p–ISSN: 2723 - 6609 and-ISSN: 2745-5254
Vol. 5, No. 11, November 2 024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5448
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the
Indonesia Stock Exchange
Ayu Sheila Soraya1*, Dianwicaksih Arieftiara2
National Development University Veteran Jakarta, Indonesia
Email: [email protected]1*, [email protected]2
*Correspondence
ABSTRACT
Keywords: financial
distress, earnings
management, Indonesia
stock exchange.
This study examines the relationship between financial
distress and earnings management among non-financial
firms listed on the Indonesia Stock Exchange during the
period 2018–2022. The research employs a quantitative
approach using the modified Jones model to measure
discretionary accruals, with leverage, firm size, and
profitability included as control variables. The findings
reveal that profitability has the strongest positive influence
on earnings management, indicating that firms with higher
profitability are more likely to manipulate earnings to
enhance financial results and meet market expectations.
Conversely, leverage demonstrates a significant negative
effect, suggesting that firms with higher debt levels are less
likely to engage in earnings manipulation due to increased
creditor scrutiny and financial discipline. Meanwhile,
financial distress and firm size have minimal impacts, with
their coefficients showing no significant influence on
discretionary accruals. These results highlight the
importance of profitability and leverage as key drivers of
earnings management while suggesting that financial
distress and firm size play lesser roles in this context. The
study acknowledges limitations, including its focus on non-
financial firms in Indonesia, a five-year observation period,
and the exclusion of additional factors like governance and
macroeconomic conditions. Future research could address
these limitations by expanding the dataset, incorporating
more variables, and exploring other emerging markets.
Introduction
Financial stability is crucial for the sustainability of any organization, especially
in the competitive landscape of modern business (Abu-Visit, 2018). Companies
Vol. 5, No. 11, November 2 024 http://jist.publikasiindonesia.id/
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5448
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the
Indonesia Stock Exchange
Ayu Sheila Soraya1*, Dianwicaksih Arieftiara2
National Development University Veteran Jakarta, Indonesia
Email: [email protected]1*, [email protected]2
*Correspondence
ABSTRACT
Keywords: financial
distress, earnings
management, Indonesia
stock exchange.
This study examines the relationship between financial
distress and earnings management among non-financial
firms listed on the Indonesia Stock Exchange during the
period 2018–2022. The research employs a quantitative
approach using the modified Jones model to measure
discretionary accruals, with leverage, firm size, and
profitability included as control variables. The findings
reveal that profitability has the strongest positive influence
on earnings management, indicating that firms with higher
profitability are more likely to manipulate earnings to
enhance financial results and meet market expectations.
Conversely, leverage demonstrates a significant negative
effect, suggesting that firms with higher debt levels are less
likely to engage in earnings manipulation due to increased
creditor scrutiny and financial discipline. Meanwhile,
financial distress and firm size have minimal impacts, with
their coefficients showing no significant influence on
discretionary accruals. These results highlight the
importance of profitability and leverage as key drivers of
earnings management while suggesting that financial
distress and firm size play lesser roles in this context. The
study acknowledges limitations, including its focus on non-
financial firms in Indonesia, a five-year observation period,
and the exclusion of additional factors like governance and
macroeconomic conditions. Future research could address
these limitations by expanding the dataset, incorporating
more variables, and exploring other emerging markets.
Introduction
Financial stability is crucial for the sustainability of any organization, especially
in the competitive landscape of modern business (Abu-Visit, 2018). Companies
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5449
must have appropriate resources to continue operations, including enough cash flow to
fulfill their financial responsibilities. When these resources are insufficient, businesses
frequently experience financial distress, which can disrupt operations and damage their
market reputation (Lazzem & Jilani, 2018). In such circumstances, management may
resort to earnings management, which involves the manipulation of accounting figures
to present a more favorable financial position than what is reflected in reality.
Companies must have appropriate resources to continue operations, including enough
cash flow to fulfill their financial responsibilities. When these resources are insufficient,
businesses frequently experience financial distress, which can disrupt operations and
damage their market reputation. Earnings management is particularly relevant in
situations of financial distress, as managers may seek to prevent adverse reactions from
the market, which could negatively affect stock prices, investor confidence, and the
company’s overall valuation. Research has shown that such behavior is not uncommon
among managers under pressure (Kalbuana, Taqi, Uzliawati, & Ramdhani, 2022).
Companies require adequate resources to sustain their operations, including enough
cash to meet lender obligations, therefore when resources are insufficient, financial
distress occurs which creates a situation where companies may manipulate accounting
profits as a means to present favorable performance, with management adjusting
accounts to influence reported earnings (Ranjbar & Amanollahi, 2018). This
opportunism often includes adjusting financial statements in a way that delays the
disclosure of financial difficulties, providing the company with additional time to
address underlying issues. However, while this tactic might offer short-term relief, it
carries significant risks, including regulatory penalties, a loss of investor trust, and long-
term damage to the company’s reputation (Istiqomah & Adhariani, 2017).
This study examines the relationship between financial distress and earnings
management, with a specific focus on non-financial companies listed on the Indonesia
Stock Exchange. Emerging markets like Indonesia provide a unique context for such
research, given the distinct regulatory environment, market dynamics, and economic
challenges. (Heniwati & Essen, 2020). Previous studies have extensively explored
earnings management practices in developed markets, yet there is limited research on
how financial distress influences such practices in emerging economies. By addressing
this gap, this study contributes to the growing body of literature on earnings
management while offering insights that are particularly relevant to regulators,
investors, and corporate managers operating in similar environments. (Giarto &
Fachrurrozie, 2020).
In addition to financial distress, this research incorporates leverage, firm size, and
profitability as control variables. These factors are essential for providing a
comprehensive understanding of the dynamics that influence earnings management.
Leverage reflects a company’s debt burden and is often linked to financial distress,
while firm size can impact a company's ability to access resources and withstand
economic shocks (Fachrudin, 2020). Profitability, on the other hand, serves as a key
indicator of financial health, often influencing managerial decisions regarding earnings
reporting. By analyzing these variables in conjunction, the study seeks to uncover the
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5449
must have appropriate resources to continue operations, including enough cash flow to
fulfill their financial responsibilities. When these resources are insufficient, businesses
frequently experience financial distress, which can disrupt operations and damage their
market reputation (Lazzem & Jilani, 2018). In such circumstances, management may
resort to earnings management, which involves the manipulation of accounting figures
to present a more favorable financial position than what is reflected in reality.
Companies must have appropriate resources to continue operations, including enough
cash flow to fulfill their financial responsibilities. When these resources are insufficient,
businesses frequently experience financial distress, which can disrupt operations and
damage their market reputation. Earnings management is particularly relevant in
situations of financial distress, as managers may seek to prevent adverse reactions from
the market, which could negatively affect stock prices, investor confidence, and the
company’s overall valuation. Research has shown that such behavior is not uncommon
among managers under pressure (Kalbuana, Taqi, Uzliawati, & Ramdhani, 2022).
Companies require adequate resources to sustain their operations, including enough
cash to meet lender obligations, therefore when resources are insufficient, financial
distress occurs which creates a situation where companies may manipulate accounting
profits as a means to present favorable performance, with management adjusting
accounts to influence reported earnings (Ranjbar & Amanollahi, 2018). This
opportunism often includes adjusting financial statements in a way that delays the
disclosure of financial difficulties, providing the company with additional time to
address underlying issues. However, while this tactic might offer short-term relief, it
carries significant risks, including regulatory penalties, a loss of investor trust, and long-
term damage to the company’s reputation (Istiqomah & Adhariani, 2017).
