Analyses of Earnings Management Practice in Nigeria: Evidence from Listed Non- Financial Industries

Abstract

Manipulations of accounting numbers negatively for the purpose of increasing the economic performance lead to the poor quality of the real economic performance of a firm. This study investigates earnings management practice and compare it among some selected Non-Financial listed industries in Nigeria using sample of 81 companies from 10 sectors. OLS method of estimation was used to estimate discretionary accruals as proxy of earnings management using Modified Jones Model (1995). The result revealed that all the listed Non-financial firm manipulate earnings. Natural resources sector is the sector with high earnings management while, Heath care is the sector that manage earnings least. The study recommended that SEC should come up with a policy that will heavily regulate the financial and operations activities of Non-financial sector, especially, natural resources sector that manage earnings most.

Full text

KASU Journal of Accounting Research and Practice Vol. 7 No. 2 December, 2018 ISSN: 2360-8889
1
Analyses of Earnings Management Practice in Nigeria: Evidence from Listed Non-
Financial Industries
By
Nuraddeen Usman Miko Ph.D
Department of Accounting, Faculty of Management Science
Kaduna State University, Kaduna
and
Ladan Sahnun Ph.D
Department of Business Administration, School of Business
Ahmadu Bello University, Zaria
Abstract
Manipulations of accounting numbers negatively for the purpose of increasing the economic
performance lead to the poor quality of the real economic performance of a firm. This study
investigates earnings management practice and compare it among some selected Non-Financial
listed industries in Nigeria using sample of 81 companies from 10 sectors. OLS method of
estimation was used to estimate discretionary accruals as proxy of earnings management using
Modified Jones Model (1995). The result revealed that all the listed Non-financial firm
manipulate earnings. Natural resources sector is the sector with high earnings management
while, Heath care is the sector that manage earnings least. The study recommended that SEC
should come up with a policy that will heavily regulate the financial and operations activities of
Non-financial sector, especially, natural resources sector that manage earnings most.
Keywords: Earnings Management, Discretionary Accruals, Non-Financial firms, Nigeria
1. Introduction
Investors and other stakeholders have interest in financial reporting because financial reporting
contains information about earnings of their investments. Reported earnings are considered to be
of valued relevance for shareholders in estimating their future returns (Das & Kim, 2013).
However, some financial reports contained manufactured reports that are full of lies. Financial

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analysts can find out the effect of earnings management if it is included in future earnings
forecast through large accruals (Abarbanell & Lehavy, 2003).
Earnings management can be seen either as a booster or a destroyer of a firm’s earnings quality
(Hui & Fatt, 2007). Earnings management can be good which can boost the company’s earnings,
and also can be bad, which destroys company’s earnings quality. Kin (2008) groups earnings
management into two categories: real-based earnings management and accrual-based earnings
management. Real-based earnings management has to do with manipulation of real activities,
such as reducing discretionary expenditure; while accrual-based earnings management is the
alteration of accruals or revisal of accruals through changes of accounting estimation. Real-based
earnings management has a direct effect on the cash flow, while accrual-based earnings
management has no direct effect on the cash flow (Roychowdhury, 2006). Managers use either
of the methods to manipulate income to boost firms’ earnings and report unrealistic figures in the
financial report.
Earnings management may lead to the loss of investments. For instance, investers have been
concerned with the collapse and scandals of giant companies, such as Enron, WorldCom and
Xerox in developed countries because they suffered investment losses (Fodio, Ibikunle, & Oba,
2013). In Nigeria, corporate scandals have involved large companies, such as African Petroleum
PLC, Cadbury Nigeria PLC and Lever Brothers PLC (Ajibolade, 2008; Miko & Kamardin,
2015). This study investigates earnings management practice and compare it among some
selected Non-Financial listed industries in Nigeria.
2. Conceptual Framework and Literature Review

