Earnings Quality of Cross-Listed Firms on the U.S. Market
and the Adoption of International Financial Reporting Standards
Singgih Wijayana*
Department of Accounting, Gadjah Mada University, Yogyakarta, Indonesia
ABSTRACT
The convergence of accounting standards motivates this study to examine the
earnings quality of cross-listed firms, i.e., firms from countries across the world that listed
on the U.S. market. With the expectation that IFRSs adoption increases the quality of
financial information, this study examines whether earnings quality of cross-listed firms
increase after the adoption of IFRSs. This study finds some evidence to support that the
globalisation of accounting standards leads to the higher quality of financial information as
indicated by the increasing cross-sectional average earnings quality of cross-listed firms
following the increasing number of cross-listed firms’ financial statement under IFRSs
overtime. This study also investigates how the IFRSs financial information is affected by
country-level attributes. This study finds no evidence to support for home country investor
protection impacting the earnings quality of cross-listed firms in the U.S. market. An
association between control variables, i.e., size, analyst following, business risk, liquidity
risk and earnings quality is suggested.
Keywords: Earnings Quality, IFRSs, Investor Protection, Cross-Listed Firms
*Corresponding author: Tel.:+6281391117864; e-mail addresses: singgihw@ugm.ac.id
1
1. Introduction
This study investigates whether the earnings quality of cross-listed firms increase
overtime, following the increasing adoption of IFRSs by cross-listed firms in the U.S.
market. It is motivated by the convergence of accounting standards, culminating in the
IFRSs adoption around the globe. The IFRSs are claimed to be high-quality accounting
standards (e.g., Gordon et al., 2008; SEC, 2007). Following the adoption of IFRSs by firms
from countries across the world in the last decade, a number of studies examine the quality
of financial reporting under IFRSs (Barth, 2008; Barth et al., 2008; Barth et al., 2006;
Houqe, 2012; Soderstrom and Sun, 2007). Firms adopting IFRSs have less earnings
management, timelier loss recognition, and greater value relevance of earnings (Barth et al.,
2008). While these studies documents that financial reporting under IFRSs is likely to result
in higher accounting quality compared to firms applying non-U.S. domestic standards (nonIFRSs),1 they do not examine whether such factor also apply in the context of U.S. market
where firms across country dual listed on the U.S. market. The bonding hypothesis, which
predicts that the earnings quality of U.S. cross-listings increases because they must comply
with U.S. securities laws, is supported by empirical evidence (e.g., Doidge et al., 2004; Hail
and Leuz, 2009; Reese and Weisbach, 2002). Thus, in the context of U.S. market, high
quality of cross-listed firms may relate to the bonding with U.S. regulation rather than
IFRSs adoption. To investigate whether the earnings quality changes over time, this study
calculates and compares the cross-sectional average of earnings quality between IFRSs and
non-IFRSs adopters (before the adoption of IFRSs).
Beyond the issue of earnings quality under IFRSs, this study addresses variables
related to variations in firms’ home country institutional factors across countries. Crosscountry institutional differences affect the implementation of accounting standards and
therefore affect the quality of earnings (e.g., Lang et al., 2006; Lang, M. H. et al., 2003).
Investor protection, a key institutional factor affecting corporate policy in disclosure and
accounting rules (La Porta et al., 2000; Shleifer and Vishny, 1997), is investigated. Since
investor protection varies across countries (La Porta et al., 2000; Shleifer and Vishny,
1997), its effect on the earnings quality of cross-listed firms is expected to vary. Crosscountry variations in investor protection and control variables are simultaneously
investigated using country-level cross-sectional analysis.
This study finds some evidence to support that the earnings quality of cross-listed
firms increased slightly during 2002–2007 (greater in the recent years that those in the later
years). This study finds no evidence that country investor protection is associated with
country-level earnings quality. The bonding hypothesis literature may explain this result.
This study supports that size, analyst following, business risk, liquidity risk is factors that
affects the earnings quality of cross-listed firms. These association have been documented
in the context of home country analyses (e.g., DeFond et al., 2007; Landsman et al., 2012;
Leuz et al., 2003). These results are consistently well supported by this study’s primary and
sensitivity analyses.
This study contributes to the academic literature by adding empirical evidence of
earnings quality under IFRSs over time following the increasing number of cross-listed
firms adopt IFRSs. This study also contributes to the international accounting and crosslisting literature by presenting a link between country-level variables, that is, size, analyst
following, business risk, liquidity risk and earnings quality for cross-listed firms in the U.S.
1
Similarly, Barth et al. (2006) find that the earnings quality of IFRSs firms is higher after the adoption of
IFRSs than before (when using domestic GAAP).
2
market. It also presents an important contribution to the literature and further understanding
on the linkage between country-level factors and earnings quality in an international crosslisting setting where the issue related bonding hypothesis remains unresolved. Evidence
from this study is helpful in accounting standards setting, corporate financial disclosure
decisions, and capital market investment decisions.
The remainder of this study is organized as follows. Section 2 presents the
theoretical background and hypotheses development. Section 3 demonstrates the data,
sample, and research methodology to investigate the usefulness of reconciliation
information over time. Section 4 presents and analyses the results from testing hypothesis.
The final section, section 5, concludes this study.
2. Theoretical background and hypothesis development
2.1 Earnings Quality of Cross-listed Firms
The theoretical literature suggests that a better quality of financial information is
associated with a lower cost of capital because a higher quality of financial information
reduces transaction costs and/or reduces information risk (Habib, 2006). A higher quality of
financial information enhances securities demand and hence increases stock market
liquidity, leading to lower transaction costs and reducing the cost of equity (Botosan, 1997;
Easley and O'Hara, 2004; Habib, 2006; Healy et al., 1999; Leuz and Verrecchia, 2000).2
Firm policy on the selection of accounting standards influences the cost of capital through
the quality and quantity of information available to investors (Clinch and Lombardi, 2011;
Easley and O'Hara, 2004).
It is suggested that informative earnings are important for communicating a firm’s
inside information to investors (Collins et al., 1997; Francis et al., 2002a; Lev, 1989). The
more informative the earnings, the more effective the reduction in information asymmetry3
(Christensen et al., 1999; Lennox and Park, 2006; Warfield et al., 1995). Furthermore,
empirical work documents that investors react to high and low earnings quality differently
(Christensen et al., 1999; Francis et al., 2005; Mikhail et al., 2003; Warfield et al., 1995).
Francis et al. (2005) find that earnings (accrual) quality affects market perceptions of equity
risk and the market reactions associated with differences in earnings quality. Based on
theoretical work by Easley and O’Hara (2004) that shows that lower information risk results
in lower costs of equity, Francis et al. (2005) argue that higher earnings quality reduces
information asymmetry between firms and investors and therefore investors face lower
information risk, leading to lower costs of capital (e.g., Francis et al., 2008). Consequently,
higher earnings quality is valued more by investors because investors anticipate lower
expected returns due to lower costs of equity.
Whether adopting high-quality IFRSs accounting standards leads to higher or lower
earnings quality of cross-listed firms motivates this study to investigate the earnings quality
of cross-listed firms over time, in the context of cross-countries studies. On one side, the
bonding hypothesis, which suggests that the quality of financial information of U.S. cross2
A lower cost of capital is reflected in higher stock prices (Amihud and Mendelson, 1986). Providing a high
quality of financial information reduces a firm’s cost of capital because it attracts investors and therefore
increases the liquidity of the firm’s securities (Diamond and Verrecchia, 1991).
3
Information asymmetry here refers to information asymmetry between management and investors, known as
the agency problem, first noted by Jensen and Meckling (1976) and information asymmetry between investors,
known as the lemon problem, first noted by Akerlof (1970). Firm financial reporting plays an important role in
mitigating both types of information asymmetry (Frankel and Li, 2004; Healy and Palepu, 2001; Lev, 1988).
3
listings increases because they must comply with U.S. securities laws, is supported by
empirical evidence (e.g., Doidge et al., 2004; Hail and Leuz, 2009; Reese and Weisbach,
2002). Effective bonding increases firm’s quality of financial information for at least the
following reasons: 1) U.S. capital markets typically require more disclosure than home
country capital markets (e.g., Bailey et al., 2006; Lang, M. et al., 2003; Lang, M. H. et al.,
2003). Therefore, effective bonding decreases in information risk from compliance with
U.S. disclosure regulation (Botosan, 1997; Daske et al., 2008; Leuz and Verrecchia, 2000);
2) countries with greater differences with U.S. GAAP need to make greater adjustments to
reconcile to U.S. GAAP than firms from countries with smaller differences with U.S.
GAAP, requiring greater bonding effort (e.g., Durand and Tarca, 2005; Pagano et al., 2002);
and 3) by listing in U.S. capital markets, investor protection is strengthened,4 which in turn
increases the earnings quality of cross-listed firms (Doidge et al., 2004). Thus, one can
argue that the quality of financial information of cross-listed firms may have increased.
They are listed on the U.S. market and the adoption of IFRSs may not increase earnings
quality because their earnings quality is already high.