This study examines the relationship between financial distress and earnings
management, with a specific focus on non-financial companies listed on the Indonesia
Stock Exchange. Emerging markets like Indonesia provide a unique context for such
research, given the distinct regulatory environment, market dynamics, and economic
challenges. (Heniwati & Essen, 2020). Previous studies have extensively explored
earnings management practices in developed markets, yet there is limited research on
how financial distress influences such practices in emerging economies. By addressing
this gap, this study contributes to the growing body of literature on earnings
management while offering insights that are particularly relevant to regulators,
investors, and corporate managers operating in similar environments. (Giarto &
Fachrurrozie, 2020).
In addition to financial distress, this research incorporates leverage, firm size, and
profitability as control variables. These factors are essential for providing a
comprehensive understanding of the dynamics that influence earnings management.
Leverage reflects a company’s debt burden and is often linked to financial distress,
while firm size can impact a company's ability to access resources and withstand
economic shocks (Fachrudin, 2020). Profitability, on the other hand, serves as a key
indicator of financial health, often influencing managerial decisions regarding earnings
reporting. By analyzing these variables in conjunction, the study seeks to uncover the
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5450
nuanced ways in which financial distress and other factors drive earnings management
practices. To achieve these objectives, a quantitative approach is adopted, leveraging
statistical computations and systematic analysis to derive evidence-based conclusions.
The study draws data from 342 non-financial companies listed on the Indonesia Stock
Exchange over the period 2018 to 2022 (ElHawary & Hassouna, 2021).
Through this analysis, the research aims to answer critical questions: Do
financially distressed companies engage in earnings management more frequently than
their stable counterparts? How do leverage, firm size, and profitability interact with
financial distress to influence such practices?
The findings of this study are expected to provide valuable insights for investors,
regulators, and corporate managers. (Chhillar & Lellapalli, 2022). Investors can use this
knowledge to make more informed decisions by identifying red flags indicative of
earnings manipulation. Regulators can better understand the conditions under which
earnings management is more likely to occur, thereby enabling more targeted
interventions. Lastly, corporate managers can benefit from these insights by adopting
more ethical and sustainable practices to navigate financial distress without
compromising stakeholder trust. This study not only sheds light on the interplay
between financial distress and earnings management but also offers actionable
recommendations to help companies mitigate the risks associated with financial
instability.
Method
This study employs a quantitative research method, which is particularly well-
suited for achieving precision and objectivity in data analysis. The quantitative approach
allows for the systematic collection and analysis of numerical data, enabling the
generation of evidence-based conclusions that are both reliable and accurate. By
incorporating statistical computations and structured methodologies, this study seeks to
uncover the relationships between financial distress and earnings management practices
in a manner that is transparent, replicable, and grounded in empirical evidence.
Furthermore, the quantitative approach is expected to yield results that are not only
reliable but also verifiable, ensuring that the findings can be generalized to a broader
population while maintaining statistical rigor.
The dataset used in this study is derived from a comprehensive sample of 342
companies operating within the non-financial sector, all of which are listed on the
Indonesia Stock Exchange. The selected data spans five years, from 2018 to 2022,
thereby providing a robust temporal framework for analyzing trends and patterns. This
timeframe allows the study to capture variations in financial distress and earnings
management practices over different economic conditions, ensuring a more nuanced
understanding of the dynamics at play. The decision to focus on non-financial
companies was made to minimize the potential confounding effects of financial sector-
specific regulations and practices, which may differ significantly from those of other
industries.
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5450
nuanced ways in which financial distress and other factors drive earnings management
practices. To achieve these objectives, a quantitative approach is adopted, leveraging
statistical computations and systematic analysis to derive evidence-based conclusions.
The study draws data from 342 non-financial companies listed on the Indonesia Stock
Exchange over the period 2018 to 2022 (ElHawary & Hassouna, 2021).
Through this analysis, the research aims to answer critical questions: Do
financially distressed companies engage in earnings management more frequently than
their stable counterparts? How do leverage, firm size, and profitability interact with
financial distress to influence such practices?
The findings of this study are expected to provide valuable insights for investors,
regulators, and corporate managers. (Chhillar & Lellapalli, 2022). Investors can use this
knowledge to make more informed decisions by identifying red flags indicative of
earnings manipulation. Regulators can better understand the conditions under which
earnings management is more likely to occur, thereby enabling more targeted
interventions. Lastly, corporate managers can benefit from these insights by adopting
more ethical and sustainable practices to navigate financial distress without
compromising stakeholder trust. This study not only sheds light on the interplay
between financial distress and earnings management but also offers actionable
recommendations to help companies mitigate the risks associated with financial
instability.
Method
This study employs a quantitative research method, which is particularly well-
suited for achieving precision and objectivity in data analysis. The quantitative approach
allows for the systematic collection and analysis of numerical data, enabling the
generation of evidence-based conclusions that are both reliable and accurate. By
incorporating statistical computations and structured methodologies, this study seeks to
uncover the relationships between financial distress and earnings management practices
in a manner that is transparent, replicable, and grounded in empirical evidence.
Furthermore, the quantitative approach is expected to yield results that are not only
reliable but also verifiable, ensuring that the findings can be generalized to a broader
population while maintaining statistical rigor.
The dataset used in this study is derived from a comprehensive sample of 342
companies operating within the non-financial sector, all of which are listed on the
Indonesia Stock Exchange. The selected data spans five years, from 2018 to 2022,
thereby providing a robust temporal framework for analyzing trends and patterns. This
timeframe allows the study to capture variations in financial distress and earnings
management practices over different economic conditions, ensuring a more nuanced
understanding of the dynamics at play. The decision to focus on non-financial
companies was made to minimize the potential confounding effects of financial sector-
specific regulations and practices, which may differ significantly from those of other
industries.
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5451
Through the application of systematic statistical analysis, this study aims to
rigorously evaluate the relationship between financial distress and earnings
management, while also considering the influence of control variables such as leverage,
firm size, and profitability. These control variables are included to account for
additional factors that may impact the observed relationships, thereby enhancing the
comprehensiveness and validity of the analysis. Overall, the methodological approach
adopted in this research is designed to provide clear and actionable insights into how
financial distress influences earnings management practices, particularly within the
context of emerging markets like Indonesia.
Measurement of Earnings Management
In earnings management, discretionary accruals are typically used, assuming that
non-discretionary accruals are determined by the company’s operational conditions,
while discretionary accruals are determined by managers exercising discretion over
applicable accounting policies and estimates within a company (Luu Thu, 2023).
The calculation of discretionary accruals using the Modified Jones Model
involves the following equation:
Total Accruals I,t = Net Income I,t – Cash Flow From Operations I,t
The total accruals value is measured using the following multiple regression equation:
Total Accruals i,t /A i,t-1 = α1(1/A i,t-1) + α2 (ΔREV i,t / A i,t-1) + α3 (PPE i,t /A i,t-1) + ε
Non-discretionary accruals are calculated using the following formula:
NDA i, t = α1(1/A i,t-1) + α2 (ΔREVi,t / A i,t-1 - ΔREC i,t / A i,t-1 ) + α3 (PPE i,t /A i,t-1)
Next, discretionary accruals can be calculated as follows:
DA i, t = (Total Accruals i,t /A i,t-1) - NDA i, t
Measurement of Financial Distress
In this study, financial distress will be assessed using the Altman Z-Score method,
which is recognized as a reliable tool for evaluating financial health (Zainudin et al.,
2023). The Altman Z-Score is calculated using the following formula:
Z-Score = 1.2 A+ 1.4B + 3.3C + 0.6D + 1.0E
Z-Score = Financial Distress
A = Working Capital / Total Assets
B = Retained Earnings / Total Assets
C = EBIT / Total Assets
D = Market Value of Equity / Total Liabilities
E = Sales / Total Asset
Measurement of Variable Control
To ensure a comprehensive analysis, this study incorporates several control
variables that are known to influence earnings management: profitability, leverage, and
firm size. (Ardillah & Vesakhadevi, 2021). These variables are crucial for capturing the
broader financial and operational context in which earnings management practices
occur. The methods used to measure these control variables are as follows:
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5451
Through the application of systematic statistical analysis, this study aims to
rigorously evaluate the relationship between financial distress and earnings
management, while also considering the influence of control variables such as leverage,
firm size, and profitability. These control variables are included to account for
additional factors that may impact the observed relationships, thereby enhancing the
comprehensiveness and validity of the analysis. Overall, the methodological approach
adopted in this research is designed to provide clear and actionable insights into how
financial distress influences earnings management practices, particularly within the
context of emerging markets like Indonesia.
