CORPORATE GOVERNANCE AND EARNINGS MANAGEMENT.
FIRST DRAFT, MARCH 2000
Thomas Jeanjean
CEREG, University of Paris-Dauphine, France.
Key words: corporate governance, earnings management, independent directors.
Abstract: This article investigates the role of independent directors to monitor earnings
management. Using a latent variable approach to assess earnings management, this article
shows that external monitoring of the CEO (big six or five auditor, significant stockholder,
percentage of independent board members) constraint the manager to engage in opportunistic
income increasing decisions.
1
Introduction
Recent announcements (Chairman Lewitt of the SEC in 1998, the 1999 blue book of the
CNCC - the professional association of auditors in France), echoed by the financial press, cast
a doubt on the reliability of financial statements. Corporate governance structures are
frequently seen as a major device to guarantee the quality of financial statements. Those
structures include auditor quality, ownership structure, capital structure, and board of director
(size, existence of independent director(s), existence of specialized committees).
Previous studies show the influence of a high quality audit on earnings management (Becker
et al., 1998; Francis, Maydew and Sparks, 1999): a quality auditor (i.e.: a big auditor,
DeAngelo, 1981) tends to reduce discretionary accruals.
The impact of ownership structure on earnings management is also well documented (Smith,
1976; Niehaus, 1993, Warfield, Wild and Wild, 1995): the presence of a major stockholder is
associated with less income increasing accounting choices because the manager of a
controlled business is more monitored than the CEO of a managerial firm.
Surprisingly, there are very few articles of the impact of board of directors on earnings
management (henceforth, EM). Beasley (1996) and Dechow et al. (1996) compare the
percentage of independent directors between firms that do violate GAAP to overstate their
earnings and matched businesses that do not. They find that violation of GAAP is associated
with a lower presence of independent board members. Both studies treat extreme cases
(violation of GAAP). Peasnell, Pope and Young (1999, henceforth PPY) concentrate on a
more subtle form of earnings management. After partitioning their sample of UK firms
according to their unmanaged earnings (above or below zero), they find that the more
independent directors, the less likely positive discretionary accruals.
2
This papers differs from PPY in three ways.
First, hypothesis are validated on a sample of French listed firms instead of UK businesses.
UK and French contexts are different in terms of corporate governance. French firms adopted
later internationally recognized governance practices (audit committee, independent board
members,…), this can generate different empirical results.
Second, more incentives to manage earnings are taking into account. In particular, stimuli
related to financing decisions (such as issuing debt or equity) are controlled.
Finally, I try to address earning management measurement problems using a latent variable
approach. Various algorithms are proposed by the literature to extract the discretionary
component of accruals (e.g.: Healy, 1985; DeAngelo, 1986; Jones, 1991; Jones modified by
Dechow, 1995,...). All of those models seem to have a rather low power and a great
measurement error. PPY try to mitigate this problem by testing hypothesis with two different
measures of discretionary accruals. In this paper, EM is considered as a latent variable, i.e.: a
synthetic measure of accruals activity is computed..
Conclusions are slightly different from PPY article. The presence of one independent director
tends to lessen discretionary accruals, but the relation is non linear. This result suggest that an
optimal board composition exists to constrain earnings management: to minimize abnormal
accruals, a percentage of 30 to 40% of independent board members seems optimal. As in PPY
study, the presence of an audit committee do not influence earnings management.
The remainder of this paper is organized as follows. Section 1 presents hypotheses
development. Section 2 reports sampling criteria and proxy issues. Section 3 concentrates on
earnings management measurement problems. Empirical results are reported in section 4,
concluding remarks and avenues for future research are presented in section 5.
3
Section 1: hypotheses development.
Corporate governance mechanisms help to control agency costs either by improving the
alignment of manager's interests with those of outside shareholders or by monitoring the
manager to deter him from engaging in opportunistic actions (Charreaux, 1997). The role of
external and internal monitoring is all the more important that interests-alignment is low
because high alignment should create a powerful enough incentive for the manager to
maximize the value of the firm.
Financial reporting in general and earnings management in particular is a major stake for
corporate governance because it conveys information about firm value and thus the quality of
the management. Moreover, compensation contracts usually heavily rely on accounting
numbers (see: Watts and Zimmerman, 1986).
However, the way corporate governance mechanisms relate to earnings management is not
clear. Holthausen (1990), Aria, Sunder and Glover (1998) remark that earnings management
is not necessarily opportunistic. Signaling long term value of the firm or efficient contracting
motives can as well explain reported earnings manipulation (within the generally accepted
accounting principles). They do not have reasons to oppose to signaling or efficient
contracting earnings management. The difficulty is that monitors can not unambiguously
determine the actual motive for earnings management.