On the other side, high-quality home country earnings under IFRSs leads to high
quality of earnings released in the U.S. market and thus, reduces U.S. investor uncertainty
(Dechow, 1994; Francis et al., 2005; Francis et al., 2008). The current convergence
accounting standards is expected to change accounting standards over time and thus
increase cross-sectional average earnings quality of cross-listed firms. It is noted that the
number of cross-listed firms that adopt IFRSs, or a version thereof, increased from 12.16%
in 2004 to 31.76% in 2005, and continued to increase to 41.89 in 2007 (Table 2). It is
plausible that the increase in firms using IFRSs, or a version thereof, lead to the increase in
earnings quality because IFRSs are considered as high−quality accounting standards (e.g.,
Hopkins, 2008). The different time schedules of IFRSs adoption across countries can lead to
different inferences on the earnings quality. Firm from countries, such us the U.K. and
Australia, France and Germany adopted IFRSs earlier than firm from countries, such as,
Canada, Korea, Argentina, and Brazil. If the adoption of IFRSs increased earnings quality,
then firms from countries of German/French that adopted IFRSs will have better quality of
earnings than firms from countries that have not yet adopted IFRSs, such as Canada which
continued to use domestic GAAP until 2011.
Given that higher home country earnings quality leads to a higher quality of
earnings released on the U.S. market, it is expected that financial reporting under IFRSs is
likely to result in higher accounting quality compared to firms applying non-U.S. domestic
standards (non-IFRSs).5 If this effect of IFRSs adoption on earnings quality is stronger than
the effect of bonding with U.S. regulation, then higher cross-sectional average of earnings
quality in more recent year (along with the increasing number of firms adopting IFRSs) are
expected. 6
H1: Earnings quality of cross-listed firms increased during 2002–2007
4
It has been suggested that U.S. securities laws have some of the best investor protection in the world (Hail
and Leuz, 2009).
5
Firms adopting IFRSs have less earnings management, timelier loss recognition, and greater value relevance
of earnings (Barth et al., 2008), suggesting that financial reporting under IFRSs is likely to result in higher
accounting quality compared to firms applying non-U.S. domestic standards (non-IFRSs).
6
Some of prior studies suggest that SEC regulations do not fully overcome local environment effects, even
after the implementation of the Sarbanes-Oxley Act (Hail and Leuz, 2009; Mahoney, 2009; Soderstrom and
Sun, 2007).
4
2.2 Country-Level Factors and the Earnings Quality of Cross-Listed Firm
Agency theory suggests that shareholders require protection because management
may not always act in the interests of shareholders (Fama, 1980; Fama and Jensen, 1983;
Jensen and Meckling, 1976). In the context of U.S. cross-listing studies, the need to protect
U.S. investors has been greater than in the context of local listing per se due to at least two
factors: 1) a multiplicity of national accounting standards used by cross-listed firms (SEC,
2007); and 2) variations in firms’ home country intuitional factors across countries (Ball et
al., 2000; Ball et al., 2003; Gaio, 2010; Hopkins, 2008; Jamal et al., 2007). The SEC
Regulation S is required to reduce the information risk from the first factor. The second
factors are simultaneously investigated by examining variables related to variations in home
country institutional factors of cross-listed firms across countries, namely, investor
protection.
It has been suggested that a country’s legal protection of investors can affect a firm’s
quality of financial information (Chen et al., 2009; Hail and Leuz, 2009). Prior literature
suggests that investor protection is a mechanism for reducing agency costs,7 and therefore
firms in countries with stronger investor protection have higher quality of financial
information than firms in countries with weaker investor protection (e.g., 1998; 2002; La
Porta et al., 1997). Investors are better protected by effective investor protection through
high quality of financial information because it reduces the information risk for investors,
which in turn reduces the cost of capital, thereby affecting firm value (e.g., Chen et al.,
2009). This view is supported by theoretical work by Easley and O’Hara (2004), who show
that lower information risk results in a lower cost of equity capital. 8 This view is further
supported by Clinch and Lombardi (2011). Lower investor protection appears to result in
poor quality of financial reporting (e.g., Leuz et al., 2003) or lower disclosure quality (e.g.,
Lambert et al., 2007), leading to higher information risk, higher cost of capital, and lower
firm value.9
A positive association between investor protection and the quality of earnings
information is suggested by the prior cross-country earnings literature (e.g., DeFond et al.,
2007; Leuz et al., 2003). In the context of cross-listed firms, investor protection is expected
to be associated with earnings quality based on the following arguments.
First, managers in countries with strong investor protection are less likely to use
their discretion to increase the quality of earnings, as opposed to managers in countries with
weak investor protection, because regulations in countries with strong investor protection
limit managerial incentive and ability to manipulate reported firm performance (DeFond et
al., 2007; Gaio, 2010; Leuz et al., 2003). Ensuring that a reliable financial reporting process
through regulations limits insiders’ abilities to manipulate reported firm performance will
promote high-quality financial reporting and reduce information asymmetry between
insiders and outsiders (Healy et al., 1999; Healy and Palepu, 2001; La Porta et al., 2000).
Potential shareholders and creditors are protected through this mechanism and are therefore
motivated to finance firms (La Porta et al., 2000).
Agency costs refer to an agent’s bonding expenditures, residual loss, and the principal’s monitoring
expenditures, such as measuring or observing agent behavior and efforts to control agent behavior through
budget restrictions, compensation policies, and operating rules (Jensen and Meckling, 1976).
8
A recent paper by Christensen et al. (2010) shows another view, that capital is offset by an equal increase in
cost of capital for the period leading up to the release of the information that decreases the cost of capital.
9
This information effect is expected to be captured in the earnings quality variable and is discussed further
with the third hypothesis (H3).
7
5
Second, it is argued that the national accounting systems of countries with weaker
investor protection, such as the code law countries of France, Germany, Finland, Argentina,
and Italy, are influenced more by government preferences to establish and enforce national
accounting standards (Ball et al., 2000). They have greater differences from U.S. GAAP and
therefore need to make greater adjustments to comply with the U.S. regulations than do
firms from common law countries (e.g., Durand and Tarca, 2005; Pagano et al., 2002)
where the properties of accounting standards are determined primarily by demand of
disclosure in the market (e.g., Ball et al., 2000; Durand and Tarca, 2005). Consequently,
lower quality of earnings from firms in code law countries are expected because the
bonding effect for these firms is greater than for common law firms (Coffee, 2002; Doidge,
2004; Doidge et al., 2004; Durand and Tarca, 2005).
Third, in addition to investor protection, the high quality of financial reporting may
be due to the high-quality accounting standards underlying the preparation of financial
statements. Following the adoption of IFRSs by firms from countries across the world in the
last decade, a number of studies examine the quality of financial reporting under IFRSs
(Barth, 2008; Barth et al., 2008; Barth et al., 2006; Soderstrom and Sun, 2007). Firms
adopting IFRSs have less earnings management, timelier loss recognition, and greater value
relevance of earnings (Barth et al., 2008), suggesting that financial reporting under IFRSs is
likely to result in higher accounting quality compared to firms applying non-U.S. domestic
standards (non-IFRSs). 10 If countries with strong investor protection have more IFRSs
preparers than countries with weak investor protection, higher earnings quality of firms
from strong investor protection countries than from weak investor protection countries are
expected.
When complying with U.S. capital market regulation, firms from countries with
weaker investor protection is strengthened more for those with stronger investor protection
(Reese and Weisbach, 2002). Firms from weaker investor protection countries improve their
disclosure more than firms from stronger investor protection countries to comply with U.S.
capital market regulations.11 Consequently, information provided by firms from countries
with weaker investor protection will be valued more by U.S. investors than firms from
stronger investor protection countries (e.g., Bailey et al., 2006). 12 Indeed, prior studies
document that bonding effect varies according to the home country’s degree of investor
protection (e.g., Doidge et al., 2004; Hail and Leuz, 2009; Lang et al., 2006; Reese and
Weisbach, 2002). The SEC regulations do not fully overcome local environment effects,
even after the implementation of the Sarbanes-Oxley Act (Hail and Leuz, 2009; Mahoney,
2009; Soderstrom and Sun, 2007). Thus, although firms face the same level of investor
protection under U.S. securities law (La Porta et al., 1998), the home country’s legal system
affects quality of financial information in U.S. capital markets.
H2: Earnings quality is greater for cross-listed firms from countries with weaker investor
protection than for those from countries with stronger investor protection.
Control variables
10
Similarly, Barth et al. (2006) find that the earnings quality of IFRSs firms is higher after the adoption of
IFRSs than before (when using domestic GAAP).
11
Weaker investor protection countries are characterized by lower disclosure requirements compared to
stronger investor protection countries.
12
Bonding effects are sustained for many years after cross-listing in U.S. capital markets (Hail and Leuz,
2009).
6
Prior studies document that the release of financial information are affected by firm
size (Atiase, 1985; Bamber, 1987). Larger firms are more likely to disclose more
information than smaller firms are. For example, Plumlee and Plumlee (2008) report that
firms with large market capitalization disclose more financial information than firms with
medium or small market capitalization. Indeed, Hora et al. (2004) argue that the market
responds more to the information of smaller firms than to that of larger firms, because
information disclosed by a small firm is worth more than that disclosed by a large firm.
Following these authors, this study includes firm size as a control variable.
Rees (1995) argues that exchange rate fluctuations have a significant influence on
U.S. returns for non-U.S. companies. A new equilibrium share price can decrease (increase)
due to the weakening (strengthening) of a foreign currency relative to the U.S. dollar and
therefore affect returns for non-U.S. companies. A positive value indicates the dollar has
weakened relative to the foreign currency (Rees, 1995). Following Rees (1995), this study
includes the exchange rate as a control variable.
