Earnings management and the
informational and disciplining role of debt:
evidence from Iran
Arash Ghorbani and Mahdi Salehi
Abstract
Purpose – The agency theory predicts that there are conflict of interests between managers and
shareholders over free cash flow and major operating decisions. Earnings management can help
managers hide and retain their private benefits of control. Given that, the purpose of this study is to
investigate whether financial leverage reduces agency and information problems caused by earnings
management.
Design/methodology/approach – The research uses a sample of annual data of 200 firms listed on the
Tehran Stock Exchange during 2002-2016. The data required is obtained from the Rahavard Novin
database. The research uses multivariate regression models that regress financial leverage on earnings
management proxies and other determinants of capital structure.
Findings – The research documents that firms with higher income smoothing and the absolute value of
discretionary accruals, as the proxies for earnings management, have higher financial leverage. The
results suggest that a higher level of financial leverage can discipline managers and generate useful
information about firm quality.
Arash Ghorbani is based at
the Department of
Economics and
Administrative Sciences,
Islamic Azad University,
Bojnourd, Iran.
Mahdi Salehi is based at
the Department of
Economics and
Administrative Sciences,
Ferdowsi University of
Mashhad, Mashhad, Iran.
Originality/value – The study highlights the informational and disciplining role of debt in the presence of
severe uncertainty about firm quality in a developing country.
Keywords Agency conflicts, Capital structure, Earnings management
Paper type Research paper
1. Introduction
The theory of capital structure explains how asymmetric information and agency costs play
roles when the firm chooses between alternative sources of finance. The pecking order
theory predicts that information asymmetry between the firm’s insiders and outsiders
determines capital structure decisions (Myers and Majluf, 1984). Information asymmetry
increases the costs of financing, which implies that the use of internal sources should be
preferred over external financing. The theory also proposes that firms should favor debt
over equity as the latter, which brings external ownership into the firm, signals the capital
market that the firm’s share price is overvalued. From the perspective of the agency theory,
however, the agency cost is a determinant of capital structure. The firm increases its
leverage when agency problems are meaningful/major because it reduces agency costs of
free cash flow or convinces shareholders of the managers’ ability to generate sufficient
earnings to repay their debt (Jensen, 1986; Harris and Raviv, 1990).
Previous studies have shown that the presence of extreme agency costs and asymmetric
information affect the choice of a capital structure by a firm. Harvey et al. (2004) report
empirical evidence on the role of financial leverage as a governance mechanism. Ross
(1977) suggests that if the manager possesses inside information, then the choice of a
DOI 10.1108/JABS-11-2019-0336
© Emerald Publishing Limited, ISSN 1558-7894
Received 12 November 2019
Revised 12 January 2020
22 April 2020
Accepted 21 May 2020
j JOURNAL OF ASIA BUSINESS STUDIES j
financial structure signals information to the capital market. Rehman (2018) reports that
during losses and financial crises, firms do not follow a target level of leverage policy. While
some empirical studies conducted by Iranian researchers also have reported the effects of
asymmetric information and agency costs on financial leverage (Setayesh et al., 2011;
Kordestani and Fadaee Koloozi, 2012), no attention has been paid yet to the possible
influence of earnings management on the capital structure. Healy and Wahlen (1999) define
earnings management as the alteration of financial report by insiders/managers to either
mislead outsiders about the firm’s true economic performance or to influence contractual
outcomes. If earnings management intensifies agency and information problems, then one
may ask whether it influences the firm’s capital structure. To address this question, the
present research seeks to examine the effect of income manipulation on the capital
structure using data from listed public companies in Iran. The worldwide literature on
earnings management suggests that managers may engage in income-increasing earnings
manipulation to avoid debt covenant violations (Watts and Zimmerman, 1986; Defond and
Jiambalvo, 1994). This may make any attempt to establish a causality relationship from
earnings management to financial leverage subject to endogeneity concerns. The purpose
of our study, nonetheless, is to explore an interesting ceteris paribus association between
those two variables after controlling for potential endogeneity. Our examination relies on this
argument that firms with higher earnings management activities suffer higher agency costs
due to severe information asymmetry and mismatch of interests between managers and
shareholders. Given the influence of income manipulation on agency costs and information
asymmetry, we hypothesize that holding other factors constant earnings management can
explain the cross-sectional variation of financial leverage.
The agency theory and pecking order theory have contributed to the development of our
research hypothesis. We argue that the disciplining function of debt, the informational role
of debt and the higher costs of equity financing when asymmetric information is present,
individually or together, suggest that the financial leverage is likely to be positively
associated with earnings management. From the perspective of the agency theory, Jensen
(1986) predicts that there is a conflict of interests between managers and shareholders, as
managers are unwilling to pay out free cash flow to shareholders as the dividend. They also
have incentives for overinvestment in low-return projects to increase their power and
compensation. The presence of such a conflict of interest suggests that managers have
incentives to hide their private benefits of control from outsiders (Leuz et al., 2003). Earnings
management can help managers conceal their private information about the firm’s true
underlying economic performance and thereby reduce the possibility of outsiders’
intervention (Leuz et al., 2003; Harvey et al., 2004). The detection of managers’ private
benefits of control, however, may motivate some or all categories of equity owners
(including atomistic shareholders, institutional investors and large-block shareholders) to
restrict their arbitrary actions. Regarding that, McConnell and Servaes (1995) propose that
shareholders have two solutions to this problem. First, they may demand or force selfserving managers to issue debt, as it discourages overinvestments. Second, they are
disinclined to contribute equity funds in the future.