Prior research associate aggressive income-recognition with opportunistic behavior (e.g.:
Holthausen, 1990; Watts and Zimmerman, 1986; Christie and Zimmerman, 1994: Peasnell,
Pope and Young, 1998). The reason is that if earnings management for opportunistic reasons
do not take place, discretionary accruals (whatever the way they are computed) should be less
important.
We can state the following prediction:
4
H1: All other things being equal, the extent of income increasing earnings management
is negatively related to external and internal monitoring.
Because, this monitoring is important when interests divergence between managers and
outside stockholder is important, we can test:
H2: All other things being equal, the impact of external and internal monitoring is more
pronounced when interest alignment is low.
To empirically test H1 and H2 predictions, we need to determine in what consist in
monitoring devices and how to measure interests alignment.
External monitoring devices are: the nature of the auditor, the existence of a major
stockholder and the board of directors (Maati, 1999)
Following, Hirst (1994) and Phillips (1994), we can suppose that big6 auditors are more
conservative than non b6 auditors.
(H1.1) If the auditor is big five (or six), positive discretionary accruals are less likely.
If a significant Stockholder exists, he will engage in a close monitoring of CEO’s actions. At
the opposite, a small shareholder do not have an economic incentive to do so. Call V: the
agency costs generated by an absence of monitoring, F: the (supposedly) fixed cost of
monitoring, : the percentage of equity controlled by a stockholder. On a strict economic
point of view, monitoring will take place if:
 * V>F
that is equivalent to:
5
>F/V
That is only significant stockholders engage in close monitoring. This control constraints
CEO opportunism and, in turn, the level of discretionary accruals.
(H1.2) The extent of income increasing earnings management is negatively related to the
existence of a major stockholder.
According to the standard agency theory, the primary mission of the board of directors is to
monitor the managers, i.e.: to minimize the agency costs. Fama (1980) presents a model in
which the monitoring architecture of the firms is structured along three major devices: the
capital and job markets (an inefficient CEO is sanctioned by a share price fall and a reduction
of his or her job opportunities), the other managers inside the firm (if they work in a poor
perspective firm, their job opportunities lessen) and the board of directors.
The efficiency of the board depends on three characteristics (John and Senbet, 1998):
(1) the size of the board: adding an extra director has two opposite effects. On the one hand,
the monitoring ability of board is increased, on the other hand, decision process and
communication procedures are longer (Jensen, 1983). This marginal cost increases with
board size, as a consequence, an optimal board size should exist. The problem is that the
optimal size varies according the authors from 4 (Yermack, 1986) to 9 (Lipton and
Lorsch, 1992).
(2) The nature of board members. Baysinger and Butler (1985) distinguish three categories of
board members: insiders (salaried of the firm), independent board members (outsiders:
they are not paid by the firm and have no authority relations with the CEO) and gray
members (directors that do not match with previous definitions). All those three categories
participate to board efficiency: insiders bring their knowledge of the business (but their
6
ability to critique the CEO is probably low), outsiders their independence (but they suffer
from information asymmetry). Because, all categories are necessary, the relation between
the proportion of independent board members and board efficiency is probably non linear.
(3) The structure of the board, that is the existence of specialized committees such as an audit
committee. Its presence is supposed to increase board attention to financial reporting
issues.
An efficient board can monitor more accurately the manager and thus limiting the extent of
the income increasing accruals decisions:
(H1.3) Ceteris paribus, if the board is small or large, the income increasing accrual
decisions tend to be more frequent.
(H1.4) Ceteris paribus, the proportion of independent board members influences accrual
decisions.
(H1.5) The extent of income increasing accrual decisions is negatively related to the
existence of an audit committee.
One empirical problem to test hypotheses H1.1 through H1.5 is to know whether or not, the
different external monitoring devices are alternative or complementary. If the are alternative
forms of control, than all five hypotheses can be treated in a single regression. If they are
complimentary, it is not possible to do so because of collinearity.
Following, Warfield, Wild and Wild (1995) we will use CEO ownership as a proxy of
interests alignment. Those authors remark that if a director is major stockholder than his
7
utility is aligned on other shareholders interests. Thus earnings management, for opportunistic
reasons at least, will not take place.
(H2.1) All other things being equal, the impact of external and internal monitoring is
more pronounced when CEO ownership is low.
