hong Kong
New ZealandIreland
Philippines
Finland
NorwayNetherlands
Sri Lanka
Denmark
MexicoIsraelThailand
Switzerland
Chile
Pakistan
JordanTaiwanIndia
Malaysia
Singapore
Belgium
Germany
Nigeria
Austria
Indonesia
Spain

Panel B: Score for REM
AEM1
0.704
0.669
0.665
0.563
0.647
0.504
0.487
0.504
0.415
0.545
0.533
0.486
0.434
0.535
0.463
0.444
0.508
0.494
0.405
0.451
0.520
0.422
0.511
0.701
0.506
0.425
0.362
0.413
0.464
0.351
0.417
0.307

AEM measures
AEM2-0.532-0.747-0.659-0.614-0.564-0.690-0.714-0.813-0.749-0.783-0.816-0.738-0.781-0.803-0.777-0.826-0.733-0.832-0.785-0.839-0.886-0.801-0.854-0.860-0.837-0.803-0.835-0.797-0.916-0.847-0.846-0.899

AEM3
0.421
0.495
0.579
0.606
0.635
0.561
0.677
0.576
0.512
0.727
0.662
0.695
0.616
0.728
0.689
0.565
0.799
0.663
0.621
0.607
0.730
0.759
0.780
0.909
0.817
0.787
0.726
0.810
0.751
0.735
0.821
0.696

Score for AEM
1
4.7
5
5.7
6.3
8
12.7
13
13.3
13.3
14
14.3
15
15.3
15.7
16
16.3
17.3
18
18.3
21.7
22
22
23
23.3
23.3
24.7
24.7
26.3
28
29
29.3

CountryJapan
Austria
MexicoNetherlands
GreeceSwitzerland
South Korea
Chile
Ireland
Belgium
Denmark
TaiwanThailandFinlandGermany
Indonesia
Spain
Italy
Nigeria
FranceUnited States
Singapore
IsraelUnited Kingdom
NorwayPortugal
India
South Africa
SwedenHong Kong
JordanPakistan

REM measures
REM1
REM2
0.928
0.753
0.865
0.677
0.853
0.732
0.859
0.651
0.861
0.645
0.827
0.714
0.895
0.597
0.828
0.641
0.824
0.645
0.846
0.600
0.817
0.646
0.877
0.549
0.852
0.575
0.818
0.609
0.824
0.570
0.797
0.585
0.782
0.640
0.796
0.556
0.825
0.479
0.781
0.560
0.791
0.535
0.796
0.456
0.774
0.517
0.765
0.524
0.788
0.451
0.804
0.402
0.747
0.490
0.792
0.404
0.733
0.483
0.776
0.422
0.715
0.467
0.726
0.444

Score for REM
1
4
4.5
5.5
6.5
7
7.5
9.5
10
10.5
11
11
11.5
13
15
16
17
18.5
18.5
21
21
23.5
24.5
24.5
25.5
25.5
26
27
27.5
28
29.5
30.5

29

France

0.347-0.869

0.779

30.7

Malaysia

0.744

0.345

33.5

South Korea
Japan
Italy
GreecePortugal
MeanMedian
Std.

0.383
0.354
0.304
0.364
0.243
0.470
0.464
0.110

-0.869-0.875-0.907-0.924-0.932-0.799-0.815
0.094

0.877
0.819
0.816
0.906
0.798
0.704
0.727
0.116

32
33
34.7
35
35

Philippines
New Zealand
CanadaSri Lanka
Australia
MeanMedian
Std.

0.644
0.556
0.695
0.701
0.622
0.787
0.796
0.076

0.412
0.418
0.372
0.371
0.336
0.533
0.542
0.113

34
34.5
35
35
37.5

In Panel A, countries are sorted by AEM scores. Each variable is computed using 222,513 firm-year observations from 1991 to 2010. AEM1 is based on the ratio of the standard deviationof the operating income to that of operating cash flow, which is calculated by time-series data from each firm. AEM1 is the median of the ratio in each country. AEM2 is the correlation
between changes in accruals and changes in operating cash flows and is computed from the pooled data in each country. AEM3 is the ratio of the absolute value of accruals to that ofoperating cash flows, and is calculated in each firm-year. AEM3 represents each country’s median data. The score for AEM is the average rank of AEM1, AEM2, and AEM3.
In Panel B, countries are sorted by REM. Each variable is computed using 222,513 firm-year observations from 1991 to 2010. REM1 and REM2 are the correlation between the change
in sales and the change in production costs and the change in discretionary expenses. REM1 and REM2 are computed from the pooled data in each country. The score for REM is theaverage rank of REM1 and REM2.

