EARNINGS MANAGEMENT AND CROSS LISTING IN BRAZIL
Alexsandro Broedel Lopes*
Associate Professor – University of São Paulo
PhD Student – Manchester Business School
[email protected]
phone: 55 11 3091-5820
Yhurika Tukamoto
Instituto Superior de Educação - CERES
Fernando Caio Galdi
Assistant Professor – FUCAPE Business School
[email protected]
phone: 55 27 4009-4433
ABSTRACT
In this paper we investigate the impact of cross listing and of adjustments to US GAAP on the
earnings management practices of Brazilian firms. The institutional environment in Brazil is
characterized by poor investor protection and uninformative accounting numbers. In this
environment firms with better prospects could try to opt out of the country’s poor institutional
environment and to commit themselves to superior governance systems by cross-listing in the
US. However, these firms continue to face an institutional environment at home that does not
provide incentives to the production of credible and informative financial reports making the
impact of cross listing on the properties of accounting reports an interesting research question.
We investigated earnings management practices of cross listed Brazilian firms using numbers
prepared under US and Brazilian GAAP and also compare earnings management practices of
cross listed versus non-cross listed firms using numbers prepared according to Brazilian
GAAP. Using five proxies of earnings management commonly used in the literature we found
evidence that neither cross listing nor adjustments to US GAAP have a significant impact on
earnings management for our sample of Brazilian firms.
JEL classification: G14, G15, M41
Key Words: Earnings management. Cross listing. ADR. Brazil
1
INTRODUCTION
This paper investigates the effects of cross listing and adjustments to US GAAP on the
earnings management practices of Brazilian firms. First, we examine whether cross listed
Brazilian firms present lower levels of earnings management than not cross listed firms.
Second, we investigate whether accounting reports prepared under US GAAP present lower
levels of earnings management than reports prepared under Brazilian GAAP for the sample of
cross listed firms. This research design allows us to identify the marginal effects of cross
*
Corresponding author. Av. Prof. Luciano Gualberto, 908 – FEA 3, São Paulo, Brazil – 05508-900,
[email protected]. We would like to thank Martin Walker, Fábio Moraes, Nelson Carvalho and Iran Lima for
useful comments. Alexsandro wants to thank FIPECAFI and CNPq for financial assistance in conducting this
research. Fernando wants to thanks FAPESP and FIPECAFI for financial assistance in conducting this research.
listing and of adjustments to US GAAP on earnings management. Our sample originates from
Brazil because Brazilian firms have strong incentives to opt out of the country’s poor
institutional environment and to commit themselves to superior governance models at the
same time that they face a local environment inimical to the informativeness of financial
reports. A natural question arising is which of these factors has a greater role in shaping the
actual level of earnings management.
There is a significant literature trying to explore the impact of the actual incentives
managers face on the properties of accounting reports. This literature (Ball et all 2000; 2003)
has shown that the properties of accounting numbers do not depend solely on standards and
regulations but also on the incentives managers face to produce informative numbers. Ball et
all (2000) have shown that common law countries with developed debt and equity markets
(outsider model) present more conservative and informative accounting reports than code law
countries with credit oriented financial systems (insider model). Ball et all (2003) also
demonstrated that beyond accounting rules economic incentives do play a major role on the
determination of the ‘quality’ of accounting statements using a sample of firms from four East
Asian countries which despite being inserted on the common law tradition do not provide the
actual economic incentives that informative accounting reports need to flourish. This
literature examines the incentives firms face to produce accounting reports at the country level
without an explicit consideration to firms’ specific features. As a contribution to this debate,
Houlthausen (2003) comments that firms with good economic prospects immersed in
countries with poor institutional environments could find attractive to opt out of their
countries’ poor environment and ‘borrow’ more strict accounting and governance rules in
order to be able to attract foreign investors. Cross listing seems to be an effective way for
these firms to bind themselves to governance models that provide superior investor
protection. Supposedly, these superior firms (cross listed ones) located in poor institutional
countries would have the appropriate incentives to produce informative accounting reports.
Houlthausen (2003) argument does not take into account, however, that cross listed firms
may not have the adequate incentives at home to produce informative reports. The incentives
to produce more informative reports to attend foreign demand have to be considered against
the incentives firms face locally. Basically, we have two arguments posing into different
directions regarding the impact of cross listing on earnings management. On the pro side
cross listed firms have the incentives to provide less manipulated earnings in order to attract
foreign investors and to report higher earnings quality. On the con side these firms do not
posses - at home - the appropriate incentives to report more credible numbers. The economic
environment in Brazil does not provide significant incentives to the production of informative
and credible financial reports for several reasons. First, Brazil has been classified as a poor
enforcement, legal regime and transparency country (Durnev and Kim, 2005). Brazilian
financial markets are undeveloped and financial contracting operates under extremely
inimical conditions (Anderson, 1999). This setting provides the incentives for some Brazilian
firms to look abroad to attract foreign capital 1 . Second, the Brazilian accounting model
complies negatively with the Ali and Hwang (2001) criteria for accounting informativeness.
As Lopes (2006) has shown, Brazilian accounting system is (i) usually classified under the socalled Continental model (Nobes, 1998), (ii) the government issues all accounting rules and
professional bodies do not have any de facto power to influence standards, (iii) Brazilian
firms rely on private deals to obtain funding which clearly reduces the informativeness of
1
This is exactly what happens: Brazil is the emerging market with the highest number of American Depositary
Receipts issuers (second higher issuer country) and the participation of foreign investors in Bovespa is constantly
increasing moving from 25% in 2001 to 35% of the volume traded in 2006. The average daily volume traded in
Bovespa for the year ended on December 31st, 2006 was above US$ 1.1 billion, what makes Brazil the biggest
capital market in Latin America.
accounting reports (Leuz and Wusteman, 2004), (iv) ownership concentration is very high on
small public debt and equity markets. Third, Brazilian managers have considerable discretion
on how to prepare accounting reports. Brazilian managers have the option to capitalize or not
research and development costs, can revaluate fixed assets, do not have to provide statement
of cash flows and can make prior period adjustments without restate prior financial
statements. This high level of discretion combined with a poor institutional environment and
low level of monitoring creates a fertile ground for earnings management to emerge. This
scenario provides an interesting opportunity to test which factor dominates in terms of forging
the properties of accounting reports: cross listing and external demands versus local
incentives.