Analyst following, a measure for the flow of information into price (Bushman et al.,
2005), can indicate the amount of information disclosed by a firm, also indicating
information risk (e.g., Habib, 2006) and information asymmetry (Lang and Lundholm,
1996). More information about the firm becomes available in the markets as the number of
analysts following the firm increases. This study includes analyst following as the control
variable to mitigate problems of potentially omitted correlated variables. One can also argue
that a higher analyst following indicates lower information asymmetry, because more
information disclosed to the public reduces information asymmetry between insiders (firm
managers) and outsiders (investors).
Following Jain and Rezaee (2006), this study controls for firm performance (risk
and expected growth) using the market-to-book ratio of common equity (Fama and French,
1993). The market-to-book ratio is defined as the market value of equity (market
capitalization) divided by shareholders’ equity, where the market value of equity is the total
dollar market value of all of a company’s outstanding shares (calculated by multiplying the
company’s current stock price by its number of outstanding shares).
Despite any reconciliation information provided, firms are required by the SEC to
provide private enforcement information (risk information) in Form 20-F beyond any risk
information disclosed in annual reports (Danckaert et al., 2010). This additional risk
information includes quantitative and qualitative updates of risk disclosure presented in the
annual report (market and liquidity risk information) and business risk. The purpose of this
risk exposure requirement is to provide information to users (investors) so that they can use
it to access firm risk and hence make risk judgments for investment decisions.
The incremental risk information disclosed by cross-listed firms is useful for
investors (e.g., Danckaert et al., 2010; Jorion, 2002). This study includes the three elements
of risk disclosure information required by the SEC, namely, credit risk (CR), liquidity risk
(LR), and business risk (BR). They are included in the cross-sectional model to eliminate
the potential confounding effect caused by the exposure of credit risk, liquidity risk, and
business risk.
The issue of differences in accounting standards across countries leads this study to
examine the association between earnings quality with earnings (book value of equity)
differences. Prior studies document that cross-country variations in the use of domestic
GAAP, IFRSs as issued by the IASB, and other versions of IFRSs are expected to be
reflected in the quality of earnings (DeFond et al., 2007; Leuz et al., 2003). Countries with
national accounting standards have greater differences with U.S. GAAP than countries with
7
IFRSs as issued by the IASB or other versions of IFRSs.13 Greater accounting standards
differences, as indicated by earnings (book value of equity) differences lead to cross firms
variation of earnings quality. Therefore, this study expects earnings (book value of equity)
differences to be positively associated with earnings quality. Thus the earnings (book value)
differences are used as control variables.
3. Sample, data, and research methodology
The period 2002–2007 is appropriate for this study. The year 2002 is the beginning
of the convergence of IFRSs and U.S. GAAP. 14 The period of analysis finishes in 2007
because the number of cross-listed firms that disclose reconciliation statement has
decreased since the SEC’s 21 December 2007 announcement of the removal of the
reconciliation requirement. The sample for this study comprises firms listed on the New
York Stock Exchange (NYSE), American Stock Exchange (AMEX), or National
Association of Securities Dealers and Quotation (NASDAQ) during 2002–2007. The
number of foreign firms registered with the SEC in each year spanning 2002 to 2007, as of
31 December, was 1,319, 1,232, 1,244, 1,231, 1,145, and 1,058, respectively, resulting in
7,229 firm-year observations (SEC, 2009). The sample for investigating H1 is selected
based on the following criteria:
1) Firms are listed on the NYSE, AMEX, or NASDAQ and issue level II and III
American depository receipts (ADRs);
2) Firm specific data are available for earnings quality calculation.
3) Firms are continuously listed on the NYSE, AMEX, or NASDAQ during 2002–
2007;
One selection criterion is added to obtain the sample for investigating H2, namely, that data
for investor protection, earnings differences, book value of equity differences, and control
variables are available. Table 1 present the outcomes of applying the sample selection
criteria and the number of observations employed to examine H1 and H2, respectively.
Table 1 Sample selection
Criteria
Panel A: Sample for testing H1
Foreign firms registered with the SEC
1. Firms do not issue level II or level III
ADRs
2. Unavailability of firms’ annual report
3. Firms are not continuously listed on the
U.S. exchange during 2002–2007
4. Earnings and book value of equity
differences information is unavailable:
- Firms use U.S. GAAP
Number of Sample Firms
2004
2005
2006
2002
2003
2007
Total
1,319
-499
1,232
-454
1,244
-454
1,231
-431
1,145
-362
1,058
-318
7,229
-2,518
820
-54
766
-316
778
-38
740
-290
790
-41
749
-299
800
-45
755
-305
783
-50
733
-283
740
-33
707
-257
4,711
-261
4,450
-1,750
450
450
450
450
450
450
2,700
-198
-198
-198
-198
-198
-198
-1,188
13
Chen and Sami (2009) present evidence that trading volume reactions to reconciliation information from
IFRSs as issued by the IASB are weaker than trading volume reactions to reconciliation information from
other versions of IFRSs.
14
A memorandum of understanding between the Financial Accounting Standards Boards (FASB) and the
International Accounting Standards Board (IASB) for converging accounting standards was signed in October
2002 in the Norwalk Agreement (IASB, 2008a).
8
Criteria
- Reconciliation information is
unavailable
Less: Firm-specific data are unavailable
Final sample for testing H1
Less: Country-specific data are unavailable
Final sample for testing H2
Number of Sample Firms
2004
2005
2006
-6
-6
-6
2002
-6
2003
-6
246
246
246
246
-186
148
88
60
-186
148
88
60
-186
148
88
60
-186
148
88
60
2007
-6
Total
-36
246
246
1,476
-186
148
88
60
-186
148
88
60
-1116
888
528
360
Table 2 presents the final sample classified by the accounting standards used by
firms to prepare financial statements. The accounting standards are classified into domestic
GAAP, IFRSs as issued by the IASB (including financial statements that are prepared under
International Accounting Standards, the predecessor term for IFRSs), and other versions of
IFRSs such as IFRSs as adopted by the European Union, Mexican Financial Reporting
Standards (FRSs), New Zealand FRSs, and Hong Kong FRSs. There is a steady increase in
the number of firms using IFRSs (IFRSs as issued by the IASB and other versions of
IFRSs) during 2002–2004, followed by a considerable increase during 2005–2007. More
than half of this study’s sample comprises firms that still used domestic GAAP up to 2007.
Their domestic GAAP may have partially converged with IFRSs. 15 This convergence of
domestic GAAP may also have increased the quality of financial reporting. The effect of
partial IFRSs adoption on earnings quality is likely to have been more than that of IFRSs,
given that domestic standards are converged with IFRSs more in recent years.
Table 2 Accounting
standards employed by the sample of cross-listed firms
Year
2002
2003
2004
2005
2006
2007
Firm Numbers
Domestic GAAP
No.
136
%
91.89
No.
135
%
91.22
No.
130
%
87.84
No.
101
%
68.24
No.
91
%
61.49
No.
86
%
58.11
IFRS:
12
8.11
13
8.78
18
12.16
47
31.76
57
38.51
62
41.89
- IFRSs as issued
by the IASB
- Other versions of
IFRSsa
Total firms
4
2.70
4
6.08
5
3.38
17
11.49
20
13.51
21
14.19
8
5.41
9
2.70
13
8.78
30
20.27
37
25.00
41
27.70
148
100
148
100
148
100
148
100
148
100
148
100
a
For example, IFRSs as adopted by the European Union, Mexican FRSs, New Zealand FRSs, and Hong Kong
FRSs.
The following databases are used for the source data in this study. The SEC IDEA
(EDGAR) database is used to obtain: 1) a list of firms from 2002 to 2007;16 2) firm fiscal
year-end; and 3) CIK numbers. The CIK number is used to collect firm-specific data from
the Compustat database from Wharton Research Data Services (WRDS) such as earnings,
shareholders’ equity, operating cash flow, working capital data, the market value of equity,
total assets, current assets, accounts receivable, inventory, cash and short-term investments,
other current assets, current liabilities, accounts payable, taxes payable, debt in current
liabilities, and other current liabilities necessary for calculating earnings quality. Analyst
following data, measured by the number of analysts providing earnings estimates (number
of estimates) (Ackert and Athanassakos, 2003; Lang and Lundholm, 1996), are obtained
15
For example, Chinese Accounting Standards have somewhat converged with IFRSs since 2006, even though
China’s standard setters have not officially adopted IFRSs.
16
A list of cross-listed firms is available at http://www.sec.gov/divisions/corpfin/internatl/companies.shtml.
9
from the I/B/E/S Summary History, the Institutional Brokers' Estimate System (I/B/E/S)
from Thomson Reuters.
3.1 Earnings Quality
This study measures earnings quality by using accrual quality (Dechow and Dichev,
2002), 17 which is calculated from the standard deviation of residuals from firm-specific
regressions of changes in working capital on past, present, and future operating cash flows
(Equation 1).
∆WCt = α0 + α1CFOt-1 + α2CFOt + α3CFOt+1 + et
(1)
where
∆WCt = change in working capital accruals of firm i for period t;18
CFOt-1 = cash flow from operations of firm i for period t - 1;
CFOt = cash flow from operations of firm i for period t; and
CFOt+1 = cash flow from operations of firm i for period t + 1.