Measurement of Earnings Management
In earnings management, discretionary accruals are typically used, assuming that
non-discretionary accruals are determined by the company’s operational conditions,
while discretionary accruals are determined by managers exercising discretion over
applicable accounting policies and estimates within a company (Luu Thu, 2023).
The calculation of discretionary accruals using the Modified Jones Model
involves the following equation:
Total Accruals I,t = Net Income I,t – Cash Flow From Operations I,t
The total accruals value is measured using the following multiple regression equation:
Total Accruals i,t /A i,t-1 = α1(1/A i,t-1) + α2 (ΔREV i,t / A i,t-1) + α3 (PPE i,t /A i,t-1) + ε
Non-discretionary accruals are calculated using the following formula:
NDA i, t = α1(1/A i,t-1) + α2 (ΔREVi,t / A i,t-1 - ΔREC i,t / A i,t-1 ) + α3 (PPE i,t /A i,t-1)
Next, discretionary accruals can be calculated as follows:
DA i, t = (Total Accruals i,t /A i,t-1) - NDA i, t
Measurement of Financial Distress
In this study, financial distress will be assessed using the Altman Z-Score method,
which is recognized as a reliable tool for evaluating financial health (Zainudin et al.,
2023). The Altman Z-Score is calculated using the following formula:
Z-Score = 1.2 A+ 1.4B + 3.3C + 0.6D + 1.0E
Z-Score = Financial Distress
A = Working Capital / Total Assets
B = Retained Earnings / Total Assets
C = EBIT / Total Assets
D = Market Value of Equity / Total Liabilities
E = Sales / Total Asset
Measurement of Variable Control
To ensure a comprehensive analysis, this study incorporates several control
variables that are known to influence earnings management: profitability, leverage, and
firm size. (Ardillah & Vesakhadevi, 2021). These variables are crucial for capturing the
broader financial and operational context in which earnings management practices
occur. The methods used to measure these control variables are as follows:
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5452
Firm Size
Firm size is another important control variable, as larger firms often have greater
resources and more established reputations, which can influence their financial
reporting behavior. Firm size is measured using the logarithm of total assets, calculated
as:
Firm size i,t = Log (Total Asset i,t)
Larger firms may have more stringent regulatory oversight and higher stakeholder
scrutiny, potentially reducing their inclination to engage in earnings management
compared to smaller firms.
Data Analysis Technique
This study employs Microsoft Excel and STATA version 17 MP Parallel Edition
for data analysis. Microsoft Excel will be used for initial data preparation, cleaning, and
basic descriptive statistics, ensuring the dataset is ready for advanced analysis. STATA,
known for its robust statistical capabilities, will handle regression analyses, estimate
discretionary accruals using the Modified Jones Model, and examine relationships
between variables. The combination of these tools ensures efficient, accurate, and
comprehensive data analysis, supporting the study’s aim to generate reliable and
evidence-based conclusions.
Descriptive Statistics
This study uses descriptive analysis to summarize the characteristics of the
research sample, representing the population. Key statistical measures, including the
mean, standard deviation, minimum, and maximum values, are analyzed to provide
insights into data distribution, variability, and range. (Aljughaiman, Nguyen, Trinh, &
Du, 2023). These measures help identify patterns, trends, and anomalies, serving as a
foundation for further statistical analysis and ensuring the dataset aligns with the study’s
assumptions. Descriptive analysis offers a clear overview of the data, facilitating
transparency and preparing for more advanced techniques.
Regression Model Feasibility Testing
Panel data analysis is a statistical method that accounts for data variation across
two dimensions: cross-sectional, representing different entities, and time series,
representing observations over multiple periods. This dual-dimensional approach allows
for a more nuanced understanding of the relationships between variables by capturing
both inter-entity and intra-entity variations. To determine the most suitable model for
analyzing panel data, several diagnostic tests will be conducted. These include the
Chow test, which evaluates whether a fixed-effects model is more appropriate than a
pooled ordinary least squares (OLS) model by testing for significant differences in
intercepts across entities. Additionally, the Hausman test will be applied to compare
fixed-effects and random-effects models, helping to identify the best model based on the
assumptions of homogeneity and consistency. The Lagrange Multiplier (LM) test will
also be performed to assess whether a random-effects model is preferable to a pooled
OLS model. By conducting these tests, the study ensures the selection of a robust and
statistically appropriate model for analyzing the relationship between financial distress,
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5452
Firm Size
Firm size is another important control variable, as larger firms often have greater
resources and more established reputations, which can influence their financial
reporting behavior. Firm size is measured using the logarithm of total assets, calculated
as:
Firm size i,t = Log (Total Asset i,t)
Larger firms may have more stringent regulatory oversight and higher stakeholder
scrutiny, potentially reducing their inclination to engage in earnings management
compared to smaller firms.
Data Analysis Technique
This study employs Microsoft Excel and STATA version 17 MP Parallel Edition
for data analysis. Microsoft Excel will be used for initial data preparation, cleaning, and
basic descriptive statistics, ensuring the dataset is ready for advanced analysis. STATA,
known for its robust statistical capabilities, will handle regression analyses, estimate
discretionary accruals using the Modified Jones Model, and examine relationships
between variables. The combination of these tools ensures efficient, accurate, and
comprehensive data analysis, supporting the study’s aim to generate reliable and
evidence-based conclusions.
Descriptive Statistics
This study uses descriptive analysis to summarize the characteristics of the
research sample, representing the population. Key statistical measures, including the
mean, standard deviation, minimum, and maximum values, are analyzed to provide
insights into data distribution, variability, and range. (Aljughaiman, Nguyen, Trinh, &
Du, 2023). These measures help identify patterns, trends, and anomalies, serving as a
foundation for further statistical analysis and ensuring the dataset aligns with the study’s
assumptions. Descriptive analysis offers a clear overview of the data, facilitating
transparency and preparing for more advanced techniques.
Regression Model Feasibility Testing
Panel data analysis is a statistical method that accounts for data variation across
two dimensions: cross-sectional, representing different entities, and time series,
representing observations over multiple periods. This dual-dimensional approach allows
for a more nuanced understanding of the relationships between variables by capturing
both inter-entity and intra-entity variations. To determine the most suitable model for
analyzing panel data, several diagnostic tests will be conducted. These include the
Chow test, which evaluates whether a fixed-effects model is more appropriate than a
pooled ordinary least squares (OLS) model by testing for significant differences in
intercepts across entities. Additionally, the Hausman test will be applied to compare
fixed-effects and random-effects models, helping to identify the best model based on the
assumptions of homogeneity and consistency. The Lagrange Multiplier (LM) test will
also be performed to assess whether a random-effects model is preferable to a pooled
OLS model. By conducting these tests, the study ensures the selection of a robust and
statistically appropriate model for analyzing the relationship between financial distress,
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5453
earnings management, and control variables, while accounting for the complex structure
of the panel dataset (Agrawal & Chatterjee, 2015).