To investigate these questions we split our sample into four groups: the first one consists
of Brazilian firms listed on the São Paulo Stock Exchange (BOVESPA) that do not posses
ADRs traded on the New York Stock Exchange (NYSE) and the second group is composed of
firms listed at BOVESPA that have ADRs traded on the NYSE. We test earnings
management using accounting reports prepared under Brazilian GAAP for these two sets of
firms. Additionally, we test earnings management metrics for cross listed firms using reports
prepared under Brazilian GAAP (third group) and US-GAAP (fourth group). Those cross
listed firms have to present financial statements locally according to Brazilian GAAP and
based on US-GAAP to comply with SEC regulations. This set of Brazilian firms (groups three
and four) provides a good opportunity to test whether adjustments to US GAAP improve the
quality of accounting numbers since this is the only factor to differentiate the numbers used to
compute the earnings management metrics. We used the same four metrics of earnings
management as proposed by Leuz et all (2003) plus an additional measure. Our results show
that there are no significant differences between the earnings management metrics over the
four samples. These results indicate that cross listing and adjustments to US GAAP do not
impact significantly the level of earnings management in Brazil. These results also suggest
that local factors seem to be the major influence over the preparers of financial statements.
Recently, Lang et all (2006) show that cross listed firms present higher earnings
management figures than comparable American firms. They also show that earnings
management is higher for firms located in countries with weak investor protection. Our paper
contributes to this literature by comparing earnings management activities of a sample of
Brazilian firms cross listed and not cross listed (using numbers prepared under Brazilian
GAAP) and by comparing numbers prepared under US and Brazilian GAAP for the cross
listed firms. Our research design allows us to investigate the marginal effects of cross listing
and of the use of an alien set of accounting rules (US GAAP) on earnings management. Our
results show that listing on a ‘better’ corporate governance regime is not sufficient to increase
the quality of a firm’s accounting numbers. On the same way that the use of a supposedly
superior set of rules (US GAAP for the cross listed firms) also does not impact significantly
on the properties of accounting reports. Despite being traded at the NYSE these Brazilian
firms continue to show high levels of ownership concentration, poor investor protection and
all other features commonly associated with poor governance practices. Basically, cross
listing did not change manager’s incentives to report. Once the incentives are the same the
properties of accounting numbers do not change. Our results support the argument and the
results presented by Lang et all (2006) and suggest that the additional SEC oversight does not
compensate for the effect of local incentives on preparers.
Our paper also contributes to a related strand of the literature related to cross listing.
Siegel (2005) argues that the level of SEC actual enforcement over cross listed firms is
relatively small when compared to American firms and shows that jurisdiction and priorities
reduces the will of the SEC to pursue claims against foreign registrants. Frost and Pownall
(1994) also show that disclosure enforcement by the SEC is significantly lower for ADR
issuers than for local firms and found evidence of noncompliance with annual and interim
reporting requirements. These results question the actual degree of “legal bonding” by cross
listed firms (Coffe, 2002) and suggest that listing in the US may entail some level of
“reputational bonding” instead of legal bonding. These results emphasize the importance of
differences in managerial incentives and regulatory environments to explain the actual
differences in reported accounting figures. Our results show that Brazilian firms face a
combination of pressure to manage earnings in the local market, relaxed oversight of the local
accounts and reduced scrutiny by the SEC as Lang et all (2006) report for their sample of
foreign registrants. These results suggest that local forces are the main factors driving the
actual choices managers make in terms of the properties of financial reports.
For accounting regulators our results show that imposing a new set of accounting rules
(US-GAAP on Brazilian firms) on a high discretionary accounting environment where
managers do not have the economic incentives to report credible accounting numbers does not
affect the properties of actual financial reports being produced. Our results suggest that in
order to change the properties of accounting numbers it’s necessary to modify the economic
incentives involved in their production. These results have strong implications to the current
debate over the convergence of accounting standards towards a common set of rules and
regulations. As previously noted (Ball, 2006) the imposition of a supposedly superior set of
accounting standards2 over a population of firms that do not have the incentives to produce
informative accounting standards is not likely to produce de facto more informative numbers.
Our results show that earnings management metrics are similar for statements prepared under
US and Brazilian GAAP showing that the marginal effect of the accounting rules ceteris
paribus is insignificant. These results support previous research (Ball et all, 2003) which
shows that for a sample of firms from countries which are influenced by Anglo-Saxon
accounting rules (British and IFRS) the properties of accounting reports are similar to what is
found on code law countries. This happens because firms in those countries, despite being
influenced by Anglo-Saxon standards, do not have the incentives to prepare informative
accounting reports. Our results suggest that the same phenomenon happens for our sample of
Brazilian firms. Despite having to comply with US rules they do not face the actual incentives
to present less earnings management than their counterparts that do not report under US rules.
Another contribution of this paper is to the current debate about the listing requirements
demanded by American Securities and Exchange Commission on foreign firms. These
requirements are designed to improve the quality of information provided to investors in the
US and assume that local GAAP are of inferior quality. Our results do not support such a
claim and show that, at least in terms of earnings management, the outcomes do not differ
significantly. These results are not comprehensive enough to claim that SEC requirements do
not add value to American investors. However they show that cross listing and US GAAP
rules alone do not reduce the level of earnings management presented by Brazilian firms.
From an investor’s perspective our results emphasize the importance of being cautions in
comparing US GAAP statements of American firms with adjusted data from Brazilian firms.
Our results show that the level of earnings management observed in numbers prepared under
Brazilian GAAP is similar to the level observed in reports prepared under US GAAP. As
Lang et all (2006) show this level is significantly higher than the level presented by American
firms. We do not make this comparison directly but our results suggest the same.
The remaining of the paper is organized as follows. Section 2 reviews the literature and
motivates the paper; Section 3 presents the models used to measure earnings management.
Section 4 presents the results and Section 5 concludes the paper.
2
He is referring to International Financial Reporting Standards (IFRS) issued by the International Accounting
Standards Board (IASB) but the main argument extrapolated to other situations alike.
