Earnings management funding and diversification strategies of banks in Africa

Description
This paper aims to investigate the implications of earnings management for funding and
diversification strategy within the context of developing and emerging economies.

Accounting Research Journal
Earnings management, funding and diversification strategies of banks in Africa
Mohammed Amidu Ransome Kuipo
Article information:
To cite this document:
Mohammed Amidu Ransome Kuipo , (2015),"Earnings management, funding and diversification
strategies of banks in Africa", Accounting Research J ournal, Vol. 28 Iss 2 pp. 172 - 194
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Earnings management, funding
and diversifcation strategies of
banks in Africa
Mohammed Amidu and Ransome Kuipo
Department of Accounting, University of Ghana Business School,
Legon, Ghana
Abstract
Purpose – This paper aims to investigate the implications of earnings management for funding and
diversifcation strategy within the context of developing and emerging economies.
Design/methodology/approach – The authors raise two issues pertinent to bank earnings
management: frst, whether there is evidence of earnings management of banks in the selected African
countries; and second, what must have accounted for the banks to engage in such practices?
Findings – The results show that almost all the 330 banks in the 29 African countries sampled are
found to have engaged in some management of their earnings during the period 2002-2009. The authors
also fnd evidence that bank activity mix and funding modes explain bank earnings quality. Overall
results indicate that the sensitivity of earnings management to revenue diversifcation across interest
income decreases, as bank market shares increases.
Originality/value – The authors investigate how earnings management is affected by banks
intermediation strategies.
Keywords Diversifcation strategy, Earnings management, Funding modes
Paper type Research paper
1. Introduction
The aim of this study is to shed more light on the relationship among bank funding
sources, diversifcation strategy and discretionary accruals as a proxy for earnings
management in a specifc sample of banks in Africa. Studies on earnings management
has continue to attract attention fromthe following incidents of the accounting fraud at
Enron, WorldCom, Xerox, Royal Ahold and HealthSouth (Shen and Chih, 2007). A case
of interest is that of Lehman Brothers repurchase agreement known in accounting
jargon as “Repo 105”[1]. Levitt (2000) also contends that the increasing attention to the
quality of reported earnings makes the study of earnings management important.
According to Healy and Wahlen (1999, p. 368), earnings management occurs:
[…] when mangers use judgement in fnancial reporting and in structuring transactions to
alter fnancial reports to either mislead some stakeholders about the underlying economic
performance of the company or to infuence contractual outcomes that depend on reported
accounting numbers.
This implies that earnings management can be used to reduce outsider interference and
to protect insiders’ private control benefts. For instance, Schipper (1989) argues that
JEL classifcation – G21, M41 N27
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/1030-9616.htm
ARJ
28,2
172
Received22 July2013
Revised26 May2014
Accepted23 June 2014
Accounting Research Journal
Vol. 28 No. 2, 2015
pp. 172-194
©Emerald Group Publishing Limited
1030-9616
DOI 10.1108/ARJ-07-2013-0045
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insiders can use their discretion in fnancial reporting to overstate the true level of
earnings and conceal unfavourable earnings realisations which would prompt outsiders
to take actions against insiders. Moreover, in the event of extensive earnings
management, fnancial reports inaccurately refect frm performance and consequently
weaken outsiders’ ability to monitor the frm (Leuz et al., 2003).
Extant literature has documented a number of studies that attempt to measure
earnings management (Jones, 1991; Dechow et al., 1995; Burgstahler and Dichev, 1997;
and Leuz et al., 2003), to identify situations where earnings management is likely and
assesses if earnings management can be detected (Perry and Williams, 1994; and Teoh
et al., 1998) and to examine particular structure of the frm and investigate whether
earnings management is facilitated or mitigated by those structures (Klein, 2002; and
Xie et al., 2003). Similarly, prior research in banking regarding factors that affect
earnings management have been centred on auditor reputations (Kanagaretnam et al.,
2010), management of loan loss provisions (LLPs), (Fronseca and Gonzalez, 2008), banks
under fnancial duress (Shrieves and Dahl, 2003), corporate governance (Shen and Chih,
2007) and initial public offerings (Adams et al., 2009). Strikingly, none of these studies
addresses the infuence of bank funding sources and diversifcation strategy on
earnings management. This gap is what the paper is seeking to address.
The motivation for this paper is that, while traditional portfolio and intermediation
theories and promotion of information asymmetry provide a link between earnings
management, funding and diversifcation strategies by banks, there is very little
empirical evidence on the effect of funding and diversifcation on earnings management.
The study therefore seeks to respond to a number of these remaining research questions:
RQ1. Do banks in Africa engage in earnings management?
RQ2. Is funding and diversifcation strategy responsible for creating and
facilitating an environment where earnings management is practiced?
RQ3. Is there any relationship between earnings management on one hand and
bank diversifcation and funding sources on the other?
