DP2013-34

A Cross-Country Study on the
Relationship between Financial
Development and Earnings Management
Masahiro ENOMOTO
Fumihiko KIMURA
Tomoyasu YAMA GUCHI
Revised October 27, 2017

A Cross-Country Study on the Relationshipbetween Financial Development and Earnings Management

Masahiro Enomoto *Kobe University, Kobe, Japan
Fumihiko Kimura †Tohoku University, Sendai, Japan
Tomoyasu Yamaguchi ‡Tohoku Gakuin University, Sendai, Japan
Southern Cross University, Gold Coast, Australia
October 2017*

Corresponding author
Research Institute for Economics and Business Administration, Kobe University, 2-1 Rokkodaicho, Nada-ku, Kobe
657-8501, Japan. E-mail: menomoto@rieb.kobe-u.ac.jp. Phone: +81-78-803-7031; fax: +81-78-803-7031.
† Graduate School of Economics and Management, Tohoku University, 2-2-1 Katahira, Aoba-ku, Sendai 980-8576,
Japan. E-mail: fkimura@tohoku.ac.jp. Phone: +81-22-217-6282; fax: +81-22-217-6282.
‡ Faculty of Business Administration, Tohoku Gakuin University, 1-3-1 Tsuchitoi, Aoba-ku, Sendai 980-8511, Japan.
E-mail: yamaguchi@mail.tohoku-gakuin.ac.jp. Phone: +81-22-721-3471; fax: +81-22-721-3471.
School of Business and Tourism, Southern Cross University, Southern Cross Drive, Bilinga, Queensland 4225,
Australia. E-mail: tomoyasu.yamaguchi@scu.edu.au (On sabbatical leave from September 2017 to August 2018)

A Cross-Country Study on the Relationshipbetween Financial Development and Earnings Management

Abstract
This paper investigates whether a country’s level of financial development is associatedwith earnings management in an international setting. Financial development is likely toheighten the monitoring and scrutiny of accounting numbers because of strengthened
investor protection laws and regulations as well as sophisticated market participants.
Therefore, we first hypothesize that both accrual-based and real earnings management
decrease with greater financial development. However, research shows that managers
tend to apply real earnings management, instead of accrual-based earnings management,
under strict accounting standards, regulations, and close auditor scrutiny. Thus, we
explore the alternative hypothesis that accrual-based earnings management decreases
but real earnings management increases along with higher financial development. We
examine the relationship between financial development and both types of earnings
management using 56,830 observations in 37 countries covering the period 2009 to
2012. The results indicate that both types of earnings management are more restrained
under higher levels of financial development.

Keywords: financial development, accounting institution, accrual-based earnings
management, real earnings management
JEL classification: M41

1. Introduction
This paper investigates whether a country’s level of financial development is associated withearnings management. Beck and Levine (2002) define “financial development” as the degree to which
national financial systems assess firms, monitor managers, facilitate risk management, and mobilizesavings. Financial accounting is required to provide investors with useful information on decision-making
under the accounting system of each country and assist in furthering financial development.
However, it is not clear whether higher-quality accounting information is available in countries withhigher levels of financial development. For instance, several countries with highly developed financial
systems have suffered accounting scandals such as those involving Enron and WorldCom (the U.S.), Tesco(the U.K.), Biovail (Canada), One.Tel (Australia), and Olympus (Japan). In response to such scandals,
national financial authorities and legislatures have enacted laws to strengthen audits and internal control
systems (Brown et al., 2014a), which have improved the transparency of firms and enabled stakeholders to
obtain high-quality accounting information. These events illustrate how levels of financial development areassociated with advances in financial accounting regulation. Financial development is likely to promoteenhanced monitoring and scrutiny through the legal/regulator system, as well as incentives fortransparency and reduced information asymmetry. Hence, financial development probably influences thefinancial accounting information provided by managers.
According to Dechow et al. (2010), earnings management is a crucial factor affecting accounting
information quality. The research shows that earnings management is related to monitoring, transparency,
and information asymmetry, which are potential effects of financial development. For example, Yu (2008)
finds that firms followed by more analysts engage less in earnings management, indicating that earnings
management is restrained by analyst monitoring. Abad et al. (2016) find that real earnings management isassociated with higher information asymmetry. Jo and Kim (2007) indicate that transparency-increasing
disclosure reduces managers’ incentives to manage earnings. Thus, it is useful to investigate therelationship between financial development and earnings management.
Earnings management can be divided into accrual-based earnings management (AEM) and realearnings management (REM). AEM alters the accrual process in order to manage earnings. This leads to areversal of accruals in subsequent periods but does not have a direct effect on cash flows. REM reflects afirm’s real activities and manages earnings by changing the timing and/or structure of an operating,1

investment, or financial decision. REM can reduce future cash flows via non-optimal decisions such asopportunistic reductions in R&D and advertising costs.
Cross-country research on earnings management has often considered investor protection as one of
the important factors affecting managerial behaviors. Leuz et al. (2003) show that earnings management isnegatively associated with the quality of minority shareholder rights. They use an anti-director rights index
from La Porta et al. (1997) and a legal enforcement index, measured as the average score of three legal
variables, used by La Porta et al. (1998). Although the legal aspects of investor protection are the mostimportant factors in financial development (Beck et al., 2001), they do not capture all financial
development factors. We adopt the index provided by the World Economic Forum (WEF) to measurefinancial development. The WEF defines “financial development” as the factors, policies, and institutions
that lead to effective financial intermediation and markets, as well as deep and broad access to capital andfinancial services (World Economic Forum, 2012). The WEF index consists of 121 comprehensive
financial development variables and thus covers more financial development aspects than are considered inprior research. Our financial development scores consider the evaluation of laws and regulations and theeconomic impacts of banking and security market systems.
Financial development is likely to heighten the monitoring and scrutiny of accounting figures
because it involves strengthened investor protection laws and regulations and increases the number ofsophisticated market participants. Here, financial development would likely act to discipline managers,
thereby reducing their involvement in earnings management. We therefore hypothesize that both AEM and
REM decrease with greater financial development. However, the research shows that managers tend to
apply REM instead of AEM when facing stricter accounting standards and related regulations (Ewert andWagenhofer, 2005; Cohen et al., 2008; Enomoto et al., 2015). We thus alternatively hypothesize that, while
AEM decreases, REM increases with higher financial development.
This paper examines the relationship between two types of earnings management and financial
development using 56,830 observations in 37 countries covering the period 2009 to 2012. Our results showthat managers are more restrained from using both AEM and REM in countries with higher financial
development. Our empirical results withstand several robustness checks.
We extend earnings management research by investigating the relationship between financial
development measures and two types of earnings management. Our financial development score, based on
2

WEF as the institutional factor, is more comprehensive than the scores used in other studies (e.g., Leuz etal., 2003; Degeorge et al., 2013). This score includes the economic dimensions of financial systems as wellas the quality of relevant laws and regulations, which is the focus of La Porta et al. (1997, 1998).
Moreover, we add REM measures to capture managers’ real activities, which are predicted to vary
according to national financial development levels.
To the best of our knowledge, our study is the first to show that financial development restrains both
AEM and REM. The results indicate that financial reporting processes improve as financial developmentlevels increase by reducing managerial intervention in the accounting accrual process and that firm
activities become more appropriate through reduced myopic behavior, such as lower R&D costs.
Our study should be important to wider audiences. Previous studies have shown that tighter
accounting standards restrict AEM but increase REM (Ewert and Wagenhofer, 2005; Cohen et al., 2008).
Considering that REM likely has a negative impact on firm performance (Bhojraj, 2009; Cohen andZarowin, 2010), regulatory authorities and researchers should be interested in exploring the institutional
environment that restrains both AEM and REM simultaneously. Our findings also suggest that accounting
numbers reported by firms in countries with lower financial development are managed more extensively,
which should be a serious concern for the globally oriented investor.
The remainder of the paper is organized as follows. Section 2 reviews the literature and considers
the relationship between earnings management and financial development. Section 3 presents the research
design of the study. Section 4 describes the results of our econometric analysis. Finally, section 5
concludes the paper and discusses suggestions for future research.

2. Literature Review and Hypotheses
2.1 Literature review
Bushman and Smith (2001, 305) state that powerful interactions occur between financial accounting
regimes and other institutional characteristics, including auditing regimes, communication infrastructures,
analyst followings, financial architectures, legal environments, and human capital. Similarly, Wysocki(2011) and Leuz and Wysocki (2016) describe “new institutional accounting research” focusing on thelinks between accounting institutions (such as accounting standards, disclosure systems, and audit) andnon-accounting institutions (such as legal systems, corporate governance mechanisms, and the3

enforcement of laws governing investor protection). They point out that accounting institutions are key
economic institutions and that a link exists between accounting institutions and non-accounting
institutions. Bartov et al. (2001) also find that both national financial accounting regimes and institutional
factors play important roles in the informativeness of earnings.
In new institutional accounting research, cross-country research on earnings management is one of
the most important streams. Leuz et al. (2003) present comparative evidence on corporate earnings
management across 31 countries from 1990 to 1999, and show that earnings management will decrease
under strong legal protection. Using a sample of firms in 42 countries from 1994 to 2004, Francis andWang (2008) find that earnings quality increases for firms with Big 4 auditors in countries with stronger
investor protection, whereas the earnings quality of firms with non-Big 4 auditors are largely unaffected byregimes’ investor protection levels. By contrast, Jaggi et al. (2012) indicate that audits by industry
specialists are associated with higher earnings quality in countries with weak (rather than strong) investor
protection. Most of the cross-country research on earnings management has dealt with the relationshipbetween the legal aspects of institutional factors and AEM.
Degeorge et al. (2013) shed more light on financial development as an institutional factor, analyzing
the effect of financial development and analyst coverage on AEM in various countries. For a sample offirms from 21 countries covering 1994 to 2002, they find that increased analyst coverage is associated withless earnings management in countries with high levels of financial development, while, in countries withlow levels of financial development, analyst coverage is not associated with reductions in earnings
management.
Several cross-country studies focus on both AEM and REM. Doukakis (2014) analyzes a sample of
15,206 firm-years from 22 countries covering 2000 to 2010. However, he does not find a significant effectof mandatory IFRS adoption on either real or accrual-based earnings management. Bramm et al. (2015)
suggest that political connections relate to the choice of earnings management methods. Using data from30 countries covering 1997 to 2001, they show that politically connected firms tend to substitute morecostly but invisible REM for AEM than do non-connected firms. Enomoto et al. (2015) examine therelationships between two types of earnings management and investor protection using data from 222,513
firm-year observations covering 1991 to 2010, finding that outside investor rights are negatively correlatedwith REM and positively correlated with AEM. Francis et al. (2016) show that REM is encouraged and4

