Enomoto

Accrual-Based and Real Earnings Management:

An International Comparison for Investor Protection

Masahiro Enomoto*

Kobe University, Kobe, Japan

Fumihiko Kimura

Tohoku University, Sendai, Japan

Tomoyasu Yamaguchi

Tohoku Gakuin University, Sendai, Japan

March 2013

* Corresponding author.E-mail: [email protected]: +81-78-803-7031; fax: +81-78-803-7031.

Research Institute for Economics & Business Administration, Kobe University, 2-1 Rokkodaicho, Nada-ku, Kobe 657-8501, JAPAN. E-mail: [email protected]

Tel: +81-22-217-6282; fax: +81-22-217-6282.

Graduate School of Economics and Management, Tohoku University, 2-2-1 Katahira, Aoba-ku, Sendai 980-8576, JAPAN. E-mail: [email protected] Tel: +81-22-721-3471; fax: +81-22-721-3471.

Faculty of Business Administration, Tohoku Gakuin University, 1-3-1 Tsuchitoi, Aoba-ku, Sendai 980-8511, JAPAN.

Accrual-Based and Real Earnings Management:

An International Comparison for Investor Protection

Abstract

The purpose of this paper is to examine the differences between accrual-based and real earnings management across 38 countries. Following the international comparative research on earnings management and the research on the choice of the method of earnings management, we predict that real earnings management is preferred over accrual-based earnings management in countries with stronger investor protection. We regard legal enforcement, outside investor rights, disclosure regulations, and analyst following as constituting investor protection. Our examination is based on the data of 222,513 firm-year observations from 1991 to 2010. We show that accrual-based earnings management is inhibited, while real earnings management is implemented in the countries with stronger investor protection except for analyst following. With regard to analyst following, we find evidence that real earnings management is limited in the presence of analysts. Moreover, this result is not affected by the control of audit quality and the calculation of earnings management measures according to country and year.

JEL Classification: G34 K22 M41 G38

Key Words: Corporate Governance, Corporation and Securities Law, Accounting,

Government Policy and Regulation

1. Introduction

The purpose of this paper is to examine the differences in accrual-based and real earnings management across 38 countries. Following Scott (2011), we define earnings management as the choice by a manager of accounting policies, or real actions that affect earnings so as to achieve a specific reported earnings objective. The methods of earnings management are categorized as either the change in the accrual process or the deviation from normal business activity. The former is called Accrual-based Earnings Management (AEM) and the latter, Real Earnings Management (REM).

Previous international comparative studies on earnings management have concentrated on AEM. Leuz et al. (2003) applied investor protection as an institutional factor that influences managerial behavior, investigated whether or not investor protection in each country is related to AEM, and examined the relationship between outside investor protection and earnings management in 31 countries from 1990 to 1999. Investor protection is defined as the power to prevent manager from expropriating minority shareholders and creditors within the constraints imposed by law (La Porta et al., 2002; Leuz et al., 2003). Researchers have found that earnings management decreases in countries with stronger investor protection. Boonlert-U-Thai (2006) also investigated the relationship between investor protection and earnings management in 31 countries from 1996 to 2002, and suggested that earnings are smoothed in countries where investor protection has progressed. Shen and Chih (2005) focused on banks in 48 countries and showed that earnings management declined in countries with stronger investor protection and more transparent accounting disclosure.Prior research mainly employs accrual-based measures as a proxy of earnings management. However, aside from the accrual process, managers can also use real activities to manage earnings. REM also serves as an important topic in recent researches on

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earnings management (e.g., Roychowdhury, 2006). While REM could make a negative impact on the future value of the firms (e.g., Bhojraj et al., 2009; Cohen and Zarowin, 2010), neither regulators nor auditors could restrain it.1 Moreover, the scrutiny of REM by regulators and auditors could not exceed that of AEM. Based on such arguments, we predict that REM, rather than AEM, is implemented in countries with stronger investor protection.

We measure AEM in three proxies, following Leuz et al. (2003): (1) the ratio of the standard deviation of operating income to that of operating cash flow, which is calculated by time-series data from each firm, (2) the correlation between changes in accruals and changes in operating cash flow computed from the 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. On the other hand, we measure REM in two proxies: (1) the correlation between changes in sales and production costs and (2) the correlation between changes in sales and discretionary expenses.2

As variables of investor protection, we use the strength of legal enforcement and the extent of outside investor rights that are related to corporate law and security law (La Porta et al., 1998; La Porta et al., 2006). Furthermore, we emphasize the disclosure regulations and the role of the analyst as important factors for investor protection, including the existence of analyst following and the disclosure index.

Our examination is based on the data of 222,513 firm-year observations from 38 countries between 1991 and 2010. Our findings suggest show that AEM is less prevalent than REM in the countries that have stronger investor protection. Moreover, this result is not affected by the control of audit quality and the calculation of earnings management measures according to country and year.

Contrary to Cohen and Zarowin (2010), Guuny (2010) suggests that REM makes a positive impact on the future value of the firms.

The correlation between the changes in sales and cash flows from operations, which is employed in Roychowdury (2006), is not included. The reason is shown in footnote 4.

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Most international comparative researches on earnings management (e.g., Leuz et al., 2003; Shen and Chih, 2005; Boonlert-U-Thai, 2006), has only analyzed AEM. We, however, also investigate REM based on the theoretical background of REM and AEM. Moreover, compared to previous studies, our results are more reliable because of the larger sample size and longer term.

The remainder of this paper is organized as follows. Section 2 provides a discussion on AEM and REM and institutional background. Section 3 shows sample selection procedures and the measure of earnings management and investor protection. Section 4 contains the results for the relationship between earnings management and investor protection. Section 5 discusses additional analyses, and Section 6 concludes.