Chow Test
The Chow Test is conducted to determine whether the common effect model or
the fixed effect model is the most appropriate for analyzing the dataset. This test
evaluates the F-probability value to assess whether the fixed effect model provides a
significantly better fit than the common effect model by checking for differences in
intercepts across entities. The hypotheses for the Chow Test are as follows:
H0: common effect model (prob. > 0.05)
H1: fixed effect model (prob. < 0.05)
Hausman Test
The Hausman Test is used to choose between the fixed effect model and the random
effect model by examining the relationship between the predictors and the individual
effects. This test determines whether the individual-specific effects are correlated with
the independent variables. The hypotheses for the Hausman Test are:
H0: random effect model (prob. > 0.05)
H1: fixed effect model (prob.0.05)
Regression Analysis
The regression analysis technique employed in this study is designed to test the
research hypothesis by evaluating the relationship between financial distress and
earnings management while accounting for the influence of control variables such as
profitability, leverage, and firm size. The model is represented by the following
equation:
EMi,t=α+β1FDi,t+β2LEVi,t+β3SIZEi,t+β4PROFi,t+ε
EM i,t = Earning Management
FD i,t = Financial Distress
LEV i,t = Leverage
SIZE i,t = Firm size
PROF I,t = Profitability
α = Constant
β1, β2, β3, β4, β5 = Regression Coefficient
ε = error estimate
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5453
earnings management, and control variables, while accounting for the complex structure
of the panel dataset (Agrawal & Chatterjee, 2015).
Chow Test
The Chow Test is conducted to determine whether the common effect model or
the fixed effect model is the most appropriate for analyzing the dataset. This test
evaluates the F-probability value to assess whether the fixed effect model provides a
significantly better fit than the common effect model by checking for differences in
intercepts across entities. The hypotheses for the Chow Test are as follows:
H0: common effect model (prob. > 0.05)
H1: fixed effect model (prob. < 0.05)
Hausman Test
The Hausman Test is used to choose between the fixed effect model and the random
effect model by examining the relationship between the predictors and the individual
effects. This test determines whether the individual-specific effects are correlated with
the independent variables. The hypotheses for the Hausman Test are:
H0: random effect model (prob. > 0.05)
H1: fixed effect model (prob.0.05)
Regression Analysis
The regression analysis technique employed in this study is designed to test the
research hypothesis by evaluating the relationship between financial distress and
earnings management while accounting for the influence of control variables such as
profitability, leverage, and firm size. The model is represented by the following
equation:
EMi,t=α+β1FDi,t+β2LEVi,t+β3SIZEi,t+β4PROFi,t+ε
EM i,t = Earning Management
FD i,t = Financial Distress
LEV i,t = Leverage
SIZE i,t = Firm size
PROF I,t = Profitability
α = Constant
β1, β2, β3, β4, β5 = Regression Coefficient
ε = error estimate
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5454
Results and Discussion
Chow Test
The Chow test results indicate a probability value of 0.9577, which is greater than
the significance level of 0.05. This suggests that there is no significant difference in the
intercepts across the entities being analyzed. Consequently, the common effect model is
determined to be the most appropriate model for analyzing the panel data in this study.
The common effect model assumes that all entities share the same intercept, simplifying
the analysis by treating the dataset as homogenous without entity-specific effects.
Hausman Test
The results of the Hausman test indicate a probability value of 0.000, which is less
than the significance threshold of 0.05. This implies that there is a statistically
significant difference between the fixed-effects and random-effects models. As a result,
the fixed-effect model is deemed the most appropriate model for the analysis. The fixed-
effect model accounts for entity-specific characteristics that do not vary over time,
ensuring that unobservable factors unique to each entity are controlled for, leading toF test that all u_i=0: F(341, 1363) = 0.86 Prob > F = 0.9577
rho .39515224 (fraction of variance due to u_i)
sigma_e .17312595
sigma_u .13993339
_cons .392691 .2140221 1.83 0.067 -.0271574 .8125393
roa .2169164 .0227103 9.55 0.000 .1723654 .2614674
firm_size -.0289389 .0162192 -1.78 0.075 -.0607562 .0028784
debt_to_asset_ratio -.0185554 .003259 -5.69 0.000 -.0249485 -.0121622
z_score .0016516 .000598 2.76 0.006 .0004785 .0028248
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
corr(u_i, Xb) = -0.3599 Prob > F = 0.0000
F(4,1363) = 81.14
Overall = 0.3387 max = 5
Between = 0.4866 avg = 5.0
Within = 0.1923 min = 4
R-squared: Obs per group:
Group variable: id Number of groups = 342
Fixed-effects (within) regression Number of obs = 1,709Prob > chi2 = 0.0000
= 50.14
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test of H0: Difference in coefficients not systematic
B = Inconsistent under Ha, efficient under H0; obtained from xtreg.
b = Consistent under H0 and Ha; obtained from xtreg.
roa .2169164 .1503039 .0666125 .0128632
firm_size -.0289389 .0003914 -.0293303 .0161851
debt_to_as~o -.0185554 -.0316403 .0130849 .0027179
z_score .0016516 .0000816 .00157 .0004587
fe re Difference Std. err.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5454
Results and Discussion
Chow Test
The Chow test results indicate a probability value of 0.9577, which is greater than
the significance level of 0.05. This suggests that there is no significant difference in the
intercepts across the entities being analyzed. Consequently, the common effect model is
determined to be the most appropriate model for analyzing the panel data in this study.
The common effect model assumes that all entities share the same intercept, simplifying
the analysis by treating the dataset as homogenous without entity-specific effects.
Hausman Test
The results of the Hausman test indicate a probability value of 0.000, which is less
than the significance threshold of 0.05. This implies that there is a statistically
significant difference between the fixed-effects and random-effects models. As a result,
the fixed-effect model is deemed the most appropriate model for the analysis. The fixed-
effect model accounts for entity-specific characteristics that do not vary over time,
ensuring that unobservable factors unique to each entity are controlled for, leading toF test that all u_i=0: F(341, 1363) = 0.86 Prob > F = 0.9577
rho .39515224 (fraction of variance due to u_i)
sigma_e .17312595
sigma_u .13993339
_cons .392691 .2140221 1.83 0.067 -.0271574 .8125393
roa .2169164 .0227103 9.55 0.000 .1723654 .2614674
firm_size -.0289389 .0162192 -1.78 0.075 -.0607562 .0028784
debt_to_asset_ratio -.0185554 .003259 -5.69 0.000 -.0249485 -.0121622
z_score .0016516 .000598 2.76 0.006 .0004785 .0028248
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
corr(u_i, Xb) = -0.3599 Prob > F = 0.0000
F(4,1363) = 81.14
Overall = 0.3387 max = 5
Between = 0.4866 avg = 5.0
Within = 0.1923 min = 4
R-squared: Obs per group:
Group variable: id Number of groups = 342
Fixed-effects (within) regression Number of obs = 1,709Prob > chi2 = 0.0000
= 50.14
chi2(4) = (b-B)'[(V_b-V_B)^(-1)](b-B)
Test of H0: Difference in coefficients not systematic
B = Inconsistent under Ha, efficient under H0; obtained from xtreg.
b = Consistent under H0 and Ha; obtained from xtreg.
roa .2169164 .1503039 .0666125 .0128632
firm_size -.0289389 .0003914 -.0293303 .0161851
debt_to_as~o -.0185554 -.0316403 .0130849 .0027179
z_score .0016516 .0000816 .00157 .0004587
fe re Difference Std. err.
(b) (B) (b-B) sqrt(diag(V_b-V_B))
Coefficients
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5455
more reliable and robust results in the context of this study (Rusci, Santosa, & Fitriana,
2021).
Lagrange Multiplier Test
The results of the Lagrange Multiplier (LM) test show a probability value of 1,
which is significantly greater than the significance threshold of 0.05. This indicates that
the random-effects model is not appropriate, as there is no evidence to suggest that the
random-effects model provides a better fit than the pooled ordinary least squares (OLS)
model. Therefore, the common effect model is chosen as the most suitable model for
analyzing the panel data in this context. The common effect model assumes uniformity
across entities, treating all observations as homogenous without accounting for entity-
specific effects.