AEM discouraged in countries with stronger legal environments using a sample of 38 countries covering
1994 to 2009.
These studies do not investigate the relationship between financial development and REM, although
they focus on AEM and REM in an international setting. The financial development score of this study ismore comprehensive than the scores used in other studies, providing new insights into the relationshipbetween financial development and earnings management.

2.2 Hypothesis development
Financial development is known to be a crucial factor in economic growth (Beck and Levine, 2002)
and, as mentioned, is probably related to managers’ accounting behaviors. In this subsection, we develop
our hypothesis on the relationship between financial development and earnings management from theperspective of stakeholders, such as regulatory authorities, auditors, and investors.
First, financial development generates a greater variety and number of stakeholders. To balanceinterests among them, regulatory authorities create relevant laws and regulations that require a
sophisticated accounting system. As a result, financial development increases the importance of theaccounting system through laws and regulations and improves stakeholders’ general ability to process
accounting information. Leuz et al. (2003, 506) explain that “strong and well-enforced 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.” The mitigation of managers’
opportunistic incentives may reduce overall managerial discretionary behaviors (i.e., AEM and REM).
Following the arguments and findings of Leuz et al. (2003), the strengthening of various laws andregulations resulting from the development of financial systems discipline managers and thus mitigate
managers’ earnings management incentives. 1
Like regulatory authorities, auditors play a key function in the governance of financial development
environments and in restraining earnings management. For example, Becker et al. (1998) and Francis et al.
(1999) have found that the degree of AEM among firms with Big 6 auditors tends to be lower than among
those with non-Big 6 auditors. Moreover, to foster financial development, firms should provide investors
and debt holders with high-quality accounting information that allows them to make rational investment
decisions. They are also likely to be concerned with a firm’s earnings management. Lambert et al. (2007)5

have demonstrated that accounting information quality can affect the cost of capital. Investors tend toregard earnings management as an undesirable behavior and thus seek to detect it, which is reflected inpricing. Francis et al. (2004) indicate that firms with low accruals quality have higher equity cost than dofirms with high accruals quality. In addition, Francis et al. (2005) have found that lower accruals quality is
associated with higher costs of debt. Because earnings management reduces earnings quality (Dechow and
Schrand, 2004), the quality of accounting (earnings) information examined in these studies is strongly
related to earnings management.
Meanwhile, sophisticated investors have both the incentive and ability to constrain a manager’s
REM in order to avoid reductions in future cash flow. 2 They can consider a firm’s AEM and REM when
making decisions pertaining to the pricing of securities. Wongsunwai (2013) and Kim and Sohn (2013)indicate that sophisticated U.S. market participants monitor managers’ value-destroying behaviors andeffectively restrain managers from engaging in AEM and REM. In financially developed countries such as
the U.S., stakeholders are typically more sophisticated than are those in less developed countries, anddemand the least-managed earnings possible. As such, managers face stricter monitoring and scrutiny inthese countries and are likely to be prevented from carrying out value-destroying behaviors involving
AEM and REM. 3
While the above explanation indicates that financial development suppresses managers’ incentives tomanage earnings owing to stakeholders’ heightened monitoring and scrutiny abilities, financial
development also curbs earnings management via accounting and audit standards. For instance, newaccounting standards that narrow a manager’s discretion in achieving a target income can result in greater
constraints on the manipulation of accruals (i.e., AEM). Thus, countries with high levels of financial
development are likely to have higher quality and tighter standards resulting from stakeholder demands.
Managers therefore face more difficulty in implementing AEM in countries with greater financial
development. 4 We thus hypothesize that earnings management, including both AEM and REM, decreases
as financial development levels increase because managers tend to be disciplined in such cases. We
therefore propose the following:
H1. Both AEM and REM decrease as financial development levels increase.

Since the growing use of accounting information due to financial development produces greater
6

private benefits for managers engaging in earnings management, managers may continue to have sufficientincentives to carry out earnings management. We assume that financial development heightens themonitoring and scrutiny of accounting figures and that AEM is less (more) prevalent under conditions ofhigh (low) financial development. This is why AEM becomes more costly than REM when revealed.
Managers are more likely to engage in REM when the incentives to manage earnings are not weakened andonly AEM is restrained.
Many studies have examined the substitution between AEM and REM. Some have found that, whenstricter accounting standards and other regulations become effective, managers tend to apply REM insteadof AEM (Ewert and Wagenhofer, 2005; Cohen et al., 2008; Enomoto et al., 2015) and that higher-quality
audit conditions lead to similar results (Chi et al., 2011). It is more difficult for regulatory authorities andauditors than for investors to interfere in a firm’s real activities because they are independent of the firm.
Many studies have pointed out that managers tend to employ REM rather than AEM to achieve the target
income, since AEM is more likely to face scrutiny from auditors, regulators, and other parties (Graham etal., 2005). Kothari et al. (2012) argue that REM is easier to camouflage as a normal activity than AEM isand that discretion relating to operating and investment activities is inherently provided to managers byshareholders. Based on interviews with executives, Graham et al. (2005) indicated that managers prefer
REM to AEM because they fear overzealous regulators. The research argues that managers may also prefer
REM because they hope to avoid the scrutiny and oversight of stakeholders, in spite of the higher future
costs their firms may incur.
Therefore, if the substitution effect between the two types of earnings management occurs and has adominant effect on REM, despite monitoring constraints and scrutiny in countries with high levels offinancial development, AEM (REM) will be less (more) prevalent in countries with higher levels offinancial development. We thus propose the following:
H2. AEM decreases, while REM increases, as financial development levels increase.

3. Research Design
3.1 Earnings management measures
3.1.1 Accrual-based earnings management measure
Following many earnings management studies (e.g., Warfield et al., 1995; Becker et al., 1998;

7

Cohen et al., 2008), we use the absolute value of abnormal accruals as an AEM measure in order to capture
the effects of both income-increasing and income-decreasing AEM. To measure abnormal accruals, we use
the cross-sectional modified Jones (1991) model (Dechow et al., 1995). Specifically, we estimate thefollowing regression model for each industry-year combination in each country, where the industry is
identified by a two-digit SIC code:
ACCijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β1 ((ΔSijt - ΔARijt) / Aijt -1) + β3 (PPEijt / Aijt -1) + εijt

(1)where ACC is accruals, calculated as net income minus operating cash flow reported in the statement ofcash flow; A is total assets; ΔS is the change in net sales; ΔAR is the change in accounts receivable; PPE is
net property, plant, and equipment; and the subscripts refer to firm i, country j, and time t. Abnormal
accruals are calculated as the estimated residuals from equation (1), and the absolute value is our proxy for
AEM (|A_ACC|). Definitions of all variables are provided in the appendix.

3.1.2 Real earnings management measures
Following Roychowdhury (2006), Cohen et al. (2008), Cohen and Zarowin (2010), and Ho et al.
(2015), we develop a proxy for three REM methods: (1) sales manipulation, (2) reduction of discretionary
expenses, and (3) overproduction. According to Roychowdhury (2006), sales manipulation andoverproduction lead to abnormally high production costs relative to sales and abnormally low cash flowfrom operating activities relative to sales, while the reduction of discretionary expenditures leads toabnormally low discretionary expenses. 5
To measure an abnormal level of CFO (A_CFO), discretionary expenses (A_DE), and production
costs (A_PD), we estimate the following regression models. Similar to equation (1), the models are
estimated for each industry-year combination in each country, where the industry is identified by a twodigit SIC code:
CFOijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt / Aijt-1) + β3 (ΔSijt / Aijt-1) + εijt

(2)DEijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt-1 / Aijt-1) + εijt

(3)PDijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt / Aijt-1) + β3 (ΔSijt / Aijt-1) + β4 (ΔSijt-1 / Aijt-1) + εijt

(4)where CFO represents the operating cash flow reported in the statement of cash flow; DE represents
selling, general, and administrative expenses; PD represents production costs and is calculated as the cost8

of goods sold plus change in inventory; and S represents net sales. 6 A_CFO, A_DE, and A_PD are
calculated as the estimated residuals from equations (2), (3), and (4), respectively.
Since the three types of REM described above might be implemented to decrease earnings,
consistent with Francis et al. (2016) and Kim and Sohn (2013), we convert A_CFO, A_DE, and A_PD toabsolute values and use them as our REM proxies (|A_CFO|, |A_DE|, and |A_PD|, respectively). 7 In
addition, we combine these three measures to capture the total effect of REM. Consistent with Cohen andZarowin (2010), we multiply A_CFO and A_DE by negative one and add them to A_PD, so that higher
values indicate greater income-increasing earnings management. Again, considering the possibility ofincome-decreasing REM, we convert the aggregated REM measure to an absolute value and use it as ourfourth REM proxy (|REM|).