The residuals from the regression using Equation 1 (et) reflect the accruals that are unrelated
to cash flow realizations. At least eight years of data from each firm are required to estimate
the intercept (α0) and coefficients (α1, α2, and α3) from the regression model of Equation 1
(e.g., Dechow and Dichev, 2002). This study uses 11 years of data estimated for each firm,
from t - 11 (i.e., 1990) to t - 1 (i.e., 2001). To determine the residuals, yearly data over the



estimation period together with the estimated intercept (  0 ) and coefficients (  1 ,  2 , and

 3 ) are entered into (Equation 2):




et = ∆WCt – (  0 +  1 CFOt-1 +  2 CFOt +  3 CFOt+1)
(2)
For example, the residual for the year 1991 (e1991) is calculated using the estimated intercept




(  0 ); the estimated coefficients (  1 ,  2 , and  3 ); the data of the 1991 change in working
capital accruals (∆WC1991); and the data of the 1990, 1991, and 1992 cash flows from
operations (CFO1990, CFO1991, and CFO1992). 19 The residual for the year 1992 (e1992) is




calculated using the estimated intercept (  0 ); the estimated coefficients (  1 ,  2 ,and  3 );
the 1992 change in working capital- accruals (∆WC1992); and the 1991, 1992, and 1993 cash
17
Various measures of earnings quality developed by prior studies include the predictability of future
performance; earnings variability; accruals quality; the correlation between cash, accruals, and income; the
abnormal accruals component; and earnings persistence (Cohen, 2003; Schipper and Vincent, 2003).
18
Following Dechow and Dichev (2002), the change in working capital accruals (∆WCt) is ∆AR + ∆Inventory
+ ∆Other Current Assets - ∆AP - ∆TP - ∆Other Current Liabilities, where AR is accounts receivable, AP is
accounts payable, and TP is taxes payable. The change in working capital accruals can also be calculated
from the equation (e.g., Richardson et al., 2005) ∆WC = WCt – WCt-1. WC = current operating assets (COA) current operating liabilities (COL), where COA = current assets - cash and short-term investments and COL =
current liabilities - debt in current liabilities.
19
All the data to determine earnings quality are obtained from the home country of the foreign registrant’s
financial data. These are obtained from Compustat, Datastream, the Osiris database, or a firm’s annual reports
if the data from Compustat are unavailable or missing. All variables are deflated by average total assets. The
Compustat data are presented in the domestic currency of each firm. Some firms change their currency during
the sample period (e.g., from Dutch guilders in 2005 to euros in 2006). Consequently, before calculating
average total assets as deflators, this study translates the currencies of all variables to determine earnings
quality in U.S. dollars by using the exchange rate at the end of the financial year.
10
flows from operations (CFO1991, CFO1992, and CFO1993); and so on. For 11-year time-series
data, this procedure results in 11 residuals.
The standard deviation of the above residuals is a firm-level measure of accrual
quality, where higher standard deviations denote lower quality (Dechow and Dichev, 2002).
The 2002 standard deviation is calculated from the 11-year time-series residuals from 1990
to 2001 (e1991, e1992, …., e2001) to determine a firm’s 2002 earnings quality. Similarly, the
2003 standard deviation is calculated from the 11-year time-series residuals from 1992 to
2002 (e1992, e1993, …., e2002) to determine a firm’s 2003 earnings quality. The same
procedure is applied for the calculation of firms’ 2004, 2005, 2006, and 2007 earnings
quality.
The standard deviation represents the level of earnings of a single firm. The countrylevel measure of earnings quality is a country’s mean firm-level standard deviation of
residuals. Higher values of this measure indicate that, ceteris paribus, insiders exercise
accounting discretion, resulting in a low quality of earnings. To indicate that higher values
represent higher earnings quality, this value of the standard deviation is multiplied by -1.
3.2 Country-Level Factors and Earnings Quality of Cross-Listed Firms
To test the second hypothesis (H2), this study performs a country-level crosssectional analysis. The model is presented in Equation 3. In this country-level model, each
country represents a single observation. The dependent variable is measured by the countrylevel of earnings quality (EQNjt). The country-level test variables in the model is the
investor protection regime (IPjt). Prior studies document that the quality of financial
information is affected by country-level variables other than those above, including firm
size (SIZENjt) (e.g., Atiase, 1985), exchange rates (ERNjt) (e.g., Rees, 1995), analyst
following (AFNjt) (e.g., Hora et al., 2004), the market-to-book ratio (MTBNjt) (e.g., Fama
and French, 1993), credit risk (CRNjt), liquidity risk (LRNjt) and business risk (BRNjt) (e.g.,
Danckaert et al., 2010). All these variables are firm-specific measures, except for the
exchange rate (ERNjt). Given that these firm-specific variables must be addressed in the
country-level model as control variables to avoid potentially omitted correlated variables,
this study aggregates firm-level measures to country-level measures by using the average20
of firm-level measures. This average is determined by using the arithmetic mean.
To investigate the hypothesis, an OLS regression model for pooled observations
across countries and over time is employed. Relevant country- and time-specific variables
are included in the model to avoid the potential problem of omitted variables. The country
dummy variable, CAN, is set to one for Canadian firms and zero for non-Canadian firms.
Firms from Canada play a major role in U.S. markets, because about 27% of firms listed on
U.S. exchanges are Canadian. In this study, 33% of the sample consists of Canadian firms.
The total number of Canadian firms in the sample is 54 (324 firm-year observations) from a
total sample of 163 (978 firm-year observations). Theoretically, the differences between
Canadian and U.S. GAAP are smaller relative to those between other countries’ and U.S.
GAAP (Amir et al., 1993; Etter et al., 1999; Hora et al., 2003). Canadian firms are also
categorised as being from a high investor protection country, and firms from such countries
have a high quality of earnings (DeFond et al., 2007; Leuz et al., 2003).
The time dummy variable d1 is coded as one if the sample year is 2002, and zero
otherwise; the time variable d2 is coded as one if the sample year is 2003, and zero
20
The average central tendency of a dataset is a measure of its middle value. This can be determined using the
mean, median, or mode.
11
otherwise; and so on up through 2006 with the time variable d5. Note that, over time,
globalisation accounting standards lead to a improve the quality of financial reporting as a
result of the use of high-quality IFRSs accounting standards (e.g., Barth et al., 2008). The
time dummy variables are designed to capture this effect over time, and therefore the
coefficients of investor protection should represent the correct values of the parameters,
indicating the level of impact of the independent variables on the dependent variable. To
support H2, the coefficient estimate (α1) of the variable IP must be positive. It determines
whether higher earnings quality occurs when firms are from countries with stronger investor
protection.
Table 3 summarises all the variables employed in this study and their measurements.
Table 3 Summary of variables and their measurements
Variable
Measurement
Panel A: To Test Hypothesis 1
Firm-Level Measure of Earnings Quality
Earnings Quality for
The firm-level earnings quality measure (EQit) is the standard deviation of the
individual firm (EQit)
residuals from a regression of a least eight years of data using the equation
∆WCt = α0 + α1CFOt-1 + α2CFOt + α3CFOt+1 + e t
where
∆WCt= change in working capital of firm i for period t;
CFOt-1= cash flow from operations of firm i for period t - 1;
CFOt = cash flow from operations of firm i for period t; and
CFOt+1= cash flow from operations of firm i for period t +1.
Panel B: To Test Hypothesis 2 and 3
Dependent Variables
Country-level earnings The country-level measure of earnings quality is the average firm-level earnings
quality measure
quality of N firms from country j for period t (EQit).
(EQNjt)
1
EQNjt=
Nj
Test Variables
Investor protection
(IP)
Country-level earnings
differences (EDNjt)
Nj
 EQit
i 1
The anti-director rights index is an index that aggregates six components of investor
rights: (1) voting by mail; (2) shares not deposited; (3) cumulative voting; (4) an
oppressed minority; (5) capital to call a meeting; and (6) preemptive rights. This
measure ranges from zero to six, with higher scores for stronger investor rights
(Djankov et al., 2008).
The country-level earnings differences are measured by the average earnings
differences of N firms from country j for period t:
1 N j US
 (E  EitNonUS) / EitNonUS
N j i 1 it
where EDNjt = country-level earnings differences; Nj = number of firms from
country j; EDit = absolute firm-level earnings differences; E itUS = reconciled
EDNjt=
Country-level book
value of equity
differences (BVDit)
earnings reported under U.S. GAAP for firm i for period t; and E itNonUS = earnings
reported under non-U.S. GAAP for firm i for period t.
The country-level book value of equity differences are measured by the average
book value of equity differences of N firms from country j for period t:
12
Variable
Measurement
1 Nj
US
 (BV it  BV itNonUS) / BV itNonUS
N j i1
where BVDNjt = country-level book value of equity; Nj = number of firms from
country j; BVitUS = reconciled book value of equity reported under U.S. GAAP for
BVD Njt=
Country-level notes to
reconciliation (NP)
Control Variables
Country-level firm
size (SIZEjt)
firm i for period t; and BVitNonUS = book value of equity reported under non-U.S.
GAAP for firm i for period t.
The country-level notes to reconciliation are measured by the average of the total
number of pages of notes to reconciliation statement of N firms from country j for
period t.