Scholars view the institutional environment and debt as the alternative or complementary
mechanisms for mitigation of agency costs. However, why should shareholders of Iranian
firms that face high managerial agency costs prefer higher leverage, given that highly
leveraged firms can incur the risk of bankruptcy? We argue that Iran’s Legal system that is
close to French civil and commercial laws plays a role in this regard (Baldwin, 1973; Owsia,
1991). French civil law countries are believed to provide the least protection for both
shareholders and creditors compared with common law countries (La Porta et al., 1997).
The cross-country studies suggest that debt as a substitute governance mechanism helps
the reduction of agency and information problems, especially in those countries where
investor protection is weak (Leuz et al., 2003; An et al., 2016). The study of Harvey et al.
(2004) also show that firms suspected of having extreme agency costs use higher financial
j JOURNAL OF ASIA BUSINESS STUDIES j
leverage as a governance mechanism. They suggest that debt creates value because it
discourages overinvestments or signals that the manager is willing to be monitored by
lenders. Moreover, domestic firms are expected to be less diversified and have a lesser
degree of risk tolerance compared to multi-national firms. The study of Khaw (2019)
documents that domestic companies are more likely to sustain more long-term debt than
multi-national companies to reduce the costs related to agency problems. Thus, we expect
that, given the relative weakness of the Institutional environment in Iran, the use of debt as
an alternative mechanism for investor protection is likely to serve the interests of
shareholders against misaligned managerial incentives.
We also expect that the informational role of debt explains the effect of earnings
management on the capital structure. Debt encourages the mitigation of agency conflicts in
another way. Harris and Raviv (1990) propose a theory that predicts that the mere ability of
the firm to repay its debt generates information about its quality, as it signals that the firm
generates sufficient earnings to repay its debt. Thus, the implication of the theory for our
study is as follows. Earnings management reduces the informativeness of financial
statements and thereby diminishes the ability of the capital market to observe the firm’s
quality. This increases the firm’s risk and contributes to the share price falling. As the quality
of the firm can be observed by its ability to service the debt, the higher leverage can help
mitigate the loss in the firm value. Furthermore, consistent with the pecking order theory,
while the firm’s outsiders know less about its prospects, risks, and value and earnings
management activities intensify this information asymmetry, the manager may favor higher
leverage as it signals his/her confidence that the firm is profitable (a positive signal). The
manager also anticipates that outsiders may interpret external equity financing as a lack of
confidence and overvaluation (a negative signal). Combined with the higher costs of
financing through equity due to the information asymmetry caused by earnings
management, this again makes the external equity financing disproportionately even less
desirable than debt. This analysis suggests that firms with higher income manipulation are
likely to have higher financial leverage.
The rest of this paper is organized as follows. Section 2 gives an overview of previous
research and develops the hypothesis. Section 3 describes the research methodology.
Section 4 reports our empirical results, and Section 5 concludes.
2. Literature review and hypothesis development
Jensen and Meckling (1976) provide a theoretical framework that explains why and how
agency costs determine the capital structure of publicly held firms. Agency costs are
generated in the agency relationship between manager and shareholders and due to the
separation of ownership and control. As a result of such an agency relationship, Jensen
(1986) and Hart and Moore (1994) suggest that debt contracts can prevent managers from
inefficient investments of free cash flows. Free cash flows, by definition, are excessive cash
resources that have no place of consumption for funding profitable projects elsewhere.
Jensen (1986) predicts that managers prefer to overinvest free cash flow in less profitable
projects rather than distributing them among shareholders. Paying free cash flow to
shareholders drains resources under the control of the manager. These resources can be
used by the manager for growing the size of the firm beyond its optimal size, as firm growth
increases the manager’s compensation. Disgorging free cash flows also may increase the
need for external financing, which raises the possibility of more intensive supervision in the
future.
Literature suggests that debt creation alleviates the agency costs of free cash flow in three
ways. First, debt can function as an external control mechanism that establishes more
supervision and restrictions on earnings (Jensen and Meckling, 1976; Harris and Raviv,
1990; Harvey et al., 2004). Second, debt commitments, in the form of debt repayments, can
decrease or prevent the necessary funds from being available to the manager to invest
j JOURNAL OF ASIA BUSINESS STUDIES j
arbitrarily or waste (Hart and Moore, 1994). Third, the risk that the firm may be unable to
meet its obligations heightens the risk of legal intervention, which works as a motivational
force to make the firm more efficient (Jensen, 1986). In the presence of agency costs of free
cash flow, Hart and Moore (1994) also suggest that, in firms with low financial leverage, it is
more easily for managers to finance low-return projects by transferring future profits to the
current period. The advantage of debt is that it holds as mortgage the return derived from
fixed assets, which prevents managers from using them for investing in inefficient projects.
Earnings management is a way for managers and insiders to hide their private information
from shareholders and outsiders. Such private information can help them retain and extract
the benefits they hold from the control of firms’ assets in place (Leuz et al., 2003).
Burgstahler and Dichev (1997) document that managers tend to avoid reporting losses or
decline in earnings through earnings manipulation. DeFond and Park (1997) show that
managers tend to smooth earnings through accrual management. Alareeni (2018)
documents that when the possibility of loss increases, managers are more likely to engage
in earnings management. Either by hiding the losses or by decreasing the earnings
volatility, managers can lower the possibility of outsiders’ intervention. Hence, when the
manager frequently engages in earnings management activities, he or she has some
private benefits which are not in line with the interests of shareholders (Salehi et al., 2019).
This suggests that earnings management can be indicative of the mismatch of interests and
severity of agency conflicts between managers and shareholders (Salehi et al., 2018).