3.2 Financial development measures
We adopt the financial development score used by the WEF because it takes a comprehensive view
when assessing the factors contributing to the long-term development of financial systems (World
Economic Forum, 2012, xiii). It also includes various factors used in prior cross-country research on
financial accounting, such as corporate governance, legal and regulatory issues, and contract enforcement.
The WEF has provided a score and rank for the breadth, depth, and efficiency of 62 of the world’s leading
financial systems and capital markets since 2008. The index analyzes drivers of financial systemdevelopment that support economic growth and thus compares the overall competitiveness of financial
systems (World Economic Forum, 2012, xiii).
As shown in Panel A of Table 1, World Economic Forum (2012, 4-5) defines seven pillars grouped
into three broad categories (Factors, policies, and institutions; Financial intermediation; Financial access).
(1) Institutional environment encompasses financial sector liberalization, corporate governance, legal
and regulatory issues, and contract enforcement. (2) Business environment considers human capital,
taxes, infrastructure, and costs of doing business. (3) Financial stability captures the risk of currency
crises, systemic banking crises, and sovereign debt crises. (4) Banking financial services measure size,
efficiency, and financial information disclosure. (5) Non-banking financial services include IPO andM&A activity, insurance, and securitization. (6) Financial markets encompass foreign exchange andderivatives markets as well as equity and bond market development. (7) Financial access evaluates

9commercial and retail access. Each pillar is scored from one to seven. The financial development score is
the average of the scores for all seven pillars. Panel B of Table 1 reports their correlation matrix.
Institutional environment and Financial markets are highly associated with other pillars. Although all of thecorrelations have positive coefficients, some correlation coefficients are not high. Non-banking financial
services have relatively low correlations with other pillars.
[Insert Table 1 here]
Since not all of the pillars are associated with financial accounting (and resulting earnings
management), we extracted scores from four of the seven pillars (Institutional environment, Banking
financial services, Non-banking financial services, and Financial markets) as the factors most closely
related to financial accounting. In our study, these four pillars include much broader factors than do theinstitutional factors examined in prior research. First, (1) Institutional environment involves not only theindex of minority shareholders’ interests, legal enforcement, and corporate governance, as measured in La
Porta et al. (1998) and Leuz et al. (2003), but also financial sector liberalization, regulation of securities
exchanges, and the ethical behavior of firms. Financial accounting is essential for all of these to work
efficiently. Next, (4) Banking financial services reflect efficiency and financial information disclosure aswell as the extent of banking services, such as the ratio of the sum of bank debt in the private sector andoutstanding nonfinancial bonds to GNP (La Porta et al., 1997) and the ratio of GDP to claims from thebanking sector on the private sector (Beck and Levine, 2002). (5) Non-banking financial services consistof the extent of M&A activities and the size of initial public offerings (e.g., La Porta et al., 1997 and 1998),
measured by the ratio of equity issued by newly listed firms to population or GDP. Finally, the (6)Financial market pillar includes the level of bond market development in addition to the size of the stock
market, represented by the ratio of domestic firms listed in a given country to its population (La Porta etal., 1997), the value of listed shares divided by GDP, and the value of stock transactions as a share ofnational output (Beck and Levine, 2002).
As shown in Table 1, Financial intermediation includes (4) Banking financial services, (5) Nonbanking financial services, and (6) Financial markets. Financial intermediation is defined as the variety,size, depth, and efficiency of the financial intermediaries and markets that provide financial services. Ourfinancial development score can therefore be interpreted as the financial intermediaries and theinstitutional environment supporting them.
10Unlike the four pillars, (2) Business environment, (3) Financial stability, and (7) Financial accesscan be considered to not have a direct linkage to the financial accounting of listed (relatively large) firms inour sample, as per their definition in WEF (2012, 7-11). (3) Financial stability is excluded because thispillar focuses on the financial crises that affect economic growth. Similarly, (7) Financial access is
dropped because it is an indicator built from the viewpoint of individuals and small and medium-sizedenterprises. At first glance (2) Business environment, including taxation policy and the costs of doing
business, seems to relate to financial accounting, but it focuses on the availability of human capital andstate of physical capital. Judging from the definition of the indices that constitute the pillar, major parts of
the index are remotely related to the necessity for financial accounting among the firms in our sample. It istherefore eliminated from our financial development score. 8
Through this process, we can calculate a comprehensive score reflecting not only laws andregulations but also the economic impact of banking and security market systems.

3.3 Models for hypothesis tests
To examine the relationship between financial development and earnings management, we estimate
the following regression model:
EMijt = β0 + β1 FDjt + β2 Leverageijt-1 + β3 Sizeijt-1 + β4 MTBijt-1 + β5 ROAijt + β6 NOAijt-1+ Σ β Year_Fixed_Effect + Σ β Firm_Fixed_Effect + εijt

(5)where EM represents earnings management proxies, either |A_ACC|, |A_CFO|, |A_DE|, |A_PD| or |REM|;
FD is the mean value of the four pillars (Institutional environment, Banking financial Services, Nonbanking financial services, and Financial markets) in the financial development report of the WEF;
Leverage is total debt divided by total assets; Size is the natural logarithm of the market value of equity;
MTB is the market-to-book ratio; ROA is net income divided by lagged total assets; and NOA is net
operating assets divided by sales. 9
When the dependent variable is the proxy for AEM (i.e., |A_ACC|), we predict that the coefficient of
FD will have a negative sign. On the other hand, when the dependent variable is the proxy for REM (i.e.,|A_CFO|, |A_DE|, |A_PD|, or |REM|), the sign of the coefficient of FD should be negative (positive) if
REM decreases (increases) in countries with higher levels of financial development. In addition to FD,11other variables are included to control for other factors likely to affect earnings management proxies.
Leverage is included because the research shows that it is related to earnings management measures (e.g.,
DeFond and Jiambalvo, 1994; Becker et al., 1998; Roychowdhury, 2006). Following Roychowdhury(2006) and Gunny (2010), we include SIZE, MTB, and ROA. SIZE and MTB are included to control for size
effects and growth opportunity, respectively. ROA is added to control for firm performance. Following
Barton and Simko (2002) and Zang (2012), we include NOA as a proxy for the extent of AEM in previous
periods. Due to limited flexibility within GAAP and the reversal of accruals, AEM in previous periods
affects managers’ ability to manipulate accruals, with a consequent impact on REM (Zang, 2012). Finally,
consistent with approaches taken by Degeorge et al. (2013), Year_Fixed_Effect and Firm_Fixed_Effect arealso included in our regression to control for firm effects and year effects.

3.4 Sample selection
Financial development and other relevant data covering 2009 to 2012 are obtained from Global
Note. 10 The sample period is chosen because the WEF’s Financial Development Score (FD) is available in
Global Note from 2009. The countries in this paper are selected from among the 49 used in La Porta et al.
(1998). Ecuador, New Zealand, Taiwan, Uruguay, Sri Lanka, and Zimbabwe are dropped, as their FDscores are not included in the WEF report. Zimbabwe is also eliminated due to hyperinflation during thesample period. 11
The sample comprises data from Capital IQ, from which we obtain 81,317 firm-year observations,covering sales and total assets of over 1 million dollars. Next, the data for financial services firms (2,108
firm-years) are eliminated. To calculate earnings management measures, we require at least seven firmyear observations for each industry-year combination in each country (13,825 firm-years are excluded). To
calculate other measures, we exclude Austria, Colombia, Egypt, Kenya, Portugal, and Venezuela (8,326
firm-years) from our sample due to data availability issues. Using this sample selection process, we obtain
56,830 observations from 37 countries after excluding observations with missing data to compute theindependent and dependent variables.

124. Empirical Results
4.1 Descriptive statistics
Panel A of Table 2 shows the number of firm-years in 37 countries and the mean value of FD. The
U.S. has the highest number of firm-years (10,750 observations, 18.9%) while Japan is a close second.
Observations for the U.S. and Japan account for 37.7% of total observations. Greece has the lowest
number of firm-years (31 observations). The third column of Panel A reports the mean values of thefinancial development scores (FD). FD is the mean value of the four pillars (Institutional environment,
Banking financial services, Non-banking financial services, and Financial markets). The U.K. has thehighest score, at 5.45, while the U.S. has the second-highest (5.39). The lowest score is 2.11, for Nigeria.
Only the U.K. and the U.S. have scores exceeding five. 12 These ranking results are similar to those of
Beck and Levine (2002) and Degeorge et al. (2013). Panel B presents the number of firm-years in thesample period.
[Insert Table 2 here]
Table 3 shows the descriptive statistics for the dependent and independent variables in equation(5). The mean values of |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are 6.6%, 7.5%, 9.5%, 11.4%, and22.1%, respectively. These values are slightly larger than in Francis et al. (2016) and smaller than in Kim
and Sohn (2013). 13[Insert Table 3 here]
Table 4 is the correlation matrix. Since no high correlation coefficient is observed, the results of
the regressions in this section will not be influenced by multicollinearity. 14 FD is negatively associatedwith |A_ACC|, but positively correlated with |A_CFO|, |A_DE|, |A_PD|, and |REM|.
[Insert Table 4 here]