Classic Assumption Testing
Multicollinearity Test
The results of the multicollinearity test reveal a Variance Inflation Factor (VIF)
value of 2.25, which is well below the threshold of 10. This indicates that there is no
significant multicollinearity among the independent variables in the regression model. A
low VIF value suggests that the predictor variables are not highly correlated with each
other, ensuring that the regression coefficients are stable and reliable. This confirms that
multicollinearity is not a concern in this study, allowing for an accurate interpretation of
the relationships between variables.
Heteroskedasticity TestProb > chibar2 = 1.0000
chibar2(01) = 0.00
Test: Var(u) = 0
u 0 0
e .0299726 .1731259
mod_jon~c .0618831 .2487631
Var SD = sqrt(Var)
Estimated results:
mod_jones_dac[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effectsMean VIF 2.25
firm_size 1.01 0.993516
z_score 1.87 0.533855
roa 2.71 0.369150
debt_to_as~o 3.39 0.294594
Variable VIF 1/VIFProb > chi2 = 0.0000
chi2(1) = 77567.62
H0: Constant variance
Variable: Fitted values of mod_jones_dac
Assumption: Normal error terms
Breusch–Pagan/Cook–Weisberg test for heteroskedasticity
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5455
more reliable and robust results in the context of this study (Rusci, Santosa, & Fitriana,
2021).
Lagrange Multiplier Test
The results of the Lagrange Multiplier (LM) test show a probability value of 1,
which is significantly greater than the significance threshold of 0.05. This indicates that
the random-effects model is not appropriate, as there is no evidence to suggest that the
random-effects model provides a better fit than the pooled ordinary least squares (OLS)
model. Therefore, the common effect model is chosen as the most suitable model for
analyzing the panel data in this context. The common effect model assumes uniformity
across entities, treating all observations as homogenous without accounting for entity-
specific effects.
Classic Assumption Testing
Multicollinearity Test
The results of the multicollinearity test reveal a Variance Inflation Factor (VIF)
value of 2.25, which is well below the threshold of 10. This indicates that there is no
significant multicollinearity among the independent variables in the regression model. A
low VIF value suggests that the predictor variables are not highly correlated with each
other, ensuring that the regression coefficients are stable and reliable. This confirms that
multicollinearity is not a concern in this study, allowing for an accurate interpretation of
the relationships between variables.
Heteroskedasticity TestProb > chibar2 = 1.0000
chibar2(01) = 0.00
Test: Var(u) = 0
u 0 0
e .0299726 .1731259
mod_jon~c .0618831 .2487631
Var SD = sqrt(Var)
Estimated results:
mod_jones_dac[id,t] = Xb + u[id] + e[id,t]
Breusch and Pagan Lagrangian multiplier test for random effectsMean VIF 2.25
firm_size 1.01 0.993516
z_score 1.87 0.533855
roa 2.71 0.369150
debt_to_as~o 3.39 0.294594
Variable VIF 1/VIFProb > chi2 = 0.0000
chi2(1) = 77567.62
H0: Constant variance
Variable: Fitted values of mod_jones_dac
Assumption: Normal error terms
Breusch–Pagan/Cook–Weisberg test for heteroskedasticity
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5456
The heteroskedasticity test results show a Prob > chi2 value of 0.000, which is
less than the threshold of 0.05. This indicates the presence of heteroskedasticity in the
regression model, meaning that the variance of the residuals is not constant across
observations. To address this issue, a remedial measure was applied by using robust
standard errors, which adjust the standard errors of the coefficients to remain consistent
even in the presence of heteroskedasticity. By implementing this adjustment, the
reliability of the p-values and confidence intervals is maintained, ensuring accurate
statistical inference despite the heteroskedasticity detected.
Hypothesis Testing
F-Statistic Test (Simultaneous Test)
F-Statistic Value: The F-statistic is 481.13.
Prob > F: The p-value associated with the F-statistic is 0.0000.
The F-statistic test is used to determine whether all the independent variables
included in the regression model collectively have a statistically significant effect on the
dependent variable. In this study, the results of the F-statistic test show an F-statistic
value of 481.13 with an associated p-value (Prob > F ) of 0.0000. Since the p-value is
significantly lower than the standard significance threshold of 0.05, the null hypothesis
(H0) is rejected. The rejection of the null hypothesis implies that the independent_cons .0205058 .0172342 1.19 0.234 -.0132967 .0543082
roa .1503039 .1691668 0.89 0.374 -.1814926 .4821003
firm_size .0003914 .0006204 0.63 0.528 -.0008255 .0016083
debt_to_asset_ratio -.0316403 .0197508 -1.60 0.109 -.0703786 .0070981
z_score .0000816 .0006802 0.12 0.905 -.0012526 .0014158
mod_jones_dac Coefficient std. err. t P>|t| [95% conf. interval]
Robust
Root MSE = .17067
R-squared = 0.5304
Prob > F = 0.0005
F(4, 1704) = 5.02
Linear regression Number of obs = 1,709.
_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
Total 105.696297 1,708 .061883078 Root MSE = .17067
Adj R-squared = 0.5293
Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5456
The heteroskedasticity test results show a Prob > chi2 value of 0.000, which is
less than the threshold of 0.05. This indicates the presence of heteroskedasticity in the
regression model, meaning that the variance of the residuals is not constant across
observations. To address this issue, a remedial measure was applied by using robust
standard errors, which adjust the standard errors of the coefficients to remain consistent
even in the presence of heteroskedasticity. By implementing this adjustment, the
reliability of the p-values and confidence intervals is maintained, ensuring accurate
statistical inference despite the heteroskedasticity detected.
Hypothesis Testing
F-Statistic Test (Simultaneous Test)
F-Statistic Value: The F-statistic is 481.13.
Prob > F: The p-value associated with the F-statistic is 0.0000.
The F-statistic test is used to determine whether all the independent variables
included in the regression model collectively have a statistically significant effect on the
dependent variable. In this study, the results of the F-statistic test show an F-statistic
value of 481.13 with an associated p-value (Prob > F ) of 0.0000. Since the p-value is
significantly lower than the standard significance threshold of 0.05, the null hypothesis
(H0) is rejected. The rejection of the null hypothesis implies that the independent_cons .0205058 .0172342 1.19 0.234 -.0132967 .0543082
roa .1503039 .1691668 0.89 0.374 -.1814926 .4821003
firm_size .0003914 .0006204 0.63 0.528 -.0008255 .0016083
debt_to_asset_ratio -.0316403 .0197508 -1.60 0.109 -.0703786 .0070981
z_score .0000816 .0006802 0.12 0.905 -.0012526 .0014158
mod_jones_dac Coefficient std. err. t P>|t| [95% conf. interval]
Robust
Root MSE = .17067
R-squared = 0.5304
Prob > F = 0.0005
F(4, 1704) = 5.02
Linear regression Number of obs = 1,709.
_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
Total 105.696297 1,708 .061883078 Root MSE = .17067
Adj R-squared = 0.5293
Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5457
variables—financial distress, leverage, firm size, and profitability—have a significant
simultaneous impact on the dependent variable, earnings management.
This result highlights the collective importance of these independent variables in
influencing earnings management practices. It suggests that the variations in the level of
earnings management cannot be adequately explained by any single independent
variable alone but are instead the result of the combined effect of financial distress,
leverage, firm size, and profitability. The statistical significance of the F-statistic further
validates the overall fit of the regression model, confirming that the included
independent variables provide meaningful insights into the determinants of earnings
management.
By demonstrating the simultaneous influence of these variables, the findings
underscore the importance of considering a multidimensional approach to understanding
earnings management practices. This conclusion supports the theoretical framework of
the study and provides a strong foundation for further analysis of the individual
contributions of each independent variable through additional tests, such as t-tests for
individual significance.
Coefficient of Determination (R²)
R-squared: The R-squared value is 0.5304.
Adjusted R-squared: The Adjusted R-squared value is 0.5293.