Two incentives to manage earnings are included in the model: hypotheses Ha and Hb are
connected to financing decisions, Hc and Hd to the desire to meet targets.
(Ha)
Firms with high increase in leverage will choose the accounting procedures that
shift reported earnings from the future periods to the present period.
This hypothesis looks similar to the debt hypothesis of Watts and Zimmerman (1986, p. 216).
But it defers:
(1) from its specification: only firms with high increase in leverage are supposed to manage
their reported earnings,
(2) from its conceptual basis. Debt covenants are unusual in France. A connection can be
established between debt ratio and reported earnings on a signaling basis. By increasing
income, managers signal and justify the relevance of their financial policy.
Hypothesis Hc relies on the same basis, a conservative accounting policy during the equity
offering period allows the managers to present income increasing forecasts and makes it easy
to place the equity.
(Hb)
Firms will choose the accounting procedures that shift reported earnings from
the future periods to the present period before an equity offering.
8
Burgstahler and Dichev (1997) showed that CEO manage earnings in order to avoid earnings
decreases or losses.
(Hc) When unmanaged earnings are below the target: null results, CEO engages in
income increasing decisions.
(Hd) When unmanaged earnings do not meet last year reported earnings, CEO engages
in increasing decisions.
Degeorge, Patel and Zeckhauser (1999) show the existence of a hierarchy between the null
result and last year reported earnings: managers are all the more motivated to meet last year
reported earnings that the null result has been met. To allow the incentive to vary across the
situations, the four possible cases are empirically distinguished, by partitioning the sample
according unmanaged Earnings (< or > to zero)
Section 2: Sampling and research design.
Hypotheses are tested over a sample of 289 firm-years. The selection procedure was the
following:
(1) random selection of 150 firms on DAFSA files.
(2) Dafsa des groupes is used to gather data on board composition, ownership data and
auditor name in 1998 and 1999.
(3) Annual reports (available on the COFISEM-COFIPRO database) were gathered.
The final sample size is 280 firm-years because of missing data.
9
In '000 French francs.
total revenue
sales growth
property, plant and equipment
Minimum
-77,83%
280%
Std. Deviation
40.866.040
15,89%
33,88%
10.000 564.923.287 14.700.249
48.657.702
464 168.072.582
6.663.827
17.536.901
56.192 531.207.098 29.218.739
69.694.548
property, plant and equipment scaled
NS
371%
56,59%
39,08%
2,95%
428%
123%
71,68%
NS
172%
25,92%
19,63%
by lagged assets
total revenue scaled by lag assets
Mean
8.793 276.669.543 21.705.281
Financial liabilities
Total assets
Maximum
Financial liabilities scaled by lagged
assets
Minimum
Maximum
Mean
Std. Deviation
26
14
86
9,92
1,66
17,86
19%
72%
40%
4,20
2,49
25,29
board size
3
number of independent board members
0
% of equity hold by managers
0
Distinction between CEO and president functions
Big six auditor
Audit committee
Research design
To test hypotheses H1 and H2 (and their potentially various specifications as explained in
section 1), we regress our measure of earnings management (EM) on various proxies that
represent incentives or constraints to earnings management.
To take into account a hierarchy between the thresholds of earnings, the sample is partitioned
according to the sign of unmanaged earnings.
Subscript i refers to firm-years:
10
EM i    1 * aud _ b6i   * block i   * small _ board i   * l arg e _ board i
  * ibmi   * ibmi   * ACi   2 * monitoring i ,t _ CEOownershipi
2
  a * Hi _ increase _ levi   b * equity _ offeringi
  c *UME _ inf LYRE i
 1 * past _ disc _ accrualsi
  2 * sizei  1998,1999 * YEAR1998,1999   3 * CFOi   i
Variable
Construct
Explained variable
EM
Measure of earnings management (section 3)
Monitoring and interest alignment variables
Aud_b6
Dummy variable coded 1 if the auditor is a big, 0 otherwise.
block
Dummy variable coded 1 if a single shareholder owns more than 10% of
the equity.
Small_board
Dummy variable coded 1 if board size is less than 5.
Large_board
Dummy variable coded 1 if board size is larger than 15
% ibm
Percentage of independent board members
AC
Dummy variable, coded 1 if an audit committee exists.
CEO_ownership
Percentage of the stock belonging to the CEO
Monitoring_CEO Interaction term between monitoring devices and CEO ownership
Incentives to earnings management
UME
Unmanaged earnings (for measurement issues, see table A)
Equity_offering Dummy variable coded 1 if the number of share increases more than 10%.