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2.1 Earnings Management
Earnings management has many definitions due to its receiving the attention of researchers,
investors and practitioners. There is no consensus on a single definition for earnings management
(Beneish, 2001), because there are many ways of defining it (Healy, 1985; Healy & Wahlen,
1999; Meek & Thomas, 2004). For example, earnings management is considered as management
measures, which decrease the quality of the reported earnings (Kinney, Palmrose, & Scholz,
2004). Fields, Lys, and Vincent (2001) explain that earnings management happens when
managers make judgment over the accounting figures. Managers will only engage in earnings
management if they believe that users of accounting information cannot completely adjust the
accounting numbers to remove the effect of earnings management. Earnings management leads
to lower earnings quality as it reduces the predictive ability of future earnings and cash flows
(Lev, 2003), to the extent that earnings are managed to mislead investors, which is generally
considered as unethical (Siregar & Utama, 2008).
Schipper (1989) defines earnings management as, “the process of taking deliberate steps within
the constraints of Generally Accepted Accounting Principles (GAAP) to bring about the desired
level of reported income”. Earnings management is also seen as, “an intentional structuring of
reporting or production/investment decisions around the bottom line impact” (Hui & Fatt, 2007).
Meek and Thomas (2004) see it as, “an intentional manipulation or opportunistic action of
reported measures from the unbiased amounts to achieve and lead to incorrect decisions by
investors and others”. Similarly, Burgstahler and Dichev (1997) state that earnings management,
“….generally encompasses a broad range of actions that affect earnings ranging from ‘real’
operating, and financing actions to pure ‘bookkeeping’ actions that affect only accounting
measures of earnings”. Healy and Wahlen (1999) state that, “earnings management occurs when

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managers use judgment in financial reporting in structuring transactions to alter financial
reports, to either mislead some stakeholders about the underlying economic performance of the
company, or to influence contractual outcomes that depend on reported accounting”.
Based on the above definitions, it is clear that earnings management can be used positively as it
is provided by GAAP to improve economic performance of a firm as shown by Schipper (1989).
It can also be used in a negative way to increase economic performance which may lead to the
poor quality of the real economic performance of a firm (Healy & Wahlen, 1999). This study
adopts the definition of Healy and Wahlen (1999).
Several preceding studies have investigated whether or not earnings management exists in firms’
financial report (Burgstahler & Dichev, 1997; DeAngelo, DeAngelo, & Skinner, 1994; Dechow,
Sloan, & Sweeney, 1995; Healy, 1985). Some studies have attempted to find out the earnings
management types (Beneish, 2001; Siregar & Utama, 2008); some have looked at the earnings
management motives (Healy & Wahlen, 1999); factors similar to reward of management
incentives of contract (Dechow & Sloan, 1991; Guidry, Leone, & Rock, 1999); motivation of
regulators to earnings management (Key, 1997); motivation of capital market to earnings
management (Teoh, Welch, & Wong, 1998); and incentives of external contract (Watts &
Zimmerman, 1986). Evidences show clearly that earnings management exists in firms’ financial
report, and research on earnings management has existed for a long time with different findings.
Researchers have established that earnings management exists in financial reports where
efficient earnings management colors the reported earnings; while opportunistic earnings
management (which is the main focus of the present study) destroys the reported financials. The
main difference between efficient and opportunistic earnings management is the destructive
nature of the accounting reports by the opportunistic method, which often misleads owners.