30

Table 3
Institutional variables in each country.
Outside Investor
Legal Enforcement
Disclosure Index
Analyst Following
Country
RightsAustralia
4
9.51
8
2.496
4.647
Austria
2
9.36
2
5.650
Belgium
0
9.44
8
2.651
Canada
5
9.75
8
1.112
Chile
5
6.52
8
4.755
Denmark
2
10.00
7
7.590
Finland
3
10.00
6
5.665
France
3
8.68
10
5.509
Germany
1
9.02
5
2.212
Greece
2
6.82
1
2.230
Hong Kong
5
8.91
10
1.967
India
5
5.58
7
2.970
Indonesia
2
2.88
8
9.362
Ireland
4
8.36
10
1.482
Israel
3
7.72
7
5.204
Italy
1
7.07
7
2.244
Japan
4
8.24
7
0.365
Jordan
1
6.16
5
2.011
Malaysia
4
7.72
10
4.227
Mexico
1
5.37
7
9.692
Netherlands
2
10.00
4
2.525
New Zealand
4
10.00
10
0.872
Nigeria
3
4.34
5
5.642
Norway
4
10.00
7
1.036
Pakistan
5
3.67
6
1.285
Philippines
3
3.47
1
5.403
Portugal
3
7.19
6
1.811
Singapore
4
8.93
10
3.211
South Africa
5
6.45
8
2.384
South Korea
2
5.55
6
9.349
Spain
4
7.14
5
0.155
Sri Lanka
3
4.66
4
4.270
Sweden
3
10.00
2
7.990
Switzerland
2
10.00
2
2.200
Taiwan
3
7.37
6
2.730
Thailand
2
4.89
10
6.046
United Kingdom
5
9.22
10
5.209
United States
5
9.54
7
Mean
3.846
3.13
7.62
6.58
2.850
Median
3
7.98
7
Std
2.571
1.40
2.14
2.64
Outside Investor Rights is anti-director rights, an index aggregating shareholder rights. The anti-director rights index is formedby adding 1 when (1) the country allows shareholders to mail their proxy vote to the firm; (2) shareholders are not required todeposit their shares prior to the general shareholders’ meeting; (3) cumulative voting or proportional representation ofminorities in the board of directors is allowed; (4) an oppressed minorities mechanism is in place; (5) the minimum percentageof share capital that entitles a shareholder to call for an extraordinary shareholders’ meeting is less than or equal to 10% (thesample median); or (6) shareholders have preemptive rights that can be waived only by a shareholders’ vote (La Porta et al.,1998). In our study, the index ranges from 0 to 5. Following Leuz et al. (2003), we compute Legal Enforcement as the averagescore across three variables used in La Porta et al. (1998): (1) the efficiency of the judicial system, (2) an assessment of ruleof law, and (3) the corruption index. All three variables range from 0 to 10. Disclosure Index is obtained from the World Bank
website. Analyst Following is the number of analyst following per firm-year in each country.

31

Table 4
Correlation between AEM, REM, and institutional variables.
OutsideLegal
Score for
Score forInvestor
Enforcement
AEM
REM
Rights-0.435
Score for REM(0.006)-0.4280.465
Outside Investor(0.007)(0.003)Rights-0.326-0.0760.073
Legal(0.046)(0.649)(0.665)
Enforcement-0.137
0.174
0.321
0.081
Disclosure(0.412)(0.296)(0.050)(0.629)Index-0.032-0.411-0.164
0.525
Analyst(0.851)(0.010)(0.325)(0.001)
Following
The variables are defined in Tables 2 and 3; n=38; the p-values are in parentheses.

Disclosure
Index-0.048(0.774)32

Table 5
Regression results of earnings management and investor protection.
Panel A: Regression results for AEM
Dependent variable = AEM
Constant

28.434***27.014***23.298***20.881***(8.397)(7.831)(6.291)(5.616)

Outside Investor Rights-0.458***(6.896)-0.392**(-3.012)
Legal Enforcement

34.183***(-2.412)-0.385**-0.418**(-2.497)(-2.316)Disclosure Index-0.195-0.049(-1.172)(-0.319)-0.071

0.106(-0.426)(0.575)

Analyst Following
R2 (or Adjusted R2)

0.201

0.148

0.037

0.005

0.253

Number of Observations

38

38

38

38

38

Panel B: Regression results for REM
Dependent variable = REM
Constant
Outside Investor Rights

10.074***20.561***

16.160***27.002***15.949***(3.019)(5.511)(4.345)(7.849)(3.220)0.483***---

0.373**(3.224)
Legal Enforcement-(2.298)-0.054--(-0.326)0.160(0.887)

Disclosure Index

-

-

0.171

Analyst Following

-

-

-

R2 (or Adjusted R2)

0.224

0.003

0.028

-

0.065

-0.385**-0.416**(-2.502)(-2.262)

0.148

0.254

(1.026)(0.421)

Number of Observations
38
38
38
38
38
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). For
equation (11), R2 shows the adjusted R2. All variables are defined in Tables 2 and 3.

