Does public/private status affect SMEs earnings management
practices? A study on French case
Yves Mard1
Professor
Clermont University
CRCGM, Centre de Recherche Clermontois
en Gestion et Management
Ludovic Vigneron2
Assistant Professor
University of Valenciennes
IDP, Institut du Développement et de la
Prospective
Abstract:
In this article, we study accounting quality among small and medium enterprises (SMEs) in the
French context. Using a sample of 925 firms observed over the 2002-2010 period, we compare the
earnings management practices between public and private SMEs. We find evidence that private
SMEs manipulate more frequently their net income report to avoid small losses, than public ones.
We also notice a more pronounced earnings smoothing behavior among private SMEs than among
public ones. The analysis of discretionary accruals shows that both public and private SMEs use
accounting flexibilities to influence earnings, but they do it with a different purpose. Public SMEs use
accruals to increase their apparent performance while private SMEs manage earnings downward, in
order to reduce tax payments.
Résumé
Dans ce papier nous examinons la qualité de l’information comptable publiée par les petites et
moyennes entreprises (PME) dans le contexte français. A partir d’un échantillon de 925 PME
observées sur la période 2002-2010, nous comparons les pratiques de gestion des résultats des PME
cotées et non cotées. Les analyses montrent que les PME non cotées gèrent leurs résultats pour
éviter les pertes, alors que les tests ne sont pas significatifs pour les PME cotées. Nous constatons
également que ces mêmes PME lissent davantage les résultats que leurs homologues cotées. L’étude
des « accruals » met en évidence une utilisation plus intense des techniques d’optimisation du
résultat par les sociétés cotées de même qu’une utilisation différente de ces dernières. Les sociétés
cotées les utilisent plus fréquemment pour augmenter leur performance apparente tandis que les
non cotées tendent à les utiliser pour minimiser leurs résultats de manière à en réduire l’imposition.
JEL classification: M41; G34
Keywords: earnings management, SMEs, Public, Private, income smoothing
1
2
Contact: yves.mard@udamail.fr
Contact: ludovic.vigneron@univ-valenciennes.fr
1
1. Introduction:
This paper tests whether there is a difference in earnings management between public and
private Small Business Enterprises (hereafter SMEs) in the French context. Previous studies,
mostly conducted on US firms, have reported that markets’ pressure reinforce earnings
management incentives when firms go public. However, several researches prove that
earnings quality is higher in public firms than in private firms.
In continental Europe financial markets are less developed and so play a less important role
in business funding. This institutional difference influences the demand for accounting
information, and the characteristics of that information, even if some convergences are in
progress with the adoption of IFRS standards. It can also have an impact on the factors which
influence the quality of accounting disclosures, earnings disclosures in particular. For
example, Leuz et al (2003) report that earnings management is stronger in code law contexts
than in common law contexts. In a country where investor protection is lower than in the
Anglo-Saxon context, we ask whether being public is beneficial to earnings quality.
Our research contributes to the literature on earnings management on three points. Firstly,
where most researches have been conducted on large firms, or on mixed samples,
regrouping SMEs and large firms, we study earnings management in SMEs exclusively. SMEs
have specific characteristics in terms of organizational structure, investment and financial
policies. Dealing only with SMEs is a first factor of homogeneity in our study, because these
firms tend to have common motivations and constraints. For example, in France, SMEs have
a special tax rate, lower than that of large firms.
A second factor of homogeneity in our study is the focus on the French context. All firms in
our sample belong to the same institutional and financial environment, and thus have to
conform to a unique legislation, with the same social, fiscal or governance rules. The
national context can influence accounting policies in several ways. It can explain some
incentives to manage earnings. For example, factors such as political context, relations with
banks, or tax pressure are potential incentives to earnings management. The national
context also influences the environment of control, with specific rules about corporate
governance or audit. These factors limit earnings management practices. Finally, firms of the
same country use the same national accounting standards, although public firms in Europe
have to use international accounting standards.
Thirdly, only a few works addressed the question of earnings quality in private and public
SMEs in the French context. The French environment is interesting for several reasons. As in
other code law countries, investor protection and shareholders rights are lower than in
common law countries. In that context, the impact of financial markets on earnings quality is
an open question. Another difference with the US context is the influence of taxes on
accounting policies, because accounting figures are used to evaluate tax payments. Finally,
financial statements in most SMEs have to be audited, even if they are private. This
difference with the US context can lead to a better quality of financial information, even
among private firms.
2
After some preliminary analyses of firms’ net incomes distribution shape, we regress
different measures of earnings quality on a dummy associated with the public status of the
SMEs and a set of control variables. We use both incomes smoothing indicators and
discretionary accruals to evaluate earnings management intensity. Our results show that
earnings management to avoid small losses only concerns private SMEs. We also observe
that private SMEs smooth earnings with more intensity, than public ones. The analysis of
discretionary accruals provides evidence that public SMEs adopt different earnings
management strategies than private SMEs. The first ones more frequently increase their net
incomes through discretionary accruals in order to maximize their appearing performance
whereas the second ones prefer to decrease their net incomes in order to avoid taxes.
The remainder of the paper is organized as follows. We first develop in section 2 the
conceptual framework of earnings management, and the literature on earnings
management in SMEs. We propose three hypotheses related to earnings management
practices in public and private French SMEs. Section 3 describes methodologies and data
used in the study. The empirical results are discussed in section 4. Finally, the main
conclusions are exposed in section 5.
2. Literature and Hypothesis
We first present theories about earnings management and suggest some explanations to
those practices (2.1). We then propose hypotheses related to earnings management in SMEs
(2.2) and the link between listing status and earnings management (2.3).
2.1.Theories and practice of earnings management
Davidson et al. (1987), in Schipper (1989), defined earnings management as “the process of
taking deliberate steps within the constraints of generally accepted accounting principles to
bring about a desired level of reported income”. The practice of earnings management is
associated with an objective of reported income. Another characteristic of earnings
management is the respect of accounting standards, which makes it different from fraud. To
meet earnings management objectives, managers can use accounting decisions (such as
depreciations or provisions) or decisions related to real activities, such as production, sales,
financing or investment.
In a context of information asymmetry, earnings management can be seen as a signal
addressed to the users of financial information. For example, according to Trueman and
Titman (1988), firms smooth earnings to influence stakeholders’ behaviors and modify their
perception of firms’ earnings variability. Two competitive approaches are often proposed to
explain accounting choices and earnings management: the efficient approach and the
opportunistic approach. Adopting an efficient point of view, earnings management can be
seen as a way to bring information about expected cash flows of the firm (Holtausen, 1990).
Aria and al (1998) also consider that earnings management can be beneficial to both
shareholders and managers.
3
Another point of view is to consider earnings management as an opportunistic practice. This
approach is based on the functional fixation hypothesis 3, which considers that users of
financial information are not able to correctly interpret and understand accounting numbers
and accounting choices (Hand, 1990). In that case, earnings management can be seen as a
way to mislead investors and other users of accounting figures about the performance of the
firm.