Jiraporn et al. (2008) argue that diversifcation exacerbates earnings management, as
diversifcation strategy provides banks with the opportunity to escape debt holders’
monitoring activities and is thus able to mitigate the impact of debt on earnings
management. It should be noted that, though most studies view earnings management
as opportunistic, particularly in the light of recent bank scandals/failures, some studies
argue that earnings management can be used to improve the informativeness of the
reported earnings (Subramanyam, 1996). This means that earnings management may
serve as a signal that conveys information to the public and the stockholders. On the
other hand, the offsetting of accruals hypothesis contends that diversifed banks tend to
fnance their operations from diverse sources to manage their earnings. For instance,
Amidu and Wolfe (2012) reveal that banks that use internal funding have a propensity
to diversify into non-interest-generating activities. The accruals generated by these
funds are less than perfectly correlated and thus neutralise each other. It is therefore
diffcult for managers of diversifed banks to manage earnings substantially. Thus, by
this argument, diversifed banks fnance their operations from various sources, giving
them the opportunity to reduce earnings management, as some sources provide a less
potent instrument for earnings management.
173
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This paper blends the research on diversifcation and funding modes with that of a
study on earnings management. Apart from an extension in the scope of the current
literature, the paper also makes the following two important contributions regarding
developing and emerging economies: frst, the study contributes to the earnings
management literature by arguing that earnings management could be mitigated in
banks that have diversifed their activities, and the second contribution to the literature
relates to bank revenue diversifcation strategy. Research on bank diversifcation has
been varied and extensive, covering the impact of diversifcation across markets on
deposit rates (Barros, 1999), the effect of diversifcation on bank market valuation
(Laeven and Levine, 2007) and the linkage between diversifcation and performance
(Berger et al., 2010; and Amidu, 2013). To this end, this paper analyses how earnings
management is affected by banks intermediation strategies.
The accruals-based earnings management measure is used to fnd out the
relationship between banks earnings management, funding modes and diversifcation
strategy. This methodology has been used in a large number of studies in earnings
management (Altamuro and Beatty, 2010; Kanagaretnam et al., 2010 and Leuz et al.,
2003)[2]. We focus on diversifcation into business lines and use measures of
diversifcation between net interest income and non-interest income generating
activities. Three funding modes have been identifed in the sample as bank funding
strategies: deposits, non-deposits/wholesale and internal capital funds.
Our results demonstrate that almost all the banks in the 29 African countries
sampled have managed their earnings during the period 2002-2009. With regard to the
relationship among the key variables, we fnd evidence that supports the fact that the
bank activity mix and funding modes explain bank earnings quality. Overall, our
results point to the fact that earnings management among banks with a higher market
share is signifcantly more sensitive to internally generated funds (IGFs) than it is with
deposits and wholesale funding.
The remainder of this paper is organised into four additional sections. Section
2describes the recent evolution of banking in Africa and reviews relevant literature on
earnings management, funding and diversifcation strategy. Section 3 discusses the
research methodology, the measurement of key variables used in the study and data and
descriptive statistics. Section 4 discusses the regression results and robustness tests,
and fnally, Section 5 provides the conclusions and policy implications.
2. Background and literature
This section provides a brief discussion of recent developments in banking in Africa. It
also provides an overview of salient literature on earnings management, funding and
diversifcation strategy of banks.
2.1 Banking in Africa
Africa’s fnancial systems are predominately bank-based and the level of fnancial
intermediation is low compared with the rest of the world (IMF, 2006). Credit to the
private sector stands at an average of 78 (as a percentage of gross domestic product
[GDP]) for Sub-Saharan African (SSA) banks as compared to 132.5 per cent in 2011 for
other emerging markets in East Asia and the Pacifc. The poor bank credit in Africa,
according to Honohan and Beck (2007), is in itself a function of widespread poverty and
the large share of the population engaged in subsistence agriculture. More so, the large
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concentration of populations in subsistence production limits the fnancial resources
available for intermediation. Demirguc-Kunt et al. (2004) argue that low-income
countries’ private sector correlates positively with GDP per capita income and
negatively with the size of the agricultural sub-sector. The lowperformance of banks in
SSA in the area of credit has occurred in an environment of high liquid reserves, broad
money and extreme risk aversion in the banking system. In addition to excess liquidity
and the high ratio of non-performing loans in the SSAbanking system, the debt position
of SSA countries has also accounted for the poor performance in bank credit extension
(Nissanke and Aryeetey, 2006). Furthermore, SSAhas the lowest deposit institutions in
the world with an average of 16.6 per cent compared to 63.5 per cent in developing
economies with the level of penetration of 166 banks per 1,000 adults for the SSAregion
(Kimenyi and Ndungu, 2009).
In the past three decades, African countries have embarked on fnancial sector
restructuring involving deregulation and gradual opening up of the fnancial sector to
foreign participation. The reform of the fnancial sector and developments are a crucial
channel for global integration and keeping Africa at the cutting edge of best
international practices (Senbet and Otchere, 2006). The reforms and fnancial policy
changes were conducted along seven different dimensions: credit controls and reserve
requirements, interest rate controls, entry barriers, state ownership, policies on
securities markets, prudential regulations and supervision of the banking sector and
restrictions on the capital account. The reforms saw the establishment of capital
markets in many countries including the regional market that serves CFA countries –
Benin, Burkina Faso, Cote d’Ivoire, Guinea-Bissau, Mali, Niger, Senegal and Togo.