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Earnings management happens in three ways: by the use of certain income structuring and/or
transaction of expense; by changes of accounting procedures; and by the use of accruals
management (Mcnichols & Wilson, 1988; Schipper, 1989). Out of these techniques of earnings
management, accruals management is the most destructive to the accounting report’s value
because the shareholders are not aware of the amount of accruals (Mitra & Rodrigue, 2002).
Accruals are simply defined as the difference between the cash flow from operating activities
and earnings. Accruals can be categorized into discretionary accruals and non-discretionary
accruals (Rao & Dandale, 2008). Discretionary accruals are alterations to cash flows selected by
managers, whereas non-discretionary accruals are accounting adjustments to a firm’s cash flows
approved by the accounting standard-setting bodies (Rao & Dandale, 2008).
The differences between theoretical definitions and categorizations of earnings management
have shaped many opportunities for the researchers to investigate the practices, motivations and
consequences of earnings management. Several studies have been conducted in the international
arena on earnings management with many different variables (Al-Fayoumi & Alexander, 2010;
Al-Khabash & Al-Thuneibat, 2009; Burgstahler & Dichev, 1997; Cheng, Man, & Yi, 2013;
DeAngelo et al., 1994; Dechow et al., 1995; Hamad, 2007; Healy, 1985; Kanagaretnam, Lobo,
& Mathieu, 2003; Liu & Sun, 2010; Nelson, Elliott, & Tarpley, 2002; Perramon, Amat Salas, &
Oliveras, 2013; Qarran, 2005; Roychowdhury, 2006; Song, 2013; Wongsunwai, 2013; Xie et al.,
2003; Yang & Bay, 2013). Studies in the local context (Nigeria) are very few however, for
instance, Fodio et al. (2013).
2.2 Opportunistic and Efficient Earnings Management
Studies have shown that efficient earnings management maximizes shareholders’ wealth while
opportunistic earnings management enhances management’s private wealth (Mitra & Rodrigue,

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2002). Similarly, previous studies have differentiated the two forms of earnings management
(Balsam, Bartov, & Marquardt, 2002; Gul, Leung, & Srinidhi, 2000; Siregar & Utama, 2008;
Yang & Krishnan, 2005). The main difference between efficient and opportunistic earnings
management is destructive financial report with unrealistic earnings in opportunistic earnings
management method, while efficient earnings management method is terming private
information to reflect in earnings.
Efficient earnings management is used by managers to enhance the quality of earnings through
communicating private information to reflect the economic value of the firm, while opportunistic
earnings management is used by managers within the constraint of “GAAP” to engage in
aggressive reporting of accruals that harm the reported earnings (Siregar & Utama, 2008;
Stubben, 2010). For example, Siregar and Utama (2008) report that companies listed on the
Jakarta Stock Exchange engage in efficient earnings management which have positive and
significant influence on future profitability. However, studies have indicated that the
consequences of opportunistic earnings management is the same with efficient earnings
management because they all have a significantly positive relationship with the future
profitability of firms (Gul et al., 2000; Siregar & Utama, 2008; Subramanyam, 1996; Yang &
Krishnan, 2005).
2.3 Estimation Methods of Discretionary Accruals
Previous studies have suggested various methods of estimating earnings management using
discretionary accruals (Chang & Sun, 2010; Cohen, Dey, & Lys, 2008; Fodio et al., 2013;
Peasnell, Pope, & Young, 2005). Some models of estimating discretionary accruals as a proxy
for earnings management such as Jones (1991) model, Modified Jones (1995) model, Dechow
and Dichev (2002) model and Kothari, Leone and Wasley (2005) model. The Modified Jones

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Model (1995) is commonly employed to estimate discretionary accruals because it is a model
that best estimates discretionary accruals (Fodio, Ibikunle, & Oba, 2013).
3. Methodology
This study uses all listed 137 Non-financial companies as the population of the study as at 31st
December, 2013. Out of 137 non-financial companies, 56 companies were excluded because of
missing information which left the sample size of the study 81 companies representing 59% of
the Non-financial companies. The details of the sample size are presented in Table 3.1.
The period of the study covers 5 years (2013–2017). Multiple regression was used to estimate
discretionary accruals. The modified Jones Model (1995) used to estimate discretionary accruals
is as follow:
DA = TAC/Ait-1-[α1(1/Ait-1)+α2(∆REVit/Ait-1-∆RECit/Ait-1)+α3(PPEit/Ait-1)]
Where:
TAC/Ait-1 is the total accruals,
1/Ait-1 is the 1 divided by lagged total asset,
∆REV- ∆REC/Ait-1 is the changes in revenue divided by lagged total asset and
PPE/Ait-1 is the plant, properties and equipment.
4. Result and Discussions
Table 3.1
Population and Sample of the Study
Population from 2013 to 2017
Units
%
Total Population (Non-Financials Firms)
137
100
Minus:
Companies with Incomplete Information and Data
56
41
Total Sample
81
59