Most studies adopt the opportunistic approach of earnings management. Seminal work to
explain firms’ accounting policies were developed by Watts and Zimmerman (1978), with the
positive theory of accounting. Their theory explains accounting choices through the
contractual and political contexts of firms. For example, CEO pay, debt contracts and
regulation influence accounting strategies developed by managers. Some empirical evidence
of the positive accounting theory is proposed in the Swiss context by Cormier et al. (1998)
and in the Canadian context by Labelle and Thibault (1998).
Capital market pressure is another explanation to earnings management. Several studies
show that earnings management takes place in contexts such as public offerings (Cormier
and Magnan, 1995), delisting (Martinez and Serve, 2011) or LBO operations (Le Nadant,
1999).
Earnings management practices also depend on the firm’s corporate governance
characteristics. For instance, ownership structure, board composition, auditors, and financial
analysts, can influence accounting policies and constraint earnings management. Finally, an
institutional context where shareholders protection is developed is also favorable to
earnings quality (Leuz et al, 2003).
2.2.Earnings management in SMEs
Most works about earnings management have studied large companies because financial
information published by these firms is easily accessible. Large companies are generally
listed companies with publicly available financial information. The case of SMEs is different
because most of them are private firms, with less demand for financial information. Small
companies are less subject to agency problems, especially when shareholders and managers
are the same people, like in family firms. Nevertheless, if the company needs external
financing, for example from banks, the classical agency relationship between shareholders
and lenders can drive to an incentive to earnings management. Small companies are also
subject to political costs, such as tax costs. Tax purposes are often advanced to explain
accounting choices in small firms, especially when alignment between financial and tax
reporting is high (Ball et al, 2000). So it appears that incentives to manage earnings also exist
in SMEs, even if these firms are less observed than large companies.
Several studies tried to explain the objectives of financial reporting in SMEs. Lavigne (2002)
shows that, according to the managers of Canadian SMEs, financial reporting responds to
both internal management and tax purposes. He shows that structural factors, such as firm
size, ownership structure and debt also influence accounting policies. In the same context of
3
The functional fixation hypothesis can be seen as an alternative to the market efficiency hypothesis, applied
to the behavior of users of accounting information.
4
Canadian firms, Maingot and Zeghal (2006) find that the objectives of financial reporting are
linked to taxes and debt.
Firm’s performance can also influence financial reporting. Saboly (2001) shows that
managers of small distressed firms can manage earnings to influence stakeholders. In
Australia, Mc Mahon (2001) finds that financial reporting quality in SMEs is associated with
firm size, but not with performance and growth.
Like larger firms, SMEs can manage earnings in different ways. They can use “big bath
accounting”, by recording and liquidating severe losses. New managers often use this
technique when they take their function. Earnings can be managed downward, for tax
purposes, for instance. Earnings can also be managed upward, to improve perceived
performance and meet threshold, such as the zero target. Another objective of earnings
management is earnings smoothing, which consists in minimizing the dispersion of earnings
series, to limit perceived risk for example.
In this study, we first investigate whether SMEs, independently of their listing status, engage
in earnings management. We test two different practices of earnings management: earnings
management to avoid losses and earnings smoothing. According to previous developments,
we propose a first group of hypotheses:
H1: SMEs manage their earnings
H1a: SMEs use earnings management to avoid losses.
H1b: SMEs use earnings management to smooth earnings
2.3. Listing status and earnings management
Market demand for a high financial reporting quality
The listing status influences the quality of financial information. Investors use public firms’
financial communication to assess their performance. The demand for high quality
information is an incentive to limit earnings management practices among listed firms. The
going public process also offers a certain guarantee about the quality of the firm’s financial
reporting. Private firms are less subject to demand that. They can communicate with their
shareholders via private channels (Burgstahler et al, 2006). Moreover, private firms often
have strong incentives to manage earnings, to influence tax or dividend payments. These
firms can also suffer from weaknesses in their corporate governance structure, such as the
absence of independent directors or of an audit committee. These factors can lead to less
qualitative earnings among private firms than among public ones.
Financial market pressure and earnings management
On the other hand, financial markets exert pressure on listed companies. This can drive
managers to manipulate earnings. For example, the financial market allows a bonus to
companies which present a regular and increasing trend of earnings (Myers et al., 2007) or
which exceed the analysts forecasts (Bartov et al., 2002). Reversely, companies that record
earnings disappointment will see their stock market price drop (DeAngelo et al., [1996],
Skinner and Sloan, [2002]).
5
Earnings management can be conducted to reach precise thresholds, such as the zero, or the
previous or forecasted earnings. Hayn (1995) notices an abnormally weak proportion of
companies recording weak losses. According to her, this discontinuity can be explained by
earnings management to avoid losses. Burgstahler and Dichev (1997) and Degeorge et al.
(1999) confirm this discontinuity by analyzing earnings distributions of listed companies.
These authors also show that managers use earnings management to avoid earnings
decreases. Evidence of earnings management to achieve analyst forecasts is proposed in the
literature (Degeorge et al., 1999; Payne and Robb, 2000; Moehrle, 2002). Market demand
for a high level of performance is an incentive to manage earnings, especially when CEOs are
rewarded with stock-options (Bergstresser and Philippon, 2006; Burns and Kedia, 2006).
Listing status and earnings management: empirical evidence
In the recent years, several researches compared earnings management practices in private
and public firms. Does listing status influence earnings management? Even if there is no
consensus on that question in the literature, most studies conclude that earnings
management is more pronounced among private firms. For example, Ball and Shivamakumar
(2005) show that earnings quality is higher among listed companies in the UK, in a context
where public and private firms face similar regulation on auditing, accounting standards and
taxes. Van der Bauwhede et al (2003) study earnings management among Belgian firms, and
prove that both private and public firms engage in earnings smoothing to meet benchmark
targets, yet listed companies are less engaged in income-decreasing earnings management
than private firms. In Spain, Arnedo et al (2007) also show lower levels of income decreasing
among public firms, but find income smoothers and increasers among both public and
private firms. However, they find that firms in the Ibex 35 index exhibit lower levels of
manipulation than a parallel group of smaller listed firms.
Coppens and Peek (2005), studying European companies, show that private firms try to
avoid small losses, but not always earnings decreases. They conclude that market pressure
can drive to specific motivations for public firms, while accounting choices among private
companies answer more to tax motivations. Burgstahler et al (2006) also study earnings
management in European firms. They report that earnings management is higher among
private firms, and that strong legal systems are associated with less earnings management in
private and public firms. In Russia, Goncharov and Zimmermann (2006) prove that earnings
management for tax purposes is less pronounced among public firms.
A few studies provide evidence that nonlisted companies propose a higher quality of
financial reporting. For example, Beatty and Harris (1999) compare tax incentives and
earnings management among US private and public firms, and observe that private firms
manage earnings less aggressively. Beatty et al (2002) study earnings management in US
banks, and find that public banks report fewer small earnings declines than private banks,
and are more likely to use loan loss provisions and security gain realizations to eliminate
earnings decreases. In the context of Korea, Kim and Yi (2006) also report that earnings
management is higher in public firms than in private firms.
6
According to empirical evidence, we suppose that earnings management is more
pronounced in private SMEs than in public SMEs. We use three measures of earnings
management to test a second set of hypothesis:
H2: Earnings management is more pronounced in private SMEs than in public SMEs.