When shareholders detect such private benefits, they may institute some measures to lower
the increased agency costs (La Porta et al., 1997; Shleifer and Wolfenzon, 2002; Kardan et al.,
2016). Institutional environment mechanisms provide some measures for this purpose.
Institutional environments include regulation, customs and norms that form the organizational
behavior and its results. The Institutional environment shapes and limits organizational actions
and policies (Swaminathan and Wade, 2016; Fiechter and Novotny-Farkas, 2017; Anginer,
Demirgüç-Kunt and Mare, 2018). Among these institutional environment mechanisms is the
right legal systems reserve for shareholders to discipline the manager (e.g. the right to fire the
manager) or sign a contract to monitor hi/her private benefits. Leuz et al. (2003) present some
evidence as to how the institutional environment lowers the manager’s willingness for
manipulating earnings. Financial leverage is a costly complement mechanism that can reduce
agency costs. Harvey et al. (2004) suggest that financial leverage contributes to the mitigation
of agency costs in those environments where agency costs are expected to be high
n-Peramato
(especially in the developing countries where investor protection is weak). Villaro
et al. (2018) provide some evidence of the role of debt as a control mechanism against the
arbitrary behaviors of the manager to secure his/her position.
The informational role of debt also can explain the benefits of more debt in the capital
structure. As Harris and Raviv (1990) suggest, debt generates useful information about the
firm and quality of its manager in two ways:
1. First, debt reduces the unobservability of income for outsiders and shareholders, as
repayment of the debt may be perceived as indicative of the excess of the income over
the required payment.
2. Second, default is likely to lead to an investigation by creditors which results in the
revelation of current and past true income.
Shareholders, thus, can exploit the generated information for disciplining managers and
earning a say in implementing efficient operating decisions (Harris and Raviv, 1990).
An et al. (2016) find that there is a positive relationship between earnings management and
financial leverage. They suggest that the finding shows that firms with higher earnings
management activities have higher financial leverage, as they suffer more agency costs
and information asymmetry. In this paper, we suggest that, along with the role of debt in the
j JOURNAL OF ASIA BUSINESS STUDIES j
reduction of agency costs, the informational role of debt combined with the higher costs of
external equity financing may explain additional benefits of debt in the presence of severe
uncertainties about the firm quality and its financial reports. We predict that holding other
factors constant a higher level of debt is observed in firms with a higher level of earnings
management. The research hypothesis, therefore, is developed as follows:
Research hypothesis:
H1. Financial leverage tends to be higher in firms with higher earnings management
activities.
3. Research methodology
3.1 Research data and sample
The research sample includes all Iranian listed firms on the Tehran Stock Exchange for
which the required annual data from 2001 to 2016 are available in the Rahavard Novin
database. Investment firms, financial institutions and banks are excluded from the sample.
We also drop industries and firms with no sufficient data for estimating industry-specific
accruals models and other required firm-level variables. All firm-year observations are
scaled by the average total assets. All variables are winsorized at the 1% and 99% levels.
Our sample selection process leads to a sample of 2,832 firm-years from 200 unique firms
and 17 industries over the period 2002-2016. We use this sample for estimating the
earnings management measures.
3.2 Empirical model
In this paper, to test the impact of earnings management on the capital structure (proxied
by financial leverage), the following empirical regression model is used:
F LE V i;t ¼ a þ b 1 E M i;t1 þ
X
m k X k;i;t1 þ w DBV i;t1 þ indk þ yeart þ ei;t
(1)
where the definitions of variables are as follows:
FLEV: financial leverage, which is the debt to total assets ratio.
EM: earnings management proxy. In this paper, seven measures for earnings management
are used, which are: JDA: the average of the absolute value of discretionary accruals from the
Jones (1991) model over the past five years. MJDA: the average of the absolute value of
discretionary accruals from the modified Jones model, as introduced by Dechow et al. (1995),
over the past five years. EXJDA: the average of the absolute value of discretionary accruals
from an extended version of the Jones model over the past five years. EXMJDA: the average
of the absolute value of discretionary accruals from the extended modified Jones model over
the past five years. ISMTH: our first measure for income smoothing effort. CORR: the second
measure for income smoothing effort. FCP: the overall measure for earnings management,
which is the first principal component of all the above-mentioned earnings management
proxies. The definitions of EM proxies are explained in the next section.
X: the firm-level control variables of capital structure, including SIZE: firm size, which is
defined as the logarithm of total assets. DSM: growth opportunity, which is the difference
between sales and median sales in the industry. PROF: firm profitability, which is operating
income scaled by average total assets. TANG: tangibility, which is the ratio of fixed tangible
assets to average total assets.
DBV: a dummy variable with the value of one if equity is negative and value of zero,
otherwise. We include in the model this variable to control for the impact of negative equity
observations.
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We also include in the model ind and year as the control for industry fixed effects and year
fixed effects, respectively. We confirm that the same results are obtained if firm fixed effects
and year fixed effects are controlled instead.
Following An et al. (2016), to mitigate the concerns that the results are driven by the possible
reverse causality and endogeneity biases, we use the past five-year average of unsigned
discretionary accruals, lag all earnings management proxies by one year in the leverage
regressions and Additionally use the fixed effects of industry and year to control for
unobserved heterogeneities. The reverse causality bias may arise due to the bi-directional
relationship between financial leverage and earnings management. As we examine the impact
of the lagged value of earnings management proxies on the financial leverage, and the
unsigned discretionary accruals are their averages over the past five years, the reverse
causality problem is alleviated to some extent [1]. The endogeneity bias may occur if the
dependent variable (financial leverage) and independent variable (earnings management)
change due to an unobserved variable (omitted variable) which is not included in the model.