Hi_increase_lev Dummy variable coded 1 if the leverage ratio increases significantly (more
than 20%)
UNE_inf_LYRE Dummy variable coded 1 if unmanaged earnings are less than last year
11
reported earnings.
Control variables
Pda
Past discretionary accruals: dummy variable coded 1 if last year
discretionary accruals are significantly positive, i.e.: belong to the 4th
quartile of the sample.
Size
Log(Market value of equity)
YEAR1998
Dummy variables coded 1 if the data is from year k (for YEARk), 0
otherwise.
YEAR 1999
CFO
Cash flow from operations
Control variables
The self reversing property of accounting accruals is such that income-increasing (decreasing)
choices made in one period will inevitably lead to understated (overstated) income in some
future period(s). The empirical problem is to set the horizon of the accrual reversal. In this
paper we concentrate on short term accruals (working capital accruals) that is the reason why
we set the horizon (h) to one year. Variable PDA controls for this prior activity.
Size , CFO (Cash Flow from Operations) and YEAR are control variables usually included in
regressions.
Section 3: Earnings management measurement.
Earnings management has been measured by discretionary accruals since Healy seminal
article in 1985. Accruals are adjustments to the cash flows to generate net earnings, thus:
Earningst  cash flowt  accrualst
The problem is to identify the (unobservable) discretionary component of accruals.
12
accrualst  discretion ary accrualst  normal accrualst
According to Healy (1985, p. 89), non discretionary accruals are "the adjustments to the cash
flows mandated to by the accounting standard-setting bodies", where as discretionary accruals
are "adjustments to cash flows selected by the manager".
Many authors proposed models of “normal” (or non discretionary) accruals (e.g.: Healy,
1985; DeAngelo, 1986, Jones, 1991; Dechow et al., 1995; Peasnell, Pope and Young, 1998).
Discretionary accruals can thus be computed as:
Discretionary accruals = total accruals – normal accruals
The main concern about discretionary accruals models is about their ability to detect earnings
management. Dechow (1995) and Young (1999) address this issue. Their conclusions are
relatively similar: models capture earnings management but with a great measurement error.
For empirical research, those results have two important consequences. First, if the
measurement error is correlated with the variable used to partition the sample, than spurious
correlation can appear and blur the conclusions. Even if there is no significant correlation
between the partitioning variable and the omitted variables, non significant results can be due
to measurement error.
In order to address this issue, we propose to consider earnings management as a latent
variable.
A latent variable is "not observable but can be deducted from observable variables called
indicators" (P. Valette-Florence, 1988). This basic idea is to consider EM as a latent variable
whose indicators are the different measures of discretionary accruals.
In more simple terms, we have:
13
discretion ary accruals1  1 * Earnings mangement   t
...


discretion ary accruals k  k * Earnings mangement   k
discretion ary accruals n  n * Earnings mangement   n
where "discretionary accrualsi" refers to the result of a particular procedure to compute
discretionary accruals, i is the measurement error of this particular method. Because Earnings
management is a latent variable, an indicator such that i = 0 and  i =1 does not exist.
How can we be confident that various estimations of discretionary accruals correctly measure
the same underlying construct?
According to Carmines & Zeller (1979) a measure can be seen as the sum of three
components:
M
 
measure
V

true score

S
systematic error
 alea

alea
If the systematic (that is, the error that comes from the instrument used to measure the latent
variable) error is low, than the measure is valid. If the alea (random error) is low, the measure
is reliable. In other words, validity refers to the "degree to which instruments truly measure
the construct which they are intended to measure", and reliability "to the degree to which
measures are free from error" (Peters, 1979, p. 10)
Convergent validity (to what extent do all indicators measure the same underlying construct)
helps us to evaluate, in this case, to the validity of a measure. T tests of the regression weights
between the indicators and the latent variable allow to check convergent validity.
Reliability is measured by Cronbach's alpha or Joreskog's rho. The later is preferable because
it does not rely on sample size (P. Valette – Florence, 1998). Both measures range from 0 (no
reliability) to 1 (perfect reliability).
14
In this study, discretionary accruals are calculated according three popular algorithms (Healy,
Modified Jones, Margin Model). Those algorithm are chosen because each of them has the
ability to capture a specific form of earnings manipulation. Healy algorithm capture for sure
earnings management but with a great measurement error. The strength of the modified Jones
Model is to capture sales based manipulations, where as the margin model is able to capture
bad debt manipulation.