H2a: Earnings management to avoid losses is more pronounced in private SMEs than
in public SMEs.
H2b: Earnings smoothing is more pronounced in private SMEs than in public SMEs.
H2c: Earnings management through accruals is more pronounced in private SMEs
than in public SMEs.
Accounting choices among private firms are less constrained by market pressure, but more
by tax purposes. We consequently suppose that private firms just want to publish a small
profit, to minimize the resulting tax. The expected accruals should be lower for profitable
private firms than for profitable public firms.
Recently, earnings management in private firms and SMEs was studied in several contexts.
These studies test tax motivations to manage earnings. Van Tendeloo and Vanstraelen
(2006) show that big4 auditors can limit earnings management in private firms, especially
when financial and tax reporting are aligned. Marques et al (2011) prove that Portuguese
private firms manage earnings to limit tax payments. They find that firms with higher rates
of income taxes reduce earnings near zero. In the Slovenian context, Garrod et al (2008)
show that small private firms use asset write-offs to minimize taxes. Obviously, more
profitable companies are more likely to write-off than less profitable ones.
This leads to a third hypothesis:
H3: Private firms manage their earnings downward more frequently than public firms.
3. Data and Methodology
We first describe and justify sample selection and data collection (3.1). Then we expose our
general model of earnings quality among private and public SMEs and develop our different
measures of earnings management: earnings distributions, earnings smoothing and
discretionary accruals (3.2).
3.1. Sample description
In order to conduct our investigations, we use the Altares4 database, which provides
accounting information for about one million of French firms and covers a period of ten
years. We use the European commission’s definition to identify SMEs. The companies’ parts
of our sample employ between 20 and 250 people, and they either have a turnover of 2 to
50 million Euros or total assets of 2 to 43 million Euros. Two main criterions have to be met
for a company to be part of our sample: to be a SME and to be head of a group with
obligation to publish consolidated accounts. It allows us to deal with a relatively
4
Edited by Dun and Bradstreet and distributed via IODS (48, rue de Provence 75009 PARIS).
7
homogeneous sample, composed of the biggest SMEs. According to Marques et al (2011),
we use individual accounts because these accounts are generally used to determine tax
payments.
We only keep observations corresponding to accounting periods of twelve months and
select firms with at least two consecutive years of available accounts. Finally, to deal with
potential outliers problems, we drop the first and last percentiles of net income over total
assets, our main variable. As a result, we obtain an unbalanced panel composed of 7 451
observations from 925 different firms over the 2002-2010 period.
Table 1 provides descriptive statistics for the sample. Panel A considers it globally. Panel B
and panel C split it between public and private firms. We notice that the first ones present a
bigger size than the second ones. They have higher amounts in total assets. The difference in
terms of turnover or of number of employees between the two groups is not considerable.
We also notice that, with an average age around twenty years old, private and public SMEs
are not different from each other. The most interesting difference is that private SMEs
appear to be more profitable than public ones. This observation can be observed on both
operating income and net income. For the first category of earnings, we report a mean ratio
of operating income over net total asset of 2.98% against 1.89% and for the second a mean
ratio of net income over net total asset of 4.28% against 1.53%. Panel D describes our
sample by industry. The most represented sectors are wholesale and retail activities, and
services to companies. They represent about half of our sample. Manufacturing sectors are
underrepresented with a sum of all the related sectors reaching about 17% of the firms. We
also note that for 2% of the observations we have too little information to determine the
sectorial membership.
[Insert Table 1]
3.2.Tests specifications and variables descriptions
Earnings distribution methodology
As a preliminary analysis, we examine the shape of the firm’s earnings distribution’s curb.
Previous studies, following Burgstahler and Dichev (1997), report evidence of irregularities
associated with firm’s earnings management around identified thresholds such as the null
net income threshold. They show that firms try to avoid small losses. Thus, the number of
firms which realize a net income just below zero is inferior to the expected number, and the
number of firms which realize a net income just above zero is higher than the expected
number. This phenomenon creates a discontinuity in the distribution of net incomes around
zero. In order to assess whether our SMEs manage earnings to avoid small losses, and if
public SMEs and private SMEs exhibit different behaviors, we build a histogram of firms’ net
incomes standardized by their total assets in t-1. This standardization allows us to compare
firms’ earnings without the bias associated with size differences. Indeed without this
transformation an identical level of net income can be falsely interpreted as equivalent for
the biggest and smallest firms. We choose a histogram interval width of 0.005 to conduct
our analysis. This width is the most frequently used in the literature.
8
General explanatory model
In this subsection we present the econometric models we run to test our different
hypothesis. We also describe the variables included in the specifications. Each model is built
in the same way. We regress a measure of earnings quality on a dummy variable, that we
call Public, which takes the value one if the firm is public the years of the earnings disclosure,
and a set of control variables. These variables are firm size, leverage, growth, performance
and industry sector. Those factors have been identified by the literature as associated with
the level of earnings management.
(1)
Firm size is measured by the natural logarithm of net total assets. Moses (1987), through the
political cost hypothesis, suggests that large firms less frequently engage in earningsmanagement because they are more controlled by the public and government which
discipline them. The considered form of leverage corresponds to the ratio of total debts over
total assets. Following Rajan and Zingales (1995), we consider debt a way to deal with
agency cost and asymmetric information problems. We define growth as the increase in
total assets that we measure as the ratio of net total assets t minus net total assets t-1 over
net total assets t-1. Growth makes the need for external funds more important and with it
the need for firms to present suitable earnings track records. Consequently, we expect a
positive relationship between Growth and the intensity of earnings management. The
performance is measured by the ROA computed through the ratio of the firm’s operating
income over its net total assets. We use it as a proxy of pre-managed earnings. We include it
in our specifications as a control variable considering that too low a performance fosters
earnings management to improve appearing performance and a too high one reduces
earnings management incentives. Finally, we use a set of dummy variables associated with
the firms’ industry sectors that we code on the base of the first digit of the INSEE NAF2
revised list. This allows us to check for industry specific accounting practices. Model (1) is
tested with two measures of earnings quality: earnings smoothing and discretionary
accruals.
Earnings smoothing measures
Our first series of models is based on incomes smoothing indicators. We compute two of
these indicators and adopt each one as an explained variable in our model. The indicators
are all built in the same way. They oppose in a ratio a measure of the variations of earnings
and a measure of the variations of the level of activity. Our smoothing indicator 1 comes
from Leuz et al. (2003). It is the ratio of the standard deviation of the firm’s operating
incomes over total assets, over the standard deviation of the firm’s operating cash flows
over total assets. Our smoothing indicator, 2 previously used by Lang (2006), is identical
except for net income replacing operating income. All indicators are computed over the full
period of the study, seven years for the first one and eight for the others.
We estimate using OLS a model based on our general specification. Our different measures
of smoothing are regressed by the mean of our different explanatory variables over the total
period of study. We obtain the following equation:
9
(2)
The smoothing indicators decrease with the level of earnings management. The predicted
relationships between the explained variable and our explanatory variables are therefore
opposite to those previously discussed for the general specification. The different tests
associated with the coefficients are conducted following the White’s robust to
heteroscedasticity method.