2. Accrual-based and real earnings management, and institutional background

Managers can opportunistically manage earnings by changing the accrual process because various estimations and judgments go into the process of preparing financial statements. Thus, managers can implement AEM after the end of the fiscal year. However, AEM is more visible than REM due to scrutiny from auditors and regulators, among others, and has the potential for 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. These activities are referred to as real earnings management (REM). REM could be more costly with respect to future firm 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., 2008). However, it is hard for stakeholders to detect REM in comparison to AEM, as REM is easier to camouflage as normal activities compared to AEM (Kothari et al. 2012).

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Thus, managers may choose AEM and/or REM under the various economic contexts. Our work focuses on both AEM and REM.

Regulatory authorities impose various restrictions on managers when the latter carry out AEM because earnings management has the potential to reduce the outside investors profits. The degree of restrictions is believed to vary with the institutional factors of each country. Many studies attach importance to investor protection, including corporate law, accounting standards, and security markets as institutional factors. La Porta et al. (1997; 1998) show that each countrys investor protection is based on its legal origin, and countries with stronger investor protection have larger and open capital markets. Leuz et al. (2003) suggest that AEM decreases in countries with stronger investor protection. In a similar vein, disclosure systems and analyst following serve as components of investor protection, thereby reducing AEM (for example, Yu, 2008 and Degeorge et al., 2013). However, even though AEM is restrained, the motives for earnings management are not necessarily controlled. Moreover, in countries with developed capital markets, investors should be cautious about earnings information. Therefore, it is believed that in countries with stronger investor protection, managers tend to choose REM over AEM.Previous research supports our prediction. In a survey of chief financial officers, Graham et al. (2005) present evidence suggesting that managers opt for REM because they fear the risk wrought by overzealous regulators. Through the analysis of mathematical models, Ewert and Wagenhofer (2005) also suggest that as an effect on tighter accounting standards, manager prefer REM. Cohen et al. (2008) imply that managers switched from AEM to REM after the passage of Sarbanes Oxley Act (SOX) in United States, which was seen as strengthening investor protection. In summary, in the presence of stricter regulations, managers tend to avoid AEM but implement REM to achieve income targets. In other words, the tightening of regulations leads to restrain AEM but tends to induce REM. Given the

foregoing discussion, we propose two hypotheses. 4

H1: Ceteris paribus, accrual-based earnings management is more constrained in countries with stronger investor protection.

H2: Ceteris paribus, real earnings management is more often implemented in countries with stronger investor protection.

3. Research design

This study tests hypotheses pertaining to the relationship between both types of earnings management and investor protection. The following subsections present a detailed explanation of two earnings management and four investor protection variables.

3.1. The variables of accrual-based and real earnings management

Earnings management is classified as either AEM or REM. The purpose of this paper is to explore the relationship between two types of earnings management on the one hand and investor protection, on the other. We calculate three measures for AEM (based on accruals) and two measures for REM (correlated to sales), then aggregate the results.

Though our country-level earnings management measures may be parsimonious due to the limited amount of balance sheet and income statement information for each firm in Standard & Poors Capital IQ database, we can make a comparison between our results and those of Leuz et al. (2003) by using these measures. Thus, we compute aggregate the measures for each type in order to evaluate the countrys level of earnings management, avoiding the concerns from calculation methods as much as possible. The measures are ranked from the lowest to highest level of earnings management (e.g. the country with the least amount of earnings management gets rank one). The aggregate measure of accrual-based (or real) earnings management is the average rank of the three (two)

measures. For aggregation, all the measures should have similar tendencies to some extent. 5

3.1.1. The variables of accrual-based earnings management

Three measures of AEM are employed in this studythree of the four Leuz et al. (2003) employedand indicate the extent of earnings management by using accruals. The explanation of the three measures was drawn from Leuz et al. (2003). We concentrate on the comparison between AEM and REM, and small loss avoidance in Leuz et al. (2003) is not adopted.3

To obtain these measures, accruals are calculated as follows:

Accruals (Acc) = (CAit Cashit) (CLit STDit TPit) Depit (1), CAit = change in total current assets, Cashit = change in cash/cash equivalents, CLit = change in total current liabilities, STDit = change in short-term debt included in current liabilities, TPit

= change in income taxes payable, and Depit = depreciation and amortization expense. The subscripts i and t refer to firm i and year t. It is the same as in Dechow et al. (1995) and used generally. We can obtain operating cash flow by subtracting accruals from earnings:

CFO = Earnings Accruals. We do not directly compute accruals from the cash flow statement because the data is 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 and bottom 1% of accrual and operating cash flow data. All of the variables to compute the measures of AEM and REM are divided by the total assets at the beginning of the year.

The first measures of AEM are the standard deviation of operating earnings divided by the standard deviation of operating cash flow (AEM1). This measure assumes that a manager might use accruals to mitigate the variation of operating cash flow. If so, this

3 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 loss by using REM (for example, Roychowdhury 2006).

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measure becomes smaller. AEM1 is calculated by time-series data of each firm, and the

median of AEM1 in each country is its representative value.

AEM1 =(E)(2)

(CFO)

(E) the standard deviation of the operating income, and (CFO) 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 = (Acc, CFO)(3)Acc change in accruals and CFO 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 by various kinds of manager behaviors in achieving certain earnings targets. Therefore, we use the absolute value of accruals as an earnings discretion measure. The reason for dividing by the operating cash flow is to control firm size and performance. AEM3 is calculated in each firm-year, and we use each countrys

median data.

AEM3 =|Acc|(4)

|CFO |

|Acc| absolute value of accruals and |CFO| absolute value of operating cash flows.

AEM1 and AEM2 are employed as smoothing measures, and AEM3 is the measure of discretion used by Leuz et al. (2003). We regard these three measures as the degree of earnings management through accruals.