We concentrate on short term accruals because they are the most likely to be manipulated to
manage reported earnings (Defond and Jiambalvo, 1994; Peasnell, Pope and Young, 1998)
Healy (1985) estimated normal accruals as:
Normal accruals t 
1 t h
*  total accruals k
h k t 1
DeAngelo (1986) model is based on an accruals random walk hypothesis. It is a particular
case of Healy model (h=1):
Normal accrualst  accrualst 1
This specification is used to compute short term discretionary accruals:
Normal accruals t  WC t 1
Where, WC refers to the change of working capital net of short term depreciation.
As Kaplan (1985) suggested Healy model is probably not relevant because it fails to control
the economic drivers of total accruals.
Jones (1991) addresses this issue by specifying factors of normal accruals. Depreciation and
change in working capital are the main constituents of total accruals. Gross Property, plant
and equipment (henceforth, PPE) is supposed to explain the level of amortization. Change in
turnover explains change in working capital net of short term depreciation.
15
Jones (1991) uses a longitudinal approach which has the major drawback of requiring a quite
long historical data. To overcome this shortcoming, some authors (e.g.: DeFond and
Subramanyam, 1998; Becker at al. 1997) use a cross sectional approach (firm – years are
pooled by industry). More over, some authors recommend to control for sales manipulation by
subtracting to change in revenue, the change in creditors (Dechow et al., 1995).
We concentrate on short term accruals, thus normal accruals are defined by (scaling by lag
assets to mitigate heteroscedasticity problems):
REVt  creditors
short accruals
1
 *(
)  1 *
 t
total assetst 1
total assetst 1
total assetst 1
Finally, we use the margin model developed by Peasnell, Pope and Young (2000). Using a
cross sectional approach, we estimate 0,1,2:
REVt
cash received t
short accruals
1
  *(
)  1 *
 2 *

total assetst 1
total assetst 1
total assetst 1
total assetst 1
Discretionary accruals are computed according to:
discretion ary accruals i ,t  WC i ,t


 Healy  WC i ,t 1

^
^
(rev i ,t  creditors i ,t )


 normal accruals Modifed  Jones :
  1*
assets i ,t
assets i ,t

^

^
^
rev i ,t
cash received i ,t
0

m arg in : assets  1 * assets   2 *
assets i ,t
i ,t
i ,t

Where, coefficients with a ^ are the estimation of the coefficient from the coefficient fitting
stage.
16
Fitting parameter stage.
A sample of French listed firms was designed on DIANE data base according to the following
procedure:
(1) firms must be listed on the first or second market.
(2) consolidated accounts must be published.
(3) Availability of the necessary data to compute accruals (and discretionary accruals) during
at least two consecutive years on the 1995-1999 period (DIANE database).
This procedure generated a sample of 504 firms (1484 firm-years).
Level 6 industrial codes were retrieved from DATASTREAM1. Industries with less than 7
firm years, or related to insurance, banks, investments funds and financial activities (because
their accrual process is particular) were deleted. Firm-years with extreme total accruals (1%
highest and lowest) were also deleted.
The final sample is of 1383 firm-years.
This sample was used to fit the parameters necessary to compute discretionary accruals (m-j
model and margin model).
Discretionary accruals estimation
Here are the descriptive statistics on discretionary accruals according those three models:
1
NAF codes (French equivalent of SIC codes) indicated by DIANE database are not reliable (NAF are attributed
according to the activity of the holding, i.e.: portfolio management activity).
17
Short discretionary accruals
Minimum Maximum
Margin model
-27,77%
23,40%
Healy model
-37%
81%
Modified Jones model
-20,58%
24,33
T test were conducted. Mean value are not significantly different
Mean Std. Deviation
0,06% 6,295135E-02
0,085%
,1060
0,074% 6,367902E-02
from zero for the margin
model and Healy model. Modified Jones model produces significantly slightly positive mean
discretionary accruals.
A graphical translation of the set of equation presented earlier is:
1
1
Healy Short Disc. Accruals
Earnings
management
1
M- Jones Short Disc. Accruals
Margin Short Disc. Accruals
1
2
1
3
In order to get the model determinable, we have to set arbitrarily a regression weight to 1.
Here are the results retrieved from AMOS 4.02:
Regression weight
Ad_Short mJ Earnings management
Ad_Short Healy Earnings management
Ad_Short Margin Earnings management
Estimate
1
1.182
1.029
Standard
error
Critical ratio
p
0.099
0.059
11.892
17.324
<0.000
<0.000
Critical ratios are all superior to two and are highly significant: all indicators measure the
same underlying construct: the criterion of convergent validity, in this case a measure of
validity, is met.