Capital markets provide capital mobilisation and allow for risk allocation and risk
sharing among market participants. The intervention also saw the nationalisation of
private banks, establishment of entirely new state banks and non-bank fnancial
institutions. These developments appear to have improved the fnancial soundness of
SSA banks in the past decade including the 2008 global fnancial crisis.
Inspite of the lowlevel of credit to the private sector, there is evidence to suggest that
over the past decade, average bank earnings were signifcantly higher than the earnings
in other parts of the world (Flamini et al., 2009). A close look at Figure 1 suggests that
African banks earnings are higher and stable while the rest of the world has been low
and fuctuating. For example, during the period 2000-2007, while the average earnings
change in growth rate of African banks was (9.1 per cent), that of Asia-Pacifc was 18.9
per cent, Central and Eastern Europe 39.9 per cent and Latin America ?155.5 per cent.
The relative size of cross-African profts also appears to be persistent over the period.
What is not certain is whether the funding and diversifcation strategies are responsible
for the higher and persistent earnings in these economies. This is what the paper seeks
to address.
2.2 Literature review
The theoretical principles underlying the earnings management, funding and
diversifcation strategy of banks can be described either in terms of information
asymmetry or internal capital market frameworks. The information asymmetry
hypothesis suggests that banks that operate in many sectors[3] are likely to have more
complex structure than those that operate in single industry. Similarly, banks that
operate in many countries need to have their resources spread out over wide
175
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geographical areas. There is, thus, a need for more sophisticated structures to control the
operations of the bank. More so, banks that are both industrially and geographically
diversifed are likely to have the most complex structure. In analysing earnings reports
of diversifed banks, the public and analysts may require more resources and expertise
to accurately examine earnings that emanate from the various sources and from
different countries. Thus, the level of information asymmetry is likely to be more acute
in diversifed banks. Furthermore, managers in diversifed banks may exploit the
additional informational asymmetry and engage in earnings management than they
otherwise would be if the bank is more focused. Accordingly, informational asymmetry
hypothesis suggests that banks that are more diversifed engage in a larger degree of
earnings management.
Corollary to this is that the degree of information asymmetry between managers and
outsiders may be signifcantly greater for diversifed banks than stand-alone banks. For
a diversifed bank, outsiders must rely on the information provided by the bank,
whereas internal managers can identify the performance of each unit of the bank.
Despite the regulatory and supervision efforts to mandate diversifed banks to disclose
information at the segment level, investors in these banks may face higher information
asymmetry compared to those of focused banks, for at least two reasons. First, reporting
of divisional information is subject to considerable managerial discretion. Managers
could simply transfer funds across different units, as they wish for reporting purposes.
Second, even if correct information is available, deciphering of such information
requires considerable time, effort, resources and expertise that are not likely present for
an average investor. Managers intentionally smooth earnings to gain some benefts
(Trueman and Titman, 1988), achieve a better refection of the fundamental frm value
(Watts and Zimmerman, 1986), improve predictability of earnings (Degeorge et al., 1999)
and for career concerns (DeFond and Park, 1997). However, it could be argued that those
in diversifed frms would have less need for earnings management, as the imperfect
correlation in funds across different units would naturally result in less variability in
loanable funds. We also argue that if funds of units offset each other due to imperfect
correlation, the unit accruals would likewise counterbalance each other, leaving fewer
accruals at the frm level. Thus, the managers of diversifed banks would have fewer
Figure 1.
ROAs in developing
countries from four
different regions
including SSA,
Asia-Pacifc, Central
and Eastern Europe
and Latin America
(the grouping of
countries into
different regions
follows the World
Bank Development
Indicators
classifcation). The
proftability of the
banks is proxied by
the ROAs
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accruals to manipulate as compared to those of focused banks. This argument is in line
with Thomas’s (2002) in which the errors in analysts’ forecast for units in non-fnancial
frms are considered to offset one another, mitigating the overall deviation of the
analysts’ earnings forecasts from the actual earnings for a diversifed frm.
The internal capital market or the notion of “corporate socialism” argues that the
allocation of capital among different units within the same bank is more effcient than
raising capital from external sources. Again, the presence of internal funds within a
diversifed bank lends further support for a possible negative relation between
diversifcation and earnings management (Stein, 1997). Thus, earnings management
can be explained between two different time dimensions: the current and the future. For
instance, a bank manager would borrow (save) “current” earnings in anticipation of
promising (undesirable) “future” earnings. In contrast, in diversifed banks, earnings
management could be achieved contemporaneously across different units on the
condition that divisions are not perfectly correlated. Amanager could therefore attempt
to alleviate the poor performance of a division by “borrowing” earnings from a
proftable unit. Consequently, the earnings of a diversifed frm would be more stable,
thus requiring less earnings management at the frmlevel. On the basis of this and given
the various funding sources of a bank, this study predicts the existence of a negative
relationship between diversifcation and earnings management.
3. Data and methodology
3.1 Diversifcation strategy
In line with Sanya and Wolfe (2011), revenue diversifcation strategy is measured by
constructing the Herfndahl-Hirschmann Index for each bank. This measure accounts
for diversifcation between major activities: net interest income (NET) and non-interest
income (NON). The revenue diversifcation DIV
( REV)
for each bank is therefore
calculated as follows:
DIV
( REV)
?