Discretionary accruals models
Our second series of models is based on discretionary accruals. As for smoothing indicators,
we use various methods to estimate discretionary accruals and include the resulting value as
an explained variable in our general specification. Here we use three methods. They are all
built on the same base. We regress total accruals measured by the difference between
operating cash flows and the income before extraordinary items 5 over a set of explanatory
variables associated with the evolution of firms’ activity and their fixed assets and use the
residuals as the estimation of discretionary accruals. The first estimation follows the
modified version of the classical model of accruals proposed by Jones (1991) following the
recommendation of Defond and Subramanyam (1998). We have:
(3)
is change in turnover,
the end of the year, and
DACC 1.
the difference in accounts receivable from start to
property, plant and equipment.
gives the variable
The second estimation modifies the first one to consider the link between changes in
accounts receivable and change in sales. In order to do so, we use a factor k computed by
the following regression:
(4)
We include this factor in the previous specification to obtain:
(5)
gives us a second estimation of discretionary accruals the variable DACC 2.
Following Dechow et al. (2003), we add two elements to get the last specification. At first, to
take into account the fact that by definition accruals reverse through time and are less
persistent than cash flows, as Chambers (1999) does, we include the one year lagged value
of total accruals, lagTA, in our specification. Then, to take into account the fact that an
increase in stock for a firm anticipating growth can’t be analyzed as a manipulation, we also
include the firm expected growth, Exp. Gro., in our equation. We estimate it by using the
5
We use this category of income in order to obtain accruals which are not affected by exceptional operations.
10
growth in turnover over the period t to t+1. This solution was proposed by Mc Nichols
(2000). We obtain the following specification:
(6)
gives us our third estimation of discretionary accruals the variable DACC 3.
Each of these models of accruals is estimated through pooled panel method (OLS) with the
variables others than ratios normed by firm’s total assets in t-1 to deal with
heteroscedasticity problems.
The resulting estimations of discretionary accruals’ absolute value are included in our
general specification as the explained variable. So we estimate the following equation in
which we control for year fixed effect:
(7)
The expected signs of our different coefficients are the same as those we discussed for the
general specification. The different tests associated with the coefficients are conducted
following the White’s robust to heteroscedasticity method.
4. Empirical evidences
Empirical evidences about earnings management in public and private SMEs are exposed for
each measure of earnings management: earnings distribution analysis, income smoothing
and discretionary accruals measures.
4.1. Earnings distributions analysis
Figure 1 provides an image of the standardized net income histogram for the range of -0.200
to +0.200 for the total sample. A vertical line points the mean value of firms’ net income and
another one the value zero. The resulting histogram is very different to that of a random
variable associated with a normal distribution. The Shapiro-Wilk test that we perform rejects
the normality hypothesis. The distribution of standardized net income is skewed and has fat
tails. It also shows an irregularity around zero with low frequencies, 109, for the values
between -0.005 and 0, and high frequencies, 296, for the values between 0 and 0.005. This
report is in line with the hypothesis (H1a) of an active earnings management by firms to
avoid small losses. Too much of them realize small losses and too much of them realize small
gains.
[Insert Figure 1]
We perform the same kind of graphic analysis over the sub-groups of public SMEs (figure 2)
and private SMEs (figure 3). Both distributions appear asymmetric and with fat tails like the
11
one that we have drawn for the total sample. The hypothesis of normality is rejected for the
two series. We also report irregularities around zero in both cases, but the irregularity
appears to be less important for public firms than for private ones. The frequency of small
losses is similar in both cases, around 1.4% of the observations, but the frequency of small
gains is higher for private SMEs than for public SMEs, 4.3% of the observation against 2.4%.
[Insert Figure 2 and 3]
These simple graphic observations do not provide a clear measure of the importance of
earnings management practices. In order to fill this gap, we conduct statistical analyses of
the irregularity. Two categories of methods are used. To start with, we compare each side of
the irregularity, small losses and small gains, with their theoretical frequencies. There are
estimated using different procedures: arithmetic mean of the two classes neighboring the
considered side of the irregularity (Burgstahler and Dichev, 1997), linear, exponential
(Dechow et al., 2003) and logarithmic interpolation (Vidal, 2008), based on the appropriate
form of regression of the four classes of standardized net income that preceed the estimated
class as well as the four that follow. We then globally evaluate the irregularity’s size through
a measure of the distribution’s asymmetry around the standardized net income value of
zero.
Table 2 displays estimations and tests performed on the irregularity associated with the class
of observations corresponding to small losses.
[Insert Table 2]
Throughout the total sample, we find evidence of significant differences between small
losses estimated theoretical frequencies and observed frequencies. We get these results
using our different methods except for the exponential interpolation one. The measured lack
of observations is around 35 to 45 percent of estimated theoretical frequencies, depending
of the method used to determine it. We conduct a similar analysis on the sub-sample of
public SMEs. We don’t notice any significant difference between small losses theoretical and
empirical frequencies for this category of firms. From this observation, we deduce that the
differences reported on the total sample are driven by private SMEs behavior. This
conclusion is confirmed by the analysis of their specific distribution of standardized net
income. For their sub-sample, we find significant difference between theoretical and
empirical frequencies, for each method but the exponential interpolation one. The
estimated size of the irregularity is here included between 38 and 48 percent of the
theoretical frequency, depending on the method of determination.
Table 3 displays the same kind of information but considers the class of observations
associated with small gains.
[Insert Table 3]
For the total sample, as for small losses, we find evidence of significant differences between
small gains estimated frequencies and measured frequencies using our different methods,
except here for the arithmetic mean estimation. The estimated surplus of observations
relatively to theoretical frequencies is around 51 to 113 percent, depending on the method
12
used to determine it. The analysis of the public SMEs sub-sample, as for small losses, doesn’t
show significant differences between theoretical frequencies of small gains and empirical
ones. It seems that the hypothesis of an active earnings management to avoid small losses
and to force small gains for public SMEs doesn’t match the data. This last conclusion is in line
with our second hypothesis (H2a). The differences reported for total sample are also here
driven by private SMEs behaviors. The differences between theoretical and empirical
proportion of small gains computed using each method, expect of the arithmetic mean one,
are significant. The estimated size of the irregularity for those firms is around 65 to 142
percent of the theoretical frequency.
In table 4, we report the analyses of a statistic measuring the standardized net income
distribution asymmetry around the threshold zero. We use the Glaum et al. (2004) metric,
which is computed as the ratio of the difference between the frequency of the located on
the right side of the threshold and the one on its left side over the sum of the two
frequencies. This indicator is equal to zero if the two classes are perfectly symmetric. This
indicates that the small gains are as frequent as small losses.
[Insert Table 4]
Here, the indicator is positive. It shows that the part of the distribution we analyze is on its
growing side. The small gains frequency is more important than the small losses frequency
as we saw in the different figures.
We start from the postulate that the bigger the absolute value of the statistic of asymmetry
is, the bigger the irregularity around the threshold of the distribution of standardized net
incomes is. The first elements displayed in the table are related to the time series of the
statistic for the total sample and the two sub-samples. None of these series show any
trends. At the aggregated level, for the full period, we report an important difference
between public and private SMEs. The value of the asymmetry is 0.23 for the former and
0.50 for the latter. The second part of the table displays statistical tests for difference in
mean, median and standard deviation of the annual asymmetry for public and private SMEs.