Commonalties between the indicators and the latent variable are indicated table A:
2
To mitigate problems raised by non normality, bootstrapping technique is used to evaluate regression weights.
18
Table A
Margin model
Discretionary accruals Accruals Healy
Short discretionary accruals (mj model)
Commonalties
,856
,631
,840
Similar to previous studies, we find that discretionary accruals extracted by Healy model are
the less connected to earnings management, indicating a great measurement error.
Joreskog’s rho formula is:
p

(  yi ) 2 * var( )
i 1
p
p
i 1
i 1
(  yi ) 2 * var( )   var(  yi )
Where yi is the communality factor of the ith indicator, var() is the variance of the latent
variable, var(yi), the variance of the error term (i.e.: 1-yi2).
Since, var()=0.867, rho value is 0.81.
According to Valette-Florence (1998), in marketing studies, a rho superior to 0.7 indicates a
good reliability.
Thus, using those three models of short term discretionary accruals seems to be valid and
reliable.
Unmanaged earnings (UME) and the extent to which UME falls below or above last year
reported earnings (LYRE) are calculated along the same procedure in order to mitigate
measurement error due to discretionary accruals models. Table A reports T-tests and
Joreskog's rho calculus for both constructs. Reliability and validity seem fairly good.
19
Section 4: empirical results.
To test empirical predictions, total sample is partitioned according to the sign of unmanaged
earnings. Table B reports descriptive statistics for explanatory variables, no significant
differences appear along the partitioning variable except for the following control variables:
CFO (cash flow from operations), high increase in leverage ratio, market value. As would be
expected, firms with unmanaged earnings below zero are more likely to lack last year
earnings target.
Table C presents correlation between monitoring devices. It shows that the percentage of
independent board members are positively related to a large board size and to the nature of the
firm. Controlled firms have smaller boards with less independent board members.
A principal component analysis failed to extract significant commons factors suggesting a non
regularity of the joint use of monitoring devices. However, the significant correlation (even if
low: less than 0.5) may create collineariy problems during regression analyses. To mitigate
this problem, we decided to eliminate from the regression the board size variables (to waive
the variables that exhibit the most important correlations).
Table D presents the regression results. A level of significance of 10% is used due to the
small sample size.
Our results show that the various forms of monitoring have different ability to limit earnings
management.
The impact of board monitoring on earnings management is significant but non linear. It is
non conditional to the performance of the firm.
20
The presence of an audit committee do not constraint significantly earnings management. This
result is consistent with prior studies (see Peasnell, Pope and Young, 1998; ThieryDubuisson, 2000).
Big six auditors constraint earnings management when firm performance is correct (UME
above 0 or last year reported income).
Surprisingly, ownership structure seems relevant to explain earnings management activity, but
the sign of the coefficient is not expected.
Same tests were conducted when the sample is partitioned according to the sign of the
difference between last year reported earnings and unmanaged earnings. Results (not
tabulated) are similar.
Test of hypothesis H2
Results reported Table E fail to confirm hypotheses H2 of a monitoring ability of independent
board members different according to CEO ownership. The fact that independent board
members are more likely in managerial firms (firms without significant block holder, either
the manager or an outside shareholder) can explain this result.
Additional tests.
Additional tests were conducted to control for model specification errors. Following tests
were conducted:
(1) the variable "significant block holder" was recoded to take into account the nature of the
block holder (CEO, institutional or other shareholder): results were non significant.
(2) Interaction terms between the stimulus and the monitoring device were included to control
for a specific ability (or willingness) of a monitoring mechanism to constraint a particular
21
incentive. Results were generally non significant non significant. However, big six
auditors seem to be able to limit EM during an increase in leverage or an equity offer
(significance test respectively 19.6% and 15,3%). Tests on a larger sample should allow to
check this point.
(3) Tests were also conducted using m-j and margin model to compute UME and as a
measure of earnings management. Results are qualitatively similar.
Section 5: concluding remarks.
Like previous research, this study confirm that board independence is important in
constraining manager discretion in earnings management, at least as far as GAAP are not
violated. This is the only lever of control able to limit earnings management when
performance is low (falls below zero or last year reported earnings). However this result is
conditional to the proportion of independent board members: to minimize earnings
management, a proportion of 30-40% (according to the sample data) seems optimal.
This suggests that adding an independent board members tend to increase board size:
adjustment and communication costs overbalance increased monitoring ability in terms of
board efficiency.
However, methodological issues can blur the interpretation of the results. Two levels of
problems should be distinguished.