33

Table 6
2SLS results of earnings management and investor protection.
AEM
Constant
Outside Investor Rights
Legal Enforcement
Disclosure Index
Analyst Following
Adjusted R2

REM

43.725***

7.847(6.114)(1.128)-1.050***0.974***(-2.877)(2.741)-0.247-0.071(-0.947)(-0.281)0.180-0.143(0.863)(-0.706)-0.125-0.162(-0.489)(-0.652)-0.119-0.06038
Number of Observations
38
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). The
regressions are estimated using two-stage least squares (2SLS). Legal origin and GDP ranks are employed as instrumental
variables for outside investor rights and legal enforcement. Following Leuz et al. (2003), English, French, German, andScandinavian legal origins based on the classifications from La Porta et al. (1998) are transformed into three dummy variables.
GDP is computed as average per capita GDP from 1981 to 1990 in each country. The other variables are defined in Tables 2
and 3.

34

Table 7
Regression results of earnings management and investor protection: Controlling for Big 4 audits ratio.
AEM
REM
Constant
Outside Investor Rights
Legal Enforcement
Disclosure Index
Analyst Following
Big4 Ratio
Adjusted R2

29.134***14.771***(6.461)(3.203)-0.438**0.371*(-2.337)(1.934)-0.336*0.132(-1.831)(0.703)

0.011

0.048

(0.068)(0.285)0.177-0.466**(1.013)(-2.603)-0.1280.046(-0.721)(0.254)

0.311

0.279

33
Number of Observations
33
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). Big4 Ratio
is the ratio of firms audited by the Big 4 in each country. The other variables are defined in Tables 2 and 3.

35

Table 8
Regression results of REM and investor protection: using REM_Roy based on Roychowdhury (2006) model.
REM_RoyConstant

1.574(0.352)

Outside Investor Rights

0.269*

Legal Enforcement

0.573***(1.782)(3.365)Disclosure Index

0.053(0.369)Analyst Following

0.021(0.121)

0.374
Adjusted R2
Number of Observations
37
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). The
dependent variable, REM_Roy, is calculated as follows. To obtain the abnormal level of production costs (A_Prod) anddiscretionary expenses (A_DE), our estimation uses the following regression models. Regression models are estimated foreach industry-year combination in each country, where industry is identified by a two-digit SIC code:
Prodijt / Assetijt-1 = β0 + β1 (1 / Assetijt-1) + β2 (Salesijt / Assetijt-1) + β3 (ΔSalesijt / Assetijt-1)+ β4 (ΔSalesijt-1 / Assetijt-1) + εijt
DEijt / Assetijt-1 = β0 + β1 (1 / Assetijt-1) + β2 (Salesijt-1 / Assetijt-1) + εijt

(12)
(13)

Standard deviations of A_Prod and A_DE are computed from the pooled sample from each country (σ(A_Prod) and σ(A_DE))and used as the country-level representative value. The aggregated REM score (REM_Roy) is computed as the average rankof σ(A_Prod) and σ(A_DE). Other variables are defined in Tables 2 and 3.

36

Table 9
Regression results of earnings management and investor protection: Another proxy for Legal Enforcement.
AEM
REM
Constant

34.483***(6.843)(3.235)

Outside Investor Rights-0.390**0.391**(-2.372)(2.375)

Legal Enforcement-0.375**0.065(-2.156)(0.376)Disclosure Index
Analyst Following
Adjusted R2

16.291***-0.064

0.062(-0.405)(0.396)0.061-0.354*(0.343)(-1.992)

0.239

0.240

38
Number of Observations
38
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). Legal
Enforcement is the average score across three legal variables: (1) the efficiency of the judicial system, as used in La Porta etal. (1998); (2) an assessment of rule of law, based on Kaufmann et al. (2004); and (3) the corruption index, based on Kaufmannet al. (2004). The first variables range from 0 to 10 and the second and third from -2.5 to +2.5. The other variables are definedin Table 2 and Table 3.

37

Table 10
Regression results of earnings management and investor protection based on country-year earnings management measures.
AEM
REM
Constant
Outside Investor Rights
Legal Enforcement
Disclosure Index

42.915***7.510**(14.137)(2.174)-0.369***0.154***(-9.310)(3.420)-0.348***0.087*(-7.829)(1.719)-0.0540.131***(-1.426)(3.044)-0.035-0.153***(-0.758)(-2.922)

Year_Dummy

included

Included

Adjusted R2

0.355

0.164

Analyst Following

515
515
Number of Observations
The t-statistics are in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels (two-tailed). Allvariables are defined in Tables 2 and 3.

38