We deal with the endogeneity problem by controlling for the fixed effects of industry and year,
which can capture the effects of the unobserved heterogeneities.
3.3 Earnings management measures
In this paper, we use earnings management as a measure for agency conflicts between
shareholders and management. Discretionary accruals are frequently used as a measure
for earnings management. We define JDA and MJDA as the past five-year average of
the absolute value of residuals from the industry-specific Jones model and modified Jones
model, respectively. Following Dechow et al. (2012) and Christodoulou and Sarafidis
(2008), we use a panel version of the accruals models.
TAi;t ¼ b0 þ b1 1=Ai;t þ b2 DSi;t þ b3 PPEi;t þ « i;t
Jones model
TAi;t ¼ b0 þ b1 1=Ai;t þ b2 DSi;t DARi;t þ b3 PPEi;t þ « i;t
Modified Jones
model where the definitions of variables are as follows:
TA: total accruals. Total accruals are measured as the difference between net income and
cash flows from operation (CFO), scaled by average total assets. 1/A: One scaled by
average total assets. DS: Change in sales revenue scaled by average total assets. PPE:
Gross property, plant and equipment scaled by average total assets. DAR: Change in
accounts receivable scaled by average total assets. Following Kothari et al. (2005, p. 173),
an Intercept is included in the accruals models, for additional control for heteroscedasticity.
In this paper, we also use two additional measures of earnings management. We found that
there is a positive serial correlation in the time series of both total accruals and discretionary
accruals in our sample, which indicates that the standard accrual models fail to fully
separate nondiscretionary accruals from discretionary accruals. Given that the true
discretionary accruals must reverse in subsequent periods, the times series of residuals
from accrual models are expected to be negatively autocorrelated (Allen et al., 2013).
Kothari et al. (2005) suggest that economic reasons may cause total accruals to be
correlated, which can result in serially correlated estimates of discretionary accruals. As a
proportion of current accruals is predictable based on the past year’s accruals,
Dechow et al. (2003) include in their accruals model the first lag of total accruals.
Accordingly, in this paper, we also use an extended version of both the Jones model and
the modified Jones model, which includes the lagged value of total accruals. EXJDA and
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EXMJDA are defined as the past five-year average of the absolute value of residuals from
the extended Jones model and the extended modified Jones model, respectively.
TAi;t ¼ b0 þ b1 1=Ai;t þ b2 DSi;t þ b3 PPEi;t þ b4 TAi;t1 þ « i;t
Extended Jones model
TAi;t ¼ b0 þ b1 1=Ai;t þ b2 DSi;t DARi;t þ b3 PPEi;t þ b4 TAi;t1 þ « i;t
Extended modified Jones model.
The definitions of the variables in the extended models are similar to the previous ones.
Income smoothing is our next measure for earnings management. Income smoothing is the
manager’s attempt to hide earnings volatilities and economic shocks through accruals. In this
paper, following Leuz et al. (2003), we construct two measures for income smoothing. The first
income smoothing measure (ISMTH) is computed as the standard deviation of operating income
scaled by the standard deviation of operating cash flows over the past five years, multiplied by
1. We require at least three observations for each firm. ISMTH measures the reduction of
reported earnings variability due to the manager’s exercise of discretion over accruals.
The second measure for earnings smoothing (CORR) is constructed as the correlation between
changes in total accruals (i.e. DTA) and changes in (i.e. DCFO) over the past five years,
multiplied by 1. Again, we require at least three observations for each firm. CORR measures
the extent manager disguises the effect of economic shocks on cash flows by exercising
discretion over accruals. For example, when there is a positive economic shock, the manager
can report less income to preserve earnings for future periods. Similarly, in a case where a
negative economic shock takes place, the manager can transfer a proportion of current period
costs to the future to hide the poor performance of the current period. In both cases, accounting
accruals absorb cash flow surprises, which results in the generation of a negative correlation
between cash flows and accruals. Though Dechow (1994) suggests that there is a negative
correlation between accruals and cash flows due to the nature of accrual accounting processes,
larger negative correlations are likely to be indicative of income smoothing (Leuz et al., 2003).
Multiplying the income smoothing variables by 1 means that higher value of ISMTH and CORR
imply higher income smoothing.
Finally, FCP is the first principle component of all the earnings management proxies (i.e.
JDA, MJDA, EXJDA, EXMJDA, ISMTH and CORR). This variable is used as our overall
measure for earnings management.
3.4 Control variables
In this paper, we expect a ceteris paribus relationship between financial leverage and
earnings management measures. It implies that the leverage regression model must
include some control variables for other determinants of capital structure. We use the
following variables as control variables in our research model.
PROF: Profitability. Based on the pecking order theory, the cost of financing rises when
information asymmetry increases. Outsiders (i.e. creditors and investors) have less
information about firm quality than the manager, which implies that the firm faces a higher
cost of financing through external funds. As a consequence, the theory predicts that internal
funds, which are generated form operation, are the first source of finance. Thus, we expect
that there is an inverse relationship between financial leverage and profitability ratio, as the
higher the firm’s profitability, the lower the probability of using external financing.
SIZE: Firm size. Large firms are less likely to be bankrupted and are diversified enough to
generate stable cash flows. Furthermore, information asymmetry is lower for them due to
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their analyst coverage. Therefore, we expect large firms to have higher financial leverage,
which implies that there is a positive relationship between firm size and financial leverage.