The first relates to measurement issues. Earnings management evaluation has already been
evoked. Qualifying a director of independent raises the same problem (how can we be
confident in our classification?). Criteria applied to identify independent directors are (1) the
director is not a significant owner of the firm equity, (2) the director is not salaried or a former
22
manager of the firm. Those data were collected on databases (Dasfa, Cofisem and Who's
Who).
However, characteristics that attenuate the director's independence (such as friendship with
the manager, belonging to the same club,…) can not be identified. Along the same argument,
the criteria to determine significant ownership, managerial firm,… are, if not arbitrary, largely
conventional.
The second issue relates to stimuli to earnings management. If opportunistic relevant
incentives to manage earnings are omitted, than situations in which the monitoring role of
independent board members is important are waived from the analysis. Thus, we loose an
opportunity to study the relation between board independence and earnings management. The
problem is all the more important, that one the incentive studied here (the desire to meet
targets) is not strictly speaking opportunistic: Myers and Skinner (1999) show that a pattern of
increasing result is associated with abnormal market returns. This policy is in shareholders
best interests.
The conclusions of this paper should be further investigated by increasing sample size and
refining indicators to define independent directors, a "block" of ownership,…. Another
avenue for developing future research is to test independent directors sensitivity to earnings
management:
(1) under situations of potentially high opportunism (such as a CEO succession that creates an
incentive for a big bath accounting, cf. Pourciau (1993)).
(2) With a qualitative approach using a quasi experimental approach. A mini case study about
financial reporting matters is submitted to actual directors and they are asked to behave as
if they were independent board members of the firm. This approach allows to check their
23
attitude about financial reporting. In this way, it is complimentary to a quantitative
approach.
24
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27
Table A: Unmanaged earnings (UME) measure and difference between UME and last
year reported earnings measures (diff_UME_LYRE)
For UME:
(1) Unmanaged earnings are defined as net earnings minus discretionary accruals.
(2) Three indicators of UME (ume1, ume2, ume3) are computed: one for each of the
discretionary accruals model (Healy, m- Jones, Margin models).
(3) Ume1, ume2, ume3 are the indicators of a latent variable "unmanaged earnings"
(1) The difference between last year reported earnings and UME is defined as (last year
reported earnings minus UME).
(2) Three indicators of diff_UME_LYREi (i=1, 2, 3) are computed using ume1, ume2, ume3 as
measures of UME.
(3) diff_UME_LYREi (i= 1, 2, 3) are three indicators of the latent variable "difference
between last year reported earning and UME"
28
Latent
variable
Convergent validity
Indicators
Ume1
Ume2
Ume3
diff_UME_
Difference
between last LYRE1
year reported diff_UME_
earnings and LYRE2
unmanaged
diff_UME_
earnings.
LYRE3
Unmanaged
earnings
estimate
Standard
error
Critical
ratio
Reliability
commonaliti
es
1
0.935
0.964
NS
0.051
0.026
NS
18.414
36.693
0.923
0.758
0.914
1
NS
NS
0.883
0.810
0.072
11.261
0.596
1.023
0.049
20.776
0.876
Joreskog's
rho
0.89
0.83
All critical ratios are greater then 2, they are highly significant. Rhos are greater than 0.7.
Indicators seems both reliable and valid measure of the underlying factors.
29
Table B: explanatory variable partitioned according to UME. Tests of equality of the
means.