Two of them are significant: the Student test and the Fisher test. They show that on average
public SMEs present a greater asymmetry as well as a higher standard deviation. These
results are in line with those of the previous analyses. They also provide evidence that
private SMEs manage earnings ones to avoid small losses more frequently than public.
4.2.Income smoothing
The second part of our analysis focuses on income smoothing behavior, which consists in
firms stabilizing their earnings relatively to the variation of their level of activity. We use two
indicators from the literature to measure the intensity of the firm’s income smoothing
practices. They are all built on the same model. They oppose, through a ratio, a measure of
earnings variation to a measure of activity variation. The smoothing intensity decreases
when the ratio increases. If the earnings variation is lower than the activity variation, it
indicates that firms act to stabilize their level of performance.
The first indicator of earnings smoothing is the ratio of the standard deviation of the ratio of
operating income normed by total assets, over the standard deviation of the ratio of
13
operating cash flow normed by total assets. This measure was first used by Leuz et al. (2003).
The second indicator is built on the same model but with net income instead of operating
income. It was first used in Lang et al. (2006).
Table 5 displays statistics on the elements which enter in the computation of our smoothing
indicators, and the indicators themselves. We observe that the indicators’ mean values are
respectively 0.462 and 0.648 for our two measures of earnings smoothing. These values are
lower than 1, which shows the presence of earnings smoothing practices among firms of the
sample. This result goes beyond the hypothesis that SMEs smooth their earnings (H1b).
[Insert Table 5]
Bivariate analyses provide evidence that income smoothing behaviors are less important for
the group of public SMEs than for the group of private ones. The two indicators are
significantly higher for public SMEs than for private ones. This report is in line with our
second hypothesis (H2b). A quick comparison between the values of our smoothing
indicators, which have the same denominator, shows evidence that there is a greater
difference between operating income standard deviation and net income standard deviation
for public firms, 0.028, than for private ones, 0.016. We interpret these figures as
consequence of the fact that accruals, costs of debts and exceptional profits seem to affect
public firms more widely than private ones. Private firms exhibit less natural earnings
variability, but also smooth more aggressively their earnings.
In table 6, we present a multivariate analysis of firms’ income smoothing activities. We
regress our income smoothing indicators against our test variable, a dummy which takes the
value one if the firm is quoted and zero if it isn’t, and a set of control variables.
[Insert Table 6]
Once again results confirm that public SMEs are less intensely engaged in earnings
smoothing actions. We note a positive and significant relationship between our firms’
quotation status dummy and each of our smoothing indicators. As previously discussed, our
smoothing indicators are reverse proxies of the firms’ effective smoothing behaviors. Thus a
positive correlation between them and the fact that a firm is quoted highlight that those
firms have a less important smoothing activity than private ones. This result is coherent with
our previous reports and our second hypothesis (H2b).
4.3. Discretionary accruals study
The last part of our analysis focuses on discretionary accruals. We use different methods to
estimate the importance of earnings manipulation through accounting choices. Table 7
displays descriptive statistics about total accruals, the absolute value of discretionary
accruals variables and their positive and negative components.
[Insert Table 7]
We notice that total accruals are more negative for public SMEs than for private ones. This
difference can be associated with a size effect. As we have previously noticed, public firms
14
exhibit higher assets than private firms. The absolute value of discretionary accruals
represents on average near 2 percent of firms’ gross total assets. This value appears to be
more important for private firms than for public ones. The difference is caused by negative
discretionary accruals. We do not find any differences in positive discretionary accruals
between private and public SMEs but we report evidence of an average level of negative
ones more important for public firms than for privates ones.
To complete our analysis, we include the three estimations discretionary accruals absolute
value and their different transformations as explained variables in our general specification.
Table 8 reports the results of the regressions for the total sample.
[Insert Table 8]
The first category of regressions considers discretionary accruals absolute value. We find
that earnings management intensity appears to be lower for public firms. Two of our
estimations of discretionary accruals (DACC2 and DACC3) are negatively and significantly
related to the dummy quoted versus unquoted. The other estimation (DACC1) presents a
negative but non-significant coefficient. These findings tend to support our hypothesis (H2c)
of private SMEs managing earnings through accruals more aggressively than public ones.
A more precise analysis of discretionary accruals shows that public and private SMEs follow
different objectives in dealing with earnings management. We separate firms with positive
discretionary accruals from firms with negative discretionary accruals. We find no strong
relationship between positive discretionary accruals and public status. On the other hand,
we find a negative and significant relationship between absolute value of negative
discretionary accruals and public status in our three estimations of discretionary accruals.
These results can be interpreted as consequences of the wish for private firms to minimize
their income taxes. This last point confirms our third hypothesis (H3) which states that
private firms manage their earnings downward.
In order to study these motivations more precisely, we analyze accounting policies among
profitable firms, which are the only ones interested in reducing income taxes. They are the
only ones which must pay such taxes. Table 9 reports results of regressions for profitable
firms.
[Insert Table 9]
Our findings confirm the last statement. Listed firms manage earnings upward more
aggressively than private firms, while unlisted firms manage earnings downward more
aggressively than listed firms. This difference can be explained by differences in targeted
earnings between the two categories. Public firms can be more interested in manipulating
their earnings to increase their appearing performance to seduce potential investors on the
market in order to maximize their estimated value than private ones. It can explain why
profitable public firms exhibit more positive discretionary accruals than profitable private
ones. We consider this particular hypothesis in our fourth category of regression, which uses
positive discretionary accruals among profitable firms as an explained variable. We report a
15
positive relationship between a firm being listed and the importance of its positive
discretionary accruals. This result is in accordance with our hypothesis on targeted earnings.
On the other hand, incentives to use positive accruals in order to present more important
net income is less important for private SMEs. These firms might be more interested in
manipulating their earnings to decrease their taxable profit. This reasoning is the base of our
third hypothesis (H3). We therefore expect private SMEs to use more negative discretionary
accruals than public ones. We consider this particular hypothesis in our last category of
regression which uses absolute value of negative discretionary accruals as the explained
variable. Our results are coherent with this explanation, since profitable public firms record
less negative accruals than profitable private firms.
5. Conclusion
In this paper, we study earnings management practices among SMEs in the French context.
The French context is specific in terms of institutional and financial environment. The French
institutional context is characterized by lower investor protection and shareholder rights
than in common law countries. On the other hand, auditing is widespread because most
SMEs have to be audited. Another specific point is the tight link between accounting and
taxation. In this context, we question the influence of quotation on earnings quality, by
comparing accounting policies in public and private SMEs. We use three different
methodologies to test earnings management on a sample of 925 small and medium firms
over the period 2002-2010. These methodologies are earnings distributions’ shape analysis,
multivariate income smoothing, and discretionary accruals regressions analysis.