4.2 Regression results
Table 5 reveals evidence of the influence of financial development on managerial behavior. 15 Fivetypes of dependent variables are used in the analyses. Column (1) in Table 5 displays the regression resultsof |A_ACC|. The coefficient of FD is significantly negative, supporting our prediction that AEM isrestrained in countries that are highly financially developed.
This result implies that an increase in the financial development score by one (e.g., the difference in13

score between the U.S. and Germany) is associated with a 0.008 decrease in the absolute value of
discretionary accruals. When considering financial development as an institutional factor, this result is
consistent with Leuz et al. (2003) in that earnings management is negatively associated with thedevelopment of the institutional environment. Financial development serves as an institutional factor that
inclines managers to avoid accrual-based discretionary behaviors for earnings management purposes. 16[Insert Table 5 here]
In the regression where the dependent variable is |A_CFO| (column [2] of Table 5), FD also has asignificantly negative coefficient. When the dependent variables are |A_DE|, |A_PD|, and |REM| (columns[3], [4], and [5] respectively), the coefficients of FD are also significantly negative. These results indicatethat both AEM and REM are negatively associated with higher levels of financial development. In more
highly financially developed economies, managers presumably reduce noise in earnings and avoid
reductions in future revenues caused by earnings management for fear that stakeholders would have anegative view of these behaviors.
Taken together, these results may indicate that stakeholders pay more attention to accounting
numbers under higher financial development and therefore that managers try to avoid the costs resulting
from earnings management, such as scrutiny from auditors and regulators, litigation, declines in future
sales, and increasing costs of capital. This evidence supports our hypothesis (H1) that both types of
earnings management are restrained under higher levels of financial development. However, this is notconsistent with prior research showing that substitution occurs between AEM and REM (e.g., Zang 2012;
Francis et al., 2016). We infer that managers are disciplined and that their earnings management (bothAEM and REM) is limited under conditions of high financial development.

4.3 Additional tests
To capture the financial development affecting accounting information, we calculate FD as the meanvalue of the four components: Institutional environment, Banking financial services, Non-banking
financial services, and Financial markets. 17 However, the original financial development score has three
additional components: Business environment, Financial stability, and Financial access. Hence, we reestimate equation (5) with Original_FD instead of FD, where Original_FD is the mean value of the sevencomponents mentioned above. The results in Table 6 show that the coefficients of Original_FD in all14

regressions are significantly negative, supporting the results of Table 5. Therefore, even when Businessenvironment, Financial stability, and Financial access are added to the measure of financial development,
the results again indicate that both AEM and REM decrease under higher financial development.
[Insert Table 6 here]
Although FD includes the Institutional environment component, Leuz et al. (2003) find that theinstitutional factor of investor protection has a restraining effect on earnings management. We areconcerned that our primary results may be driven only by the institutional factor in FD. Therefore, we
divide FD into two variables: Institutional environment and Financial Intermediation. The following
regressions are estimated:
EMijt = β0 + β1 Institutional_Environmentjt + β2 Financial_Intermediation + β3 Leverageijt-1+ β4 Sizeijt-1+ β5 MTBijt-1 + β6 ROAijt + β7 NOAijt-1 + Σ β Year_Fixed_Effects+ Σ β Firm_Fixed_Effect + εijt

(6)Institutional_Environment is the score for Institutional environment as reported by WEF;
Financial_Intermediation is the mean value of the Banking financial Services, Non-banking financial
services, and Financial markets scores as reported by WEF. 18 Financial intermediation represents thevariety, size, depth, and efficiency of the financial intermediaries and markets that provide financial
services (World Economic Forum, 2012, 5).
Table 7 shows the results of estimating equation (6) with Institutional environment and
Financial_Intermediation instead of FD. 19 When the dependent variable is |A_ACC|, the coefficient ofInstitutional environment is significantly negative. This result is consistent with Leuz et al. (2003) andindicates that AEM is more restrained under more stringent financial institutions. The coefficient of
Financial_Intermediation is significantly negative, indicating that AEM decreases as financial
development increases, other than the institutional factor. For REM, while the coefficient of Institutional
environment is marginally significantly negative only when the dependent variable is |A_DE| and |REM|,
the coefficient of Financial_Intermediation is negative and significant at the 1% level for all dependent
variables.
These results suggest that, under strong financial institutions, AEM is indeed restrained but REM isrestrained to a lesser extent. In addition, not only AEM but also REM is restrained to a considerable extent
under strong financial development other than financial institutions (i.e., financial intermediation). We can15thus say that financial development other than financial institutions is important in restraining REM,
probably due to monitoring by sophisticated financial markets and financial service firms.
[Insert Table 7 here]
So far, we have used the simple average of four (seven) pillars as a financial development score,
FD (Original_FD). Next, we conduct a principal components analysis in addition to a simple average tosummarize each pillar’s score. This should give us a new interpretation of the relationship between
financial development and earnings management.
We input seven pillar values of 38 countries covering 2009 to 2012 (N = 142). Principal
component analysis (not tabulated) reports that 67.5% (13.9%) of the variation is explained by the first(second) eigenvalue. Hence, 81.4% of the variation is explained by the first two eigenvalues together.
This percentage is sufficiently high and acceptable at conventional levels. Hence, we employ the first twocomponents in the regression analysis as summarized financial development indices.
Before carrying out the regression analysis, we interpret the two components. Since all of thecoefficients of the first component have positive values (from 0.230 to 0.427), the coefficients can beinterpreted as the level of each pillar’s contribution to financial development. The second component
has positive coefficients on Pillars 1, 2, 3, and 7 (from 0.096 to 0.720) and negative ones on Pillars 4, 5,
and 6 (from -0.569 to -0.092). As stated in section 3.2., Pillars 1, 2, 3, and 7 capture the institutional or
infrastructural aspect that forms the basis of financial development, while Pillars 4, 5, and 6 represent
the aspect of financial intermediation that provides financial services. Hence, we can infer that thiscomponent represents the relative development of the financial intermediation compared to the strengthof the institutional environment in each country. For simplicity, if the strength of the institutional
environment is fixed, a country with stronger financial intermediation lowers this score because thecoefficients of Pillars 4, 5, and 6 are negative. In fact, the U.S. (the U.K.) has the lowest (second-lowest)
score for the second component in our sample.
Next, we compute the two principal component scores and replace FD with them in equation (5)(non-tabulated). The coefficients on the first component score are significantly negative when thedependent variable is |A_ACC|, |A_DE|, and |REM|. These results are consistent with our primary results,
considering the first component as another summarized index of financial development. Next, the second

16component score has significantly positive coefficients when the dependent variable is |A_CFO|, |A_PD|,and |REM|. This implies that the development of financial intermediation relative to the institutional
environment is negatively associated with REM. We can thus infer that monitoring and scrutiny offinancial intermediation are negatively associated with real activities manipulation. Overall, the results ofthe principal component analysis are consistent with our primary results.
Finally, we test for the potentially differential effect of financial development on AEM versus REM.
Many studies have examined the tradeoff between AEM and REM (e.g., Cohen et al., 2008; Zang, 2012).
To test the trade-off between AEM and REM, we examine the association between the ratio of AEM to
REM and financial development. Following Evans et al. (2015), we calculate the ratio of |REM| to |A_ACC|
and regress the ratio on FD. Specifically, we employ the ratio in place of the earnings management
variables in equation (5). When a trade-off between AEM and REM occurs as financial development
increases, a significantly positive coefficient on FD should be observed. However, the coefficient is
positive but insignificant (untabulated), suggesting the possibility that both AEM and REM move in thesame direction as financial development, which does not indicate that financial development has adifferential effect on AEM and REM. These results are not inconsistent with our primary results.

4.4 Robustness checks
We provide ten robustness tests for the main results. First, we re-compute abnormal accruals using thecross-sectional Jones (1991) model. The coefficient of FD in the regression of |A_ACC| is similar to thatobserved before (not tabulated).
Second, we employ another CFO measure developed by Gordon et al. (2017), who point out that
CFO could vary with the classification of interest paid, interest received, and dividends received within thestatement of cash flow under IFRS (International Financial Reporting Standards). The classification ofthese items is at the manager’s discretion. Gordon et al. (2017) claim that their model, based on lagged
cash flow, could be more useful in an international setting. Hence, we calculate abnormal CFO using themodel below based on Gordon et al. (2017) and take its absolute value (|A_CFO_GHJL|):

CFOijt+1 / Aijt = β0 + β1 ACCijt / Aijt-1 + β2 CFOijt / Aijt-1 +β4 Sizeijt + β5 BMijt + β6 EPijt + εijt+1

17

(7)

We input |A_CFO_GHJL| as an independent variable in equation (5). We exclude Size and MTB(inverse of BM) from our main regression (equation [5]) to avoid repeatedly controlling them in both
equations (5) and (9). When we replace |A_CFO| with |A_CFO_ GHJL| as a dependent variable, thecoefficient of FD remains significantly negative, indicating that our main results are substantiallyunchanged even when employing this alternative abnormal CFO measure.
Third, Brown et al. (2014b) present a measure that reflects the institutional environment for
financial reporting in each country. The measure is divided into two parts, the quality of the auditor’s
working environment for listed firms and the degree of enforcement of accounting activity. This measure
could be substitutional to our Pillar 1 (Institutional environment). When we convert the index consistent
with the calculation method of Pillar 1 and include it to compute FD in place of Pillar 1, the results areunchanged (not tabulated). 20
Fourth, we add the absolute value of REM in equation (5) for the AEM test to control for thesimultaneous use of the two types of earnings management (Cohen et al., 2008; Kuo et al., 2014), and
AEM is also incorporated in the regression for REM measures in the same way (Achleitner et al., 2014;
Kuo et al., 2014). The untabulated results are similar to the primary results reported in Table 5.
Fifth, we input the accounting standard dummies into our main regressions (equation [5]). Since
different accounting standards are used in each country in our sample, our AEM and REM measures could
be affected by them. This procedure reduces the sample size (from 56,830 to 49,990) due to the lack of
accounting standard data. The untabulated results show that the coefficient of FD remains significantly
negative when |A_ACC| is the dependent variable. For REM, the coefficients of three of the four dependent
variables, |A_DE|, |A_PD|, and |REM|, are similar to those of Table 5. Thus, our primary results are notsignificantly affected by the accounting standard dummies.
Sixth, we employ country- , industry- , and year-fixed effects in place of firm-fixed effects inequation (5). While it is appropriate to use firm-fixed effect terms to control for time-invariant factors,some studies include country-, industry- , and year-fixed effects. The untabulated coefficients of FD in all
regressions remain significantly negative, which is consistent with the results shown in Table 5.
Seventh, we delete all of the firm-years for the U.S. and Japan and re-estimate the models in Table
5. The observations for the U.S. and Japan total 21,447, or 37.7% of our sample (see Table 2). It istherefore of concern that the evidence in Table 5 is driven mainly by the data for these two countries. Table18