2
LITERATURE REVIEW AND MOTIVATION
Earnings management is intrinsically related to earnings quality. Earnings management is
defined by Schipper (1989) as a deliberate intervention in financial accounting process in
order to provide (usually for managers3) some private gain. Burgsthaler and Dichev (1997)
show how earnings are managed to avoid earnings decreases and losses as an example of one
motivation affecting manager’s behavior. Leuz et all (2003) argue that earnings management
arise to mislead some stakeholders or to influence contractual outcomes. Dechow and Schrand
(2004) relate earnings quality to three desirable features of earnings: i) to reflect current
performance, ii) to be useful for predicting future performance and iii) to accurately annuitize
intrinsic firm value. Hence, earnings management clearly decreases earnings quality (Dechow
and Schrand, 2004). Earnings management can be implemented by manipulating real
transactions or accruals. Some authors have focused on real transactions manipulations (Hand,
1989; Bartov, 1993; Dechow and Sloan, 1991) but the main focus of the earnings
management literature has been on accrual manipulations. However, accrual manipulations
are difficult to measure from outside the firm. In this context several metrics for earnings
management based on aggregate accruals have been proposed by the literature. Healy (1985)
and DeAngelo (1986) apply total accruals and first-differences in total accrual as proxies for
management discretion over earnings. Jones (1991) designed a model that relaxes the
assumption of constant nondiscretionary accruals and estimates the discretionary accruals as
the residuals of a time series regression between total accruals and changes in revenues and
PP&E. Dechow et all (1995) modify the Jones Model by including the changing of accounts
receivable and found better empirical results than previous models. The main criticism of
these models is that they do not control for growth as accruals may not move one for one with
sales and PP&E (Dechow and Schrand, 2004).
Despite its limitations this literature has come into terms that earnings management is an
undesired feature of accounting numbers and that its occurrence should be detected. Earnings
management impact negatively the usefulness of accounting numbers as inputs into corporate
governance arrangements. If earnings are manipulated, outsiders will have to use other tools
to monitor managers. Credible reported earnings are essential to monitor performance and
consequentially to be used on contractual arrangements between managers and firm’s
stakeholders. However, managers may have incentives to manipulate earnings in order to hide
poor performance, to pay fewer taxes, to expropriate minority shareholders and so on. A
recent vein of the literature has being concerned with detecting the incentives behind earnings
management practices. Leuz et all (2003) show that earnings management is higher where
investors are protected less. This result is consistent with other studies which investigate
related properties of accounting numbers like conservatism and timeliness (Ball et all, 2000).
This research has suggested that the actual degree of informativeness in accounting reports
depend on the incentives managers face to produce it and not on the standards and regulations
per se (Ball et all, 2003). Researchers have show (Leuz and Wusteman, 2003) that firms
located in countries where the insider financing model (credit oriented financial systems)
predominates do not have the incentives to produce informative accounting numbers while
firms located in countries where the outsider financial model dominates (developed public
debt and equity markets) produce relevant accounting reports to inform a dispersed base of
shareholders.
3
This view assumes that the major conflict of interest is between shareholders and managers. Sometimes, as it’s
the case in Brazil, the major conflict can be between controlling shareholders and minority shareholders. This
clearly changes the incentives managers have to manipulate earnings.
However, some firms located in countries where the insider model predominates may
have the incentives to try to opt out of their country’s poor financial system to access external
financial markets to fund their existing projects (Holthausen, 2003). Motives for cross listing
vary with geography and firm’s sector and technology (Pagano et all, 2002), but it usually
involves access to depth capital markets and new business opportunities. These firms may
have to abide to strict corporate governance rules and disclosure requirements in order to have
access to international capital. Cross listing in the American Stock Exchanges has been the
major option for foreign firms trying to raise capital abroad. These firms have to comply with
the American rules for listing which includes adjusting its financial statements to US GAAP –
this requirement opened an important research vein trying to investigate the impact of cross
listing on the properties of accounting numbers (Bradshaw and Miller, 2004; Lang et all, 2006
are good examples). Thus, an interesting phenomenon happen: firms from countries with
credit oriented financial systems and poor investor protection with, supposedly, huge
incentives to manipulated earnings and poor local oversight, have to prepare its financial
statements under rules designed to attend firms located in a outsider financial model country
with more strict rules for preparation and disclosure of accounting reports. This situation leads
to a natural question: what happens to earnings management when firms from credit oriented,
poor investor protection countries with significant incentives to manipulate earnings and poor
oversight from local authorities do list in a country with huge public markets for equity and
debt, superior protection to investors and significant requirements on disclosure and financial
statement preparation? Which set of factors dominate? The local relaxed supervisory
environment or the external superior governance model? Does cross listing changes the
properties of accounting numbers prepared under the local GAAP?
To answer those questions Lang et all (2006) has shown that foreign firms listed in the US
present higher levels of earnings management than comparable American firms. They
compare earnings management metrics built on statements prepared under US GAAP for
American and foreign firms. Their result corroborates the argument presented by Siegel
(2005) which states that cross listing in the US does not provides the expected “legal
bonding” but only a “reputational bonding” because the American authorities – specially the
SEC – do not have the will nor the resources to enforce their requirements on foreign firms.
Lang et all (2006) results, however, are not based on reports prepared under local GAAP.
Their result is based on accounting numbers adjusted to US GAAP and cannot shed light over
the impact of cross listing on the earnings management practices of foreign firms using their
local GAAP. To answer this question it is necessary to examine the earnings management
metrics measured under local GAAP of foreign firms listed in the US Exchanges from
countries where managers face strong incentives to manage earnings. Brazilian firms
represent an interesting setting to investigate this question. Brazilian firms operate at home
under extreme flexible oversight and managers possess strong incentives to manipulate
earnings. This scenario is coupled with poor investor protection and law enforcement. On the
top of that the American SEC has limited enforcement power over Brazilian firms as with
other foreign firms (Siegel, 2005). Based on this, we expect that Brazilian firms that cross list
in the US will exhibit the same level of earnings management that firms that do not cross list.