The coefficient of determination R-squared of 0.5304 (53.04%) indicates that this
model explains a substantial portion of the variability in the dependent variable. In other
words, 53.04% of the variability in the dependent variable, accrual earnings
management, can be explained by the independent variables: financial distress,
leverage, firm size, and profitability. This shows that the model has substantial
explanatory power, as it captures more than half of the variability in earnings
management. The slightly lower Adjusted R-squared of 52.93% shows that the result
remains similar even after adjusting for the number of predictor variables in the model.
Overall, this model has moderate explanatory power as it captures about half of
the variability in the dependent variable. While this result shows a reasonably good fit,
there is still some unexplained variability, suggesting that the model could be further
improved or that other factors might influence accrual earning management. The
remaining 46.96% of unexplained variability indicates that other factors, not included in
the model, might also influence accrual earnings management. This opens the
possibility for further refinement of the model or exploration of additional variables that
could enhance its predictive accuracy. (Alfina & Sambuaga, 2021).Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5457
variables—financial distress, leverage, firm size, and profitability—have a significant
simultaneous impact on the dependent variable, earnings management.
This result highlights the collective importance of these independent variables in
influencing earnings management practices. It suggests that the variations in the level of
earnings management cannot be adequately explained by any single independent
variable alone but are instead the result of the combined effect of financial distress,
leverage, firm size, and profitability. The statistical significance of the F-statistic further
validates the overall fit of the regression model, confirming that the included
independent variables provide meaningful insights into the determinants of earnings
management.
By demonstrating the simultaneous influence of these variables, the findings
underscore the importance of considering a multidimensional approach to understanding
earnings management practices. This conclusion supports the theoretical framework of
the study and provides a strong foundation for further analysis of the individual
contributions of each independent variable through additional tests, such as t-tests for
individual significance.
Coefficient of Determination (R²)
R-squared: The R-squared value is 0.5304.
Adjusted R-squared: The Adjusted R-squared value is 0.5293.
The coefficient of determination R-squared of 0.5304 (53.04%) indicates that this
model explains a substantial portion of the variability in the dependent variable. In other
words, 53.04% of the variability in the dependent variable, accrual earnings
management, can be explained by the independent variables: financial distress,
leverage, firm size, and profitability. This shows that the model has substantial
explanatory power, as it captures more than half of the variability in earnings
management. The slightly lower Adjusted R-squared of 52.93% shows that the result
remains similar even after adjusting for the number of predictor variables in the model.
Overall, this model has moderate explanatory power as it captures about half of
the variability in the dependent variable. While this result shows a reasonably good fit,
there is still some unexplained variability, suggesting that the model could be further
improved or that other factors might influence accrual earning management. The
remaining 46.96% of unexplained variability indicates that other factors, not included in
the model, might also influence accrual earnings management. This opens the
possibility for further refinement of the model or exploration of additional variables that
could enhance its predictive accuracy. (Alfina & Sambuaga, 2021).Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5458
t-Statistic Test (Partial Test)
The t-statistic test is used to assess the individual significance of each independent
variable in explaining the dependent variable, earning management, in the regression
model. The results for each variable are as follows:
Altman Z Score
The p-value for the Altman Z Score is 0.832, which is greater than the
significance threshold of 0.05. Therefore, we fail to reject the null hypothesis (H0)
indicating that financial distress does not have a statistically significant effect on
earning management. This suggests that financial distress, as measured by the Altman Z
Score, is not a key factor influencing earnings management in this model.
Leverage
The t-statistic test result for leverage shows a p-value of 0.000, which is
significantly below the threshold of 0.05. This leads to the rejection of the null
hypothesis (H0), indicating that leverage has a statistically significant negative effect on
mod_jones_dac (modified Jones discretionary accruals). This finding implies that as a
company's leverage (measured by the debt-to-asset ratio) increases, the extent of
earnings management, as represented by discretionary accruals, tends to decrease. The
negative relationship could be attributed to the fact that higher leverage often subjects
firms to greater scrutiny by creditors and investors, thereby limiting the management's
ability to manipulate earnings. This heightened oversight may discourage opportunistic
accounting practices, promoting more transparent financial reporting.
The result underscores the role of leverage as an important factor influencing
managerial behavior in financial reporting, particularly in firms where debt obligations
play a prominent role in their capital structure.
Firm Size.
_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
Total 105.696297 1,708 .061883078 Root MSE = .17067
Adj R-squared = 0.5293
Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5458
t-Statistic Test (Partial Test)
The t-statistic test is used to assess the individual significance of each independent
variable in explaining the dependent variable, earning management, in the regression
model. The results for each variable are as follows:
Altman Z Score
The p-value for the Altman Z Score is 0.832, which is greater than the
significance threshold of 0.05. Therefore, we fail to reject the null hypothesis (H0)
indicating that financial distress does not have a statistically significant effect on
earning management. This suggests that financial distress, as measured by the Altman Z
Score, is not a key factor influencing earnings management in this model.
Leverage
The t-statistic test result for leverage shows a p-value of 0.000, which is
significantly below the threshold of 0.05. This leads to the rejection of the null
hypothesis (H0), indicating that leverage has a statistically significant negative effect on
mod_jones_dac (modified Jones discretionary accruals). This finding implies that as a
company's leverage (measured by the debt-to-asset ratio) increases, the extent of
earnings management, as represented by discretionary accruals, tends to decrease. The
negative relationship could be attributed to the fact that higher leverage often subjects
firms to greater scrutiny by creditors and investors, thereby limiting the management's
ability to manipulate earnings. This heightened oversight may discourage opportunistic
accounting practices, promoting more transparent financial reporting.
The result underscores the role of leverage as an important factor influencing
managerial behavior in financial reporting, particularly in firms where debt obligations
play a prominent role in their capital structure.
Firm Size.
_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
Total 105.696297 1,708 .061883078 Root MSE = .17067
Adj R-squared = 0.5293
Residual 49.6362927 1,704 .02912928 R-squared = 0.5304
Model 56.060004 4 14.015001 Prob > F = 0.0000
F(4, 1704) = 481.13
Source SS df MS Number of obs = 1,709
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5459
The t-statistic test result for firm size indicates a p-value of 0.710, which is greater
than the significance threshold of 0.05. As a result, we fail to reject the null hypothesis
(H0), concluding that firm size does not have a statistically significant effect on earning
management. This finding suggests that the size of a firm, as measured by the logarithm
of total assets, does not play a significant role in influencing the extent of earnings
management practices in this study. Larger firms are typically subject to higher levels of
regulatory scrutiny and stakeholder oversight, which might deter earnings manipulation,
while smaller firms may have less scrutiny but potentially lower capacity for complex
earnings management techniques. However, this result indicates that in this context,
firm size alone is not a determining factor in explaining variations in discretionary
accruals. (Almadi & Lazic, 2016)..
This non-significant relationship could also imply that other factors, such as
industry-specific characteristics, market conditions, or internal governance practices,
might have a more direct influence on earnings management than firm size. Further
investigation into these variables might provide additional insights into the drivers of
discretionary accruals.
Profitability
The t-statistic test result for profitability reveals a p-value of 0.000, which is
significantly less than the threshold of 0.05. Therefore, we reject the null hypothesis
(H0), concluding that profitability has a statistically significant positive effect on earning
management.
This finding indicates that as profitability, measured by profitability, increases so
does the extent of earnings management through discretionary accruals. This positive
relationship suggests that managers of more profitable firms might have stronger
incentives to engage in earnings manipulation to further enhance reported financial
performance. High profitability can create pressure to maintain or exceed market
expectations, leading to the use of discretionary accruals to smooth income or present a
more favorable financial position.
This result underscores the role of profitability as a critical determinant of
earnings management. It highlights the importance of closely monitoring accounting
practices in highly profitable firms to ensure that financial reports accurately reflect
their true economic performance, reducing the risk of misleading stakeholders.