8 displays the estimated coefficients from the sample, excluding observations for the U.S. and Japan. All of
the coefficients of FD are negative and significant and are consistent with those shown in Table 5. The
results are also robust even when we eliminate observations for either of these countries.
[Insert Table 8 here]
Eighth, we delete all 2009 data in the sample and re-estimated equation (5), as our sample periods
include data from periods of global financial crisis. The untabulated results show that the coefficient of FD
for |A_ACC| remains significantly negative. For REM, the coefficients of two of the three dependent
variables, |A_DE| and |A_PD|, are similar to those shown in Table 5. Thus, our findings are notsignificantly influenced by financial crises.
Ninth, we conduct a country-weighted least square (WLS) regression as a robustness test to address
countries that have few observations with small weights in the regressions. The weight of each observation
is inversely proportional to the number of observations per country. Using WLS ensures that unevencountry representation in the sample will not bias the results towards countries that are more heavily
represented (Hope et al., 2009, 191). As shown in Table 9, while the coefficient of |A_ACC| is negative but
insignificant, the coefficients of the four REM measures remain significantly negative. Thus, the primary
results of REM are robust to change, while the results of AEM are slightly influenced by the countries witha greater number of firm-years.
[Insert Table 9 here]
Finally, we conduct regressions in changes to control for a factor related to financial development aswell as earnings management that changes over time and is therefore not captured by firm-fixed effects.
The regression model is as follows:
ΔEMijt = β0 + β1 ΔFDjt + β2 ΔLeverageijt-1 + β3 ΔSizeijt-1 + β4 ΔMTBijt-1 + β5 ΔROAijt + β6 ΔNOAijt-1+ Σ βYear_Fixed_Effect + εijt.

(8)

Δ is the change and is computed as the difference between time t and t-1.

The dependent and independent variables are the differences from last year’s value in the regressions.
From the regression, we can observe whether the changes in FD reduce earnings management levels. Table
10 shows that the coefficient of |A_ACC| and three of the four REM measures remain significantly
negative, providing evidence that our main regression result does not suffer from unobserved
19heterogeneity.
[Insert Table 10 here]

5. Conclusions
This paper has investigated whether levels of financial development in specific countries affectmanagers’ earnings management based on 56,830 observations from 37 countries covering the period 2009
to 2012. Regarding earnings management, we focused on both AEM and REM, finding that a negative
relationship exists between countries’ levels of financial development and both types of earnings
management. These results indicate that both AEM and REM are constrained in countries with high levelsof financial development. These results show that (1) higher-quality accounting information is required incountries with more developed financial systems, (2) managers are disciplined and their earnings
management incentives are mitigated under high financial development, and (3) there is a link between
financial development and accounting institutions in each of the countries.
Previous studies show that AEM is restrained in countries with high levels of investor protection(Leuz et al., 2003) and that AEM and REM are used as substitutes for each other according to the strengthof the legal system (Francis et al., 2016) and outside investor rights (Enomoto et al., 2015). We contributeto the earnings management literature by showing that, at the international level, both AEM and REM are
restrained under high levels of financial development.
Given that AEM and REM are relevant to earnings quality, our results show that a country’s
financial development is positively related to the earnings quality of each firm in that country. In otherwords, with higher levels of financial development, financial reporting processes improve through the
reduction of managerial intervention in the accounting accrual process, and managers engage in more
appropriate activities by avoiding myopic behaviors such as reducing R&D costs.
Future study is needed in several areas. First, due to data availability issues, our sample period
covers only four years, from 2009 to 2012. Obtaining more reliable evidence will require testing for alonger sample period. Second, since the applicable accounting standards vary not only among countries but
also within each country, the model should be developed to adequately control for the accounting
standards adopted by each firm in estimating AEM and REM in an international setting. 21 Third, we
estimated the test models for AEM and REM separately, though the research indicates a substitution
20

between AEM and REM. Future studies should consider a simultaneous equation system approach for thetwo types of earnings management. Fourth, though we focused on earnings management as an important
aspect of accounting quality, it would also be fruitful to analyze other aspects of accounting quality, suchas smoothing, conservatism, and value relevance. Fifth, we did not clarify the linkage process between
financial development and accounting institutions. Wysocki (2011, 312) notes the “chicken and the egg”
problem of the endogeneity and complementarily between accounting institutions and non-accounting
institutions, which may apply to the relationship between financial development and accounting
institutions. It is difficult to determine which of these comes first, but it would be an interesting issue forfurther research to address.

Acknowledgments
We gratefully acknowledge the useful comments and feedback from Co-editor Sidney Gray and the twoanonymous reviewers. We appreciate helpful comments and suggestions from Robert Czernkowski, Ian
Eddie, Stefanie Kaiser, Kenji Kometani, Wei Li, Liang Liang, Le Ma, Akinobu Shuto, Toshihumi Takada,
Takashi Yaekura and participants at the 37th EAA conference at Talin (2014), the International
Conference on Accounting and Information Technology at Chiayi (2014), the Journal of International
Accounting Research Conference at Hong Kong (2014), and the Tokyo Accounting Workshop at Tokyo(2015). We also appreciate the financial support from The Japan Securities Scholarship Foundation andIshii Memorial Securities Research Foundation. Masahiro Enomoto thanks the financial support by JSPS
KAKENHI Grant Number 17K04053. Fumihiko Kimura acknowledges for the financial support by JSPS
KAKENHI Grant Number JP15K03763. Tomoyasu Yamaguchi is grateful for the financial support by
JSPS KAKENHI Grant Number 16K17211.

1

The financial development scores in this paper include many institutional factors (see sections 3.2).
Corporate governance mechanisms may also influence REM (Cheng et al., 2016).
3 To put it another way, financial development heightens the costs imposed on managers through the detection of earnings
management, depending on the sophistication level of stakeholders.
4 If managers’ incentives to manage earnings do not change, a substitution effect may occur between AEM and REM(Ewert and Wagenhofer, 2005). However, because the revision of accounting standards may influence real activities(e.g., stricter revenue recognition reduces sales manipulation) and as we assume that incentives are reduced withfinancial development, the effect of changes in accounting institutions on REM is unclear. The substitution effect is
discussed in the next hypothesis.
5 If the firm paid for discretionary expenses in cash, a reduction of discretionary expenses could also lead to abnormallyhigh cash flow (Roychowdhury, 2006; Cohen and Zarowin, 2010).
2

21

6

Following Bartov and Cohen (2009) and Gunny (2010), we use selling, general, and administrative expenses as
discretionary expenses because they frequently include discretionary expenses such as R&D and advertising costs.
7 For example, Francis et al. (2016) point out the possibility of income-decreasing real earnings management from an
income-smoothing perspective. When a firm’s performance is good in the current period, managers may choose to spend
more on R&D, advertising, and employee training. These activities have an income-decreasing effect for the current year
but an income-increasing effect for future periods (Francis et al., 2011, 9).
8 In section 4.3, we also test using the original financial development score.
9 Following Roychowdhury (2006), we use the values at the beginning of the year for Size and MTB, and use the values atthe end of the year for ROA.
10 Global Note is a website that collects various kinds of international statistics such as Gross Domestic Product(http://www.globalnote.jp/ [in Japanese]).
11 We consider hyperinflation as inflation above 100% per year.
12 Original financial development scores range from a low 2.51 for Nigeria to a high of 5.17 for the United States, andshow the same trends as our financial development score (FD).
13 These studies used the absolute value of abnormal accruals and three REM measures. The former uses international data
and the latter uses U.S. data.
14 The largest absolute value for the correlation coefficients among the independent variables in our analysis is 0.269 forPearson correlations and 0.436 for Spearman correlations. These values are less than 0.8 and are thus not likely to resultin multicollinearity (Kennedy, 2008).
15 We winsorize all dependent and independent variables at the 1st and 99th percentiles.
16 Another reason for the small |abACC| of financially developed countries is that financial development may affectaccounting institutions. For example, stakeholders pursuing high accounting quality require tighter accounting standards
in these countries.
17 The original financial development score of the WEF is also the simple mean value of the seven components.
18 See the explanation of the financial development measure components reported by the World Economic Report insection 3.2.
19 Since the correlation between Institutional environment and Financial_Intermediation is relatively high (0.751), theresults should be interpreted with caution. When each is singly included in the regression, although the coefficients ofFinancial_Intermediation are significantly negative for all dependent variables (as before), the coefficients ofInstitutional environment are significantly negative for only |A_ACC|, suggesting that, while financial institution restricts
AEM but not REM, financial development other than financial institutions restricts both AEM and REM.
20 Since Brown et al. (2014b) do not include Nigeria in their sample, it reduces our sample size by 71 firm-years.
21 For example, Gordon et al. (2017) express caution about the usage of the abnormal CFO model by Roychowdhury(2006) under IFRS. Pownall and Wieczynska (2017) find that over 17% of firms in the Europe Union had not adopted
IFRS in 2012. This means that more than one accounting standard is used in those countries.