Additionally, we expect to find similar earnings management levels for statements prepared
under US and Brazilian GAAP because managers continue to have the same incentives to
manipulate earnings and we do not expect accounting rules per se to change the properties of
accounting numbers.
3
EARNINGS MANAGEMENT METRICS
There is an intense debate about the appropriate metrics to detect earnings management.
Alternative proxies could lead to different results. Considering this limitation we use five
metrics for earnings management previously discussed in the literature (Healy and Whalen,
1999 and Dechow and Skinner, 2000) and applied by Leuz et all (2003) and Pincus and
Rajgopal (2002) to capture various dimensions of earnings management. The first metric
(EM1) is defined as follows:
EM1i =σ(OpIncomeit)/σ(OpCashFlowit)
(1)
Where σ(OpIncomeit) is the standard deviation of operating income4 scaled by lagged total
assets for firm i in year t and σ(OpCashFlowit) represents the standard deviation of cash flow
from operations5 scaled by lagged assets for firm i in year t. This metric aims to show the
level of income smoothing that managers impose on earnings. Low numbers of this metric, all
else equal, indicate income smoothing. First we calculate this metric to each firm in our
sample6 in a time-series approach. Second we compute the average of this metric (EM1) over
the pooled set of firms for each sample.
A more direct approach to identify smoothing effects on earnings is to investigate the
relation between accruals and cash flows. As commented by Dechow (1994) and Leuz et all
(2003) accruals are negatively correlated to cash flows. Accrual accounting process (e.g.
depreciation) presents this inverse correlation. For the Brazilian case, the capitalization of
R&D is another example of the negative correlation between accruals and cash flows. Land
and Lang (2002) and Leuz et all (2003) have argued that, ceteris paribus, a high negative
correlation is a indicative of smoothing of reported earnings. Thus the second metric (EM2)
deals with this issue and computes the correlation between changes in accruals and changes in
operating cash flows:
(2)
EM2i =ρ(VarTAit, VarOpCashFlowit)
Where VarTAit represents total accruals for firm i on year t minus total accruals for firm i
on year t-1 and VarOpCashFlowit is computed by changes on cash flow from operations for
firm i on year t. Both variables are scaled by lagged total assets at the beginning of the year.
High negative correlation values for this score suggest high levels of earnings smoothing. We
compute the average of this metric (EM2) over the pooled set of firms for each sample.
The third measure (EM3) deals with the magnitude of accruals as a proxy for manager’s
discretionary behavior. It computes the magnitude of accruals as a proxy for the level of
discretion over earnings. Insiders can overstate reported earnings to achieve certain earnings
targets or report extraordinary performance (Leuz et all, 2003). This measure captures, for
example, when managers inflate earnings to reach certain targets like analysts forecasts. We
compute this metric as follows:
EM3i = Σ(|TAi,t|/ |OpCashFlowi,t|)/Ti
(3)
Where TAit represents total accruals for firm i on year t, OpCashFlowit is the cash flow
from operations for firm i on year t and Ti is the total number of years for firm i on our
sample. The mean of this metric (EM3) is estimated over the pooled set of firms for each
sample.
4
Operating Income is defined as Earnings before interest and taxes (EBIT).
Statements of cash flow are not mandatory in Brazil, so we compute cash flow from operations by subtracting
accruals from earnings. Accruals are defined on equation (6).
6
Samples definition and composition are detailed on section 4.
5
Managers have limited reporting discretion to manipulate earnings (i.e. auditors play a
significant role in accounting process). Large amounts of loss are not easily manipulated,
unless there is some fraud scheme7. In this vein Degeorge et all (1999) and Burgstahler and
Dichev (1997) show that US managers exploit accounting discretion to avoid reporting small
losses. Following Leuz et all (2003) we compute the ratio of “small profits” to “small losses”
using net income scaled by lagged total assets. “Small profits” are defined to be in the range
[0.00, 0.01] and “small losses” are within the range of [-0.01, 0.00). The ratio “small profits”
to “small losses” is calculated in a firm-level approach in our different samples. Finally we
compute the total number of “small profits” and “small losses” for each sample. EM4
represents the ratio of total number of “small profits” to total number of “small losses”.
Complementarily to the four metrics used by Leuz et all (2003) we compute a fifth
measure, the smoothing rate (Pincus and Rajgopal, 2002), which aims to compare the
behavior of non discretionary earnings with net earnings. This metric shows how the
discretionary elements of earnings can be used to reduce the volatility in net earnings. The
volatility of earnings before the discretionary elements is compared with the volatility in
earnings after these discretionary items. If the first is bigger than the second there is evidence
of earnings management. In this case the smoothing rate is bigger than 1. This measure is
obtained by the ratio of the standard deviation of nondiscretionary income to the standard
deviation of net income (both scaled by lagged total assets):
(4)
EM5i =σ(NonDiscIncomeit)/σ(NetIncomeit)
Where NonDiscIncomeit represents the nondiscretionary income for firm i on year t and
NetIncomeit represent after-tax earnings scaled by lagged total assets. The nondiscretionary
income is estimated as follows:
NonDicIncomeit = OpCashFlowit + ANDit
(5)
Where ANDit are nondiscretionary accruals for firm i on year t. This number is obtained
by the Jones modified model. The first step is the estimation of total accruals for each firm.
Consistent with prior research (Healy, 1985; Jones, 1991; Dechow et all, 1995) total accruals
are estimated as follows:
TAt = (∆CAt + ∆CLt - ∆casht + ∆debtt - Deprt)/ At-1
(6)
Where TAt represents total accruals in period t; ∆CAt measures the change in current
assets on period t; ∆CLt represents changes in current liabilities on period t; ∆casht represents
changes in cash or cash equivalents in t; ∆debtt computes the changes in short term debt on
period t; Deprt represents the depreciation and amortization on period t; and At-1 represents
total assets on period t-1.