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3.1.2. The variables of real earnings management

As previously mentioned, earnings can be managed not only through AEM but also through REM. For example, previous research (Roychowdhury, 2006; Cohen et al., 2008; Cohen and Zarowin, 2010) examine sales manipulation with price discounts and lenient credit terms, the reduction of discretionary expenditure such as R&D, and overproduction, which lowers the unit cost of goods sold. Based on these researches, this paper considers three types of REM: (1) sales manipulation, (2) manipulation of discretionary expenditures, and (3) production manipulation.

According to Roychowdhury (2006), sales manipulation with price discounts and overproduction leads to abnormally high production costs relative to sales.4 Thus, sales and production manipulation induce an imbalance in production costs and sales, and result in a lower correlation between the change in production costs and the change in sales. For this reason, we use the contemporaneous correlation between the change in production costs and the change in sales (indicated by REM1) as measures of sales and production manipulation. REM1 is computed from the pooled sample in each country. A lower REM1 means that more sales and production manipulation has been carried out.REM1 = (Prod, Sales)(5),

Prod = change in production costs, production costs = cost of goods sold + change in inventory, and Sales = change in sales.

The manipulation of discretionary expenditures leads to departures from the normal level of discretionary expenses. Discretionary expenses are expressed as a linear function of sales in previous research (Dechow et al., 1998; Roychowdhury, 2006). Based on these

4 Roychowdhury (2006) also said that sales manipulation and overproduction lead to an abnormally low cash flow from operation, while the reduction of discretionary expenses leads to an abnormally high cash flow from operation. Therefore, the net effect on cash flow from operation by real earnings management is ambiguous, as stated in Roychowdhury (2006). Consequently, consistent with Gunny (2010) and Zang (2012), we do not use the proxy with cash flow from operation.

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researches, we employ the contemporaneous correlation between the change in discretionary expenses and the change in sales (REM2) as a proxy for the manipulation of discretionary expenditures. If discretionary expenditures are manipulated to manage earnings, REM2 should show a lower value. We define discretionary expenses as selling, general, and administrative expenses (SG&A), which frequently include discretionary expenses such as R&D, advertising, employment costs, etc. REM2 is also computed from the pooled sample in each country.REM2 = (DE, Sales)(6)

DE = 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 is variable if outside investors can reflect their views easily in shareholders meetings; it is calculated by adding 1 when: (1) the country allows shareholders to mail their proxy vote to the firm, (2) shareholders 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 oppressed minorities 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, and consequently, reduce the incentives of insiders to engage in accrual-based earnings management. Based on the

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argument of Leuz et al. (2003) and the discussion in section 2, we predict that the rights of outside investors negatively affect AEM and positively affect REM.

3.2.2. Legal enforcement

The research in 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 the framework of La Porta et al (1998)., Leuz et al. (2003) suggested legal enforcement as the institutional factors influencing AEM. Following Leuz et al. (2003), we measure Legal Enforcement as the mean score 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 range from 0 to 10. Similar to the rights of outside investors, legal enforcement is negatively related to AEM and positively related to REM.

3.2.3. Disclosure regulations

Disclosure regulations play an important role in investor protection because less information is available to minority investors than to insiders, and the former have to depend on the information that firms are required to disclose. We predict that since it is difficult to implement AEM in countries that have stricter disclosure regulations, which might lower earnings quality, managers tend to apply REM instead.

Previous studies have often used two indexes relating to the disclosure regulations of each country (e.g., Jirasakuldech et al., 2011). One is the index that comes from the Center for International Financial Analysis and Research (CIFAR) database and the other, from the Doing Business project of the World Bank (the World Bank disclosure index).5 We use the latter as the variable for disclosure regulation due to the limited data in the CIFAR index. The World Bank disclosure index measures the extent to which investors are protected through

5 This data can be obtained at the World Bank website, http://data.worldbank.org/ (last accessed 15th March 2013).

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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 among the important information intermediaries in securities markets (Healy and Palepu, 2001). The extent of analyst following could affects earnings management in a country. Some previous studies indicate that monitoring and pressure from analysts affects AEM. For example, Yu (2008) and Degeorge et al. (2013) provide evidence that analyst following negatively affects AEM. If analysts are sophisticated monitors of AEM, it is difficult for managers to engage in AEM, and they therefore carry out REM in the presence of analysts. Thus, we predict that analyst following will negatively affect AEM and positively affect REM.In this regard, however, Cohen and Zarowin (2010) find a negative association between analyst following and total EM, which is a metric that includes both AEM and REM.6

Their results indicate that AEM and REM are restrained because analysts scrutinize and monitor firm activities.7 Thus, note that 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 and investor protection, we constructed the following regression models, which are based on Leuz et al. (2003). Equations (7) to (10) are single regression models, and

However, they do not compare the relationships of REM and AEM with analyst following.

According to Cohen and Zarowin (2010), there could be two different views regarding analyst following: (1) AEM and REM are restrained because analysts scrutinize and monitor firm activities and (2) AEM and REM are accelerated because analyst forecasts serve as incentives for managers, who strive to meet or beat them.

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equation (11) is a multiple regression model that includes the four independent variables used in single regression models. Following Leuz et al. (2003), we estimate these models by rank regression.8

AEMi (or REMi) = 0 + 1Outside Investor Rightsi + i(7)AEMi (or REMi) = 0+ 1Legal Enforcementi + i(8)AEMi (or REMi) = 0+ 1Disclosure Indexi + i(9)AEMi (or REMi) = 0+ 1 Analyst Followingi + i(10)AEMi (or REMi) = 0+ 1Outside Investor Rightsi + 2 Legal Enforcementi+ 3 Disclosure Indexi + 4 Analyst Followingi + i(11)AEM = the aggregateaccrual-based earnings managementscore, REM = the

aggregate real earnings management score, Outside Investor Rights = the anti-director rights index from La Porta et al. (1998), Legal Enforcement = the average score across three legal variables used by La Porta et al. (1998), Disclosure Index = the disclosure index from the World Bank, which ranges from 1 to 10, Analyst Following = the average number of analyst following for all firms in each country, i = country, and = 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).