22

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26

Appendix
Variable

Definition

ACC

Accruals, calculated as net income minus operating cash flow reported in
the statement of cash flow

A

Total assets

AR

Accounts receivable

PPE

Net property, plant, and equipment|A_ACC|The absolute value of abnormal accruals, where abnormal accruals arecalculated as the residuals from the following regression for eachindustry-year combination in each country: ACCijt / Aijt-1 = β0 + β1 (1 / Aijt1) + β1 ((ΔSijt - ΔARijt) / Aijt -1) + β3 (PPEijt / Aijt -1) + εijt

CFO

The operating cash flow reported in the statement of cash flow

S

Net salesDE

The selling, general, and administrative expenses

PD

Production costs, calculated as the cost of goods sold plus the change ininventory|A_CFO|

The absolute value of A_CFO, where A_CFO is the residuals from thefollowing regression for each industry-year combination in each country:
CFOijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt / Aijt-1) + β3 (ΔSijt / Aijt-1) + εijt

|A_DE|

The absolute value of A_DE, where A_DE is the residuals from thefollowing regression for each industry-year combination in each country:
DEijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt-1 / Aijt-1) + εijt

|A_PD|

The absolute value of A_PD, where A_PD is the residuals from thefollowing regression for each industry-year combination in each country:
PDijt / Aijt-1 = β0 + β1 (1 / Aijt-1) + β2 (Sijt / Aijt-1) + β3 (ΔSijt / Aijt-1) + β4
(ΔSijt-1 / Aijt-1) + εijt

|REM|

The absolute value of total of A_CFO*-1, A_DE*-1, and A_PD

EM

|A_ACC|, |A_CFO|, |A_DE|, |A_PD| or |REM|

FD

The mean value of the four pillars (Institutional environment, Banking
financial Services, Non-banking financial services, and Financial markets)in the financial development report of World Economic Forum

Leverage

Total debt divided by total assets

Size

The natural logarithm of the market value of equity

MTB

The market to book ratio

ROA

Net income divided by lagged total assets

NOA

Net operating assets divided by sales, where net operating assets isdefined as total assets less cash and trading asset securities

Original_FD

The mean value of the seven pillars (Institutional environment, Businessenvironment, Financial stability, Banking financial Services, Non-banking
financial services, Financial markets, and Financial access) in thefinancial development report of the WEF

Institutional_Environment

The score for the Institutional environment in the financial development
report of the WEF

27

Financial_Intermediation

The mean value of the Banking financial Services, Non-banking financial
services, and Financial markets scores in the financial development reportof the WEF|A_CFO_GHJL|

The absolute value of A_CFO, where A_CFO are the residuals from thefollowing regression for each industry-year combination in each country:
CFOijt+1 / Aijt = β0 + β1 ACCijt / Aijt-1 + β2 CFOijt / Aijt-1 +β4 SIZEijt + β5 BMijt+ β6 EPijt + εijt+1

BM

Book-to-market ratio, calculated as the ratio of shareholders’ equity
divided by market capitalization at the beginning of yearEP
Net income divided by market capitalization at the beginning of year
The measures of financial development (FD, Original_FD, Institutional_Environment, andFinancial_Intermediation) are based on data from Global Note. The variables other than financial
development measures are based on data from Capital IQ.

28

Table 1
Panel A: Composition of the financial development index
Group
Pillar(1) Institutional environment
Factors, policies, and institutions(2) Business environment(3) Financial stability(4) Banking financial services
Financial intermediation(5) Non-banking financial services(6) Financial markets
Financial access(7) Financial access
World Economic Forum (2012, pp. 3-5)

Panel B: Correlation matrix of seven pillars of financial development
Pillar
(1)
(2)
(3)
(4)
(1) Institutional environment
1(2) Business environment
0.9151(3) Financial stability
0.478
0.481
1
(4) Banking financial services
0.843
0.821
0.303
1
(5) Non-banking financial services 0.418
0.466
0.159
0.535
(6) Financial markets
0.770
0.764
0.284
0.784
(7) Financial access
0.771
0.731
0.404
0.752
N = 142

29

(5)

(6)

(7)

1
0.746
0.444

1
0.667

1

Table 2
Data for each country and year
Panel A. The number of observations and the mean value of financial development scores in each country
Country
N
FD
P1
P2
P3
P4
P5
P6
P7
Original_FD
Argentina
88
2.44
3.22
3.83
3.32
2.84
2.33
1.36
2.22
2.73
Australia
1,607
4.80
5.49
5.58
5.23
5.07
4.14
4.51
5.15
5.02
Belgium
99
4.07
5.59
5.25
4.68
4.74
2.27
3.67
5.01
4.46
Brazil
474
3.17
3.68
3.74
5.03
3.39
3.46
2.15
3.43
3.55
Canada
2,339
4.72
5.82
5.78
5.16
4.71
4.19
4.16
4.83
4.95
Chile
188
2.86
4.47
4.80
5.45
3.35
1.97
1.68
3.54
3.61
Denmark
165
4.05
5.90
5.97
4.82
4.20
2.34
3.77
4.09
4.44
Finland
157
3.74
5.79
5.86
5.03
4.13
2.02
3.01
3.41
4.18
France
1,425
4.34
5.46
5.24
4.85
4.18
3.27
4.43
4.20
4.52
Germany
1,561
4.36
5.72
5.54
4.89
4.41
3.23
4.10
3.55
4.49
Greece
31
3.07
4.35
4.32
2.14
4.19
1.29
2.45
3.12
3.12
Hong Kong
2,544
4.86
5.71
5.93
5.58
5.58
3.60
4.54
4.92
5.12
India
7,284
3.17
3.23
3.42
4.06
3.11
3.69
2.65
2.81
3.28
Indonesia
633
2.44
3.49
3.38
4.42
2.69
2.18
1.41
2.85
2.92
Ireland
32
4.07
5.62
5.42
3.66
4.89
2.93
2.86
4.09
4.21
Israel
517
3.45
5.04
4.59
4.62
4.05
1.97
2.72
3.87
3.84
Italy
435
3.65
4.28
4.75
4.15
4.09
2.57
3.65
3.61
3.87
Japan
10,697
4.93
5.52
5.28
4.66
5.33
4.15
4.73
3.44
4.73
Jordan
32
3.90
4.73
4.12
3.79
4.42
2.58
3.85
3.72
3.89
Malaysia
2,435
3.83
5.05
4.69
5.40
4.64
3.01
2.62
3.73
4.16
Mexico
146
2.50
3.64
3.98
4.99
2.76
2.01
1.58
2.99
3.14
Netherlands
160
4.68
5.81
5.77
4.80
5.22
3.55
4.12
4.04
4.76
Nigeria
71
2.11
3.73
2.88
3.88
2.28
1.23
1.21
2.37
2.51
Norway
201
3.88
5.90
5.83
5.49
4.84
2.15
2.61
4.21
4.43
Pakistan
543
2.29
3.02
3.36
3.79
2.72
1.34
2.10
2.34
2.67
Peru
110
2.41
3.72
4.07
4.91
2.83
1.68
1.42
3.30
3.13
Philippines
121
2.74
3.73
3.38
4.13
2.95
2.30
1.97
2.65
3.02
Singapore
1,374
4.83
6.16
6.00
5.60
4.63
3.44
5.08
4.34
5.03
South Africa
395
3.27
4.53
4.15
4.76
3.62
2.44
2.51
3.15
3.59
South Korea
4,585
4.02
4.15
5.36
4.31
4.18
4.27
3.48
3.06
4.12
Spain
138
4.34
4.99
4.78
3.95
5.22
3.07
4.09
4.13
4.32
Sweden
731
4.20
5.98
5.83
4.82
4.83
2.33
3.68
4.57
4.58
Switzerland
440
4.46
5.68
5.83
5.75
4.54
2.90
4.72
3.87
4.76
Thailand
1,232
2.90
4.20
4.23
4.60
3.71
1.68
2.01
3.37
3.40
Turkey
541
2.70
3.80
4.50
3.54
3.29
1.74
1.97
3.25
3.16
U.K.
2,549
5.45
5.80
5.63
4.22
5.50
5.30
5.20
4.31
5.14
U.S.
10,750
5.39
5.61
5.55
4.35
4.17
6.03
5.75
4.69
5.17
Total / Mean 56,830
3.73
4.83
4.83
4.56
4.09
2.83
3.18
3.68
4.00

30

Panel B. The number of observations for each year
Year

N

2009
2010
2011
2012
Total

13,807
13,951
14,391
14,681
56,830In Panel A, P1 to P7 are the seven pillars reported in World Economic Forum (2012): Institutional environment(P1), Business environment (P2), Financial stability (P3), Banking financial (P4), Non-banking financial
services (P5), Financial markets (P6), and Financial access (P7). FD is the mean value of P1, P4, P5, and P6
and the measure of financial development in our main tests. Original_FD is the mean value of P1 to P7 and is
employed in the additional tests. Each pillar’s mean value in a country is calculated using only the data fromthe year for which all the dependent and independent variables are available. The mean value in the bottom
line is computed by averaging each country’s data.