Regression Analysis
EMi,t=0.0205058 +0.0000816 FDi,t – 0.0316403 LEVi,t+0.0003914 SIZEi,t
+0.1503039 PROFi,t
Financial Distress:_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5459
The t-statistic test result for firm size indicates a p-value of 0.710, which is greater
than the significance threshold of 0.05. As a result, we fail to reject the null hypothesis
(H0), concluding that firm size does not have a statistically significant effect on earning
management. This finding suggests that the size of a firm, as measured by the logarithm
of total assets, does not play a significant role in influencing the extent of earnings
management practices in this study. Larger firms are typically subject to higher levels of
regulatory scrutiny and stakeholder oversight, which might deter earnings manipulation,
while smaller firms may have less scrutiny but potentially lower capacity for complex
earnings management techniques. However, this result indicates that in this context,
firm size alone is not a determining factor in explaining variations in discretionary
accruals. (Almadi & Lazic, 2016)..
This non-significant relationship could also imply that other factors, such as
industry-specific characteristics, market conditions, or internal governance practices,
might have a more direct influence on earnings management than firm size. Further
investigation into these variables might provide additional insights into the drivers of
discretionary accruals.
Profitability
The t-statistic test result for profitability reveals a p-value of 0.000, which is
significantly less than the threshold of 0.05. Therefore, we reject the null hypothesis
(H0), concluding that profitability has a statistically significant positive effect on earning
management.
This finding indicates that as profitability, measured by profitability, increases so
does the extent of earnings management through discretionary accruals. This positive
relationship suggests that managers of more profitable firms might have stronger
incentives to engage in earnings manipulation to further enhance reported financial
performance. High profitability can create pressure to maintain or exceed market
expectations, leading to the use of discretionary accruals to smooth income or present a
more favorable financial position.
This result underscores the role of profitability as a critical determinant of
earnings management. It highlights the importance of closely monitoring accounting
practices in highly profitable firms to ensure that financial reports accurately reflect
their true economic performance, reducing the risk of misleading stakeholders.
Regression Analysis
EMi,t=0.0205058 +0.0000816 FDi,t – 0.0316403 LEVi,t+0.0003914 SIZEi,t
+0.1503039 PROFi,t
Financial Distress:_cons .0205058 .0145446 1.41 0.159 -.0080214 .0490329
roa .1503039 .0187162 8.03 0.000 .1135947 .1870131
firm_size .0003914 .001051 0.37 0.710 -.0016699 .0024527
debt_to_asset_ratio -.0316403 .0017984 -17.59 0.000 -.0351675 -.028113
z_score .0000816 .0003838 0.21 0.832 -.0006711 .0008343
mod_jones_dac Coefficient Std. err. t P>|t| [95% conf. interval]
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5460
Coefficient: 0.0000816
Interpretation: A one-unit increase in financial distress results in a minimal increase of
0.0000816 in earning management, assuming all other variables remain constant. This
very small positive effect indicates that financial distress, as measured by the Altman Z
Score, has very little impact on earnings management.
Leverage:
Coefficient: -0.0316403
Interpretation: A one-unit increase in leverage is associated with a decrease of
0.0316403 in earning management, assuming other factors remain constant. This
negative relationship suggests that higher leverage reduces earnings management
activities, potentially due to increased creditor scrutiny or stricter financial discipline.
Firm Size:
Coefficient: 0.0003914
Interpretation: A one-unit increase in firm size leads to a very small increase of
0.0003914 in earning management, holding other variables constant. This indicates a
negligible positive relationship between firm size and earnings management, suggesting
that firm size has little to no practical effect on earnings management in this model.
Profitability:
Coefficient: 0.150339
Interpretation: A one-unit increase in roa is associated with an increase of 0.1503039 in
earning management, assuming other variables remain constant. This strong positive
coefficient indicates that higher profitability significantly increases earnings
management activities, likely reflecting managerial incentives to enhance reported
financial performance.
Conclusion
This study provides an exploration of the factors influencing earnings
management, specifically focusing on discretionary accruals measured by the modified
Jones model. The findings highlight key insights into the roles of profitability, leverage,
financial distress, and firm size in shaping earnings management practices. Among
these variables, profitability and leverage stand out as the most significant drivers, while
financial distress and firm size exhibit minimal impacts. Profitability was found to have
the largest positive influence on earning management, with a significant coefficient
indicating a strong relationship. This suggests that companies with higher profitability
are more likely to engage in earnings management practices. The positive association
can be attributed to managerial incentives to enhance already favorable financial results,
thereby meeting or exceeding market expectations. High profitability often attracts
attention from investors and stakeholders, increasing pressure on management to sustain
this performance. This finding underscores the importance of monitoring financial
practices in highly profitable firms, as they may have both the resources and
motivations to manipulate reported earnings. Leverage exhibited a significant negative
relationship with earnings management, suggesting that higher levels of debt reduce the
likelihood of earnings management. This negative effect can be explained by the
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5460
Coefficient: 0.0000816
Interpretation: A one-unit increase in financial distress results in a minimal increase of
0.0000816 in earning management, assuming all other variables remain constant. This
very small positive effect indicates that financial distress, as measured by the Altman Z
Score, has very little impact on earnings management.
Leverage:
Coefficient: -0.0316403
Interpretation: A one-unit increase in leverage is associated with a decrease of
0.0316403 in earning management, assuming other factors remain constant. This
negative relationship suggests that higher leverage reduces earnings management
activities, potentially due to increased creditor scrutiny or stricter financial discipline.
Firm Size:
Coefficient: 0.0003914
Interpretation: A one-unit increase in firm size leads to a very small increase of
0.0003914 in earning management, holding other variables constant. This indicates a
negligible positive relationship between firm size and earnings management, suggesting
that firm size has little to no practical effect on earnings management in this model.
Profitability:
Coefficient: 0.150339
Interpretation: A one-unit increase in roa is associated with an increase of 0.1503039 in
earning management, assuming other variables remain constant. This strong positive
coefficient indicates that higher profitability significantly increases earnings
management activities, likely reflecting managerial incentives to enhance reported
financial performance.
Conclusion
This study provides an exploration of the factors influencing earnings
management, specifically focusing on discretionary accruals measured by the modified
Jones model. The findings highlight key insights into the roles of profitability, leverage,
financial distress, and firm size in shaping earnings management practices. Among
these variables, profitability and leverage stand out as the most significant drivers, while
financial distress and firm size exhibit minimal impacts. Profitability was found to have
the largest positive influence on earning management, with a significant coefficient
indicating a strong relationship. This suggests that companies with higher profitability
are more likely to engage in earnings management practices. The positive association
can be attributed to managerial incentives to enhance already favorable financial results,
thereby meeting or exceeding market expectations. High profitability often attracts
attention from investors and stakeholders, increasing pressure on management to sustain
this performance. This finding underscores the importance of monitoring financial
practices in highly profitable firms, as they may have both the resources and
motivations to manipulate reported earnings. Leverage exhibited a significant negative
relationship with earnings management, suggesting that higher levels of debt reduce the
likelihood of earnings management. This negative effect can be explained by the
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5461
heightened scrutiny and financial discipline imposed by creditors on highly leveraged
firms. Companies with substantial debt obligations are often subject to stringent
covenants and monitoring, which limit managerial discretion in manipulating earnings.
This finding highlights leverage as a potential governance mechanism, acting as a
constraint on opportunistic financial reporting practices.