DSM: Given that sales are scaled by total average assets in our sample, DSM is defined as
sales minus median sales in the industry. We use this variable as a proxy for growth
opportunities. While the change in sales (DS) exhibits a white noise behavior in our sample,
sales that are higher/lower than median sales in the industry are persistent. It suggests that
DSM can be used as a proxy for future growth opportunities. We prefer this measure of
growth opportunity over the market to book ratio, as we believe the latter is a noisy measure
of growth opportunity due to the effects of the chronic inflation over the past forty years in
Iran. As high-growth firms are mostly in the growth stage of their life cycle, it is less likely
that their internal funds (i.e. retained earnings) to be sufficient to support their growth needs.
Thus, we expect such firms to have higher leverage.
Tang: Tangibility, which is defined as the ratio of fixed tangible assets to average total
assets. We predict that firms with higher tangible assets ratio are less inclined to debt
creation. First, because of their large noncash depreciation costs and tax reduction, such
firms have large cash flows to be used in their internal financing. Second, a higher level of
tangibility can be indicative of higher competitive advantage, higher managerial ability and
higher technology (Ball and Foster, 1982). These capabilities can lead to a lower production
cost per unit and higher profitability. Thus, we expect a positive relationship between
profitability and tangibility. As it is expected that there is an inverse relationship between
leverage and profitability, we predict an inverse relation between tangibility and leverage.
4. Findings
4.1 Descriptive statistics
Table 1 presents the summary statistics for the variables used in this study. On average,
financial leverage is large (mean = 0.704) in our sample, mainly because of the upward bias
that is generated due to using the historical value for assets as the denominator of the ratio.
Non-positive equity firms have leverage ratios with values more than one (throughout the
sample 277 observations have negative equity). We did not exclude negative equity
observations, as dropping them did not change the results. Furthermore, the informational role
of debt is more important in a negative equity firm, as Harris and Raviv (1990) suggest,
shareholders can exploit the information that debt generates about firm quality to decide
whether to liquidate the firm or continue current operations. However, we use some measures
to reduce the noise of negative equity observations. First, a dummy variable is included in the
leverage regression model to control for negative equity observations. Second, a censored
Tobit regression model is used, which censors the dependent variable from above.
Table 2 reports the Pearson pair-wise correlations between the research variables using a
uniform sample. Financial leverage is positively correlated with both measures of income
smoothing (i.e. CORR and ISMTH). It is also positively correlated with the discretionary
accrual measures (i.e. JDA and EXJDA). The positive correlation between CORR and
ISMTH indicates that, on average, firms with lower earnings variability due to accruals
exhibit a more negative correlation between changes in accruals and changes in cash
flows. There is a significantly positive correlation between JDA and income smoothing
measures. EXJDA similarly exhibits a positive correlation with CORR and ISMTH which
implies that, on average, firms with higher income smoothing have higher unsigned
discretionary accruals and vice versa.
4.2 Findings
Table 3 presents the results of estimating model 1 with our seven measures of earnings
management being included in the model individually as an explanatory variable. We
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Table 1 The results of descriptive statistics of variables
FLEV
JDA
MJDA
EXJDA
EXMJDA
ISMTH
CORR
FCP
PROF
DSM
SIZE
TANG
Mean
Median
Min
Max
Std
Skew
Kurt
N
0.710
0.107
0.110
0.097
0.100
0.851
0.773
0.001
0.110
0.092
13.48
0.267
0.708
0.098
0.101
0.088
0.091
0.740
0.882
0.323
0.087
0.001
13.39
0.219
0.267
0.012
0.011
0.013
0.013
2.779
0.207
4.070
0.262
0.818
10.27
0.005
1.255
0.315
0.314
0.300
0.298
0.128
0.998
8.187
0.635
1.974
17.74
0.888
0.226
0.050
0.051
0.046
0.047
0.533
0.270
1.954
0.169
0.516
1.384
0.193
0.184
0.936
0.884
1.102
1.046
1.289
1.800
0.884
0.647
1.104
0.625
1.063
0.154
0.791
0.586
1.381
1.200
1.837
2.892
0.899
0.965
1.748
0.790
0.765
2,832
1,995
1,995
1,995
1,995
1,995
1,775
1,775
2,832
2,832
2,832
2,832
Notes: The table represents the descriptive statistics of the research variables. The required data are
obtained from the Rahavard–Novin database. All variables are winsorized at the 1st and 99th
percentiles. The sample period is from 2007 to 2016 (2008 to 2016) for all the discretionary accruals
measures and ISMTH (CORR), and from 2002 to 2016 for the remaining variables. The definitions of
variables are as follows: FLEV: financial leverage, which is defined as debt to assets ratio. JDA:
earnings management variable, which is the past five-year average of the absolute value of residuals
from the Jones model. MJDA: earnings management variable, which is the past five-year average of
the absolute value of residuals from the modified Jones model. EXJDA: earnings management
variable, which is the past five-year average of the absolute value of residuals from the extended
Jones model. EXMJDA: earnings management variable, which is the past five-year average of the
absolute value of residuals from the extended modified Jones model. ISMTH: income smoothing
measure, which is computed as the standard deviation of operating income scaled by the standard
deviation of CFO over the past five years, multiplied by 1. CORR: income smoothing measure,
which is constructed as the correlation between changes in total accruals and changes in operating
cash flows over the past five years, multiplied by 1. FCP: the overall measure of earnings
management, which is the first principle component of all the earnings management proxies used in
this study. PROF: profitability, which is operating income scaled by average total assets. DSM:
growth opportunity, which is defined as sales minus median sales in the industry. SIZE: firm size,
which is the logarithm of total assets. TANG: tangibility, which is the ratio of fixed assets to average
total assets
estimated the model using ordinary least squares with Newey–West standard error for
correcting heteroscedasticity and autocorrelation. Consistent with the research
hypothesis, the coefficients on all unsigned discretionary accruals measures, as
measures of earnings management, are positive and significant at least at the 1% level.