Mohanram (2003) states that the use of total accruals as proxies for earnings management
is rather simplistic once high accruals can be originated by genuine events (growth in Sales,
for example). However he agrees that Jone’s modified model (Dechow et all, 1995) is valid as
a proxy for earnings management. The starting point for the measurement of discretionary
accruals is the calculation of total accruals. The Jones modified model measures the
nondiscretionary accruals from the total accruals. This procedure allows for the separation
between both components of total accruals. The Jones modified model is designed to
eliminate the bias introduced in the original model due to revenue measurement (Dechow et
all, 1995). The model measures the nondiscretionary accruals as:
ANDt = α1 (1/ At −1 ) + α 2 (∆ Re vt − ∆Acc Re ct ) + α 3 ( PPEt )
7
Earnings management metrics assume that managers (and auditors) are not engaged in fraudulent reporting.
(7)
Where ∆Revt represents changes in operating revenues in t scaled by lagged total assets;
∆AccRect measures the changes in accounts receivable scaled by lagged total assets; and PPEt
represents the property, plant and equipment in t. The α1, α2 e α3 coefficients and the
nondiscretionary accruals are obtained from the specification presented on Jones original
model. The Jones model is adjusted by changes in receivables (Dechow et all, 1995). The
results for the Jones modified model are presented in appendix 1. Finally we compute EM5 as
the average of EM5i over the pooled set of firms for each sample.
4
SAMPLE SELECTION AND RESULTS
Sample Selection and Data
We collected the financial statements under US GAAP by using EDGAR database.
Additionally, we collected financial statements prepared under Brazilian GAAP for public
held Brazilian firms listed on BOVESPA from Economatica® database. The sample was
selected as follows: (i) first the financial institutions were excluded because they follow a
separate and codified accounting system in Brazil; (ii) the data was obtained from the public
available financial statements starting on December 1995 and ending on December 20038;
(iii) all periods with insufficient data were eliminated; (iv) all financial statements must be
available for at least three consecutive years. We eliminated outliers that fall on 3% superior
and inferior for each regression parameter as suggested by Dechow et all (1995) when
considering the impact of firms with extreme superior financial performance on the regression
results. We codified our sample as follow:
Sample 1: financial statements under Brazilian GAAP (in BRL) from firms that have not
issued ADRs;
Sample 2: financial statements under Brazilian GAAP (in BRL) from firms that have
issued ADRs.
We also consider all firms that have ADRs and their financial statements prepared
under BR GAAP and US GAAP respectively, as follow:
Sample 3: financial statements under Brazilian GAAP (in USD) from firms that have
issued ADRs;
Sample 4: financial statements under US GAAP (in USD) from firms that have issued
ADRs.
Another important institutional feature is that foreign companies listed on NYSE can
opt to report their financial statements9 entirely under US GAAP, or to reconcile home GAAP
to US GAAP for net income and shareholders´ equity. For robust application of earnings
management metrics we select firms that prepare their financial statements entirely under US
GAAP. However some firms change their reporting practices over time (e.g. in one year a
firm reports its financial statements under US GAAP in USD; in the next year the same firm
report its financial statements in home GAAP reconciled to US GAAP in local currency), as
presented on table 1. We selected financial statements from firms that did not change their
report to generate samples 5 and 6, as follow:
Sample 5: financial statements under Brazilian GAAP (in USD) from firms that have
issued ADRs and maintained the same reporting criteria over a matching period;
Sample 6: financial statements under US GAAP (in USD) from firms that have issued
ADRs and maintained the same reporting criteria over a matching period.
8
Information from periods before 1995 were not used because of the extensive inflation adjustments obligatory
in Brazil.
9
Foreign companies listed on American Stock Exchange must file the 20-F form with SEC.
Table 1
Accounting features for financial statements filed with SEC for Brazilian cross listed firms
This table presents firms’ financial statements that form samples 5 and 6. Sample 5 represents financial
statements under Brazilian GAAP (in USD) from firms that have issued ADRs and maintained the same
reporting criteria over a matching period; Sample 6 represents financial statements under US GAAP (in USD)
from firms that have issued ADRs and maintained the same reporting criteria over a matching period. The
numbers within the table represent: 1 for Financial Statements in USD prepared under US GAAP; 2 for Financial
Statements in BRL prepared under BR GAAP and reconciled to US GAAP (net income and stockholders’
equity) and; 3 for firms that did not file 20-F. Bold numbers represent firms’ financial statements that form
samples 5 and 6.
Firm
Listing date
1996 1997 1998 1999 2000 2001 2002 2003
ARACRUZ
05/27/1992
1
1
1
1
1
1
1
PÃO DE AÇÚCAR
05/29/1997
3
1
1
1
1
CSN
11/14/1997
1
1
1
1
1
GERDAU
03/10/1999
1
1
1
1
1
PETROBRAS
08/10/2000
1
1
1
1
AMBEV
06/04/1997
1
1
1
2
2
2
BRASKEM
12/21/1998
1
1
1
1
1
2
VALE
06/20/2000
1
1
1
2
2
EMBRAER
07/21/2000
2
2
2
1
1
1
VCP
04/14/2000
1
1
1
Firm Characteristics
Panel A from Table 2 provides the number of observations per year for each sample we
constructed. The number of Brazilian firms listed on NYSE increased from 3 in 1996 to 21 on
2003. Panel B of Table 2 shows the samples’ descriptive statistics. Interestingly, the
coefficient of variation of operating income and net income for sample 1 is considerably
higher than the same metric for any other sample. This is due to great variability within that
sample considering the different features of firms that form sample 1. As we restrict our
sample (samples 2,3,4,5 and 6) to cross listed firms, the behavior of accounting measures
became less volatile. This is an expected result considering that firms that issue ADRs are
usually large corporations, with lower likelihood to present dispersed results. The mean of
total assets under Brazilian GAAP from cross listed firms is BRL 10.1 billion (USD 5.3
billion10), while the median is BRL 7.3 billion (USD 3.8 billion). For companies that released
financial statements under US GAAP the mean total assets are USD 8.8 billion while the
median is USD 3.7 billion.
Table 2
Panel A: Firm-year observation
Sample 1 represents financial statements under Brazilian GAAP (in BRL) from firms that have not issued ADRs;
Sample 2 represents financial statements under Brazilian GAAP (in BRL) from firms that have issued ADRs;
Sample 3 represents financial statements under Brazilian GAAP (in USD) from firms that have issued ADRs;
Sample 4 represents financial statements under US GAAP (in USD) from firms that have issued ADRs; Sample
5 represents financial statements under Brazilian GAAP (in USD) from firms that have issued ADRs and
maintained the same reporting criteria over a matching period; Sample 6 represents financial statements under
US GAAP (in USD) from firms that have issued ADRs and maintained the same reporting criteria over a
matching period.