The market value of equity, that is, common stock. Country-level firm size is
measured by the average firm size of firms from all cross-listed firms’ countries:
1
SIZEjt =
Nj
Country-level
exchange rate (ERjt)
Country-level analyst
following (AFjt)
Country-level marketto-book ratio (MTBjt)
Credit risk (CRNjt)
Liquidity risk (LRNjt)
Business risk (BRNjt)
Nj
 SIZE
it
i 1
The percentage change between the foreign registrant’s home country currency and
the U.S. dollar on the release of reconciliation information.
The average number of analysts following for firms from cross-listed firms’
countries. Country-level analyst following is measured by the average number of
analysts following for firms from cross-listed firms’ country.
The market-to-book ratio of common equity. Country-level firm performance (risk
and expected growth) is measured by the average market-to-book ratio of common
equity for firms from the countries of cross-listed firms.
Dummy variable of credit risk, coded as one if credit risk disclosure in the annual
reports is the same as that in Form 20-F, and zero otherwise. Country-level credit
risk is measured by the average credit risk for N firms from country j for period t.
Liquidity risk as measured by the average liquidity ratio for N firms from country j
for period t.
Business risk as measured by the average standard deviation of at least nine years’
time-series cash flow data for N firms from country j for period t.
4. Empirical results
4.1 Results of Investigating H1
Descriptive statistics for variables used in testing the first hypothesis, EQ from
2002-2007, are summarised in Table 4. A balanced sample is employed to test the first
hypothesis, with 888 observations representing 148 firms and a six-year sample period
(2002–2007). The mean EQ from 2002 to 2005 are similar, that is, -0.0504, -0.0505, 0.0503, and -0.0503, respectively. Compared to the 2002-2005 period, mean EQ 2006 -2007
period is slightly higher with EQ 2006 is -0.0470 and EQ 2007 is -0.0462. Measured by the
arithmetic median, the central tendency is fluctuated slightly with not trend of increasing
quality of earnings over time. The value range of EQ for all observation is between -0.1754
and -0.0057.
Table 4 Descriptive statistics: earnings quality of cross-listed firms during 2002−2007
Variable
All
EQ 2002 EQ 2003 EQ 2004 EQ 2005 EQ 2006
N
888
148
148
148
148
148
EQ 2007
148
13
Mean
-0.0509
-0.0504
-0.0505
-0.0503
-0.0503
-0.0470
-0.0462
Standard Deviation
0.0447
0.0720
0.0712
0.0697
0.0708
0.0573
0.0577
Median
-0.0329
-0.0293
-0.0290
-0.0304
-0.0299
-0.0288
-0.0294
Minimum
-0.1754
-0.5629
-0.5684
-0.5685
-0.5691
-0.3885
-0.4178
Maximum
-0.0057
-9.1E-05
-7.3E-05
-8.6E-05
-9.3E-05
-8.9E-05
-9.4E-05
Variable description: EQ = the standard deviation of residuals calculated using Dechow and Dichev (2002)
multiplied by -1 (to indicate that a high value represents a high quality of earnings)
The results of recording and analysing the earnings quality over time 2002–2007
appear in Tables 5 Panel A and B. Table 5, Panel A reports the comparison of earnings
quality between the three periods (2002−2003, 2004−2005, and 2006−2007). The earnings
quality of 148 firms in two−years (296 observations) for each period is used for this
comparison. Table 5, Panel B reports the comparison of earnings quality between the two
periods (2002−2004 and 2005−2007). The statistical significance of the difference between
the periods’ earnings quality are obtained using a t−test. The average earnings quality of
each period is presented.21 The earnings quality for the period 2002−2003 is -0.0505. This
EQ for 2002−2003 are slightly lower than for 2004−2005 (EQ = -0.0503). In the
comparison of 2004−2005 with 2006−2007, EQ is higher in the later period (EQ= -0.0466)
than in the earlier one (EQ= -0.0503). The results are similar when the two period
comparison is examined. These comparison results show a continue increase in earnings
quality over time, supporting this study first hypothesis.
It is noted that the number of firms that adopt IFRSs, or a version thereof, increased
from 8.11% in 2002 to 8.78% in 2003, and continued to increase to 12.16, 31.76, 38.51,
41.89 in 2004-2007 respectively (Table 2). It is plausible that the increase in firms using
IFRSs, or a version thereof, is related to the increase in earnings quality from period to
period 2002−2003, 2004−2005 to 2006−2007 because IFRSs are considered as high−quality
accounting standards (e.g., Hopkins, 2008). The considerable increase in IFRSs adoption in
2005, 2006, and 2007 may have resulted in a better−quality financial information22 (e.g.,
Daske, 2006; Daske et al., 2008). To determine whether the considerable increase in IFRSs
adoption after 2005 is related to the use of IFRSs by cross-listed firms, this study separates
the sample into firms preparing financial statements using domestic GAAP (domestic
preparers) and those using IFRSs or a version thereof (IFRSs preparers). This division of
the sample is based on the sample classification in Table 2. The mean of cross−sectional
firms’ earnings quality of GAAP preparer firms and IFRSs preparer firms are used to
compare the average earnings quality. The results support the notion that the earnings
quality under IFRSs is higher than those under national domestic GAAP. 23
Table 5 Comparison of earnings quality during 2002−2007: three-period analysis
Period
N
Earnings Quality (EQ)
Panel A
2002−2003
296
-0.0505
2004−2005
296
-0.0503
(t−test)
0.420
2004−2005
296
-0.0503
The average earnings quality is the mean of cross−sectional firms’ earnings quality of 148 observations.
Prior studies suggest that the market reacts more to high quality of financial information than low quality
financial information due to lower information asymmetry and cost of capital (Leuz, 2003; Leuz et al., 2003).
23
For simplification, the results are not presented.
21
22
14
2006−2007
(t−test)
296
-0.0466
0.710
Panel B
2002−2004
444
-0.0504
2005−2007
444
-0.0479
(t−test)
0.576
Variable description: EQ = the standard deviation of residuals calculated using Dechow
and Dichev (2002) multiplied by -1 (to indicate that a high value represents a high
quality of earnings. The t−test is used to compare whether the mean earnings quality for
2002−2003 are greater than for 2004−2005 or 2006−2007.
Although this study finds an increasing trend of earnings quality overtime, no
(statistic) significant difference between EQ in 2002-2003, 2004−2005 and 2006−2007 is
found. One plausible interpretation for these results is that the earnings quality is not solely
affected by globalization accounting standards. Cross-country institutional differences
affect the implementation of accounting standards and therefore affect the quality of
earnings (e.g., Lang et al., 2006; Lang, M. H. et al., 2003). The issues of investor protection
and other attributes that potentially affect the quality of financial information are explored
in the H2 testing.
4.2 Results of Investigating H2
The previous section presented the empirical results of examining the earnings
quality over time. The univariate tests support the first hypothesis (H1) that the earnings
quality has increased over time. This section presents the empirical results of multivariate
analysis investigating whether country-level factors explain variations in the earnings
quality of cross-listed firms. Specifically, this study examines whether stronger investor
protection (H2), is associated with higher earnings quality. Country-level cross-sectional
regression analyses are performed to simultaneously test the hypothesis.
Table 6 presents descriptive statistics for country-level variables employed for
investigating H2. The variables include: 1) earnings quality (EQ); 2) a country-level
variables of interest (investor protection (IP)) and ; 3) nine control variables earnings
differences (ED), and book value of equity differences (BVD), firm size (SIZE), exchange
rate (ER), analyst following (AF) market-to-book value of equity (MTB), credit risk (CR),
liquidity risk (LR), and business risk (BR)); and 4) six dummy variables (one country
dummy and five time dummies).24
The numbers of firms and observations for each country are presented in the second
and third columns of Table 6, respectively. Six countries, Hungary, India, Indonesia, South
Africa, Spain, and Taiwan, are represented by a single firm in the sample. Thus, the sample
firm measure for these countries represents the country-level measure. For countries
represented by more than one sample firm, the country-level measure is the average of
sample firms from that country for the specified period. The countries with the highest
number of firms are Chile, with nine firms (54 firm-year observations), and Argentina and
Mexico, each with seven firms (42 firm-year observations).
This study employs the average firm-level earnings quality25 as the country-level
measure of earnings quality. The Table 6, column 5 presents country-level EQ, with higher
24
The country dummy variable (CAN) is set to one for Canadian firms and zero for non-Canadian firms. The
time dummy variables (d1–d5) are coded as one if the year of the sample is 2002, 2003, and so on, up to the
year 2006, and else equal to zero.
25
A firm-level earnings quality is measured by the standard deviation of residuals that are determined based on
Dechow and Dichev (2002).
15
levels indicated by higher values. The country-level EQ statistics show that Hungary has the
highest EQ (-0.013), followed by Spain, Brazil, and China with EQ of -0.016, -0.018, and 0.022, respectively. Indonesia, Korea (South), Mexico, and Israel are the four countries with
the lowest EQ values, 0.073, -0.074, -0.103, and -0.168, respectively.
It is noted that lower home country earnings quality leads to a lower quality of
earnings released in the U.S. market. Compared to home country EQ in prior studies (e.g.,
DeFond et al., 2007), EQ in this study shows some similarities to those of DeFond et al.