Specifically, the coefficients on JDA, MJDA, EXJDA and EXMJDA are 0.374, 0.387,
0.443 and 0.447, respectively. The results suggest that, on average, firms with higher
unsigned discretionary accruals tend to have higher financial leverage. Similarly, the
coefficient on the first measure of income smoothing (ISMTH) is positive and significant
(coeff = 0.32, p < 0.01). The results, however, are not robust to the second measure of
income smoothing (CORR). The coefficient on FCP (the overall measure of earnings
management) is significantly positive (coeff = 0.011, p < 0.01). The standard deviation
for FCP is 1.955 (Table 1), so one standard deviation increase in FCP results in a 2.15%
(= 0.011 1.955) increase in financial leverage (FLEV). Given that the mean value of
FLEV is 0.710, a 2.15% increase is responsible for 1.53% (= 1.56 0.71) change of
FLEV for an average firm in our sample. This analysis shows that the impact of earnings
management on financial leverage is also economically significant. If we exclude
CORR, the significant coefficients on the lagged value of EM measures imply that
earnings management provides additional information about future capital-structure
decisions. They also suggest that firms with higher information need of shareholders
and/or higher agency conflicts (both proxied by earnings management) tend to have
higher financial leverage.
j JOURNAL OF ASIA BUSINESS STUDIES j
Table 2 Pearson correlation coefficients
FLEV
JDA
EXJDA
ISMTH
CORR
FCP
PROF
DSM
SIZE TANG
FLEV
JDA
0.113
EXJDA 0.113 0.983
ISMTH 0.088 0.130 0.103
CORR 0.047
0.152 0.144 0.510
FCP
0.136 0.949 0.945 0.275 0.318
PROF 0.568 0.002
0.009 0.062 0.075 0.056
DSM 0.088 0.022 0.034 0.041 0.010
0.029 0.334
SIZE
0.055 0.043 0.040
0.073 0.057 0.041 0.078 0.086
TANG 0.108 0.021
0.014 0.105 0.181 0.027 0.049
0.007 0.044
Notes: The table reports Pearson correlations among the research variables. The sample period is
from 2008 to 2016 (n = 1,575). Superscripts ; and denote the significance levels of 1%, 5% and
10%, respectively. The definitions of variables are as follows: FLEV: financial leverage, which is the
debt to assets ratio. JDA: the past five-year average of the absolute value of residuals from the Jones
model. EXJDA: the past five-year average of the absolute value of residuals from the extended Jones
model. ISMTH: income smoothing measure, computed as the standard deviation of operating
income scaled by the standard deviation of CFOover the past five years, multiplied by 1. CORR:
income smoothing measure, computed as the correlation between changes in total accruals and
changes in operating cash flows over the past five years, multiplied by 1. FCP: the overall measure
of earnings management, which is the first principle component of all the earnings management
proxies used in this study. PROF: profitability, which is operating income scaled by average total
assets. DSM: opportunity growth, which is the firm’s sales scaled by average total assets minus the
industry’s sales median scaled by average total assets. SIZE: firm size which is the logarithm of total
assets. TANG: tangibility, which the ratio of fixed assets to average total assets
Table 3 also shows that the coefficients on all control variables in all regressions are
consistent with our predictions, as well. The significantly positive coefficient on SIZE
indicates that large firms tend to have higher financial leverage, consistent with their lower
possibility of bankruptcy and lower information asymmetry due to more analyst coverage.
DSM is also positively significant (p < 0.0001). It indicates that firms with above-median
sales in their respective industries do not generate adequate internal funds to satisfy their
own growth needs. Consistent with pecking order theory, the coefficient on profitability
(PROF) is negatively significant (the minimum coeff in all regressions = 0.706, p < 0001).
Additional analysis shows that one standard deviation increase in profitability leads to at
least an 8.5% decrease in financial leverage for average firms. The negative coefficient on
TANG supports our intuition that firms with higher tangibility are less likely to have high
financial leverage. It indicates that firms with higher tangibility, which have higher tax saving
and noncash depreciation costs, generate adequate internal funds to be used in their
financing (The correlation between CFO and TANG is 0.807 in our sample).
4.3 Results robustness
4.3.1 Tobit regression model. To reduce the noise of non-positive equity observations, we
also used a censored Tobit regression. Table 4 presents the results of the Tobit regression
model, in which the dependent variable (FLEV) is censored from above (i.e. for FLEV > 1). All
Tobit regressions include industry fixed effects and year fixed effects. Robust standard errors
are computed using Huber–White standard errors. The output for each model includes a x 2
statistic for testing the joint null hypothesis that none of the predictors has any effect on the
dependent variable. As shown in the table, the results again are qualitatively similar to our
basic results. The coefficients on all earnings management measures have the expected signs
and are statically significant at least at the1% level except for CORR. Furthermore, the
coefficients on all control variables also have expected signs and significant.