Our results first indicate that private SMEs manage earnings to avoid small losses, while tests
are not significant for public firms. We also show that earnings smoothing is more
pronounced among private SMEs than among public firms. The analysis of discretionary
accruals reveals that public and private SMEs use discretionary accruals for different
earnings management purposes. Public SMEs more frequently increase their net incomes
through discretionary accruals in order to maximize their appearing performance, whereas
private SMEs manage earnings downward. We assume that private SMEs might adopt such a
policy in order to avoid taxes.
Only a few studies addressed the question of accounting policies in SMEs. To our knowledge,
our research is the first study on earnings management in SMEs in the French context. It
appears as an open avenue of research in accounting. As an example, the impact of
corporate governance on accounting policies in SMEs could be an interesting research
direction.
16
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19
Table 1: Sample descriptive statistics
Panel A : Total sample (925 firms/ 7 451observations)
Mean
Median
Total assets
148 270.9
21 218
Turnover
21 080.67
12 993
Number of employees
80.98
56
Age
23.81
19
Debts over total asset
0.4925
0.4885
Operational income over
0.0265
0.0190
total asset
Net income over total
0.0360
0.0369
asset
Panel B : Public firms (245 firms/1 805 observations)
Mean
Median
Total assets
437 010.3
31 898
Turnover
16 520.36
11 443
Number of employees
79.18
59
Age
22.92
19
Debts over total asset
0.4268
0.4166
Operational income over
0.0189
0.0056
total asset
Net income over total
0.0153
0.0318
asset
Panel C : Private firms (680 firms/ 5 804 observations)
Mean
Median
Total assets
Turnover
Number of employees
Age
Debts over total asset
Operational income over
total asset
Net income over total
asset
Standard
Deviation
887 249.3
35 024.65
58.85
16.36
0.2365
Minimum
Maximum
44
0
20
1
0
23 630 000
851 053
249
113
2.5057
0.1024
-1.0508
0.5815
0.1092
-0.6219
0.3988
Standard
Deviation
1 722 770
22 977.2
54.97
14.77
0.2238
Minimum
Maximum
244
0
20
3
0
23 630 000
501 562
249
113
2.4653
0.1051
-0.6222
0.5261
0.1233
-0.5892
0.3988
Minimum
Maximum
44
0
20
1
0.0007
6 558 000
851 053
249
106
2.5057
55 962.21
22 538.58
81.55
24.09
0.5135
19 448.5
13 787.5
56
20
0.5159
Standard
Deviation
235 085.7
37 966.81
60.03
16.82
0.2366
0.0289
0.0224
0.1014
-1.0508
0.5815
0.0426
0.0382
0.1034
-0.6219
0.3962
20
Panel D : Sectorial repartition of firms and observations
Number Percent
of firms
of firms
Manufacturing primary transformation
62
6.70
Manufacturing secondary
84
9.08
transformation
Others manufacturing activities
22
2.37
Wholesale and retail
226
24.43
Transport, hotel business and catering
65
7.02
Communication, finance and real193
20.86
estate
Services to companies
225
24.32
Health
20
2.16
Human services and leisure activities
12
1.29
Missing values
16
1.72
Total
925
100.00
21
Number of
Percent of
observations observations
510
6.84
664
8.91
173
1 832
544
2.32
24.58
7.30
1 467
19.68
1 860
174
97
130
7 541
24.96
2.33
1.30
1.74
100.00
Table 2: Measures of the left side of earnings’ distribution irregularity (small losses)
This table reports estimations of the earnings’ distribution irregularity associated with small
losses. We consider small losses as earnings ratio (net income t / total asset t-1) value
included between -0.005 and 0. The table displays three series of elements one for the total
sample (panel A), one for public firms (panel B) and one for private firms (panel C). The first
column specifies the method implemented to assess theoretical frequencies of small losses.
The second column indicates empirical frequencies of small losses. The third one provides
estimated frequencies of small losses. The fourth one provides the estimated size of the
irregularity measured through the ratio of the empirical frequency of small losses minus
theoretical frequencies over the theoretical frequencies. The last column displays z-test
values related to test the hypothesis that the difference between empirical frequency and
theoretical frequency is not significantly different from 0.
Panel A : Total sample (925 firms/ 7 451observations)
Method
Empirical
Theoretical
Frequencies Frequencies
Arithmetic mean
109
189
(Burgstahler and Dichev, 1997)
Interpolation Linear
(Dechow, Richardson and Tuna,
109
167.75
2003)
Interpolation Exponential
(Dechow, Richardson and Tuna,
109
128.87
2003)
Interpolation logarithmic
109
194.78
(Vidal, 2008)
Panel B : Public firms (245 firms/1 805 observations)
Method
Empirical
Theoretical
Frequency
Frequency
Arithmetic mean
27
36
Interpolation Linear
27
33.62
Interpolation Exponential
27
30.06
Interpolation logarithmic
27
37.55
Panel C : Private firms (680 firms/ 5 804 observations)
Method
Empirical
Theoretical
Frequency
Frequency
Arithmetic mean
82
153
Interpolation Linear
82
134
Interpolation Exponential
82
95.71
Interpolation logarithmic
82
157.06
Irregularity
z
Test
-0.4232
-5.69***
-0.3502
-4.18***
-0.1542
-1.41
-0.4404
-6.11***
Irregularity
z
Test
-1.35
-1.00
-0.46
-1.59
-0.2500
-0.1970
-0.1018
-0.2810
Irregularity
-0.4640
-0.3880
-0.1433
-0.4779
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
22
z
Test
-5.73***
-4.19***
-1.10
-6.06***
Table 3: Measures of the right side of earnings distribution irregularity (small gains)
This table reports estimations of the earnings’ distribution irregularity associated with small
gains that we consider as an earnings ratio (net income t / total asset t-1) value included
between 0 and 0.005. The table displays three series of elements one for the total sample
(pane A), one for public firms (panel B) and one for private firms (panel C). The first column
specifies the method implemented to assess theoretical frequencies of small gains. The
second column indicates empirical frequency of gains losses. The third one provides
estimated ones. The fourth column provides the estimated measures of the size of the
irregularity measured through the ratio of the empirical frequency of small gains minus
theoretical frequencies over the theoretical frequencies. The last column displays z-test
values related to test the hypothesis that the difference between empirical frequency and
theoretical frequency is not significantly different from 0.