(2007). DeFond et al. (2007) document that Australia, Canada, and South Africa have EQ
above the average EQ in their sample. Similarly, the Table 6, column 5 shows that EQ for
Australia (-0.026), Canada (-0.028), and South Africa (-0.024) are above the average (0.049). In addition, the EQ for France (-0.052) and the Netherlands (-0.059) is close to the
average (mean), as per the study of DeFond et al. (2007).
Despite these similarities, minor differences exist between EQ in this study and EQ
in the study of DeFond et al. (2007). While Hong Kong and Spain are categorised as
countries that have EQ below the average in the study of DeFond et al. (2007), they are
categorised as countries that have EQ above the average in this study. A plausible
explanation relate to International Financial Reporting Standards (IFRSs) adoption. The
sample period of DeFond et al. (2007) is from 1995 to 2002, during which time firms in
Hong Kong and Spain used domestic GAAP. In comparison, the sample period for this
study is from 2002 to 2007, and firms from Hong Kong and Spain have been allowed to use
IFRSs since 2005. If the adoption of IFRSs increases the quality of financial statements
employed by Hong Kong and Spanish firms,26 a better earnings quality is expected. The
Table 6, column 5 shows that EQ of firms from Spain (-0.016) and Hong Kong (-0.038) are
higher than the average (-0.049).
Investor protection regimes are coded using the anti-director rights index of Djankov
et al. (2008). As shown in Table 6, column 6, the index ranges from 0 to 6, with higher
scores representing stronger investor protection regimes. The mean IP is 3.83, with seven
countries (Brazil, Hong Kong, India, Ireland, South Africa, Spain, and the U.K.) rated 5,
one country (South Korea) rated 4.5, and five countries with a 4 rating (Australia, Canada,
Chile, Indonesia, and Israel). The remaining seven countries (Argentina, China, France,
Hungary, Mexico, the Netherlands, and Taiwan) have a rating below the mean value of
3.83.
This study employs earnings differences and book value of equity differences as
proxies for accounting standards differences. Greater values of ED and BVD indicate
greater differences between firm’s accounting standards and the U.S. GAAP. The statistics
for these country-level ED and BVD variables are described in Table 6, columns 7 and 8.
The mean ED (0.827) 27 is greater than the mean BVD (0.684), suggesting that the
differences between home country and U.S. GAAP have a greater impact on earnings
differences relative to book value of equity differences. Brazil has the highest ED and the
second highest BVD. Argentina and Mexico have the second and the third highest ED,
respectively. These figures are consistent with prior studies, which suggest that Brazil,
Argentina, and Mexico are categorised as macro-uniform countries, with accounting system
that have greater differences with U.S. accounting standards than micro-uniform countries
(Hora et al., 2003, 2004). During this study’s sample period (2002–2007), firms from these
26
Hong Kong and Spain are represented by two firms and one firm in the sample, respectively.
This study’s average accounting standards differences (mean ED = 0.827) are greater than in the prior
studies by Hora et al. (2003, 2004) with a mean ED of 0.290.
27
16
countries still used domestic GAAP, and therefore large differences with U.S. GAAP are
expected.
Table 6, columns 7 and 8 also indicate that European countries such as Hungary,
Spain, the U.K., and Ireland have lower ED values than the cross-country average (mean or
median). These countries are micro-uniform and have smaller differences with U.S.
accounting standards than macro-uniform countries. Firms from these countries have also
employed IFRSs since 2005 and therefore have smaller ED values than for Brazil,
Argentina, and Mexico. Compared to the ED value of France (0.574) and the Netherlands
(0.838), that of Hungary, Spain, the U.K., and Ireland are also smaller. France and the
Netherlands have adopted IFRSs since 2005, but they are originally macro-uniform
countries. Consequently, the ED values of France and the Netherlands are higher than those
of firms from micro-uniform countries under IFRSs, but lower than those of firms from
macro-uniform countries using domestic GAAP. These ED statistics are consistent with the
notion that IFRSs adoption reduces the difference between home country accounting
standards and U.S. GAAP. The BVD figures for these European firms are similar to the ED
figures.
The investor protection regime in home countries impacts ED and BVD. Canada and
the U.S. are common law countries (La Porta et al., 1998). They have similar accounting
standards and hence smaller ED and BVD than other countries (Amir et al., 1993;
Bandyopadhyay et al., 1994; Barth and Clinch, 1996; Etter et al., 1999; Hora et al., 2003,
2004).28 Nonetheless, the Table 6, column 7 shows that the ED value of firms from Canada
is 0.958, greater than the average of 0.827. Late adoption of IFRSs by Canadian firms may
partially explain the higher value of ED than the cross-country ED average.29 Firms from
Canada adopt IFRSs in 2011. If the adoption of IFRSs decreases differences in accounting
standards from U.S. GAAP, then countries that have adopted IFRSs during 2002–2007
should have more similar accounting standards to U.S. GAAP (smaller ED) than Canada
(which used domestic GAAP during 2002–2007).
In relation to control variables, there is low variation in countries’ SIZE, CR, LR,
and BR values. In this study’s sample, greater variation is evident in the average number of
analysts following firms (AF) in each country, ranging from a minimum of zero analysts
following for Hungary and Canada, to an average of 37 and 16 analysts following for India
and Ireland, respectively. Firms from India and Ireland are characterized by higher MTB
and SIZE values than for the firms from the other countries. This implies that more analysts
follow firms with greater value and size, consistent with prior studies (e.g., Bushman et al.,
2005; Hora et al., 2003; Lang and Lundholm, 1996). Variations also exist in the MTB and
ER, with MTB ranging from 1.157 (Mexico) to 11.301 (India) and the ER30 ranging from
0.0001 (Indonesia) to 1.778 (U.K.).
28
Using data during 1983–1989, Bandyopadhyay et al. (1994) document that the magnitude of earnings
differences, as measured by U.S.–Canada earnings differences deflated by the market value of equity, is as
small as -0.022.
29
Bandyopadhyay et al. (1994) document that the reconciliations from Canadian to U.S. GAAP earnings
provided in the 10-K or 20-F filings in their sample firms contains as many as 49 types of differences, grouped
into six categories: foreign exchange, oil and gas, deferred taxes, interest capitalization, and two types of
extraordinary items. These differences are expected to remain in this study, as indicated by the Canadian ED.
30
The exchange rate is determined by the percentage change between the foreign registrant’s home country
currency and the U.S. dollar on the release of reconciliation information. The average exchange rate at the
time of the release of reconciliation information across firms within a country represents the country-level
measure of ER.
17
Table 6 Descriptive statistics: country-level variables
Country
Fir
ms
FirmYear
Obs.
Ctry
Year
Obs.
Dependent
Variables
EQ
IP
ED
BVD
SIZE
ER
AF
MTB
CR
LR
BR
Argentina
7
42
6
-0.033
2.00
2.855
0.486
7.800
0.331
1.690
1.290
0.024
1.095
0.070
Australia
3
18
6
-0.026
4.00
0.410
0.106
6.261
0.749
0.611
5.221
0.611
0.240
0.198
Brazil
2
12
6
-0.018
5.00
5.045
2.472
8.884
0.451
7.000
2.262
0.083
1.079
0.120
Canada
4
24
6
-0.028
4.00
0.958
0.071
6.580
0.841
0.000
5.661
0.875
0.363
0.120
Chile
9
54
6
-0.072
4.00
0.508
0.140
7.279
0.002
3.704
2.470
0.148
1.143
0.160
China
2
12
6
-0.022
1.00
0.356
0.044
10.761
0.127
2.083
1.878
1.000
2.353
0.111
France
2
12
6
-0.052
3.50
0.574
0.585
11.089
1.298
3.917
2.287
0.083
1.912
0.094
Hong Kong
2
12
6
-0.038
5.00
0.272
0.018
9.141
0.128
4.000
1.176
0.667
0.915
0.121
Hungary
1
6
6
-0.013
2.00
0.123
0.020
8.612
0.005
0.000
1.700
0.000
0.902
0.250
India
1
6
6
-0.024
5.00
0.004
0.654
7.491
0.023
37.167
11.301
0.167
0.136
0.303
Indonesia
1
6
6
-0.073
4.00
0.445
0.077
8.114
0.000
1.500
2.009
1.000
1.255
0.284
Ireland
2
12
6
-0.043
5.00
0.477
0.343
7.325
1.251
16.000
4.917
0.750
2.293
0.080
Israel
4
24
6
-0.168
4.00
0.524
0.050
5.784
0.238
1.583
1.872
0.833
1.611
0.071
Korea
2
12
6
-0.074
4.50
0.228
0.188
9.887
0.001
2.250
1.710
0.000
1.462
0.342
Mexico
7
42
6
-0.103
3.00
1.379
0.166
8.504
0.092
2.881
1.157
0.500
1.848
0.203
Netherlands
3
18
6
-0.059
2.50
0.838
1.477
10.116
1.280
1.111
3.570
0.611
1.541
0.171
Sth. Africa
1
6
6
-0.024
5.00
0.431
0.183
8.129
0.142
7.833
1.814
1.000
0.564
0.080
Spain
1
6
6
-0.016
5.00
0.216
0.045
10.880
1.296
5.667
1.562
0.000
1.531
0.086
Taiwan
1
6
6
-0.048
3.00
0.629
5.958
7.567
0.031
13.833
2.073
0.833
0.585
0.294
-0.052
5.00
0.276
0.595
9.552
1.778
6.717
4.040
0.800
1.493
0.103
Mean
-0.049
3.83
0.827
0.684
8.488
0.503
5.977
2.998
0.499
1.216
0.163
Median
-0.038
4.00
0.477
0.166
8.129
0.142
2.881
2.009
0.611
1.143
0.121
SD
0.037
1.22
1.167
1.378
1.544
0.579
8.503
2.386
0.389
0.638
0.088
U.K.