j JOURNAL OF ASIA BUSINESS STUDIES j
j JOURNAL OF ASIA BUSINESS STUDIES j
þ
þ
þ
þ
þ
þ
þ
þ
þ
–
–
þ
‫؟‬
0.032 (3.593)
0.061 (4.195)
0.714 (15.67)
0.122 (2.784)
0.307 (11.80)
0.372 (2.837)
0.496
1,795
0.374 (2.251)
0.032 (3.596)
0.062 (4.215)
0.714 (15.58)
0.122 (2.783)
0.309 (11.80)
0.344 (2.865)
0.497
1,795
0.387 (2.394)
0.032 (3.611)
0.061 (4.115)
0.706 (15.21)
0.121 (2.774)
0.314 (12.53)
0.351 (2.837)
0.497
1,795
0.443 (2.711)
0.032 (3.615)
0.061 (4.142)
0.707 (15.19)
0.121 (2.772)
0.314 (12.58)
0.351 (2.837)
0.497
1,795
0.447 (2.705)
Dependent variable: FLEVt
0.038 (4.289)
0.071 (4.762)
0.756 (13.72)
0.092 (1.985)
0.289 (11.72)
0.111 (0.731)
0.494
1,795
0.032 (2.751)
0.039 (4.380)
0.071 (4.684)
0.769 (14.05)
0.097 (2.108)
0.283 (11.39)
0.077 (0.516)
0.489
1,575
0.016 (0.729)
0.011 (2.717)
0.032 (3.589)
0.061 (4.160)
0.709 (15.45)
0.120 (2.744)
0.311 (12.18)
0.361 (2.975)
0.514
1,575
Notes: The table reports the results of regressing financial leverage on the determinants of capital structure. All regressions include industry fixed effects and year fixed effects (not
reported). PS indicates the predicted signs for coefficients; NObs is the number of observations. The t-statistics (shown in parentheses) are adjusted for heteroscedasticity and
autocorrelation using the Newey-West standard error correction method. Superscripts ; ; and denote the significance levels of 1%, 5% and 10%, respectively. Dependent
Variables in all models are financial leverage (FLEV). Unsigned discretionary accrual measures (including JDA, MJDA, EXJDA and EXMJDA) and income smoothing measures
(including ISMTH and CORR) are proxies for earnings management. FCP is the overall measure of earnings management. Control variables are firm size (SIZE), growth opportunity
(DSM), profitability (PROF) and tangibility (TANG). DBV is a dummy variable for negative equity observations. The sample period is from 2007 to 2016 (2008 to 2016) for all the
discretionary accruals measures and ISMTH (CORR), and from 2002 to 2016 for the remaining variables. All variables are winsorized at the 1% and 99% levels
JDA t1
MJDA t1
EJDA t1
EMJDA t1
ISMTHt1
CORRt1
FPCt1
SIZEt1
DSMt1
PROFt1
TANGt1
DBVt1
Intercept
Adj. R2
Nobs
PS
Table 3 Pooled model
j JOURNAL OF ASIA BUSINESS STUDIES j
0.033 (6.999)
0.064 (5.754)
0.896 (23.40)
0.095 (2.777)
0.100 (1.306)
987.9
0.000
1,795
0.543 (4.098)
0.033 (7.009)
0.064 (5.792)
0.897 (23.35)
0.097 (2.821)
0.104 (1.368)
977.7
0.000
1,795
0.494 (3.844)
0.033 (6.858)
0.061 (5.556)
0.884 (22.52)
0.097 (2.810)
0.126 (1.641)
954.3
0.000
1,795
0.417 (2.685)
0.033 (6.883)
0.062 (5.597)
0.885 (22.44)
0.097 (2.827)
0.128 (1.662)
943.1
0.000
1,795
0.374 (2.454)
Dependent variable: FLEVt
0.031 (6.556)
0.061 (5.470)
0.886 (22.01)
0.102 (3.046)
0.203 (2.525)
919.9
0.000
1,795
0.028 (2.827)
0.033 (6.814)
0.061 (4.459)
0.898 (22.28)
0.110 (3.266)
0.181 (2.275)
865.7
0.000
1,575
0.019 (0.858)
0.012 (3.377)
0.033 (6.860)
0.062 (5.672)
0.887 (22.94)
0.094 (2.730)
0.165 (2.126)
974.1
0.000
1,575
Notes: The table reports the results of Tobit regression models. All regressions include industry fixed effects and year fixed effects (not reported). PS indicates the predicted signs for
coefficients; NObs is the number of observations. The z-scores (shown in parentheses) are adjusted using robust Huber–White standard errors. Superscripts ; ; and denote the
significance levels of 1%, 5% and 10%, respectively. Dependent Variables in all models are financial leverage (FLEV). Unsigned discretionary accrual measures (including JDA,
MJDA, EXJDA and EXMJDA) and income smoothing measures (including ISMTH and CORR) are proxies for earnings management. FCP is the overall measure of earnings
management. Control variables are firm size (SIZE), growth opportunity (DSM), profitability (PROF) and tangibility (TANG). The sample period is from 2007 to 2016 (2008 to 2016) for
all the discretionary accruals measures and ISMTH (CORR) and from 2002 to 2016 for the remaining variables. All variables are winsorized at the 1% and 99% levels
JDA t 1
þ
MJDA t1
þ
EJDA t1
þ
EMJDA t1
þ
þ
ISMTHt1
CORRt1
þ
þ
FPCt1
SIZEt1
þ
DSMt1
þ
–
PROFt1
TANGt1
–
Intercept
‫؟‬
Wald (joint) test
p-value
Nobs
PS
Table 4 Tobit model
j JOURNAL OF ASIA BUSINESS STUDIES j
0.008 (1.924)
0.009 (0.545)
0.320 (7.370)
0.097 (3.613)
0.107 (3.995)
74.92
0.144
1,733
0.000
1,795
0.453 (8.000)
0.256 (2.257)
0.000
1,795
1,730
0.008 (1.905)
0.009 (0.555)
0.320 (7.341)
0.097 (3.623)
0.106 (3.993)
74.86 0.146
0.252 (2.252)
0.454 (8.044)
0.008 (1.931)
0.010 (0.612)
0.310 (7.039)
0.095 (3.506)
0.108 (4.011)
74.89
0.145
1,711
0.000
1,795
0.291 (2.396)
0.455 (7.940)
0.008 (1.819)
0.009 (0.545)
0.313 (7.147)
0.096 (3.532)
0.100 (3.762)
73.40
0.174
1,722
0.000
1,795
0.291 (2.453)
0.457 (7.952)
Dependent variable: FLEVt
0.011 (1.761)
0.008 (0.939)
0.339 (9.284)
0.105 (4.029)
0.122 (6.083)
78.62
0.089
845.9
0.000
1,795
0.021 (2.367)
0.343 (8.401)
0.010 (1.791)
0.011 (1.247)
0.336 (9.121)
0.109 (4.184)
0.119 (5.998)
77.51
0.103
897.5
0.000
1,575
0.017 (0.916)
0.352 (8.657)
0.007 (2.489)
0.008 (1.724)
0.009 (0.547)
0.316 (7.267)
0.096 (3.510)
0.099 (3.774)
72.71
0.189
1,737
0.000
1,575
0.459 (7.970)