31

Table 3
Descriptive statistics of dependent and independent variables
Variables
Mean
Q1
MedianQ3SD
N|A_ACC|
0.066
0.019
0.043
0.085
0.073
56,830|A_CFO|
0.075
0.021
0.050
0.098
0.081
56,830|A_DE|
0.095
0.024
0.058
0.123
0.109
56,830|A_PD|
0.114
0.032
0.077
0.152
0.120
56,830|REM |
0.221
0.065
0.150
0.294
0.230
56,830
FD
4.422
3.697
4.793
5.075
0.883
56,830
Leverage
0.307
0.172
0.283
0.417
0.172
56,830
Size
4.648
3.064
4.389
6.080
2.172
56,830
MTB
1.779
0.617
1.072
1.962
2.294
56,830
ROA
0.042
0.002
0.046
0.102
0.131
56,830
NOA
1.674
0.666
1.021
1.659
2.354
56,830|A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute value of abnormal accruals(A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses(A_DE), abnormal production costs (A_PD), and aggregated REM measure (REM), respectively.
A_ACC, A_CFO, A_DE, and A_PD are calculated as the estimated residuals in equations (1), (2),(3), and (4), respectively. REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. FD is themeasure of financial development, which is the mean value of the index of Institutional
environment, Banking financial services, Non-banking financial services, and Financial markets
reported by the WEF. Leverage is total debt divided by total assets; Size is the natural logarithm of
the market value of equity; MTB is the market-to-book ratio; ROA is net income divided by lagged
total assets; and NOA is net operating assets divided by sales.

32

Table 4
Correlation matrix of dependent variables and independent variables|A_ACC||A_CFO||A_DE||A_PD||REM |FD
Leverage
Size
MTB
ROA
NOA
|A_ACC|
1
0.341
0.092
0.139
0.163
-0.0620.057-0.1740.093-0.034-0.039|A_CFO|
0.453
1
0.144
0.238
0.334
0.009
0.011
-0.1190.177
0.038-0.062|A_DE|
0.152
0.222
1
0.460
0.552
0.168
0.096
-0.0850.157
0.060-0.290|A_PD|
0.193
0.303
0.583
1
0.791
0.107
0.090
-0.0730.138
0.078-0.226|REM |
0.223
0.406
0.695
0.879
1
0.098
0.100
-0.0870.150
0.072-0.235FD-0.037
0.031
0.167
0.111
0.101
1
-0.1560.273
0.146-0.041-0.122
Leverage
0.070
0.029
0.109
0.108
0.116
-0.1391-0.220-0.023-0.081-0.379Size-0.182-0.124-0.101-0.077-0.0890.264-0.225
1
0.436
0.293
0.159
MTB
0.160
0.253
0.198
0.193
0.205
0.093
0.102
0.249
1
0.261
-0.014
ROA-0.133-0.102-0.0180.059
0.054-0.099-0.074
0.259
0.011
1
-0.154NOA
0.015-0.008-0.156-0.126-0.126-0.013-0.2690.086-0.007-0.1591
The lower-left (upper-right) triangle of the correlation matrix displays Pearson (Spearman) correlations. |A_ACC|, |A_CFO|, |A_DE|, |A_PD| and |REM| isthe absolute value of abnormal accruals (A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses (A_DE), abnormal
production costs (A_PD), and aggregated REM measure (REM). A_ACC, A_CFO, A_DE, and A_PD are calculated as the estimated residuals in equation(1), (2), (3), and (4), respectively. REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. FD is the measure of financial development, which is themean value of the index of Institutional environment, Banking financial services, Non-banking financial services, and Financial markets reported by theWorld Economic Forum. Leverage is the total debt divided by the total assets; Size is the natural logarithm of the market value of equity; MTB is themarket to book ratio; ROA is net income divided by lagged total assets; NOA is net operating assets divided by the sales. N=56,830.

33

Table 5
Financial development scores and earnings management
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.119***
0.142***
0.179***
0.238***
0.424***(7.981)(9.529)(12.774)(12.055)(12.346)FD-0.008**-0.010***-0.013***-0.021***-0.032***(-2.441)(-3.003)(-4.155)(-4.785)(-4.216)
Leverage
0.026***
0.015**
0.005-0.020**-0.008(3.738)(2.075)(0.761)(-2.363)(-0.494)Size-0.006***-0.007***-0.009***-0.009***-0.019***(-5.319)(-7.250)(-9.140)(-6.915)(-8.261)MTB
0.002***
0.003***
0.004***
0.004***
0.008***(4.266)(6.029)(7.139)(6.333)(6.693)
ROA-0.029***0.036***
0.013
0.094***
0.177***(-2.881)(3.854)(1.597)(7.917)(8.009)NOA-0.001*-0.002***-0.001***-0.002***-0.004***(-1.887)(-3.868)(-4.370)(-3.253)(-4.874)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Firm_Fixed_Effects included
includedincludedincludedincluded
Adjusted R-squared 0.332
0.448
0.776
0.638
0.676
Observations
56,83056,83056,83056,83056,830*** **The t-statistics in parentheses are based on robust standard errors clustered by firm. , , and *indicate significance at 1%, 5%, and 10% levels respectively (two-tailed). The following regressions
are estimated:
EMijt = β0 + β1 FDjt + β2 Leverageijt-1 + β3 Sizeijt-1 + β4 MTBijt-1 + β5 ROAijt + β6 NOAijt-1+ Σ βYear_Fixed_Effect + Σ β Firm_Fixed_Effect + εijt
(5)
EM represents the earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or|REM|. |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute values of abnormal accruals(A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses (A_DE),
abnormal production costs (A_PD), and aggregated REM measures (REM), respectively. A_ACC,
A_CFO, A_DE, and A_PD are calculated as the estimated residuals in equations (1), (2), (3), and (4),respectively. REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. FD is the measure offinancial development that is the mean value of the index of Institutional environment, Banking
financial services, Non-banking financial services, and Financial markets, as reported by the World
Economic Forum. Leverage is total debt divided by total assets, Size is the natural logarithm of themarket value of equity, MTB is the market to book ratio, ROA is net income divided by lagged total
assets, and NOA is net operating assets divided by sales.

34

Table 6
Original financial development scores and earnings management
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.135***
0.142***
0.185***
0.205***
0.402***(7.569)(7.619)(10.476)(8.618)(9.298)
Original_FD-0.012***-0.010**-0.014***-0.013**-0.026***(-2.957)(-2.360)(-3.560)(-2.484)(-2.753)
Leverage
0.026***
0.014**
0.004-0.020**-0.009(3.692)(2.031)(0.693)(-2.420)(-0.551)Size-0.006***-0.008***-0.009***-0.009***-0.020***(-5.354)(-7.355)(-9.248)(-7.198)(-8.454)
MTB
0.002***
0.003***
0.004***
0.004***
0.008***(4.288)(6.070)(7.181)(6.419)(6.752)
ROA-0.029***0.037***
0.013*
0.094***
0.179***(-2.860)(3.894)(1.650)(7.990)(8.068)NOA-0.001*-0.002***-0.001***-0.002***-0.004***(-1.880)(-3.882)(-4.381)(-3.304)(-4.903)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Firm_Fixed_Effects included
includedincludedincludedincluded
Adjusted R-squared 0.332
0.448
0.776
0.638
0.676
Observations
56,83056,83056,83056,83056,830*** **The t-statistics in parentheses are based on robust standard errors clustered by firm. , , and *indicate significance at 1%, 5%, and 10% levels respectively (two-tailed). The following regressions
are estimated:
EMijt = β0 + β1 Original_FDjt + β2 Leverageijt-1 + β3 Sizeijt-1 + β4 MTBijt-1 + β5 ROAijt + β6 NOAijt-1+ Σ βYear_Fixed_Effect + Σ β Firm_Fixed_Effect + εijt
EM represents the earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or|REM|. |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute values of abnormal accruals(A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses (A_DE),
abnormal production costs (A_PD), and aggregated REM measures (REM), respectively. A_ACC,
A_CFO, A_DE, and A_PD are calculated as the estimated residuals in equations (1), (2), (3), and (4),respectively. REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. Original_FD is themeasure of financial development, the mean value of the index of Institutional environment,
Banking financial services, Non-banking financial services, Financial markets, Businessenvironment, Financial stability, and Financial access, as reported by the WEF. The latter three
indices are added to FD in our primary results. Leverage is total debt divided by total assets, Size isthe natural logarithm of the market value of equity, MTB is the market to book ratio, ROA is net
income divided by lagged total assets, and NOA is net operating assets divided by sales.