(
NON
NETOP
)
2
?
(
NET
NETOP
)
2
(1)
where NONrepresents non-interest income, net interest income is captured as NETand
NETOP stands for net operating income. DIV
( REV)
is a very simple measure of revenue
diversifcation which measures the shift into non-interest income. Equation (1) is
interpreted to mean that a rise in DIV
(REV)
shows an increase in revenue concentration
and less diversifcation. This process is repeated to construct diversifcation within
non-interest activities:
DIV
( NON)
?
(
COM
NON
)
2
?
(
TRD
NON
)
2
?
(
OTOP
NON
)
2
(2)
where DIV
( NON)
is diversifcation within non-interest activities. Revenue from
commission income is captured by COM. TRD is trading income and OTOP captures
other operating income. Higher values indicate greater concentration.
3.2 Funding modes
Three funding modes have been identifed in the samples as bank funding strategies:
deposits, non-deposits/wholesale and internal funding. DEPOSIT as a source of bank
177
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funding includes demand, saving and time deposits. Customer deposits are traditionally
considered to be the main funding source of banks and to be cheaper relative to other
sources of funding, and allow banks to maintain relatively high profts (Ianotta et al.,
2007). Deposits funding is measured as the total deposits as a percentage of total assets.
The NON-DEPOSIT is the funding resource from other banks and other sources that
include notes, debenture, short-term bills and all other related debts not covered in
the deposits modes. It is short-term funding with relatively higher interest cost
compared to deposits from customers. Non-deposit funding is calculated, as all other
debts (except deposits) divided by total assets. The measurement of IGFs is similar to
that of Houston et al. (1997), as the sum of net profts before extraordinary items and
LLPs relative to bank loans at the end of the period.
3.3 Measures of earnings management: abnormal LLP
Previous studies show that earnings management in banks commonly occur using
LLPs. Similarly, Adams et al. (2009) and Nichols et al. (2009) document the use of loan
loss reserves to manage accounting earnings. In addition, Hasan and Wall (2004, p. 132)
summarise the accounting process used to determine the level of the balance sheet loan
loss allowance (LLA) and the income statement account LLPs as follows:
Banks operating under US generally accepted accounting principles (USA GAAP) follow a
multistep process to determine their allowance for loan as well as lease losses (LLA). At the end
of each accounting period, a bank determines the probable value of the loan losses in its
existing portfolio. The bank then debits its loan loss expense or provision by an amount equal
to the difference between its estimated loan losses and the current balance in its LLA. The
offsetting credit increases the bank’s LLA. The LLA is shown on the balance sheet as a
reduction in the value of its outstanding loans (in what is termed in accounting as contra-assets
account). As the period progresses, a bank will recognise that it is unlikely to collect the full
value of selected loans and charges off the portions of those loans that are unlikely to be
collected. As individual loans are charged off, the offsetting entry is a reduction in the LLA. In
some cases, the bank will fnd that it can recover part or all of the value of a loan that had been
previously charged off. The offsetting entry for these recoveries is an increase in the LLA. The
combined effect of charge-offs and recoveries is the LLA which is often simply referred to as
charge-offs net of recoveries, or net charge-offs. At the end of the period, the process repeats.
The bank compares the remaining values of its LLA with the losses in its existing portfolio.
This process is equally used for those who prepare their accounting under International
Financial Reporting Standards (IFRS) or any local GAAP.
As discussed above and given the nature of discretionary choices associated with the
banks, we examine the income statement account of LLP for evidence of earnings
management. In addition to increasing loan loss reserves in the balance sheet, increases
in LLP decrease net earnings, return on assets (ROAs) and return on equity (ROE).
Therefore, to analyse the infuence of managerial discretion on intermediation strategy,
a two-stage approach is used to identify discretionary LLP. In the frst stage, the normal
or non-discretionary component of LLP is estimated by regressing LLP on beginning
LLA, net loan charge-offs, growth in loan, change in total loan outstanding, total loans
outstanding, non-performing loans, market share of loans, earnings before tax and LLPs
and country-specifc variables using the following model:
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LLP
it
? ?
0
? ?
1
LLA
it
? ?
2
CHGOFF
it
? ?
3
GLOAN
it
? ?
4
?LOANS
it
? ?
5
LOANS
it
? ?
6
DNPA
it
? ?
7
MKTS
it
? ?
8
EBTP
it
?
?
j?4
k
?
j
M
j
? ?
t
YEARDUMMY
t
? ?
it
(3)
where LLP
it
is the expected level loan loss provision based on coeffcient estimates from
the sample of African bank during the period 2002-2009, LLA
it
is the beginning loan loss
allowance of a bank i in period t, CHGOFF
it
is the net loan charge-off of bank i in period
t, GLOAN
it
is the growth in loans of bank i in period t, ?LOANS
it
is the change in total
loan outstanding of bank i in period t, LOANS
it
is the loan portfolio of bank i in period t
and DNPA
it
is an indicator variable that equals to one if the value for non-performing
loan is missing and zero if otherwise[4]. MKTS
it
is the loan market share of bank i in
period t, EBTP
it
is the earning before tax and proft of bank i in period t, the variables M
are a set of ?k? variables controlling for the respective countries’ macro-economic
environments and regulatory variables and ?
it
is the error term. The estimation of
discretionary loan loss provisions (DLLP) is computed by subtracting the predicted
level or the non-discretional component of LLP from the actual level of LLP[5].