Notes: The table reports the results of dynamic panel data models. PS indicates the predicted signs for coefficients; NObs is the number of observations. The z-scores (shown in
parentheses) are computed using robust standard errors. Superscripts ; ; and denote the significance levels of 1%, 5% and 10%, respectively. x 2 statistic a Wald statistic
based on the estimated covariance matrix. Dependent Variables in all models are financial leverage (FLEV). Unsigned discretionary accrual measures (including JDA, MJDA, EXJDA
and EXMJDA) and income smoothing measures (including ISMTH and CORR) are proxies for earnings management. FCP is the overall measure of earnings management. Control
variables are the firm size (SIZE), growth opportunity (DSM), profitability (PROF) and tangibility (TANG). The sample period is from 2007 to 2016 (2008 to 2016) for all the discretionary
accruals measures and ISMTH (CORR) and from 2002 to 2016 for the remaining variables. All variables are winsorized at the 1% and 99% levels
FLEVt1
þ
JDA t1
þ
MJDA t1
þ
EJDA t1
þ
EMJDA t1
þ
ISMTHt1
þ
þ
CORRt1
þ
FPCt1
SIZEt1
þ
þ
DSMt1
PROFt1
–
–
TANGt1
þ
DBVt1
Sargan test
p-value
Wald (joint) test
p-value
Nobs
PS
Table 5 Dynamic panel model
4.3.2 Dynamic panel model. According to Flannery and Rangan (2006) in a frictionless world
because of immediate adjustments toward the optimal capital structure, firms would always
maintain their target leverage. However, adjustment costs may prevent firms from immediate
adjustments. A partial or incomplete adjustment toward target leverage implies that the lagged
leverage, as an additional regressor, should be included in the leverage regressions. To do so,
we estimated dynamic panel models that carried out the estimation of dynamic panel data
models using the GMM-SYS method. The dynamic panel data models include the lagged
dependent variable. Table 5 reports the coefficients estimated for the variables and their zscores, which are computed using robust standard errors. The outcomes presented in the table
also provide the Sargan over-identification test (the results show that the null hypothesis of the
Sargan test is not rejected for all regressions) and a Wald test for the overall significance.
As can be seen in Table 5, the coefficients on the lagged dependent variable (FLEVt-1) in all
regressions are positive and significant at least at the 1% level, which is consistent with the
partial adjustment toward target leverage. Similar to the previous results, the coefficients on
JDA, MJDA, EXJDA and EXMJDA are significantly positive. The coefficient on IMSTH is
also significantly positive (coeff = 0.021, p < 0.05). The coefficients on SIZE, PROF and
TANG in all regressions have the expected signs.
5. Conclusion
In this paper, we explore the informational and disciplining role of debt in reducing agency
costs by using a sample of 200 unique firms listed on the Tehran Stock Exchange. We assume
that earnings management intensifies information asymmetry and helps self-serving managers
exploit their private benefits of control. Given that, we predict that higher leverage is likely to
mitigate agency and information problems. We use earnings management as a proxy for
agency conflicts between shareholders and managers. We document that firms with higher
earnings management activities tend to have higher financial leverage. Overall, our results
suggest that the use of higher leverage contributes to the reduction of agency costs. Our main
conclusion is consistent with the informational role of debt as proposed by Harris and Raviv
(1990). It is also consistent with An et al. (2016), which document the disciplining role of debt
in alleviating the agency costs of free cash flow.
From the perspective of practical implications, we believe that our study contributes to the
body of knowledge and enlightens the stakeholders of Iranian firms, especially when they
face asymmetric information problems. Our results highlight the benefit of higher leverage
when there are high managerial agency costs due to earnings management activities.
Given the relative weakness of the Institutional environment in Iran, the use of debt as an
alternative mechanism for investor protection may serve the interests of shareholders
against misaligned managerial incentives.
Note
1. We also provide an additional reason regarding why the reverse causality may not be a major
problem of our research conclusions. The debt covenant hypothesis predicts that covenant
violation motivates income-increasing earnings management. Thus, given that earnings
manipulation and covenant violation, respectively, are proxied by discretionary accruals and
leverage, it does not suggest that there should be a positive association between unsigned
discretionary accruals and leverage. Consistently, the results of our additional tests do not
document that firms with higher leverage have higher unsigned discretionary accruals (the results
are not reported in this paper).
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Corresponding author
Mahdi Salehi can be contacted at: mehdi.salehi@um.ac.ir
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