Panel A : Total sample (925 firms/ 7 451observations)
Method
Empirical Theoretical
Frequency Frequency
Arithmetic mean
296
282
(Burgstahler and Dichev, 1997)
Interpolation Linear
296
167.87
(Dechow, Richardson and Tuna, 2003)
Interpolation Exponential
296
138.50
(Dechow, Richardson and Tuna, 2003)
Interpolation logarithmic
296
192.17
(Vidal, 2008)
Panel B : Public firms (245 firms/1 805 observations)
Method
Empirical Theoretical
Frequency Frequency
Arithmetic mean
44
39,50
Interpolation Linear
44
35.50
Interpolation Exponential
44
32.29
Interpolation logarithmic
44
39.36
Panel C : Private firms (680 firms/ 5 804 observations)
Method
Empirical Theoretical
Frequency Frequency
Arithmetic mean
252
242,5
Interpolation Linear
252
132.25
Interpolation Exponential
252
103.80
Interpolation logarithmic
252
152.65
Irregularity
z
Test
0.0496
0.72
0.7632
6.62***
1.1370
8.14***
0.5196
5.37***
Irregularity
z
Test
0.59
1.11
1.53
0.60
0.1139
0.2394
0.3624
0.1176
Irregularity
0.0391
0.9054
1.4277
0.6508
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
23
z
Test
0.53
6.74***
8.34***
5.59***
Table 4: Measures of asymmetry around the threshold of zero net income
This table displays statistics about asymmetry between classes of observation around the threshold of zero net income. In panel A, we report
frequency of small loses (net income t over total asset t-1 between -0.005 and 0), frequency of small gains (the same ratio between 0 and
0.005), and a synthetic asymmetry indicator computed through the ratio of frequency of small gains (the right side of the threshold) minus
frequency of small losses (the left side of the threshold) over the sum of these two frequencies for the total sample and the sub-samples of
public firms and private firms, for each year and the total period. In panel B, we report statistical tests of difference in mean, median and
standard deviation of the indicator of asymmetry for the subsamples of public and private firms. The last columns provide two elements. The
first is the value of the statistics associated with the tests (Student test, Wilcoxon test and Fisher test). The second, reported in brackets, is the
p-values associated with the tests.
Panel A: Annuals composition of asymmetry around the threshold of null net income
Panel A : Total sample
Panel B : Public firms
Asymmetry
Small losses
Small gains
Small losses
Small gains
Indicator
2003
13
40
0.50
1
8
2004
13
31
0.40
4
4
2005
12
34
0.47
1
5
2006
15
30
0.33
5
1
2007
12
33
0.46
2
6
2008
18
36
0.33
5
6
2009
10
44
0.62
3
6
2010
8
36
0.63
3
5
2011
8
12
0.20
3
3
Total period
109
296
0.46
27
44
Panel B: Differences in asymmetry around the threshold between public and private firms
Indicator of asymmetry
t-test
Mean
Public
0.21
1.820*
(0.100)
Private
0.48
Median
Public
0.25
Private
0.45
Standard deviation
Public
0.14
Private
0.04
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
24
Panel C : Private firms
Asymmetry
Indicator
0.77
0.00
0.66
-0.66
0.50
0.09
0.33
0.25
0.00
0.23
Small losses
Small gains
12
9
11
10
10
13
7
5
5
82
32
27
29
29
27
30
38
31
9
252
w-test
Asymmetry
Indicator
0.45
0.50
0.45
0.48
0.45
0.39
0.68
0.72
0.28
0.50
Fisher test
1.506
(0.132)
0.100***
(0.003)
Table 5: Building of income smoothing indicators
This table displays statistical elements that relate to the computation of the income
smoothing indicators mobilized in later analysis. For each variable, it provides mean for the
total sample and groups of public and private firms. The last column reports t-statistics
associated with the Student test for difference in mean between public and private firms.
The first element is the firms operating incomes standard deviation over their total assets
ratio. The second element is the firms operating cash flows standard deviation over their
total assets ratio. Smoothing indicator 1 is the ratio of the first element over the second one.
The fourth element is ratio of the firms’ net incomes standard deviation over their total
assets. The smoothing indicator 2 is the ratio of the fourth element over the second one.
Total
sample
Standard deviation of operating
income over total assets ratio
Standard deviation operating cashflows over total assets ratio
Smoothing Indicator 1 (Leuz et al,
2003)
Standard deviation of net income
over total assets ratio
Smoothing Indicator 2 (Lang et al.,
2006)
Public
firms
Private
firms
t-test
Mean
0.047
0.061
0.043
-11.29***
Mean
0.145
0.228
0.119
-3.697***
Mean
0.462
0.557
0.431
-6.755***
Mean
0.066
0.089
0.059
-18.28***
Mean
0.648
0.812
0.595
-10.66***
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level
25
Table 6: Multivariate analysis of income smoothing
This table displays estimations for OLS regressions of each of our smoothing indicators (built
as defined in table 5) on firms’ characteristics. We consider two types of dependent
variables. The first is the means over the period of Size, the neperian logarithm of firm’s total
assets; Leverage, the total debt over total assets; Growth, net total asset t minus net total
assets t-1 over net total assets t-1; and ROA, operational income over total assets. The second
is composed of dummies variables: Public firm, which take the value one if the considered
firm is a public one in the current year, and Industry controls, a set of dummies variables
associated with business sectors following the one digit NAF2 revised classification. For each
explanatory variable, we report coefficients and the White robust estimated standard
deviation associated with (hereafter in brackets).
constant
Size
Leverage
Growth
ROA
Public firm
Industry
controls
Fisher test
R2 adjusted
Nb.
observations
Smoothing
Indicator 1
Smoothing
Indicator 2
1.865***
(0.252)
-0.117***
(0.017)
-0.047
(0.101)
1.438***
(0.336)
-0.047**
(0.024)
-0.519***
(0.137)
0.004
(0.004)
0.0003
(0.004)
-0.328
(0.473)
0.225***
(0.061)
-1.224***
(0.408)
0.153***
(0.058)
yes
yes
10.95***
0.124
4.69***
0.080
892
892
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
26
Table 7: Total accruals, absolute value of discretionary accruals and their transformations
This table displays mean for the total sample and groups of public and private firms of our
different measure of accruals. The last column reports t-statistic associated with the Student
test for difference in mean between public and private firms. Total accruals are computed as
the difference between operating cash flows and incomes before extraordinary items.
Discretionary accruals 1 and 2 are the estimations of discretionary accruals that we obtained
through the residuals of models 1, 2 and 3. For each, we isolate positive and negative
discretionary accruals which represent accounting choices made to increase and to decrease
the reported net incomes respectively.
Total
sample
Public
firms
Private
firms
t-test
Total accruals
-1 532
-5 013
-378
1.112
Absolute value disc. acc. 1
0.018
0.017
0.018
2.347**
Positive discretionary accruals 1
0.010
0.009
0.010
1.512
Negative discretionary accruals 1
-0.024
-0.023
-0.025
-0.849
Absolute value disc. acc. 2
0.017
0.016
0.017
1.828*
Positive discretionary accruals 2
0.009
0.009
0.009
-0.962
Negative discretionary accruals 2
-0.023
-0.022
-0.024
-3.053***
0.018
0.018
0.019
Positive discretionary accruals 3
0.012
0.012
0.012
0.541
Negative discretionary accruals 3
-0.025
-0.023
-0.025
2.623***
Absolute value disc. acc. 3
2.045**
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates
significance at the 0.01 level.
27
Table 8: Multivariate analysis of discretionary accruals (total sample)
This table displays estimations for OLS regression of discretionary accruals’ absolute value, positive discretionary accruals and negative discretionary accruals’ absolute
value on firms’ characteristics for the total sample. The independent variables are: Size, the neperian logarithm of firm’s total assets; Leverage, the total debt over total
assets; Growth, total asset t minus total assets t-1 over total assets t-1; ROA, operational income over total assets; Public firm, a dummy variable which take the value one if
the considered firm is a public one in the current year; Industry controls, dummy variables associated with business sectors following the one digit NAF2 revised
classification. Lines contain coefficients and the White robust estimated standard deviation associated with (in brackets).