5
30
6
Total
60
360
120
Independent Variables
18
Variable descriptions: EQNjt = average EQ for N firms from country j for time t, where EQ is the standard
deviation of residuals calculated using Dechow and Dichev (2002) multiplied by -1 (to indicate that a high
value represents a high quality of earnings); IP = an index that aggregates six components of investor rights
(see Djankov et al., 2008), ranging from 0 to 6, with higher scores for stronger investor rights; EDNjt =
average ED for N firms from country j for time t, where ED is the absolute value of earnings differences
between earnings under U.S. and non-U.S. GAAP divided by earnings under non-U.S. GAAP in U.S. dollars;
BVDNjt = average BVD for N firms from country j for time t, where BVD is the absolute value of book value
of equity differences between the book value of equity under U.S. and non-U.S. GAAP, divided by book
value of equity under non-U.S. GAAP in U.S. dollars; SIZENjt = natural log of average total assets for N firms
from country j for time t; ERNjt = average ER for N firms from country j for time t, where ER is the
percentage change between the firm’s home country currency and the U.S. dollar on the release of
reconciliation information; AFNjt = average number of analysts following firms in U.S. markets for N firms
from country j for time t; MTBNjt = average market-to-book ratio for N firms from country j for time t; CRNjt
= dummy variable of credit risk, coded as one if credit risk disclosure in the annual report is the same as that
in Form 20-F, and zero otherwise; the country-level credit risk is measured by the average credit risk for N
firms from country j for period t; LRNjt = average debt-to-equity ratio for N firms from country j for time t;
BRNjt = average BR for N firms from country j for time t, where BR is the standard deviation of at least nine
years’ time-series cash flow; and NPNjt = natural log of the average number of pages of notes to reconciliation
statement for N firms from country j for time t.
The Pearson correlations for the independent variables in the cross-sectional models
of Equation 3 are presented in Table 7. Table 7 reports that none of the variables of interest
are significantly correlated. The IP is significantly negatively correlated with the control
variable SIZE at the 10% level. This correlation suggests that countries with weaker
investor protection have larger firms than in countries with stronger investor protection.
Significant positive associations are found between IP and ER, AF, and MTB, with
correlation coefficients of 0.227, 0.322, and 0.215, respectively. These correlations suggest
that countries with stronger investor protection are characterized by stronger currency, have
greater analyst followings and greater market-to-book values of equity ratio.
No significant correlations between ED or BVD and the other independent variables
are found, except for the variables ED and CR. Table 7 shows that ED is negatively
correlated with CR (significant at the 5% level with a coefficient correlation of -0.209).
This suggests that firms from countries with greater differences from U.S. GAAP disclose
less credit risk information. This result is consistent with those of prior studies (e.g., Ball et
al., 2000; Durand and Tarca, 2005; Pagano et al., 2002). Durand and Tarca (2005) suggest
that code law countries are characterized by firms that have lower leverage than those from
common law countries. Given that firms with greater earnings differences from U.S. GAAP
are also code law countries31 (e.g., Durand and Tarca, 2005; Pagano et al., 2002), they are
likely to have less credit disclosure, because they have lower leverage than firms from
common law countries.
Table 7 also reports that SIZE is positively correlated with ER (0.311) at the 1%
level of significance. This suggests that countries with larger firms are likely to have a
stronger currency. The U.K., France, Spain, and the Netherlands are examples of countries
with large firm size in the sample of this study. They are developed countries and thus their
exchange rates are higher than those of developing countries such as Indonesia, Chile, and
31
As in the U.S., properties of accounting standards of common law countries are determined primarily by
demand of disclosure in the market (e.g., Ball et al., 2000; Durand and Tarca, 2005) and thus have fewer
differences with U.S. GAAP (e.g., Durand and Tarca, 2005; Pagano et al., 2002).
19
India. A significant negative correlation at the 1% level exists between the SIZE and CR
variables. This result suggests that countries with larger firms tend to provide less CR
information than countries with smaller firms. Given that large firms are less likely to
default (e.g., Mei and Subramanyam, 2008), countries with larger firms will have less credit
risk exposure than countries with smaller firms.
With regard to ER and BR, countries that have lower exchange rates to the U.S.
dollar are likely to have higher business risk. This is indicated by a negative correlation
between ER and BR, with a coefficient correlation of -0.488, significant at the 1% level.
Compared to the other correlations, this correlation between ER and BR is the highest.
Overall, none of the correlation coefficients exceed 0.5, and thus bias due to strong
correlations between the independent variables is minimal.
Table 7 Correlation coefficients between independent variables
Variable
EDNjt
BVDNjt
SIZENjt
ERNjt
AFNjt
MTBNjt
CRNjt
LRNjt
BRNjt
IP
EDNjt
BVDNjt
SIZENjt
ERNjt
AFNjt
MTBNjt
CRNjt
LRNjt
-0.027
-0.043
-0.161*
0.227**
0.322***
0.215**
-0.012
-0.137
-0.067
0.124
0.006
0.000
-0.074
-0.110
-0.209**
0.010
-0.150
-0.017
-0.031
0.047
-0.012
0.068
-0.030
0.099
0.311***
-0.069
-0.230**
-0.254***
0.280***
-0.069
-0.037
0.108
0.025
0.169*
-0.488***
0.323***
-0.085
-0.131
0.195**
0.040
0.203**
0.163*
0.018
-0.147
-0.181**
Variable descriptions: IP = an index that aggregates six components of investor rights (Djankov et al., 2008),
ranging from 0 to 6, with higher scores for stronger investor rights; ED Njt = average ED for N firms from
country j for time t, where ED is the absolute value of earnings differences between earnings under U.S. and
non-U.S. GAAP divided by earnings under non-U.S. GAAP in U.S. dollars; BVDNjt = average BVD for N
firms from country j for time t, where BVD is the absolute value of book value of equity differences between
the book value of equity under U.S. and non-U.S. GAAP divided by the book value of equity under non-U.S.
GAAP in U.S. dollars; SIZENjt = natural log of average total assets for N firms from country j for time t; ERNjt
= average ER for N firms from country j for time t, where ER is the percentage change between the firm’s
home country currency and the U.S. dollar on the release of reconciliation information; AF Njt = average
number of analysts following firms in the U.S. markets for N firms from country j for time t; MTBNjt =
average market-to-book ratio for N firms from country j for time t; CRNjt = dummy variable of credit risk,
coded as one if credit risk disclosure in the annual report is the same as that in Form 20-F, and zero otherwise,
where the country-level credit risk is measured by the average credit risk for N firms from country j for period
t; LRNjt = average debt-to-equity ratio for N firms from country j for time t; and BRNjt = average BR for N
firms from country j for time t, where BR is the standard deviation of at least nine years’ time-series cash
flow.
*** Significant at the 1% level, two-tail test.
** Significant at the 5% level, two-tail test.
* Significant at the 10% level, two-tail test.
To simultaneously examine H2, the cross-sectional analysis is examined using data
from 20 countries during the period 2002–2007 (120 observations). The country level
Earnings Quality (EQNjt) are the model’s dependent variables. The following equations
(Equation 3) present a model with EQNjt as the dependent variable. This model is estimated
using ordinary least squares.
20
Model:
EQNjt = α0 + α1IPjt + α2EDNjt + α3BVDNjt + α4SIZENjt + α5ERNjt + α6AFNjt +
α7MTBNjt + α8CRNjt + α9LRNjt + α10BRNjt + α11CAN + α12d1 + α13d2 + α14d3 + α15d4+
α16d5 + eNjt
(3)
where
EQNjt = country-level earnings quality as measured by the average earnings quality
score for N firms from country j for time t;
IPjt = investor protection as measured by the anti-director rights index on country j
for time t;
EDNjt = country-level earnings differences as measured by the average earnings
differences 32 between earnings reported under non-U.S. GAAP and
reconciled earnings reported under U.S. GAAP for N firms from country j
for time t;
BVDNjt = country-level book value of equity differences as measured by the average
book value of equity differences between the book value of equity reported
under non-U.S. GAAP and the reconciled book value of equity reported
under U.S. GAAP for N firms from country j for time t;
SIZENjt = average firm size for N firms from country j for time t;
ERNjt = average percentage change between the firm’s home country currency and
the U.S. dollar on the release of reconciliation information for N firms from
country j for time t;
AFNjt = average number of analysts following N firms from country j for time t;
MTBNjt = average market-to-book ratio for N firms from country j for time t;
CRNjt = dummy variable of credit risk, coded as one if credit risk disclosure in the
annual reports is the same as that in Form 20-F, and zero otherwise, where
the country-level credit risk is measured by the average credit risk for N
firms from country j for period t;
LRNjt = liquidity risk as measured by the average liquidity ratio for N firms from
country j for time t;
BRNjt = business risk as measured by the average standard deviation of at least nine
years’ time-series cash flow for N firms from country j for time t;
CAN = country dummy variable equal to one if the sample country is Canada, and
zero otherwise;
d1–d5 = time dummy variable d1 equals one if the sample year is 2002 and zero
otherwise, d2 equals one if the sample year is 2003 and zero otherwise, and
so on, up through 2006 with dummy variable d5; and
eNjt = the error term.