In the second stage, we test the link between our proxies for funding and
diversifcation strategy and the absolute value of negative DLLP. Again, we control for
bank-specifc variables (bank size, the level of leverage, market share and performance)
and country-level variables (such as infation, GDP growth and GDP per capita), as
presented in the following model:
DLLP
it
? ?
0
? ?
1
DIV
it
? ?
2
FS
it
? ?
3
DNPL
it
? ?
4
SIZE
it
? ?
5
MKTS
it
? ?
6
LOANS
it
? ?
7
LEV
it
?
?
j?4
k
?
j
M
j
? ?
t
YEARDUMMY
t
? ?
it
(4)
DLLP
it
is the estimated loan loss provision of a bank i in period t, DIV
it
is revenue
diversifcation of a bank i in period t, FS
it
is the funding strategy of bank i in period t,
DNPL
it
is an indicator variable that equals to one if the value for NPLis missing and zero
if the logarithms of total assets is used as a measure of bank SIZE
it
, MKTS
it
is the loan
market share of bank i in period t, LOANS
it
is the loan portfolio of bank i in period t,
LEV
it
is the leverage of bank i in period t, the variables M are a set of ?k? variables
controlling for the respective countries’ macro-economic environments and regulatory
variables and ?
it
is the error term.
3.4 Data and descriptive statistics
Micro-bank-level and macro-country-level data are used. Bank-level data (fnancial
statements) is taken from BankScope database maintained by Fitch/IBCA/Bureau Van
Dijk. Series are yearly, covering a sample of 330 banks across 29 countries in Africa
during the eight-year period, 2002-2009. We focus the study on the African banking
sector. Given the relationship between fnance and the real economy, the benefts of
conducting research in these sectors have a chance to make an impact beyond
African countries. Thus, the benefts and the subsequent impact of research on
emerging economies like Africa on economic growth cannot be merely measured in
179
Earnings
management
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absolute dollar terms, but in the number of people that are elevated froma desperate
subsistence level to a more adequate standard of living (Bekaert and Harvey, 2002).
This period covers both the stable period (2000-2006) and the world fnancial crisis
(2007-2009). The sample includes all commercial banks, cooperative banks,
development banks, savings banks, real estate and mortgage banks for which
annual data are available for some period of the years during the period 2002-2009.
To ensure that banks that are important players in the deposit and/or loan markets
are not omitted, medium- and long-term credit banks and specialised government
institutions are included, as they remain important in African countries.
Observations with outliers such as zero and/or negative capitalisation are dropped.
Also, observations for capitalisation above the 98th percentile were dropped. In
addition, loan growth rate observations above the 99th percentile of the distribution
were equally dropped. This is to correct for mergers, acquisitions and start-ups
during the study period. Macro-country-level data are obtained from the
International Financial Statistics database of the International Monetary Fund and
the World Bank Development Indicator.
Table I shows summary statistics for the key variables used in this study. All
bank-specifc variables are averaged by bank during the period 2002-2009, while that of
the country-level variables are averaged by country over the period under study. Banks
in Botswana are the most proftable with an average ROE ratio of 0.3473. This means
that shareholders return on investment in Botswana banks is more than 34 per cent.
However, banks operating in South Africa (SA) are the most effcient in terms of
utilising their assets to generate earnings. The average ROAs of banks in SAis 5.46 per
cent, the highest within the sample. Sudanese banks provide the highest LLP in their
income statement while banks in Tunisia have the least quality assets as they have the
highest LLA. The quality of asset of banks operating in Tunisia has resulted in
operating losses in terms of ROAs. The average ROAs of banks in Tunisia is ?0.0004.
Ugandans banks are more diversifed in terms of generating non-interest incomes, while
diversifcation strategy of Tunisian banks is geared towards interest income generating
activities. Total asset measure denominated in USAdollars, is used as a proxy for bank
size. Banks in SA are the largest banks in terms of size. The average size of the bank in
South Africa is more than $18, 251.52 million. Banks in Zimbabwe mostly use IGFs to
fnance their assets.
Table II presents the pair-wise correlation coeffcient as a preliminary analysis of the
relationship between earnings management, activities mix and funding modes. Both
diversifcations across interest income (DIV
REV
) and within non-interest-generating
activities (DIV
NON
) reduce the incidence of earnings management. Likewise the banks
that fnance their operation using deposit funds. As expected, banks in Africa engage in
earnings management through non-performing loan allowance (LLA).
4. Empirical results
This section presents results in three parts. The frst part analyses the distribution
of annual net income of 330 banks across the 29 African countries. The net income
is scaled by total equity of the banks for the period 2002-2009. The results from the
frst part are then used in the second part to test the response of bank earnings
management to diversifcation and funding strategy while controlling for
bank-specifc variables and the macro-economic environment. The third and the
ARJ
28,2
180
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Table I.