Year
Sample 1
Sample 2
Sample 3
Sample 4
Sample 5
Sample 6
1996
43
3
0
0
0
0
1997
76
8
1
1
0
0
1998
99
8
2
2
0
0
10
Computed for the average USD/BRL exchange rate from 1996 to 2003. The average exchange rate for this
period was USD 0.5238/1BRL.
1999
2000
2001
2002
2003
TOTAL
110
14
5
5
0
0
118
20
7
7
5
5
129
22
6
6
5
5
120
22
5
5
5
5
111
21
5
5
5
5
806
118
31
31
20
20
Panel B: Descriptive Statistics for samples – Firm-Year Observations from 1996 to 2003
Standard Coefficient
Deviation of Variation Median Minimum Maximum
Mean
Revenues
699,030
908,088
1.299
353,321
0
7,949,402
Total Assets
1,068,210 1,421,013
1.330
515,111
35,263 11,100,000
Equity
320,797
578,664
1.804
178,607 -6,356,925 3,750,363
Operating Income
15,259
198,992
13.041
5,363
-2,794,466 1,034,274
Sample 1 Net Income
8,766
182,054
20.767
5,881
-2,867,534 960,824
Revenues
4,790,627 4,234,121
0.884
3,350,306 535,984 19,500,000
Total Assets
10,100,000 9,046,981
0.896
7,313,147 1,152,524 40,100,000
Equity
4,674,881 4,625,600
0.989
2,879,367 438,569 21,700,000
Operating Income 397,154
721,186
1.816
258,444 -1,249,860 5,401,545
Sample 2 Net Income
418,787
722,581
1.725
215,368 -1,140,761 4,508,850
Revenues
5,304,036 8,288,029
1.563
2,592,962 443,438 33,100,000
Total Assets
8,791,873 11,000,000
1.251
4,208,938 1,679,138 47,200,000
Equity
3,366,239 4,111,265
1.221
1,466,941 621,133 17,100,000
Operating Income 981,579
2,299,852
2.343
167,019 -339,829 9,647,593
Sample 3 Net Income
804,699
1,550,666
1.927
200,952 -173,662 6,159,030
Revenues
5,362,773 8,322,069
1.552
2,635,236 487,723 30,800,000
Total Assets
8,841,195 12,800,000
1.448
3,655,000 2,454,458 53,600,000
Equity
3,064,010 4,407,373
1.438
1,191,000 507,000 17,200,000
1.985
392,001
-489
10,700,000
Operating Income 1,448,298 2,874,388
Sample 4 Net Income
810,121
1,564,802
1.932
197,692 -162,000 6,559,000
Revenues
6,971,952 9,942,310
1.426
2,625,135 575,564 33,100,000
Total Assets
10,200,000 13,400,000
1.314
4,150,999 1,679,138 47,200,000
Equity
3,749,988 4,933,014
1.315
1,407,176 621,133 17,100,000
Operating Income 1,402,211 2,785,490
1.986
240,727 -339,829 9,647,593
Sample 5 Net Income
1,060,074 1,865,337
1.760
213,499
-55,099
6,159,030
Revenues
7,164,153 9,942,598
1.388
2,967,140 574,355 30,800,000
Total Assets
10,900,000 15,600,000
1.431
3,554,717 2,454,458 53,600,000
Equity
3,526,057 5,336,070
1.513
1,049,190 507,000 17,200,000
Operating Income 2,009,779 3,469,919
1.727
443,001
82,923 10,700,000
Sample 6 Net Income
1,043,448 1,898,190
1.819
195,135 -162,000 6,559,000
N
806
806
806
806
806
118
118
118
118
118
31
31
31
31
31
31
31
31
31
31
20
20
20
20
20
20
20
20
20
20
Empirical Results
Table 3, Panel A, reports the means of earnings management metrics for each sample. Our
results can be compared to Table 2, Panel A from Leuz et all (2003) that report earning
smoothing metrics (EM1 and EM2) and earnings discretion metrics (EM3 and EM4). Leuz et
all (2003) computed earnings management metrics for 31 countries, but they did not consider
Brazil in their calculation. We computed earnings management metrics for Brazilian cross
listed and non cross listed public held firms. EM1 for sample 1 is higher than the mean EM1
reported by Leuz et all (2003), suggesting that Brazilian companies earnings are less smoother
than the average of Leuz et all (2003) countries. Actually our sample 1 EM1 would be placed
as the fourth highest compared to EM1 from Leuz et all (2003). Moving our analysis to cross
listed Brazilian firms, our EM1 for sample 2, which represents financial statements under
Brazilian GAAP from firms that have issued ADRs, would be in a similar level to the mean
EM1 from Leuz et all (2003). Sample 3 presents the lowest EM1, that indicates higher earnings
smoothing for our samples. Interestingly sample 3 shows the second higher metrics for EM2
in our sample confirming the level of earnings smoothing in financial statements under
Brazilian GAAP from firms that have issued ADRs. Sample 3 also presents high EM3. It is
important to relate that we could not compute EM4 for samples 4 and 6 because there were no
firms in these samples with “small” losses. Additionally we also compute EM5, that indicates
the variation in nondiscretionary accruals compared to net income. The highest EM5 is found
on sample 2. However, a careful analysis should consider the variance of each earnings
management metrics within the samples and across the samples. Panel B of Table 3 shows
that our earnings management metrics have high levels of standard deviations, thus we can
not infer if there are differences on this metrics just analyzing the mean results.
Table 3 – Results of earnings management metrics for all samples
Panel A: Summary Earnings Management Metrics for each Sample
The variables are computed for each sample for fiscal years 1996-2003. EM1 is the sample’s mean ratio of the
firm-level standard deviation of operating income and operating cash flow (both scaled by lagged total assets).