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This section presents the analyses of companies categories based on sectors, discretionary
accruals estimation method and discretionary accruals based on sectors.
4.1 Analysis of companies by Categories
This is the detail analysis of 81 companies used as the sample size of the study. The sectors
comprises of agriculture, conglomerates, construction and real estate, consumer goods, health
care, ICT, industrial goods, natural resources, oil and gas, and services sectors.
Table 4.1 shows the total of 81 companies from 10 groups of industries as follows:
Table 4.1
Furthermore, Table 4.1 discloses that consumer goods industry has the largest number of
companies with 19 companies representing 23.5% of the sample. This is followed by the services
industry with 15 companies representing 18.5%; industrial goods with 11 companies
representing 13.6%; oil and gas sector with 8 companies representing 9.9%; ICT industry with 7
companies representing 8.6%; and the conglomerate and healthcare industries with 6 companies
each, representing 7.4% . The agriculture, construction and real estate, and natural resources
industries comprise 4, 3 and 2 companies, representing 4.9%, 3.7% and 2.5%, respectively.
Categories of the Companies by Sectors
Industry Type
Industry Code
Number of Companies
Percentage (%)
Agriculture
1
4
4.9
Conglomerates
2
6
7.4
Construction & Estate
3
3
3.7
Consumer Goods
4
19
23.5
Health Care
5
6
7.4
ICT
6
7
8.6
Industrial Goods
7
11
13.6
Natural Resources
8
2
2.5
Oil and Gas
9
8
9.9
Services
10
15
18.5
Total Sample Size
10
81
100%

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4.2 Estimation of Discretionary Accruals
The present study follows the previous studies (Dechow et al., 1995; Iraya et al., 2015; Kasznik
et al., 1999; Mohamad et al., 2012; Rahman & Ali, 2006; Yoon et al., 2006) and uses pooled
cross-sectional OLS method of estimation and tested the model to show the ability of the
Modified Jones Model to decompose total accruals into discretionary accruals and non-
discretionary accruals. The study provides the details of the Model in Table 4.3 which discloses
the parameter estimates of discretionary accruals Model based on Dechow, Sloan and Sweeney
(1995).
Table 4.2
Descriptive Statistics of Parameters Estimation of Discretionary Accruals
DA=TAC/Ait-1-[α1(1/Ait-1)+α2(∆REVit/Ait-1-∆RECit/Ait-1)+α3(PPEit/Ait-1)]
Parameter
Mean
Min
Max
Coefficients
t-statistics
TAC/Ait-1
1.141
0.009
15.131
1/Ait-1
0.000
0.000
0.000
-39777.125
-1.478*
∆REV-∆REC/Ait-1
2.117
-4.533
3.614
1.797
2.852**
PPE/Ait-1
0.503
0.000
11.275
-1.018
-25.852***
Durbin Watson
1.767
R2
0.589
Adjusted R2
0.586
F-statistics
191.370***
***, **, * is significant at 1, 5 and 10%, respectively. TAC/Ait-1 is the total accruals, 1/Ait-1 is the 1 divided by
lagged total asset, ∆REV- ∆REC/Ait-1 is the changes in revenue divided by lagged total asset and PPE/Ait-1 is the
plant, properties and equipment.
The study estimates total accruals using cash flow approach as TAC = EBXI – CFO, where
EBXI is earnings before tax and extraordinary items; and CFO is the cash flow from operations
(Davidson, Goodwin-Stewart, & Kent, 2005). The mean of the discretionary accruals Model for
the parameters TAC/Ait-1, 1/Ait-1, ∆REV- ∆REC/Ait-1, and PPE/Ait-1 are 1.141, 2.117 and 0.503,
respectively. On average, the study expects coefficient of change in revenue (∆REV-
∆REC/Ait-1 (α2)) to be positive and lower than change in plant, property and equipment. The