35

Table 7
Effect of institutional environment and financial intermediation on earnings management
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.184***
0.164***
0.194***
0.250***
0.487***(7.163)(6.240)(8.671)(7.970)(8.540)Institutional_-0.015***-0.007-0.006*-0.007-0.020**
Environment(-3.540)(-1.570)(-1.662)(-1.418)(-2.119)-0.006**-0.007***-0.010***-0.016***-0.025***
Financial_
Intermediation(-2.521)(-2.942)(-4.157)(-4.929)(-4.308)
Leverage
0.025***
0.014**
0.005-0.020**-0.008(3.622)(2.034)(0.732)(-2.368)(-0.534)Size-0.006***-0.007***-0.009***-0.009***-0.019***(-5.296)(-7.243)(-9.125)(-6.890)(-8.237)
MTB
0.002***
0.003***
0.004***
0.004***
0.008***(4.353)(6.056)(7.162)(6.333)(6.722)
ROA-0.029***0.036***
0.013
0.093***
0.178***(-2.854)(3.863)(1.604)(7.915)(8.015)NOA-0.001*-0.002***-0.001***-0.002***-0.004***(-1.847)(-3.853)(-4.358)(-3.244)(-4.853)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Firm_Fixed_Effects
includedincludedincludedincludedincluded
Adjusted R-squared
0.332
0.448
0.776
0.638
0.676
Observations
56,83056,83056,83056,83056,830*** **The t-statistics in parentheses are based on robust standard errors clustered by firm. , , and *indicate significance at 1%, 5%, and 10% levels respectively (two-tailed). The following regressions
are estimated:
EMijt = β0 + β1 Institutional_Environmentjt + β2 Financial_Intermediation + β3 Leverageijt-1+ β4 Sizeijt-1+ β5 MTBijt-1 + β6 ROAijt + β7 NOAijt-1 + Σ β Year_Fixed_Effects(6)+ Σ β Firm_Fixed_Effect + εijt
EM represents the earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or|REM|. |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute value of abnormal accruals(A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses (A_DE),
abnormal production costs (A_PD), and aggregated REM measure (REM). A_ACC, A_CFO, A_DE,
and A_PD are calculated as the estimated residuals in equations (1), (2), (3), and (4), respectively.
REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. Institutional_Environment is theInstitutional environment score reported by the WEF. Financial_Intermediation is the mean value ofthe Banking financial services, Non-banking financial services, and Financial markets scores reportedby the WEF. Leverage is total debt divided by total assets; Size is the natural logarithm of the market
value of equity; MTB is the market to book ratio; ROA is the net income divided by lagged total assets;
NOA is the net operating assets divided by sales.

36

Table 8
Financial development scores and earnings management:
excluding observations for the U.S. and Japan
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.126***
0.135***
0.155***
0.221***
0.392***(9.099)(9.739)(11.958)(12.092)(12.307)FD-0.009***-0.009***-0.011***-0.021***-0.031***(-2.642)(-2.631)(-3.580)(-4.592)(-3.920)
Leverage
0.013*
0.014*
0.003-0.026***-0.022(1.650)(1.765)(0.391)(-2.794)(-1.223)
***
***
***
***
Size
-0.007-0.009-0.009-0.009-0.020***(-5.238)(-7.091)(-8.042)(-6.087)(-7.153)
MTB
0.002***
0.003***
0.004***
0.003***
0.007***(3.718)(5.011)(5.564)(4.555)(4.992)
ROA-0.048***0.047***
0.018*
0.076***
0.166***(-3.936)(4.369)(1.915)(6.008)(6.787)NOA-0.001-0.001***-0.002***-0.001*-0.004***(-1.605)(-3.445)(-4.447)(-1.952)(-4.038)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Firm_Fixed_Effects included
includedincludedincludedincluded
Adjusted R-squared 0.264
0.362
0.740
0.576
0.612
Observations
35,38335,38335,38335,38335,383
The t-statistics in parentheses are based on robust standard errors clustered by firm. ***, **, and *indicate significance at 1%, 5%, and 10% levels respectively (two-tailed). The following regressions
are estimated:
EMijt = β0 + β1 FDjt + β2 Leverageijt-1 + β3 Sizeijt-1 + β4 MTBijt-1 + β5 ROAijt + β6 NOAijt-1+Σ βYear_Fixed_Effect + Σ β Firm_Fixed_Effect + εijt
(5)
EM represents earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or |REM|.
|A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute value of abnormal accruals(A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary expenses (A_DE),
abnormal production costs (A_PD), and aggregated REM measure (REM). A_ACC, A_CFO, A_DE,
and A_PD are calculated as the estimated residuals in equation (1), (2), (3), and (4), respectively.
REM equals the sum of A_CFO * (-1), A_DE * (-1), and A_PD. FD is the measures of financial
development, which is the mean value of the index of Institutional environment, Banking financial
services, Non-banking financial services, and Financial markets reported by the World Economic
Forum. Leverage is the total debt divided by the total assets; Size is the natural logarithm of themarket value of equity; MTB is the market to book ratio; ROA is the net income divided by lagged
total assets; and NOA is the net operating assets divided by the sales.

37

Table 9
Financial development scores and earnings management: the country-weighted least square(WLS) regression
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.100***
0.167***
0.173***
0.229***
0.475***(4.728)(5.934)(6.657)(7.626)(8.620)FD-0.008-0.022***-0.019***-0.028***-0.062***(-1.356)(-2.949)(-2.772)(-3.406)(-4.013)
Leverage
0.012
0.006
-0.000-0.012-0.009(1.212)(0.548)(-0.024)(-0.768)(-0.327)Size-0.004**-0.006**-0.005**-0.008***-0.017***(-1.989)(-2.272)(-2.225)(-2.902)(-3.383)
MTB
0.002**
0.003**
0.002*
0.003**
0.007***(2.284)(2.455)(1.891)(2.028)(3.115)
ROA-0.0200.001
0.020
0.058***
0.089***(-1.314)(0.079)(1.420)(3.920)(2.583)NOA
0.000-0.001**-0.001**-0.001-0.002*(0.083)(-2.006)(-2.039)(-1.221)(-1.779)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Firm_Fixed_Effects included
includedincludedincludedincluded
Adjusted R-squared 0.345
0.448
0.762
0.620
0.665
Observations
56,83056,83056,83056,83056,830The coefficients were estimated using a country-weighted least square (WLS) method. The weightis inversely proportional to the number of observations per country. The t-statistics in parentheses
are based on robust standard errors clustered by firm. ***, **, and * indicate significance at 1%, 5%,
and 10% levels respectively (two-tailed). The following regressions are estimated:
EMijt = β0 + β1 FDjt + β2 Leverageijt-1 + β3 Sizeijt-1 + β4 MTBijt-1 + β5 ROAijt + β6 NOAijt-1+ Σ βYear_Fixed_Effect + Σ β Firm_Fixed_Effect + εijt
(5)
EM represents the earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or|REM|. Furthermore, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute values ofabnormal accruals (A_ACC), abnormal cash flow from operations (A_CFO), abnormal
discretionary expenses (A_DE), abnormal production costs (A_PD), and aggregated REM
measures (REM), respectively. A_ACC, A_CFO, A_DE, and A_PD are calculated as the estimatedresiduals in equations (1), (2), (3), and (4), respectively. REM equals the sum of A_CFO * (-1),
A_DE * (-1), and A_PD. FD is the measure of financial development that is the mean value of theindex of Institutional environment, Banking financial services, Non-banking financial services, andFinancial markets, as reported by the World Economic Forum. Leverage is total debt divided bytotal assets, Size is the natural logarithm of the market value of equity, MTB is the market to bookratio, ROA is net income divided by lagged total assets, and NOA is net operating assets divided bysales.

38

Table 10
Financial development scores and earnings management: differences regression
(1)
(2)
(3)
(4)
(5)
Variable|A_ACC||A_CFO||A_DE||A_PD||REM |
Intercept
0.003***
0.004***
0.008***
0.010***
0.021***(3.805)(5.447)(13.153)(11.959)(13.441)ΔFD-0.006*-0.003-0.010***-0.021***-0.027***(-1.658)(-0.755)(-3.884)(-5.143)(-3.811)
ΔLeverage
0.006
0.001
-0.006-0.037***-0.033**(0.851)(0.122)(-1.075)(-4.713)(-2.250)
ΔSize-0.007***-0.010***-0.010***-0.010***-0.021***(-6.612)(-9.507)(-12.445)(-8.982)(-9.807)
ΔMTB
0.003***
0.004***
0.004***
0.004***
0.008***(5.191)(6.623)(8.655)(6.376)(5.930)ΔROA-0.030***0.033***
0.002
0.090***
0.173***(-3.241)(3.604)(0.232)(9.460)(9.617)
ΔNOA-0.001**-0.002***-0.002***-0.003***-0.005***(-2.360)(-4.937)(-5.854)(-5.075)(-5.966)
Year_Fixed_Effects
includedincludedincludedincludedincluded
Adjusted R-squared 0.006
0.011
0.016
0.020
0.022
Observations
40,124
40,124
40,124
40,124
40,124*** **The t-statistics in parentheses are based on robust standard errors clustered by firm. , , and *indicate significance at 1%, 5%, and 10% levels respectively (two-tailed). The following regressions
are estimated:
ΔEMijt = β0 + β1 ΔFDjt + β2 ΔLeverageijt-1 + β3 ΔSizeijt-1 + β4 ΔMTBijt-1 + β5 ΔROAijt + β6 ΔNOAijt-1+ Σ βYear_Fixed_Effect + εijt
(8)
EM represents the earnings management proxies, that is, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, or|REM|. Furthermore, |A_ACC|, |A_CFO|, |A_DE|, |A_PD|, and |REM| are the absolute values ofabnormal accruals (A_ACC), abnormal cash flow from operations (A_CFO), abnormal discretionary
expenses (A_DE), abnormal production costs (A_PD), and aggregated REM measures (REM),
respectively. A_ACC, A_CFO, A_DE, and A_PD are calculated as the estimated residuals inequations (1), (2), (3), and (4), respectively. REM equals the sum of A_CFO * (-1), A_DE * (-1), and
A_PD. FD is the measure of financial development that is the mean value of the index of
Institutional environment, Banking financial services, Non-banking financial services, and Financial
markets, as reported by the World Economic Forum. Leverage is total debt divided by total assets,
Size is the natural logarithm of the market value of equity, MTB is the market to book ratio, ROA is
net income divided by lagged total assets, and NOA is net operating assets divided by sales. Δ is thechange and is computed as the difference between time t and t-1.

39