Small board (<5)
Large board (>13)
Managerial firm
Significant
blockholder
Audit committee
Ceo significant
stockholder
% ibm
% ibm²
Auditor b6 (0/1)
Significant equity
issuing
high increase in
debt ratio
High pda
UME< LYRE
(0/1)
CFO
(market value)
Equal
variances
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
assumed
not assumed
t
df
,248
,249
,764
,772
,764
,785
-,365
-,366
,106
,106
,000
,000
-,688
-,684
-,641
-,631
,190
,190
1,256
1,300
2,654
2,701
-,180
-,179
10,650
10,782
-10,272
-9,692
-2,438
-2,461
278
260,470
278
266,103
278
275,351
278
259,989
278
256,764
278
256,325
278
249,950
278
240,495
278
254,828
278
277,494
278
270,450
278
253,807
278
266,959
278
192,063
278
264,776
30
Sig. (2Mean Difference
tailed)
,804
1,042E-02
,803
1,042E-02
,446
3,542E-02
,441
3,542E-02
,445
2,083E-02
,433
2,083E-02
,716
-1,8750E-02
,715
-1,8750E-02
,915
6,250E-03
,915
6,250E-03
1,000
,0000
1,000
,0000
,492
-1,2118E-02
,495
-1,2118E-02
,522
-4,5854E-03
,528
-4,5854E-03
,849
1,042E-02
,850
1,042E-02
,210
4,167E-02
,195
4,167E-02
,008
,1500
,007
,1500
,858
-8,3333E-03
,858
-8,3333E-03
,000
,5438
,000
,5438
,000
-,1102
,000
-,1102
,015
-,8174
,014
-,8174
Std. Error
Difference
4,196E-02
4,178E-02
4,637E-02
4,585E-02
2,726E-02
2,653E-02
5,143E-02
5,123E-02
5,878E-02
5,876E-02
5,937E-02
5,938E-02
1,760E-02
1,772E-02
7,151E-03
7,262E-03
5,475E-02
5,484E-02
3,318E-02
3,206E-02
5,652E-02
5,554E-02
4,641E-02
4,654E-02
5,106E-02
5,043E-02
1,073E-02
1,137E-02
,3353
,3322
Table C: correlation between monitoring devices
small
large
ceo
Block
audit
B6
board
board
stockholde %ibm
holder committee
auditor
(<5)
(>13)
r
small board (<5)
1,000
large board (>13)
-,188(**) 1,000
Block holder
,126(*) -,246(**)
1,000
audit committee
,111
,021 -,122(*)
1,000
(**)
(**)
CEO stockholder
,029 -,190
,213
-,081
1,000
% ibm
-,303(**) ,413(**) -,240(**)
-,041
-,172(**) 1,000
,
(**)
(*)
(*)
(**)
(**)
B6 auditor
-,179
,130
-,128
,037
-,210
,226
1,000
** Correlation is significant at the 0.01 level (2-tailed).
* Correlation is significant at the 0.05 level (2-tailed).
Table D: regression results (hypotheses H1.x)
UME>0
Predicte
d sign
beta
T
sig
-1,926
,057
beta
Ume<0
T
Sig
,068
,946
(Constant)
?
significant blockholder
audit committee
%ibm
%ibm²
auditor b6 (0/1)
significant equity issuing
high increase in debt ratio
UME< LYRE (0/1)
High pda
CFO scaled by lagged
assets
Log(market value)
YEAR1998
YEAR1999
?
+
+
-
,145
-,020
-,382
,560
-,157
-,316
,233
,489
-,163
1,830
-,273
-1,715
2,486
-2,050
-4,008
2,910
6,554
-2,215
,070
,785
,089
,014
,043
,000
,004
,000
,029
,029
,013
-,309
,321
-,043
,095
,087
,289
-,095
,429
,207
-1,635
1,729
-,653
1,474
1,281
4,368
-1,506
,668
,836
,100
,086
,515
,143
,202
,000
,134
-
-,224 -2,597
,011
-,456
-6,490
,000
?
?
?
,097
,332
,382
,260
,412
,348
,108
,000
,001
1,502
-,002
,006
,135
,999
,995
F
R2 adjusted
1,132
,824
,943
7.321 0.00
40.8%
31
9.531 0.00
41.1%
Table 2: test of hypothesis 2
UME>0
Predict
ed
sign
beta
beta
T
(Constant)
?
-1,988
significant block holder
audit committee
%ibm
%ibm²
auditor b6 (0/1)
significant equity issuing
high increase in debt
ratio
UME< LYRE (0/1)
High pda
CFO scaled by lagged
assets
Log(market value)
YEAR1998
YEAR1999
Ibm_lowCEO
Ibm2_lowCEO
?
-
,160 1,901
-,013 -,177
-,233 -,732
,355 1,042
-,160 -2,077
-,320 -4,025
F
R2 adjusted
+
,239
2,953
UME<0
T
sig
sig
,049
,087
,931
,060 ,027
,860 ,014
,466 -,238
,300 ,230
,040 -,042
,000 ,095
,383
,215
-,757
,659
-,637
1,455
,702
,830
,450
,511
,525
,148
,086
1,245
,215
,004
+
-
,488 6,484
-,166 -2,237
,000 ,288 4,301
,027 -,096 -1,515
,000
,132
-
-,196 -2,052
,043 -,456 -6,413
,000
?
?
?
?
,099
,337
,383
-,205
,266
,255 ,101
,408 ,003
,349 ,007
,531 -,088
,433 ,112
,183
,991
,977
,788
,760
1,146
,831
,941
-,628
,788
6.311 0.00
40.1%
32
1,339
,011
,029
-,270
,307
8.159 0.00
40.3%