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coefficient of PPE/Ait-1 (α3) is negative on average as it indicates that decrease in property, plant
and equipment (PPE) for the firms is more of depreciation of the assets for the period (Davidson
et al., 2005). The Model is correctly specified and it decomposes total accruals into discretionary
and non-discretionary accruals (Bernard & Skinner, 1996; Davidson et al., 2005).
The minimum values for TAC/Ait-1, 1/Ait-1, ∆REV- ∆REC/Ait-1 and PPE/Ait-1 are 0.009, 0.000, -
4.533 and 0.000, respectively. The maximum values are 15.131, 0.000, 3.614 and 11.275 for
TAC/Ait-1, 1/Ait-1, ∆REV- ∆REC/Ait-1, and PPE/Ait-1, respectively. The multicollinearity test
using Durbin Watson (DW) test of 1.767 shows the absence of multicollinearity. The model
fitness of R2 is 0.586 and F-change is 191.370, which is significant at the 1% significance level,
indicating that the Model is fit to detect earnings management.
4.3 Discretionary Accruals Based on Industry
Descriptive statistics of discretionary accruals are presented in Table 4.3 based on the industries
as classified by the SEC (Nigeria). The classifications of industries are: Agriculture,
Conglomerates, Construction and Real Estate, Consumer Goods, Healthcare, ICT, Industrial
Goods, Natural Resources, Oil and Gas and Services Industry. None of the sectors is excluded
from the sample of the study.
Descriptive Statistics of Discretionary Accruals Based on Industry
Industry
Mean
SD
Min
Max
N
Agriculture
0.646
0.439
0.002
1.567
20
Conglomerates
0.644
1.213
0.014
6.077
30
Construction and Estate
0.600
1.026
0.018
4.214
15
Consumer Goods
0.444
0.363
0.001
1.590
95
Health Care
0.310
0.201
0.016
0.922
35
ICT
0.553
0.547
0.044
3.117
30
Industrial Goods
0.523
0.734
0.031
5.173
55
Natural Resources
1.279
2.753
0.280
9.109
10
Oil and Gas
0.687
0.936
0.017
4.506
40

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1
Table 4.3
The Table 4.3 shows that, the mean indicates that 0.310 is the lowest mean of discretionary
accruals from the healthcare industry with minimum value of 0.016 and the maximum value of
0.922. The result may be due to the direct relationship of the sector with human lives that leads
to the lower level of discretionary accruals in this sector. The highest mean of discretionary
accruals is 1.279 from the natural resources industry with minimum value of 0.280 and the
maximum of 9.109. The result may be due to the higher level of capital inflow required in the
sector which may lead to the high expectation of earnings that may lead to high earnings
management in the industry. The result indicates that the healthcare sector is the sector with the
lowest managed earnings and the natural resources sector is the highest. The services and
consumer goods industries are the sectors with the least discretionary accruals of 0.001 and
natural resources sector has highest discretionary accruals of 9.109. This result is in line with the
analysis of Beasley, Carcello, Hermanson and Lapides (2000) that earnings management differs
from one industry to another; one industry type of discretionary accruals may not be the same
with others.
5. Conclusion and Recommendations
This study investigates earnings management practice and compare it among some selected Non-
Financial listed industries in Nigeria. The study concludes that natural resources industry is the
industry with high earnings management practice followed by oil and gas industry, agriculture
industry, conglomerates, construction and real estate, services, ICT, industrial goods, consumer
goods and healthcare sector is the sector with the lowest managed earnings. The study
1
The periods of study are five years indicating that all industries observations are from ten and above.
Services
0.566
0.857
0.001
6.541
75

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recommends that SEC should come up with a policy that will regulate the financial and
operations activities of Non-financial sector, most especially natural resources sector. This will
enhances the quality of accounting numbers and restore confidence to the investors.
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