EM2 is the sample’s mean of firm-level correlation of the change in total accruals and the change in cash flow
from operations (both scaled by lagged total assets). EM3 is the sample’s mean of firm-level ratio of absolute
total accruals to absolute cash flow from operations. EM4 is the sample’s total number of “small profits” divided
to the total number of “small losses”. EM5 is the sample’s mean of firm-level ratio of standard deviation of
nondiscretionary income to standard deviation of net income (both scaled by lagged total assets).
Nondiscretionary income is calculated by the sum of nondiscretionary accruals to cash flow from operations.
OPI refers to operating income, CFO refers to cash flow from operations, TA refers to total accruals, NDI refers
to nondiscretionary income, NI refers to net income, #SmProfit refers to the number of small profits and
#SmLosses refers to the number of small losses. Sample 1 represents financial statements under Brazilian GAAP
(in BRL) from firms that have not issued ADRs; Sample 2 represents financial statements under Brazilian GAAP
(in BRL) from firms that have issued ADRs; Sample 3 represents financial statements under Brazilian GAAP (in
USD) from firms that have issued ADRs; Sample 4 represents financial statements under US GAAP (in USD)
from firms that have issued ADRs; Sample 5 represents financial statements under Brazilian GAAP (in USD)
from firms that have issued ADRs and maintained the same reporting criteria over a matching period; Sample 6
represents financial statements under US GAAP (in USD) from firms that have issued ADRs and maintained the
same reporting criteria over a matching period.
EM5
EM1
EM2
EM3
EM4
|TA| / |CFO|
#SmProfit / #SmLosses σ NDI/σ
σNI
σOPI/σ
σCFO
ρ(∆TA, ∆CFO)
Sample 1
0.6973
-0.7489
1.8605
2.4444
3.3370
Sample 2
0.5606
-0.7654
0.6290
2.3333
3.6225
Sample 3
0.5114
-0.8529
1.3782
2.0000
2.0977
Sample 4
0.7379
-0.6728
0.5714
NA
1.6442
Sample 5
0.8818
-0.4604
0.6903
1.0000
1.5760
Sample 6
0.5985
-0.9508
0.7727
NA
1.7438
Panel B: Detailed Earnings Management Metrics for each Sample
This table presents the mean, standard deviation, median, minimum and maximum for each metric in each
sample. EM1_s1 refers to EM1 on sample 1. EM1_s2 refers to EM1 on sample 2, and so on. EM2_s1 refers to
EM2 on sample 1. EM2_s2 refers to EM2 on sample 2, and so on. EM3_s1 refers to EM3 on sample 1. EM3_s2
refers to EM3 on sample 2, and so on. EM4_s1 refers to EM4 on sample 1. EM4_s2 refers to EM4 on sample 2,
and so on. EM5_s1 refers to EM5 on sample 1. EM5_s2 refers to EM5 on sample 2, and so on. N refers to the
number of observations for each metric.
mean
sd
Median
min
max
N
EM1_s1
0.6973
0.4924
0.6609
0.0203
3.3767
143
EM1_s2
0.5606
0.3067
0.5151
0.1087
1.2585
23
EM1_s3
0.5114
0.2577
0.4370
0.1915
0.9231
7
EM1_s4
0.7379
0.5751
0.5489
0.2797
1.9571
7
EM1_s5
0.8818
0.2995
0.9460
0.4370
1.1742
5
EM1_s6
0.5985
0.2332
0.6699
0.2538
0.8813
5
EM2_s1
-0.7489
0.4480
-0.9473
-1.0000
1.0000
143
EM2_s2
-0.7654
0.4412
-0.9357
-1.0000
1.0000
23
EM2_s3
-0.8529
0.2214
-0.9478
-1.0000
-0.4279
7
EM2_s4
EM2_s5
EM2_s6
EM3_s1
EM3_s2
EM3_s3
EM3_s4
EM3_s5
EM3_s6
EM4_s1
EM4_s2
EM4_s3
EM4_s4
EM4_s5
EM4_s6
EM5_s1
EM5_s2
EM5_s3
EM5_s4
EM5_s5
EM5_s6
-0.6728
-0.4604
-0.9508
1.8605
0.6290
1.3782
0.5714
0.6903
0.7727
0.6538
0.2500
0.0000
na
0.0000
na
3.3356
3.6225
2.0977
1.6442
1.5760
1.7438
0.7402
0.8094
0.0654
3.6114
0.3402
2.0411
0.5635
0.9653
0.9830
0.6895
0.3536
na
na
na
na
9.6129
2.5992
0.6488
0.8905
0.6733
0.5156
-0.9573
-0.7823
-0.9802
0.9538
0.5984
0.5361
0.4203
0.2902
0.2689
1.0000
0.2500
0.0000
Na
0.0000
Na
1.7424
2.6143
1.9554
1.4963
1.6377
1.9683
-1.0000
-0.9913
-0.9956
0.1143
0.1398
0.2284
0.1981
0.2092
0.1981
0.0000
0.0000
0.0000
na
0.0000
na
0.0314
0.2994
1.3527
0.3212
0.8024
1.0589
1.0000
0.9732
-0.8371
32.3940
1.6199
5.8476
1.7945
2.4156
2.4947
2.0000
0.5000
0.0000
na
0.0000
na
110.1842
8.9642
3.3335
3.0809
2.5139
2.2696
7
5
5
143
23
7
7
5
5
26
2
1
0
1
0
143
23
7
7
5
5
In order to investigate whether Brazilian firms that cross list in the US exhibit the same
level of earnings management that firms that do not cross list we perform mean comparison
test for earnings management metrics. Additionally we implement bootstrap procedure due to
our sample characteristics (small number of observations for samples 3, 4, 5 and 6). The test
based on the bootstrap method is more reliable than the parametric t-statistic. Our results are
not affected if we use the statistics based on parametric tests. Bootstrap results confirm the
classical tests. Reported bootstrapped values result from 1,000 iterations. We test three
hypothesis:
H1: There are no statistically significant differences in earnings management levels
between sample 1 (non ADR issuers) and 2 (ADR issuers);
H2: There are no statistically significant differences in earnings management levels
between sample 3 (ADR issuers with BR GAAP) and 4 (ADR issuers with US GAAP);
H3: There are no statistically significant differences in earnings management levels
between sample 5 and 6.