The results of the regressions using EQ as the dependent variable are reported in
Table 8. One of the key assumptions of the regression is that the variance of the errors is
constant across observations (homoskedastic). Residuals are plotted and no evidence of
32
The earnings differences is the absolute value of the differences between earnings under U.S. and non-U.S.
GAAP, divided by earnings under non-U.S. GAAP (Hora et al., 2004).
21
heteroskedasticity is found. The test based on White (1980) accepts the null hypothesis of
no heteroskedasticity with a statistical significance of 146.39 (p-value = 0.20). Further, no
evidence of multicollinearity is found in the regression analysis. None of the variance
inflation factors (VIF) exceed 5, suggesting that the regressions have high validity.
The regression model with EQ as the dependent variable is well fitted at the 1%
level of significance (F-statistic = 2.64) with an adjusted R2 of 7.14%. H2 predicts that the
earnings quality is greater for firms from countries with stonger investor protection than for
those from countries with weaker investor protection. Several studies that relate the quality
of financial reporting, information risk, and investor protection (e.g., Armstrong et al.,
2010; DeFond et al., 2007; Leuz et al., 2003) suggest a positive association between a
country’s legal protection and reporting quality (e.g., Armstrong et al., 2010; Leuz et al.,
2003). No evidence of an association between EQ and IP is found in the model. The Table 8
shows that neither the direction nor the significance of the association between EQ and IP
supports these prior studies. The result suggests that the variations in earning quality by
country, in the aggregate, are not explained by the cross-country variations in the level of
investor protection. This evidence can support the bonding hypothesis. The earnings quality
of U.S. cross-listings increases because they must comply with U.S. securities laws (e.g.,
Doidge et al., 2004; Hail and Leuz, 2009; Reese and Weisbach, 2002) and thus, in the
context of U.S. market, earnings quality of cross-listed firms may not vary according home
country investor protection.
Some of this study’s control variables explain the variations in the earnings quality
of cross-listed firms. The variable EQ is significantly positively associated with control
variable SIZE at the 1% level with a coefficient correlation of 0.012. This correlation
supports the view that the quality of earnings in countries is associated with the size of
firms. Large firms are likely to release higher earnings quality. Given that EQ is positively
associated with country dummy of Canadian firms (Table 8), this study presents evidence
that Canadian firm has higher earnings quality given that they have similar accounting
standards to U.S. GAAP. A negative significant association for LR and BR appears in the
result of regression analyses. This suggests that firms from countries with greater liquidity
risk and business risk experience a lower earnings quality. The argument is that greater
uncertainty in financing firms from countries with greater business risk lead to greater
earnings management and therefore, result in lower earnings quality.
This study’s cross-country variations in accounting standards do not explain the
variations in the earnings quality. The variable ED and BVD is not associated with EQ.
The correlation between EQ and AF is significant and positive at the 10% level, suggesting
that countries with higher earnings quality are followed by more analysts. In relation to ER,
prior studies suggest that exchange rate fluctuations have no association with EQ in this
study. No association between EQ and CR is found. The argument that new credit risk
disclosures reduce information asymmetry (e.g., Danckaert et al., 2010) is not supported.
No association is found for EQ and MTB.
Table 8 Cross-sectional country-level regression for the association between earnings quality and countrylevel attributes
Variable
Predicted Parameter Standard
t Value Pr > |t| Variance
Sign
Estimate
Error
Inflation
Intercept
-0.132
0.031 -4.27*** <.0001
0.000
22
IP
-0.005
0.005
-1.01
0.311
+
Control
ED
0.000
0.001
0.51 0.6071
+
BVD
-0.001
0.002
-0.36
0.7218
+
Size
0.012
0.003
4.65*** <.0001
+
ER
0.009
0.010
0.88
0.381
+
AF
0.001
0.001
1.9* 0.0585
MTB
0.002
0.001
1.33 0.1852
?
CR
-0.002
0.011
-0.17 0.8668
LR
-0.003
0.002
-2.18** 0.0301
BR
-0.068
0.041
-1.68* 0.0939
CAN
0.056
0.021
2.64*** 0.0086
d1
0.002
0.019
0.09 0.9297
d2
-0.003
0.019
-0.14 0.8902
d3
-0.010
0.019
-0.56 0.5757
d4
-0.007
0.018
-0.4 0.6886
d5
-0.003
0.018
-0.16 0.8723
F-Stat
2.64
Sig.
0.0007
Adj. R2
0.0714
*** Significant at the 1% level, two-tail test.
** Significant at the 5% level, two-tail test.
* Significant at the 10% level, two-tail test.
The reported results are estimated using the following regression equations:
1.230
1.031
1.047
1.292
1.284
1.297
1.318
1.169
1.196
1.109
1.230
2.124
2.132
2.118
2.107
2.093
Model : EQNjt = α0+ α1IPjt + α2EDNjt + α3BVDNjt + α4SIZENjt + α5ERNjt + α6AFNjt + α7MTBNjt+ α8CRNjt +
α9LRNjt + α10BRNjt + α11CAN + α12d1 + α13d2 + α14d3+ α15d4 + α16d5+ eNjt
where IP = an index that aggregates six components of investor rights (see Djankov et al., 2008), ranging
from 0 to 6, with higher scores for stronger investor rights; EQNjt = the average EQ for N firms from country j
for time t, where EQ is the standard deviation of residuals calculated using Dechow and Dichev (2002),
multiplied by -1 (to indicate that high values represent a high quality of earnings); EDNjt = average ED for N
firms from country j for time t, where ED is the absolute value of earnings differences between earnings under
U.S. and non-U.S. GAAP, divided by earnings under non-U.S. GAAP in U.S. dollars; BVDNjt = average BVD
for N firms from country j for time t, where BVD is the absolute value of book value of equity differences
between the book value of equity under U.S. and non-U.S. GAAP, divided by the book value of equity under
non-U.S. GAAP in U.S. dollars; SIZENjt = natural log of average total assets for N firms from country j for
time t; ERNjt = average ER for N firms from country j for time t, where ER is the percentage change between
the foreign registrant’s home country currency and the U.S. dollar on the release of reconciliation
information; AFNjt = average number of analysts following firms in U.S. markets for N firms from country j
for time t; MTBNjt = average market-to-book ratio for N firms from country j for time t; CRNjt = dummy
variable of credit risk, coded as one if credit risk disclosure in the annual report is the same as that in Form
20-F, and zero otherwise; the country-level credit risk is measured by the average credit risk for N firms from
country j for period t; LRNjt = average debt-to-equity ratio for N firms from country j for time t; BRNjt =
average BR for N firms from country j for time t, where BR is the standard deviation of at least nine years’
time-series cash flow; CAN = a dummy variable that equals one if the firm is from Canada, and zero
otherwise; d1–d5 = dummy variables that equal one if the year of the sample is 2002–2006, and zero
otherwise; and eNjt = the error term.
To conclude, this study finds no evidence to support hypotheses that stronger
investor protection (H2) is associated with larger earnings quality of cross-listed firms.
23
Nonetheless, the overall results show that the country-level EQ are explained by five the
control variables, that is, SIZE, AF, LR, BR and CAN, with most of the signs correctly
appears as predicted. These variables explain 7.14% of the variations in the country
aggregate of earnings quality. To check the robustness of this study’s findings, sensitivity
analyses are performed. The sensitivity analysis includes: 1) using alternative measures of
the independent variable. Three independent variable IP is surrogated by alternative
measures. The level of investor protection is replaced by dummy IP, indicating high and
low investor protection. No evidence that the variations in investor protection explain those
in the earnings quality is found; 2) using a firm-level model. The results of analyses using a
firm-level model are consistent with the main analysis, although the explanatory power of
the firm-level regression is less than that of the country-level regression.
5. Conclusion
This study provides some evidence that the earnings quality increases slightly over
time during 2002–2007, particularly in 2006 and 2007. This result can be explained by
certain studies that address the earnings quality (Barth, 2008; Barth et al., 2008; Barth et al.,
2006; Soderstrom and Sun, 2007) and investors’ familiarity with the financial statements
under IFRSs (e.g., Hopkins, 2008). This study provides no evidence that country investor
protection is associated with country earnings quality. Thus, in the context of cross-listed in
the U.S. market, investor protection is not a factor that affects the earnings quality, contrary
to this study’s hypothesis. Several studies on bonding hypothesis offer plausible
explanations. The SEC regulations may overcome local environment effects (Hail and
Leuz, 2009; Mahoney, 2009; Soderstrom and Sun, 2007). Thus, although firms have
different level of investor protection in home country, under U.S. securities law (La Porta et
al., 1998), the effects of the home country’s legal system on quality of earnings disappears
because cross-listed firms must follow the same U.S. capital markets regulations. Overall,
this study presents some support that the earnings quality is related to the size of the firm,
analyst following, liquidity risk, business risk, and Canadian firms. This relation is
evidenced by a significant association between earning quality and those variables.
24
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