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, from 1991 to 2010, which were selected from Capital IQ. The former criterion is for comparison to 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 the countries,

8 OLS regression with unranked variables produces similar results.

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at least 300 firm-years, to allow us to obtain sufficient data 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. Argentina, Brazil, Peru, Turkey, Venezuela, and Zimbabwe were thus cut from our sample, leaving a final sample of 222,513 firm-year observations from 38 countries.

Table 1 provides the descriptive statistics for each country. Panel A shows the number of firm-years in each country and year. Our total sample (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 number of firm-years in each country; the United States has the largest at 53,072, followed by Japan, India, and Canada. Nigeria has the smallest at 300. The bottom row provides the number of firm-years in each year, the smallest of which was in 1991 and the biggest, in 2010. In general, the observations we can obtain increase by the year. Canada and India, for example, had scanty data in the early 1990s, but after the second half of the decade, a large amount of data was included in Capital IQ.

Panel B shows the descriptive statistics of fundamental variables in each country. Considerable variations of other measures are also observed in Panel B due to the economic environment and the difference in the development of the capital market from country to country. Hence, all of the financial statement variables we use are divided by total assets to mitigate the effect of the size.

Panel C provides the descriptive statistics of institutional variables in each country.9

The institutional variables mainly rely on La Porta et al. (1997; 1998) and Leuz et al. (2003).

Legal Origin and Legal Tradition are shown in columns 1 and 2. There are two steps to the calculation of the Importance of Equity Market. First, in La Porta et al. (1997), the three variables that follow are ranked: (1) the ratio of the aggregate stock market capitalization held by minorities to the gross national product (GNP), (2) the number of listed domestic firms

9 The institutional variables for the regression analysis are given in Table 3.

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relative to the population, and (3) the number of IPOs relative to the population. Second, we compute the average rank of the three variables. Ownership concentration is the median ownership stake of the three largest shareholders among the 10 largest publicly traded companies (La Porta et al., 1998).

[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 their aggregated scorethe average rank of three measures (rightmost column). Lower

AEM1 and AEM2 scores imply that accrual management is used to reduce the variation of earnings and conceal economic shocks to the firms operating cash flow. For AEM1, Australia has the highest score and Portugal, the lowest, followed by Spain, Italy, Austria, and South Korea. This ratio is maintained in AEM2. Both measures are higher in Anglo-American countries than in Asia and Continental Europe, indicating that Anglo-American countries employ the lower extent of AEM. AEM3 (accruals divided by CFO) is reflected by various manager behaviors in their attempt to achieve specific earnings targets. All three variables,AEM1, AEM2, and AEM3 share similar tendencies.10 Therefore, we may use the aggregated accrual-based earnings management score (AEM), which is the average rank of the three measures.


REM1 is the extent to which production manipulation for earnings management is pervasive in a country. The mean is 0.787, with Japan posting the highest value of 0.928.

10 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 of Leuz et al. (2003) in total. 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 to the extended sample periods.

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South Korea, Taiwan, Austria and the Netherlands show a lower level of earnings management.

REM2 is the correlation between the change in sales and the change in discretionary expense. If discretionary expense is rarely managed, REM2 is expected to rise, as discretionary expenses typically increase in proportion to sales. The balance between discretionary expenses and sales is negatively affected when the former is managed. The results of REM2 are similar to those of REM1. REM2 provides the evidence that Oceanic countries tend to employ REM more than AEM.

The rightmost column of Panel B shows the aggregated score of REM measures. The result of REM2 is also similar to that of REM1. Hence, we may calculate the aggregated real earnings management score (REM) in the same manner as we calculated AEM.Table 3 contains the institutional variables for the regression analysis of the 38 countries. The Outside Investor Rights and Legal Enforcement are similar to those of Leuz et al. (2003). A characteristic of the Disclosure Index is that all the developed countries do not have high scores (e.g., Germanys index is five). Overall, the Analyst Following of European countries is relatively high. The two variables are added 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). The results imply the substitutability 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.


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4.2. Regression results

Panels A and B of Table 5 show the regression results. In Section 2, we hypothesized that AEM (REM) is constrained (more prevalent) in countries with better investor protection. Higher values for Outside Investor Rights, Legal Enforcement, and Disclosure Index mean better investor protection. Thus, the predicted signs for the coefficient on these variables are negative (positive) when the dependent variable is AEM (REM). However, we do not predict the signs for the coefficients of Analyst Following because the effect of analysts on earnings management is still obscure.

Since the results of single and multiple regressions are similar in Panels A and B, we describe only the results of 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 (-0.395, t = -2.549 and -0.474, t = -2.756). These results support Hypothesis 1 and indicate that AEM is less prevalent in countries with better investor protection. However, the coefficients of the Disclosure Index and Analyst Following are insignificant (-0.058, t = -0.391 and 0.081, t = 0.460).

Panel B carries the results for REM. As expected, they are contrary to the results for

AEM. The coefficient of Outside Investor Rights is significantly positive (0.373, t = 2.298), which is consistent with Hypothesis 2, and indicates that REM is more prevalent in countries with better investor protection. The coefficient on Legal Enforcement is not significant (0.160, t = 0.887). As with the results for AEM, the coefficient of the Disclosure Index is insignificant (0.065, t = 0.421). In contrast to the results for AEM, the coefficient of Analyst Following is significantly negative (-0.416, t = -2.262). This result contradicts our hypothesis but is consistent with Cohen and Zarowin (2010), who find a negative association between analyst following and earnings management. This finding is consistent with the notion that monitoring by analysts influences the behavior of managers and limits REM.