The variables of financial distress and firm size had negligible effects on earnings
management, as indicated by their small and statistically insignificant coefficients. For
financial distress, the lack of influence suggests that it does not play a significant role in
determining discretionary accruals in this context. This may be because distressed firms
are more focused on addressing operational and liquidity challenges than engaging in
earnings manipulation. Similarly, the non-significance of firm size indicates that
company size does not substantially impact the extent of earnings management. Larger
firms may face greater regulatory and public scrutiny, potentially discouraging earnings
manipulation, while smaller firms may lack the sophistication or resources to engage in
complex accounting practices. This result suggests that other contextual factors, such as
industry characteristics, governance structures, or external market conditions, may have
a more pronounced influence than firm size. These findings offer valuable insights for
stakeholders, emphasizing the need for enhanced scrutiny of financial reporting
practices, particularly in highly profitable firms. By focusing on the most influential
factors, practitioners, regulators, and investors can better address the challenges of
earnings manipulation and promote greater transparency and accountability in financial
reporting. For future research, it would be beneficial to expand the dataset to include a
broader range of companies across multiple sectors and countries. Extending the
observation period and incorporating additional variables—such as governance
indicators, industry effects, or macroeconomic conditions could provide a more
comprehensive understanding of earnings management practices and their determinants.
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5461
heightened scrutiny and financial discipline imposed by creditors on highly leveraged
firms. Companies with substantial debt obligations are often subject to stringent
covenants and monitoring, which limit managerial discretion in manipulating earnings.
This finding highlights leverage as a potential governance mechanism, acting as a
constraint on opportunistic financial reporting practices.
The variables of financial distress and firm size had negligible effects on earnings
management, as indicated by their small and statistically insignificant coefficients. For
financial distress, the lack of influence suggests that it does not play a significant role in
determining discretionary accruals in this context. This may be because distressed firms
are more focused on addressing operational and liquidity challenges than engaging in
earnings manipulation. Similarly, the non-significance of firm size indicates that
company size does not substantially impact the extent of earnings management. Larger
firms may face greater regulatory and public scrutiny, potentially discouraging earnings
manipulation, while smaller firms may lack the sophistication or resources to engage in
complex accounting practices. This result suggests that other contextual factors, such as
industry characteristics, governance structures, or external market conditions, may have
a more pronounced influence than firm size. These findings offer valuable insights for
stakeholders, emphasizing the need for enhanced scrutiny of financial reporting
practices, particularly in highly profitable firms. By focusing on the most influential
factors, practitioners, regulators, and investors can better address the challenges of
earnings manipulation and promote greater transparency and accountability in financial
reporting. For future research, it would be beneficial to expand the dataset to include a
broader range of companies across multiple sectors and countries. Extending the
observation period and incorporating additional variables—such as governance
indicators, industry effects, or macroeconomic conditions could provide a more
comprehensive understanding of earnings management practices and their determinants.
Ayu Sheila Soraya, Dianwicaksih Arieftiara
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5462
Bibliography
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management, signaling, and procyclicality: Evidence from Jordan. Journal of
Financial Reporting and Accounting, 16(4), 677–693.
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financial distress: Evidence from India. Global Business Review, 16(5_suppl),
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management: The implications of institutions and governance systems.
Management Decision, 54(10), 2447–2461.
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Practices in Indonesia’s Consumer Goods Companies. Accounting Analysis
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[Special Issue]. Corporate Governance and Organizational Behavior Review,
5(2), 165–183.
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Giarto, Rizka Vidya Dwi, & Fachrurrozie, Fachrurrozie. (2020). The effect of leverage,
sales growth, and cash flow on financial distress with corporate governance as a
moderating variable. Accounting Analysis Journal, 9(1), 15–21.
Heniwati, Elok, & Essen, Erlina. (2020). Which Retail Firm Characteristics Impact On
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5462
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Abu-Serdaneh, Jamal. (2018). Bank loan-loss accounts, income smoothing, capital
management, signaling, and procyclicality: Evidence from Jordan. Journal of
Financial Reporting and Accounting, 16(4), 677–693.
Agrawal, Khushbu, & Chatterjee, Chanchal. (2015). Earnings management and
financial distress: Evidence from India. Global Business Review, 16(5_suppl),
140S-154S.
Alfina, Claudy, & Sambuaga, Elfina Astrella. (2021). Pengaruh Opportunistic
Behaviour, Leverage, Financial Distress Terhadap Earnings Management.
Ultimaccounting Jurnal Ilmu Akuntansi, 13(1), 60–74.
Aljughaiman, Abdullah A., Nguyen, Tam Huy, Trinh, Vu Quang, & Du, Anqi. (2023).
The COVID-19 outbreak, corporate financial distress, and earnings management.
International Review of Financial Analysis, 88, 102675.
Almadi, Madi, & Lazic, Philip. (2016). CEO incentive compensation and earnings
management: The implications of institutions and governance systems.
Management Decision, 54(10), 2447–2461.
Ardillah, Kenny, & Vesakhadevi, Selvi. (2021). Determinant of Earnings Management
Practices in Indonesia’s Consumer Goods Companies. Accounting Analysis
Journal, 10(3), 198–205.
Chhillar, Palka, & Lellapalli, Ramana V. (2022). Role of earnings management and
capital structure in signaling early stage of financial distress: a firm life cycle
perspective. Cogent Economics & Finance, 10(1), 2106634.
ElHawary, Engy, & Hassouna, Dina. (2021). Earnings management determinants: A
study of Egyptian listed firm characteristics post the Egyptian revolution.
ElHawary, E., & Hassouna, D.(2021). Earnings Management Determinants: A
Study of Egyptian Listed Firm Characteristics Post the Egyptian Revolution
[Special Issue]. Corporate Governance and Organizational Behavior Review,
5(2), 165–183.
Fachrudin, Khaira Amalia. (2020). The relationship between financial distress and
financial health prediction model: A study in public manufacturing companies
listed on Indonesia Stock Exchange (IDX). Jurnal Akuntansi Dan Keuangan,
22(1), 18–27.
Giarto, Rizka Vidya Dwi, & Fachrurrozie, Fachrurrozie. (2020). The effect of leverage,
sales growth, and cash flow on financial distress with corporate governance as a
moderating variable. Accounting Analysis Journal, 9(1), 15–21.
Heniwati, Elok, & Essen, Erlina. (2020). Which Retail Firm Characteristics Impact On
Financial Distress and Earnings Management
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5463
Financial Distress? Journal of Accounting and Finance, 22(1), 40–46.
Istiqomah, Aisyah, & Adhariani, Desi. (2017). The influence of earnings management on
stock return with audit quality and audit committee effectiveness as variables
moderation. Journal of Accounting and Finance, 19(1), 1–12.
(2022). The
effect of profitability, board size, women on boards, and political connection on
financial distress conditions. Cogent Business & Management, 9(1), 2142997.
Lazzem, Safa, & Jilani, Faouzi. (2018). The impact of leverage on accrual-based
earnings management: The case of listed French firms. Research in International
Business and Finance, 44, 350–358.
Ranjbar , Samin , & Amanollahi , G. (2018). The effect of financial distress on earnings
management and unpredicted net earnings in companies listed on the Tehran
Stock Exchange. Management Science Letters, 8(9), 933–938.
(2021). Financial
distress and earnings management in Indonesia: The role of independent
commissioners.
An Empirical Study of Non-Financial Firms Listed on the Indonesia Stock Exchange
Indonesian Journal of Social Technology, Vol. 5, No. 11, November 2024 5463
Financial Distress? Journal of Accounting and Finance, 22(1), 40–46.
Istiqomah, Aisyah, & Adhariani, Desi. (2017). The influence of earnings management on
stock return with audit quality and audit committee effectiveness as variables
moderation. Journal of Accounting and Finance, 19(1), 1–12.
(2022). The
effect of profitability, board size, women on boards, and political connection on
financial distress conditions. Cogent Business & Management, 9(1), 2142997.
Lazzem, Safa, & Jilani, Faouzi. (2018). The impact of leverage on accrual-based
earnings management: The case of listed French firms. Research in International
Business and Finance, 44, 350–358.
Ranjbar , Samin , & Amanollahi , G. (2018). The effect of financial distress on earnings
management and unpredicted net earnings in companies listed on the Tehran
Stock Exchange. Management Science Letters, 8(9), 933–938.
(2021). Financial
distress and earnings management in Indonesia: The role of independent
commissioners.