Bank-specifc
variables: averages
for the period 2002-
2009
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(
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181
Earnings
management
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
0
:
5
3

2
4

J
a
n
u
a
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2
0
1
6

(
P
T
)
Table I.
S
a
m
p
l
e
d
c
o
u
n
t
r
i
e
s
E
a
r
n
i
n
g
s
P
r
o
v
i
s
i
o
n
s
a
n
d
r
e
s
e
r
v
e
s
D
i
v
e
r
s
i
f
c
a
t
i
o
n
s
t
r
a
t
e
g
y
F
u
n
d
i
n
g
s
t
r
a
t
e
g
y
O
t
h
e
r
c
o
n
t
r
o
l
s
R
O
E
R
O
A
L
L
P
L
L
A
D
I
V
(
R
E
V
)
D
I
V
(
N
O
N
)
D
E
P
O
S
I
T
N
O
N
-
D
E
P
O
S
I
T
I
G
F
M
K
T
S
L
O
A
N
S
I
Z
E
N
a
m
i
b
i
a
0
.
1
6
7
3
0
.
0
3
2
5
0
.
0
1
5
1
0
.
0
3
7
6
0
.
5
9
0
9
0
.
5
7
0
1
0
.
6
0
1
0
.
0
8
3
9
0
.
0
9
0
1
0
.
1
9
3
5
0
.
7
2
2
4
8
9
9
.
6
9
S
o
u
t
h
A
f
r
i
c
a
0
.
2
0
4
2
0
.
0
5
4
6
0
.
0
1
7
0
.
0
3
9
4
0
.
6
3
8
9
0
.
5
0
0
8
0
.
5
6
1
2
0
.
2
1
3
5
0
.
0
7
8
5
0
.
0
9
7
2
0
.
6
4
7
8
1
8
,
2
5
1
.
5
S
w
a
z
i
l
a
n
d
0
.
1
9
9
2
0
.
0
3
3
2
0
.
0
0
7
5
0
.
0
6
4
7
0
.
5
4
6
7
0
.
5
1
7
7
0
.
6
5
7
1
0
.
1
8
2
3
0
.
0
5
0
9
0
.
2
1
2
1
0
.
7
2
5
3
1
4
9
.
8
4
T
a
n
z
a
n
i
a
0
.
2
3
0
2
0
.
0
2
3
6
0
.
0
1
9
5
0
.
0
1
7
9
0
.
5
5
4
9
0
.
4
0
5
5
0
.
7
3
7
1
0
.
0
9
3
3
0
.
0
9
6
7
0
.
0
7
7
8
0
.
4
5
7
8
2
4
9
.
4
2
Z
i
m
b
a
b
w
e
0
.
4
5
8
2
0
.
1
3
1
8
0
.
0
5
8
7
0
.
0
1
4
4
0
.
6
8
7
9
0
.
3
5
0
2
0
.
1
8
6
7
0
.
5
6
7
4
0
.
5
5
5
6
0
.
2
2
2
2
0
.
3
5
2
5
2
,
2
2
7
.
2
6
N
o
t
e
s
:
T
a
b
l
e
I
p
r
e
s
e
n
t
s
t
h
e
m
e
a
n
v
a
l
u
e
s
o
f
c
o
u
n
t
r
i
e
s
’
b
a
n
k
-
s
p
e
c
i
f
c
v
a
r
i
a
b
l
e
s
;
R
O
E
i
s
t
h
e
b
a
n
k
e
a
r
n
i
n
g
p
e
r
e
q
u
i
t
y
c
a
p
i
t
a
l
;
R
O
A
i
s
t
h
e
r
e
t
u
r
n
o
n
a
s
s
e
t
a
n
d
c
a
l
c
u
l
a
t
e
d
a
s
p
r
o
f
t
b
e
f
o
r
e
t
a
x
d
i
v
i
d
e
d
b
y
t
o
t
a
l
a
s
s
e
t
s
;
L
L
P
i
s
t
h
e
n
o
n
-
d
i
s
c
r
e
t
i
o
n
a
r
y
c
o
m
p
o
n
e
n
t
o
f
L
L
P
;
L
L
A
i
s
t
h
e
n
o
n
-
d
i
s
c
r
e
t
i
o
n
a
r
y
c
o
m
p
o
n
e
n
t
o
f
l
o
a
n
l
o
s
s
r
e
s
e
r
v
e
s
;
D
I
V
(
R
E
V
)
a
n
d
D
I
V
(
N
O
N
)
m
e
a
s
u
r
e
r
e
v
e
n
u
e
d
i
v
e
r
s
i
f
c
a
t
i
o
n
a
c
r
o
s
s
i
n
t
e
r
e
s
t
i
n
c
o
m
e
a
n
d
w
i
t
h
i
n
n
o
n
-
i
n
t
e
r
e
s
t
i
n
c
o
m
e
g
e
n
e
r
a
t
i
n
g
a
c
t
i
v
i
t
i
e
s
r
e
s
p
e
c
t
i
v
e
l
y
;
D
E
P
O
S
I
T
i
s
d
e
p
o
s
i
t
s
o
u
r
c
e
s
o
f
f
u
n
d
i
n
g
w
h
i
l
e
N
O
N
-
D
E
P
O
S
I
T
i
s
t
h
e
w
h
o
l
e
s
a
l
e
f
u
n
d
a
n
d
i
s
c
a
l
c
u
l
a
t
e
d
a
s
a
l
l
o
t
h
e
r
d
e
b
t
s
(
e
x
c
e
p
t
d
e
p
o
s
i
t
s
)
d
i
v
i
d
e
d
b
y
t
o
t
a
l
a
s
s
e
t
s
;
I
G
F
i
s
t
h
e
f
u
n
d
s
g
e
n
e
r
a
t
e
d
i
n
t
e
r
n
a
l
l
y
;
L
O
A
N
i
s
t
h
e
l
o
a
n
p
o
r
t
f
o
l
i
o
o
f
b
a
n
k
s
c
a
l
e
d
b
y
t
o
t
a
l
a
s
s
e
t
s
;
M
K
T
S
i
s
t
h
e
m
a
r
k
e
t
s
h
a
r
e
o
f
t
h
e
r
e
s
p
e
c
t
i
v
e
b
a
n
k
;
S
I
Z
E
i
s
t
h
e
a
v
e
r
a
g
e
t
o
t
a
l
a
s
s
e
t
s
.