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Overall, the results in this section indicate that AEM is constrained in countries with better investor protection, thus making REM more prevalent. Managers in countries with better investor protection tend to engage in REM instead of AEM. In addition, we find evidence that REM is constrained by analyst following, but no evidence of a relationship between AEM (REM) and the level of the disclosure index.


5. Robustness check

Previous research considers the per capita gross domestic product (Leuz et al., 2003) and Big 4 audits (Francis and Wang, 2010) as other factors that could affect earnings management. In this paper, we omit the regression analysis of per capita GDP because of a serious multicollinearity.11 Consequently, we reestimate equation (11), including the ratio of firms audited by Big 4 in each country as an additional independent variable (indicated by theBig4 Ratio).12 The rank regression results, including the Big4 Ratio, are presented in Table 6. Although the coefficients of the Big4 Ratio are not significant (-0.051, t = -0.294 and 0.046, t = 0.254), 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.


In Section 4.2, we follow Leuz et al. (2003) and compute the legal enforcement score as the average across three variables used in La Porta et al. (1998): (1) the efficiency of the

The correlation between rank of per capita GDP and analyst followings is 0.71. In addition, the VIF for per capita GDP in rank regression is 5.63.

We exclude the Big4 Ratio from the main regression models to ensure the sample size as much as possible in the main analysis.

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judicial system, (2) an assessment of rule of law, and (3) the corruption index.13 In this section, we create a newer legal enforcement variable using available data in the sample period and reestimate equation (5). In particular, to recompute 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). Table 7 shows the rank regression results using the new proxy for legal enforcement. The results are the same as before and are thus robust when substituting them with the variable of Legal Enforcement.14


Next, due to the aggregation of EM by country in Table 5, the analysis of the relationship between investor protection and earnings management in our sample focuses on an in-depth examination of 222,513 firm-years, which is a larger sample base when compared to the standard data from 38 countries. To address this problem of decreased sample size, we use measures of EM calculated by country and year and re-estimate equation (11). 15

The recalculation increases the sample size from 38 to 549. To run the regression equation (11), two types of EM 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 in Table 8 are similar to those in Table 5 for AEM, whereas for REM, the coefficient of Legal Enforcement is significantly positive at a marginal level and the coefficient of the Disclosure Index is significantly positive. In summary, the results of this additional analysis support Hypothesis 2, which corroborates the results of previous analyses.

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

Furthermore, when the disclosure index is replaced by Disclosure Requirement in La Porta et al. (2006), the results are also similar to those shown in Table 5.

Because each country-year requires at least 30 observations to obtain two EM measures, the amount of data for each country is unbalanced (for example, Japan has data for 20 years, whereas Nigeria has data only for five years). Hence, we do not adopt country-year based analysis as the primary analysis. 18


6. Conclusion

This paper examined the relationships between earnings management and investor protection across 38 countries. Our tests were based on the data from 1991 to 2010 over 280,000 firm-years observations from the countries. Leuz et al. (2003) show evidence that strong investor protection limits earnings management. While their measures of earnings management are mainly accrual-based, we focused on both REM and AEM. REM is based on operational activities, and following Roychowdhury (2006, 338), accrual manipulation is more likely to draw the scrutiny by auditor and regulator than real decisions about pricing and production. Therefore, we hypothesized that investor protection has an adverse effect on REM as compared to AEM.

Consistent with our hypothesis, outside investor rights is negatively correlated to REM and positively correlated to AEM. However, we provide evidence that the more analysts investigate firms, the less REM is carried out. The results do not change under the control of audit quality, the alternative definitions of investor protection variables, and the use of AEM and REM measures that are calculated by country and year. Our results for REM conflict with those of Leuz et al. (2003). In summary, differences in investor protection among countries have an effect on their earnings management methods.

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 REM and AEM has a negative effect on a firms future ROA and stock price.

19

Our results show that strong investor protection restricts AEM, but induces a shift to REM. Moreover, our results also imply that strong investor protection heightens the risk of firm value reduction by REM and the existence of analysts is effective in monitoring REM.As with prior research, we have a parsimonious model in calculating the proxies for AEM and REM due to the limited data from Capital IQ. Future research is required to improve the model to estimate AEM and REM using detailed international accounting data.

Acknowledgments

The authors gratefully acknowledge the financial support from The Japan Securities Scholarship Foundation.

References

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Cohen, D. A., P. Zarowin, 2010. Accrual-based and real earnings management activities around seasoned equity offerings. Journal of Accounting and Economics. 50, 2-19.

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Dechow, P. M., S. P. Kothari, R. L. Watts. 1998. The relation between earnings and cash flows. Journal of Accounting and Economics 25, 133-168.

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Jirasakuldech, B., T. Zorn, J. Geppert, D. Dudney, 2011, Financial disclosure, investor protection, and stock market behavior: an international comparison, Review of Quantitative Finance and Accounting, 37, 181-205.Kothari, S.P., N. Mizik, S. Roychowdhury. 2012. Managing for the Moment: The Role of Real Activity versus Accruals Earnings Management in SEO Valuation. Working paper.