Results on Table 4 show two different stories. The first, relating to the comparison
between non-issuers (sample 1) and issuers (sample 2) of ADRs, tells us that the environment
upon which a given firm is immersed does not affect its earnings management activities (at
least for this sample of Brazilian firms). We can not conclude that there are differences on
EM1, EM2, EM4 and EM5 for samples 1 and 2 for a significance level of 5%. The results for
samples 3 and 4 and 5 and 6 also do not present any evidence of difference on earnings
management metrics for a significance level of 5%. The second and maybe most interesting
result shows that US GAAP prepared financial statements do not present lower earnings
management metrics than BR GAAP statements. This conclusion is supported by the results
on Table 4 regarding the comparisons of earnings management metrics for samples 3 and 4
and the comparisons for samples 5 and 6. These results have several implications for research
and accounting regulation. The first implication is that cross listing does not seem to affect
earnings management activities for this sample of Brazilian firms. Siegel (2005) present
several reasons to reinforce this conclusion. The second is that financial statements prepared
under US GAAP do not present ‘superior’ earnings management metrics, as previous
common sense would presume.
Table 4 – Two sample mean comparison test for earnings management metrics
We present the two-sample mean comparison t-test for mean earnings management metrics. Additionally we
implement bootstrap procedure. Reported bootstrapped z-statistics (p-values) result from 1,000 iterations. The
tests are performed regarding the hypothesis (i) there are no statistically significant differences in earnings
management levels between sample 1 (non ADR issuers) and 2 (ADR issuers), (ii) there are no statistically
significant differences in earnings management levels between sample 3 (ADR issuers with BR GAAP) and 4
(ADR issuers with US GAAP) and (iii) there are no statistically significant differences in earnings management
levels between sample 5 and 6. EM1_s1 refers to EM1 on sample 1. EM1_s2 refers to EM1 on sample 2, and so
on. EM2_s1 refers to EM2 on sample 1. EM2_s2 refers to EM2 on sample 2, and so on. EM3_s1 refers to EM3
on sample 1. EM3_s2 refers to EM3 on sample 2, and so on. EM4_s1 refers to EM4 on sample 1. EM4_s2 refers
to EM4 on sample 2, and so on. EM5_s1 refers to EM5 on sample 1. EM5_s2 refers to EM5 on sample 2, and so
on. N refers to the number of observations for each metric. Sample 1 represents financial statements under
Brazilian GAAP (in BRL) from firms that have not issued ADRs; Sample 2 represents financial statements under
Brazilian GAAP (in BRL) from firms that have issued ADRs; Sample 3 represents financial statements under
Brazilian GAAP (in USD) from firms that have issued ADRs; Sample 4 represents financial statements under
US GAAP (in USD) from firms that have issued ADRs; Sample 5 represents financial statements under
Brazilian GAAP (in USD) from firms that have issued ADRs and maintained the same reporting criteria over a
matching period; Sample 6 represents financial statements under US GAAP (in USD) from firms that have
issued ADRs and maintained the same reporting criteria over a matching period.
Paired Tests
EM1_s1 - EM2_s2
EM1_s3 - EM1_s4
EM1_s5 - EM1_s6
EM2_s1 - EM2_s2
EM2_s3 - EM2_s4
EM2_s5 - EM2_s6
EM3_s1 - EM3_s2
EM3_s3 - EM3_s4
EM3_s5 - EM3_s6
EM4_s1 - EM4_s2
EM4_s3 - EM4_s4
EM4_s5 - EM4_s6
EM5_s1 - EM5_s2
EM5_s3 - EM5_s4
EM5_s5 - EM5_s6
5
Mean Standard Error
Difference Difference
0.1368
-0.2265
0.2833
0.0164
-0.1801
0.4904
1.2316
0.8068
-0.0824
0.4038
na
na
-0.2869
0.4535
-0.1678
0.0761
0.2382
0.1697
0.0993
0.2920
0.3632
0.3102
0.8003
0.6161
0.2842
na
na
0.9695
0.4164
0.3793
95% Confidence Interval
Inferior
Superior
Difference
Difference
-0.0166
0.2902
-0.7722
0.3191
-0.1122
0.6789
-0.1865
0.2194
-0.8693
0.5091
-0.5128
1.4936
0.6188
1.8444
-1.0907
2.7043
-1.5033
1.3385
-1.0880
1.8957
na
na
na
na
-2.2051
1.6314
-0.4634
1.3704
-1.0529
0.7173
t- stat
1.7984
-0.9511
1.6692
0.1655
-0.6167
1.3503
3.9699
1.0081
-0.1337
1.4209
na
na
-0.2959
1.0890
-0.4424
Bootstrap
z-stat
p-value
(2-tailed) (1,000 rep)
0.0792
0.3684
0.1359
0.8697
0.5568
0.2474
0.0001
0.3474
0.8969
0.3138
na
na
0.7678
0.2995
0.6707
0.077
0.448
0.151
0.872
0.645
0.448
0.000
0.286
0.898
0.121
na
na
0.896
0.335
0.731
CONCLUSIONS
In this paper we investigated the impact of cross listing and adjustments to US-GAAP on
the earnings management practices of Brazilian firms. Brazilian managers have strong
incentives to manipulate earnings: (i) great discretion on accounting reports, (ii) poor
oversight structure, (iii) low legal enforcement and investor protection, (iv) uninformative
accounting reports, (v) strong link between financial accounting and tax. However, they list in
the American capital markets in order to raise funds to finance new projetcs and as way to
achieve some reputational bonding. An interesting question is whether cross listing and the
superior corporate governance environment Brazilian firms have to abide will change
managers incentives to report credible earnings. Recent research (Siegel, 2005) has shown
that cross listing has a very limited impact on the actual behaviour of firms because American
authorities have limited power to monitor foreign registrants. Using a sample of Brazilian
firms listed on the São Paulo Stock Exchange from 1995 to 2003 we show that cross listing
does not have a significant impact on the earnings management practices – measured using
five different models commonly used in the literature. Our results also show that adjustments
to US-GAAP also do not impact significantly the five earnings management metrics
investigated. These results suggest that cross listing in the American Stock Exchanges does
not change the actual incentives managers have to manipulate earnings in Brazil.
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