T
h
e
m
e
a
n
v
a
l
u
e
s
o
f
t
h
e
s
e
l
e
c
t
e
d
b
a
n
k
s
f
o
r
t
h
e
r
e
s
p
e
c
t
i
v
e
c
o
u
n
t
r
i
e
s
o
v
e
r
t
h
e
p
e
r
i
o
d
2
0
0
2
-
2
0
0
9
a
r
e
i
n
p
e
r
c
e
n
t
a
g
e
t
e
r
m
s
e
x
c
e
p
t
f
o
r
b
a
n
k
s
i
z
e
w
h
i
c
h
i
s
i
n
m
i
l
l
i
o
n
s
o
f
U
S
d
o
l
l
a
r
s
.
A
l
l
t
h
e
c
a
l
c
u
l
a
t
i
o
n
s
a
r
e
i
n
p
e
r
c
e
n
t
a
g
e
s
e
x
c
e
p
t
b
a
n
k
s
i
z
e
w
h
i
c
h
i
s
i
n
m
i
l
l
i
o
n
o
f
U
S
d
o
l
l
a
r
s
S
o
u
r
c
e
s
:
B
a
n
k
s
c
o
p
e
a
n
d
a
u
t
h
o
r
s
’
c
a
l
c
u
l
a
t
i
o
n
s
;
t
h
e
d
a
t
a
s
e
t
c
o
m
p
r
i
s
e
s
o
f
3
3
0
b
a
n
k
s
i
n
2
9
A
f
r
i
c
a
c
o
u
n
t
r
i
e
s
ARJ
28,2
182
D
o
w
n
l
o
a
d
e
d

b
y

P
O
N
D
I
C
H
E
R
R
Y

U
N
I
V
E
R
S
I
T
Y

A
t

2
0
:
5
3

2
4

J
a
n
u
a
r
y

2
0
1
6

(
P
T
)
Table II.
Pair-wise correlation
coeffcient between
selected variables
D
L
L
P
L
L
A
C
H
G
O
F
F
L
O
A
N
D
N
P
L
L
E
V
S
I
Z
E
M
K
T
S
D
I
V
(
R
E
V
)
D
I
V
(
N
O
N
)
D
E
P
O
S
I
T
N
O
N
-
D
E
P
O
S
I
T
I
G
F
D
L
L
P
1
.
0
0
0
L
L
A
0
.
2
1
5
9
*
1
.
0
0
0
C
H
G
O
F
F
0
.
0
2
0
0
0
.
2
1
3
0
*
1
.
0
0
0
L
O
A
N
0
.
0
2
5
6
0
.
3
6
7
0
*
0
.
1
5
9
0
*
1
.
0
0
0
D
N
P
L
0
.
2
3
9
3
*
0
.
0
9
9
8
*
?
0
.
1
0
0
1
*
?
0
.
0
4
7
7
*
1
.
0
0
0
0
L
E
V
0
.
0
1
5
3
?
0
.
1
8
3
2
*
?
0
.
2
0
2
6
*
?
0
.
0
8
0
5
*
?
0
.
0
2
4
3
1
.
0
0
0
0
S
I
Z
E
?
0
.
0
3
1
5
?
0
.
0
3
6
2
?
0
.
1
0
0
2
*
0
.
0
6
7
3
*
?
0
.
0
4
3
7
*
0
.
2
6
5
2
*
1
.
0
0
0
0
M
K
T
S
?
0
.
0
2
7
5
0
.
0
0
6
0
.
0
2
4
0
.
1
3
5
1
*
?
0
.
0
8
9
5
*
0
.
1
7
0
3
*
0
.
2
5
3
0
*
1
.
0
0
0
0
D
I
V
(
R
E
V
)
0
.
0
7
5
5
*
0
.
0
1
2
0
.
0
8
3
9
*
0
.