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22

Table 1 Descriptive statistics

Panel A: Firm-year observations in each country

Country19911992199319941995199619971998199920002001200220032004200520062007200820092010Total

Australia454950525352651072442783684916266867067488168119151,0038,165

Austria1014141515131425314249495457575758616059754

Belgium16161617152217273661666666697891948889881,038

Canada223610272014635556567167368408649149521,0431,0121,0831,14611,231

Chile13142426293735407084891011101141151191231271291301,529

Denmark28282826262833445470808386868892959595921,257

Finland25252726282730435074889197981001011021041051071,348

France95991001001001221221762233033413604024304604955095085135185,976

Germany1131151151111111001101842603434144194624704995415555485375246,531

Greece000004626375158667983931231381641761831,287

Hong Kong3032484957861131502463444395796787167608058198368508818,518

India114457525376507708751,0981,5851,7701,8891,9782,0502,1252,30617,185

Indonesia022528315263851171251321221261251281431571821962172,056

Ireland1818181819182123272835334040434449535454653

Israel001244533446577781021161231421481431652121,464

Italy161615182033305667911211321441531631761911982052072,052

Japan8569109549791,0171,0381,0051,0911,1561,5641,7281,9802,1282,2162,3152,4352,6633,1413,2333,24135,650

Jordan000000001337455358677737345390

Malaysia70737676801141642002613514294585005425946567077277597887,625

Mexico00623151526415056636872777979818487881,010

Netherlands353737373539466371899491991011031041111131151161,536

New Zealand5555791115224047536478838387859193888

Nigeria0000001110041332425654383127300

Norway1817191923252132365162698188961091231331441501,316

Pakistan000001140769786977470921101271511601751891,555

Philippines1155231022374460638993961041061031081121,064

Portugal112239712121623273537383839404041423

Singapore383838404996991151532062582923343603944144254544714914,765

South Africa181818181822244063801091271461491621761871982142222,009

South Korea00001041131361491611972983554425345996991,3231,3171,3079368,670

Spain7828414447465764747883889299991001031051031,366

Sri Lanka00000001255561518243071140158480

Sweden24252527263038771011381571742052252382623013193443663,102

Switzerland55565757596360881021281351411521571671711761781801832,365

Taiwan00011112161802402963678719851,0331,1921,3231,3991,4619,369

Thailand223639436859721121762092272472713123383533653773903,698

UnitedKingdom27227928128028126024134642049754258067369773979686588992495410,816

United States2463117491,0181,4151,9322,4982,7472,8882,9743,1723,1063,5723,6133,7303,8753,9793,6113,6983,93853,072

Total2,0602,2122,8243,1653,7454,5275,4036,7547,92410,22011,65212,56114,43816,08517,15418,26320,00520,46921,27321,779222,513

23

Panel B: Descriptive statistics of fundamental variables and investor protection variables in each country

Fundamental variables

Investor protection variables

Median ofMedian ofPercentagePer Capita

Variance of

LegalImportanceOwnership

Country

Capitalof Mfg.

InflationGDP

Legal Origin

of Equity

Firm Size

GDP

Tradition

Concentration

IntensityFirms

Growth

Markets

Australia14.5380.5850.270286522.611.23

EnglishCM29.000.28

Austria247.9070.5080.647317942.130.87

GermanCD8.670.51

Belgium193.2950.4660.577301962.070.83

FrenchCD13.330.62

Canada13.5800.6150.319281642.010.61

EnglishCM28.330.24

Chile234.3180.7210.40760805.821.57

FrenchCD23.000.38

Denmark135.0820.4510.575393612.090.79

ScandinavianCD23.170.40

Finland234.1220.4640.628307201.701.61


France147.1840.3750.460297831.720.79

FrenchCD11.000.24

Germany115.2430.4250.526303541.890.95

GermanCD6.670.50

Greece220.0730.4930.458169976.351.02

FrenchCD13.500.68

Hong Kong99.9480.4470.475248482.970.47

EnglishCM34.500.54

India25.9630.5050.7416057.690.95

EnglishCM17.670.43

Indonesia127.1260.5450.603127911.553.81

FrenchCD5.330.62

Ireland636.2820.5260.547330152.521.10

EnglishCM21.000.36

Israel77.2570.3730.479197755.900.62

EnglishCM26.330.55

Italy406.9990.4650.570254162.950.92

FrenchCD8.170.60

Japan328.6400.4420.546351470.290.81

GermanCD20.170.13

Jordan26.2050.5440.58121603.910.49

FrenchCD26.00N.A

Malaysia56.7000.5120.59848192.881.51

EnglishCM30.670.52

Mexico1007.5840.6790.444670111.691.96

FrenchCD6.500.67

Netherlands391.5580.4740.467320782.230.75

FrenchCD23.500.31

New Zealand64.7010.5910.342196682.261.79

EnglishCM25.170.51

Nigeria66.1970.4170.73059321.758.87

EnglishCM10.250.45

Norway182.3530.5690.415492842.180.88


Pakistan41.8500.5460.8566629.080.56

EnglishCM10.250.41

Philippines97.2710.6810.34412496.681.16

FrenchCD7.330.51

Portugal545.5100.6530.359147153.670.92

FrenchCD13.830.59

Singapore60.0040.4290.450257021.681.02

EnglishCM34.500.53

24

South Africa151.6700.4750.34240907.502.22

EnglishCM19.670.52

South Korea122.6850.4980.750132654.152.11

GermanCD14.330.20

Spain466.2070.5210.541204843.351.12

FrenchCD8.500.50

Sri Lanka20.9640.6600.523111810.210.48

EnglishCM8.670.61

Sweden39.4880.4540.514346081.921.41


Switzerland423.6160.4630.634452841.410.66

GermanCD29.750.48

Taiwan92.6370.4290.851140141.910.49

GermanCD16.330.14

Thailand58.3800.5590.58427233.581.49

EnglishCM17.670.48

United Kingdom88.6350.4650.378286272.390.76

EnglishCM30.330.15

United States91.9020.4720.489356822.600.05

EnglishCM28.330.12

Mean193.5180.5130.527202564.451.31

18.740.42

Median118.9640.4950.525226662.750.93

18.670.48

Std207.3380.0860.141142474.091.42

8.660.16

Firm Size is represented by US$ total assets (in million). Capital Intensity is the percentage of long-term assets to total assets. Percentage of Manufacturing Firms is computed from the firm-year percentage, whose SIC code is from 2000 to 3999. Per Capita GDP is the mean value from 1991 to 2010. Inflation is the mean value of the change in consumer prices from 1991 to 2010. Volatility of GDP Growth is the standard deviation of the GDP growth rate from 1991 to 2010.