Absolute Value of Discretionary
Accruals
Constant
Size
Leverage
Growth
ROA
Public firm
Industry
controls
Years fixed
effect
Fisher test
R2
adjusted
Nb. of obs.
Positive Discretionary
Accruals
Absolute Value of negative
discretionary accruals
DACC 1
DACC 2
DACC 3
DACC 1
DACC 2
DACC 3
DACC 1
DACC 2
DACC 3
0.0274***
(0.0024)
0.0208***
(0.0021)
0.0182***
(0.0026)
0.0040***
(0.0014)
0.0000
(0.0010)
-0.0009
(0.0016)
0.0189***
(0.0046)
0.0092**
(0.0041)
0.0058
(0.0053)
-0.0001
(0.0002)
0.0039***
(0.0011)
0.0000
(0.0000)
-0.0086***
(0.0028)
-0.0007
(0.0005)
0.0005***
(0.0001)
0.0030***
(0.0010)
-0.0000
(0.0000)
-0.0111***
(0.0022)
-0.0012***
(0.0005)
0.0009***
(0.0002)
0.0031**
(0.0013)
-0.0001***
(0.0000)
-0.0119***
(0.0029)
-0.0019***
(0.0006)
-0.0001
(0.0001)
0.0059***
(0.0008)
0.0001***
(0.0000)
0.0134***
(0.0022)
0.0008*
(0.0004)
0.0004***
(0.0000)
0.0023***
(0.0005)
0.0003**
(0.0001)
0.0055***
(0.0012)
0.0002
(0.0002)
0.0006***
(0.0001)
0.0030***
(0.0008)
0.0003*
(0.0001)
-0.0016
(0.0026)
-0.0004
(0.0004)
0.0010**
(0.0004)
0.0037**
(0.0017)
-0.0001*
(0.0000)
-0.0139***
(0.0044)
-0.0025***
(0.0009)
0.0020***
(0.0003)
0.0048***
(0.0017)
-0.0000
(0.0000)
-0.0130***
(0.0035)
-0.0033***
(0.0007)
0.0024***
(0.0004)
0.0054**
(0.0022)
-0.0001***
(0.0000)
-0.0131***
(0.0045)
-0.0037***
(0.0010)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
16.07***
15.84***
10.65***
9.02***
9.11***
5.71***
7.79***
7.36***
0.054
0.065
0.056
0.081
9.53***
0.057
0.049
0.038
0.053
0.066
6109
5954
4374
2764
2696
2144
3345
3258
2230
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
28
Table 9: Multivariate analysis of discretionary accruals (subsample of profitable firms)
This table displays estimations for OLS regressions of discretionary accruals’ absolute value, positive discretionary accruals and negative discretionary accruals’ absolute
value on firms’ characteristics for the subsample of profitable firms. The independent variables are: Size, the neperian logarithm of firm’s total assets; Leverage, the total
debt over total assets; Growth, total asset t minus total assets t-1 over total assets t-1; ROA, operational income over total assets; Public firm, a dummy variable which take
the value one if the considered firm is a public one in the current year; Industry controls, dummy variables associated with business sectors following the one digit NAF2
revised classification. Lines contain coefficients and the White robust estimated standard deviation associated with (in brackets).
Profitable SMEs
Absolute value of discretionary
accruals
Constant
Size
Leverage
Growth
ROA
Public firm
Industry
controls
Years fixed
effect
Fisher test
R2 adjusted
Nb. of obs.
Profitable SMEs
Positive discretionary accruals
Profitable SMEs
Negative absolute value of
discretionary accruals
DACC 1
DACC 2
DACC 3
DACC 1
DACC 2
DACC 3
DACC 1
DACC 2
DACC 3
0.022***
(0.002)
0.0001
(0.0002)
0.0035***
(0.0012)
0.0001***
(0.0000)
0.0004
(0.0045)
-0.0015**
(0.0006)
0.0183***
(0.0024)
0.0007***
(0.0002)
0.0021*
(0.0011)
-0.0002
(0.0001)
-0.0048
(0.0031)
-0.0020***
(0.0005)
0.0152***
(0.0030)
0.0011***
(0.0002)
0.0020
(0.0014)
-0.0002
(0.0001)
-0.0040
(0.0040)
-0.0027***
(0.0006)
0.0030*
(0.0017)
-0.0001
(0.0001)
0.0066***
(0.0009)
0.0001***
(0.0000)
0.0175***
(0.0034)
0.0012**
(0.0005)
0.0011
(0.0011)
0.0003***
(0.0000)
0.0028***
(0.0005)
0.0003**
(0.0001)
0.0030
(0.0019)
0.0007***
(0.0003)
0.0000
(0.0018)
0.0005***
(0.0001)
0.0032***
(0.0008)
0.0002*
(0.0001)
-0.0040
(0.0036)
0.0000
(0.0005)
0.0116**
(0.0048)
0.0016***
(0.0004)
0.0053**
(0.0021)
-0.0129***
(0.0022)
-0.0035
(0.0077)
-0.0029***
(0.0010)
0.0049
(0.0050)
0.0023***
(0.0004)
0.0048**
(0.0021)
-0.0092***
(0.0017)
-0.0044
(0.0053)
-0.0040***
(0.0009)
-0.0003
(0.0065)
0.0030***
(0.0005)
0.0049*
(0.0027)
-0.0049***
(0.0013)
-0.0019
(0.0066)
-0.0048***
(0.0011)
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
yes
15.23***
0.048
5014
11.08***
0.054
4883
7.33***
0.049
3616
7.79***
0.084
2432
8.57***
0.056
2364
8.20***
0.050
1857
7.01***
0.063
2582
7.14***
0.067
2519
6.23***
0.081
1759
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
29
Figure 1: Distribution of annual net income (925 firms/ 7 451observations)
histogram of the net income's distribution
mean
200
0
100
Frequency
300
zero
-.2
Net income over
total assets in t-1
-.1
0
.1
Net Income over Total Assets t-1
Mean
Median
0.045
0.040
Standard Skewness
Deviation
0.111
-0.724
.2
Kurtosis
W of Shapiro Wilk
8.327
0.882***
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.0 1 level.
30
Figure 2: Distribution of annual net income for public firms (245 firms/1 805 observations)
histogram of the net income's distribution (Public firms)
40
20
0
Frequency
60
zeromean
-.2
Net income over
total assets in t-1
-.1
0
.1
Net Income over Total Assets t-1
Mean
Median
0.027
0.035
Standard Skewness
Deviation
0.128
-0.881
.2
Kurtosis
W of Shapiro Wilk
6.414
0.918***
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.01 level.
31
Figure 3: Distribution of annual net income for private firms (680 firms/ 5 804 observations)
histogram of the net income's distribution (Private firms)
mean
150
100
0
50
Frequency
200
250
zero
-.2
Net income over
total assets in t-1
-.1
0
.1
Net Income over Total Assets t-1
Mean
Median
0.050
0.041
Standard Skewness
Deviation
0.105
-0.547
.2
Kurtosis
W of Shapiro Wilk
9.147
0.867***
* indicates significance at the 0.1 level, ** indicates significance at the 0.05 level, *** indicates significance at the 0.0 1 level.
32