Legal Origin and Legal Tradition in La Porta et al. (1998) are applied. The calculation of Importance of Equity Market has two steps in La Porta et al. (1997). We first rank the following three variables: (1) the ratio of the aggregate stock market capitalization held by minorities to the GNP, (2) the number of listed domestic firms relative to the population, and (3) the number of IPOs relative to the population. Each variable is ranked in such a way that higher scores indicate a greater importance of the stock market. Then we compute the average rank of the three variables. Ownership Concentration is the median ownership stake of the three largest shareholders among the 10 largest publicly traded companies (La Porta et al., 1998).

25

Table 2 Score for earnings management

Panel A: Score for accrual-based earnings management

Country

AEM measures

Score for AEM

AEM1AEM2AEM3

Australia0.766-0.5320.4211.3

South Africa0.759-0.7470.4954.7

Sweden0.755-0.6590.5795

United States0.679-0.6140.6066

Canada0.704-0.5640.6356.3

United Kingdom0.583-0.690.5618

Hong Kong0.599-0.7140.67711

Finland0.679-0.8160.66213

New Zealand0.591-0.8130.57613

Ireland0.492-0.7490.51213.3

Philippines0.667-0.7830.72713.7

Norway0.568-0.7380.69514.7

Denmark0.572-0.7770.68915

Switzerland0.554-0.7850.62115

Israel0.62-0.7330.79915.3

Netherlands0.51-0.7810.61615.3

Mexico0.539-0.8260.56515.7

Thailand0.592-0.8320.66316.3

Chile0.577-0.8390.60716.7

Pakistan0.61-0.8860.7321.7

Jordan0.499-0.8010.75922.3

India0.824-0.860.90922.7

Malaysia0.61-0.8370.81722.7

Singapore0.528-0.8030.78722.7

Sri Lanka0.469-0.8030.72822.7

Germany0.513-0.7970.8123

Belgium0.471-0.8350.72623.7

Taiwan0.574-0.8540.7824

Austria0.452-0.8470.73527.7

Nigeria0.508-0.9160.75128.3

Spain0.391-0.8990.69629.7

France0.432-0.8690.77930.7

Indonesia0.461-0.8460.82130.7

Panel B: Score for real earnings management

CountryREM measuresScore for REM

REM1REM2

Japan0.7530.9281

Austria0.6770.8654

Mexico0.7320.8534.5

Netherlands0.6510.8595.5

Greece0.6450.8616.5

Switzerland0.7140.8277

South Korea0.5970.8957.5

Chile0.6410.8289.5

Ireland0.6450.82410

Belgium0.6000.84610.5

Denmark0.6460.81711

Taiwan0.5490.87711

Thailand0.5750.85211.5

Finland0.6090.81813

Germany0.5700.82415

Indonesia0.5850.79716

Spain0.6400.78217

Italy0.5560.79618.5

Nigeria0.4790.82518.5

France0.5600.78121

United States0.5350.79121

Singapore0.4560.79623.5

Israel0.5170.77424.5

United Kingdom0.5240.76524.5

Norway0.4510.78825.5

Portugal0.4020.80425.5

India0.4900.74726

South Africa0.4040.79227

Sweden0.4830.73327.5

Hong Kong0.4220.77628

Jordan0.4670.71529.5

Pakistan0.4440.72630.5

Malaysia0.3450.74433.5

26

Japan0.475-0.8750.81931.3

Philippines0.4120.64434

South Korea0.452-0.8690.87733.3

New Zealand0.4180.55634.5

Italy0.406-0.9070.81634.3

Canada0.3720.69535

Portugal0.267-0.9320.79835

Sri Lanka0.3710.70135

Greece0.458-0.9240.90635.3

Australia0.3360.62237.5

Mean0.558-0.7990.704

Mean0.5330.787

Median0.561-0.8150.727

Median0.5420.796

Std.0.1170.0940.116

Std.0.1130.076

In Panel A, countries are sorted by accrual-based earnings management scores. Each variable is computed using 222,513 firm-year observations from 1991 to 2010. AEM1 is based on the ratio of the standard deviation of 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 of operating cash flows, and is calculated in each firm-year. AEM3 represents each countrys median data. The score for AEM is the average rank ofAEM1, AEM2, and AEM3.

In Panel B, countries are sorted by real management scores. 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 the average rank of REM1 and REM2.

27

Table 3 Institutional variables in each country

CountryOutside InvestorLegal EnforcementDisclosure IndexAnalyst Following

Rights

Australia49.5182.496

Austria29.3624.647

Belgium09.4485.650

Canada59.7582.651

Chile56.5281.112

Denmark210.0074.755

Finland310.0067.590

France38.68105.665

Germany19.0255.509

Greece26.8212.212

Hong Kong58.91102.230

India55.5871.967

Indonesia22.8882.970

Ireland48.36109.362

Israel37.7271.482

Italy17.0775.204

Japan48.2472.244

Jordan16.1650.365

Malaysia47.72102.011

Mexico15.3774.227

Netherlands210.0049.692

New Zealand410.00102.525

Nigeria34.3450.872

Norway410.0075.642

Pakistan53.6761.036

Philippines33.4711.285

Portugal37.1965.403

Singapore48.93101.811

South Africa56.4583.211

South Korea25.5562.384

Spain47.1459.349

Sri Lanka34.6640.155

Sweden310.0024.270

Switzerland210.0027.990

Taiwan37.3762.200

Thailand24.89102.730

United Kingdom59.22106.046

United States59.5475.209

Mean3.137.626.583.846

Median37.9872.850

Std1.402.142.642.571

Outside Investor Rights mean anti-director rights, which is an index aggregating shareholder rights. The anti-director rights index is formed by adding 1 when: (1) the country allows shareholders to mail their proxy vote to the firm, (2) shareholders 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 oppressed minorities mechanism is in place; (5) the minimum percentage of share capital that entitles a shareholder to call for an extraordinary shareholders meeting is less than or equal to 10 percent (the sample 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 average score 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. All three variables range from 0 to 10. The Disclosure Index is obtained from World Bank 2005. Analyst Following is the number of analyst following per firm-year in each country.

28

Table 4 Correlation between AEM, REM, and institutional variables

Score forScore forOutsideLegalDisclosure

InvestorEnforcemen

AEMREM

Index

Rightst

Score for REM-0.381

(0.019)

Outside Investor-0.4230.465

Rights(0.008)(0.003)

Legal-0.392-0.0760.073

Enforcement(0.015)(0.649)(0.665)

Disclosure-0.080-0.411-0.1640.525

Index(0.633)(0.010)(0.325)(0.001)

Analyst-0.1590.1740.3210.081-0.048

Following(0.341)(0.296)(0.050)(0.629)(0.774)

The variables are defined in Tables 2 and 3. n=38.

29

Table 5 Regression results of earnings management and investor protection

Panel A: Regression results for accrual-based earnings management (Dependent variable = AEM)

Dependent variable = AEM

Constant28.545***28.414***23.570***22.007***36.002***

(8.462)(8.549)(6.386)(5.956)(7.611)Outside Investor Rights-0.464***----0.395**

(-3.061)

(-2.549)Legal Enforcement--0.457***---0.474***

(-3.075)

(-2.756)Disclosure Index---0.209--0.058

(-1.260)

(-0.391)Analyst Following-

--0.1290.081

(-0.778)(0.460)R2 (or Adjusted R2)0.2070.2080.0420.0170.319Number of Observations3838383838

Panel B: Regression results for real earnings management (Dependent variable = REM)

Dependent variable = REM

Constant10.074***20.561***16.160***27.002***15.949***

(3.019)(5.511)(4.345)(7.849)(3.220)Outside Investor Rights0.483***---0.373**

(3.224)

(2.298)Legal Enforcement--0.054--0.160

(-0.326)

(0.887)Disclosure Index--0.171-0.065

(1.026)

(0.421)Analyst Following----0.385**-0.416**

(-2.502)(-2.262)R2 (or Adjusted R2)0.2240.0030.0280.1480.254Number of Observations3838383838

The t-statistics are in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels (two-tailed).For equation (11), R2shows the adjusted R2. All variables are defined in Tables 2 and 3.The equations are:AEMi (or REMi) = 0 + 1Outside Investor Rightsi + i(7),AEMi (or REMi) = 0 + 1Legal Enforcementi + i(8),AEMi (or REMi) = 0+ 1Disclosure Indexi + i(9),AEMi (or REMi) = 0+ 1Analyst Following + i(10), andAEMi (or REMi) = 0+ 1Outside Investor Rightsi + 2 Legal Enforcementi

+ 3 Disclosure Indexi + 4 Analyst Followingi + i(11).

30

Table 6 Regression results of earnings management and investor protection: Controlling for Big 4 audits ratio

AEMREM

Constant30.623***14.771***

(6.988)(3.203)Outside Investor Rights-0.451**0.371*

(-2.473)(1.934)Legal Enforcement-0.428**0.132

(-2.403)(0.703)Disclosure Index-0.0090.048

(-0.059)(0.285)Analyst Following0.138-0.466**

(0.813)(-2.603)Big4 Ratio-0.0510.046

(-0.294)(0.254)Adjusted R20.34850.2790Number of Observations3333

The t-statistics are in parentheses. ***, **, and * indicate significance at 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.The equations are as follows:

AEMi (or REMi) = 0 + 1 Outside Investor Rightsi + 2 Legal Enforcementi

+ 3 Disclosure Indexi + 4 Analyst Followingi + 5 Big4 Ratioi + i.

31

Table 7 Regression results of earnings management and investor protection: Another proxy for Legal Enforcement

AEMREM

Constant36.409***16.291***

(7.583)(3.235)Outside Investor Rights-0.391**0.391**

(-2.493)(2.375)Legal Enforcement-0.437**0.065

(-2.638)(0.376)Disclosure Index-0.0750.062

(-0.502)(0.396)Analyst Following0.036-0.354*

(0.215)(-1.992)Adjusted R20.3080.240Number of Observations3838

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

AEMi (or REMi) = 0 + 1 Outside Investor Rightsi + 2 Legal Enforcementi + 3 Disclosure Indexi + 4 Analyst Followingi + i

32

Table 8 Regression results of earnings management and investor protection based on country-year earnings management measures

AEMREMConstant42.806***11.409***

(13.812)(3.195)Outside Investor Rights-0.324***0.150***

(-8.468)(3.409)Legal Enforcement-0.393***0.085*

(-9.211)(1.73)Disclosure Index-0.0540.106**

(-1.495)(2.54)Analyst Following-0.051-0.160***

(-1.147)(-3.145)Year_DummyincludedincludedAdjusted R20.3620.153Number of Observations548548

The t-statistics are in parentheses. ***, **, and * indicate significance at 1%, 5%, and 10% levels (two-tailed). All variables are defined in Tables 2 and 3.The equations are:AEMit (or REMit) = 0 + 1 Outside Investor Rightsit + 2 Legal Enforcementit

+ 3 Disclosure Indexit + 4 Analyst Followingit + Year_Dummy +it

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