0
1
3
6
0
.
0
9
5
0
*
?
0
.
2
5
8
6
*
?
0
.
0
2
3
7
?
0
.
0
3
5
7
1
.
0
0
0
0
D
I
V
N
O
N
)
0
.
0
5
0
9
*
0
.
0
3
0
2
?
0
.
0
1
7
2
0
.
0
3
9
5
0
.
1
3
1
2
*
?
0
.
0
2
9
4
0
.
0
9
7
3
*
?
0
.
0
7
7
7
*
?
0
.
0
3
8
7
1
.
0
0
0
0
D
E
P
O
S
I
T
?
0
.
0
6
7
2
*
?
0
.
0
8
9
3
*
?
0
.
1
2
2
5
*
?
0
.
0
5
5
9
*
?
0
.
0
4
9
4
*
0
.
5
0
9
0
*
0
.
0
8
0
6
*
0
.
0
6
8
9
*
?
0
.
2
2
9
9
*
?
0
.
0
3
1
9
1
.
0
0
0
0
N
O
N
-
D
E
P
O
S
I
T
0
.
0
1
6
6
?
0
.
1
2
4
9
*
?
0
.
0
3
6
8
?
0
.
0
3
9
6
0
.
0
2
1
7
?
0
.
0
2
0
7
?
0
.
0
8
8
5
*
?
0
.
0
7
7
3
*
0
.
3
2
9
2
*
?
0
.
0
8
8
8
*
?
0
.
6
8
8
9
*
1
.
0
0
0
0
I
G
F
?
0
.
0
1
4
6
?
0
.
1
5
7
2
*
?
0
.
0
0
9
8
?
0
.
4
8
6
3
*
?
0
.
0
1
5
9
?
0
.
2
1
5
3
*
?
0
.
1
3
7
1
*
0
.
0
0
1
2
0
.
1
1
4
3
*
?
0
.
0
0
9
6
?
0
.
1
3
3
1
*
0
.
1
4
3
0
*
1
.
0
0
0
N
o
t
e
s
:
T
a
b
l
e
I
I
p
r
e
s
e
n
t
s
p
a
i
r
-
w
i
s
e
c
o
r
r
e
l
a
t
i
o
n
c
o
e
f
f
c
i
e
n
t
e
s
t
i
m
a
t
e
d
o
n
s
a
m
p
l
e
o
f
3
3
0
b
a
n
k
s
a
c
r
o
s
s
2
9
A
f
r
i
c
a
c
o
u
n
t
r
i
e
s
;
*
i
m
p
l
i
e
s
s
i
g
n
i
f
c
a
n
t
a
t
5
%
o
r
m
o
r
e
;
D
L
L
P
i
s
t
h
e
d
i
s
c
r
e
t
i
o
n
a
r
y
c
o
m
p
o
n
e
n
t
o
f
L
L
P
;
L
L
A
i
s
t
h
e
n
o
n
-
d
i
s
c
r
e
t
i
o
n
a
r
y
c
o
m
p
o
n
e
n
t
o
f
l
o
a
n
l
o
s
s
r
e
s
e
r
v
e
s
;
C
H
G
O
F
F
i
s
t
h
e
r
a
t
i
o
o
f
n
e
t
c
h
a
r
g
e
-
o
f
f
s
t
o
a
v
e
r
a
g
e
l
o
a
n
s
d
u
r
i
n
g
t
h
e
p
e
r
i
o
d
;
L
O
A
N
i
s
t
h
e
l
o
a
n
p
o
r
t
f
o
l
i
o
o
f
b
a
n
k
s
c
a
l
e
d
b
y
t
o
t
a
l
a
s
s
e
t
s
;
L
E
V
i
s
t
h
e
l
e
v
e
r
a
g
e
o
f
b
a
n
k
s
c
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183
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fnal sub-section analyses the change in bank earnings quality in response to a
change in market share of banks.
4.1 Do banks in Africa manage earnings to exceed thresholds?
This sub-section analyses the results with the aim of identifying whether banks
operating in Africa engage in earnings management. We group the countries on the
basis of regional location of the banks. The groupings are northern, central and southern
Africa countries. This categorisation of countries is made by the World Bank[6]. This
allows us to examine whether there are regional differences as to how banks manage
their earnings over time. To begin with, Figure 2 presents the earnings histogram of
banks. The earnings are scaled by banks equity capital. The results of the histogramof
banks earnings showa half-normal distribution shape for all the three regions of Africa.
Earnings less than zero occur much less frequently and occur only in central zones of the
continent. These results mean that the incentive to manage earnings of banks varies
among banks in Africa.
To explain these different levels of earnings management among banks, we turn to
the respective 330 banks earnings distribution across 29 African countries from2002 to
2009[7]. Similar to the regional results, all the banks, except those banks operating in
Angola, exhibit half bell-shaped distribution. We draw two inferences from these
particular results. First, the half bell-shaped of majority of banks earnings shows that
distribution of earnings differs fromone bank to the other. Second, even for those that do
not clearly exhibit this half-normal distribution, the left hand of the distribution is
withered. These results provide us with the important stylised facts and motivate us to
further investigate whether banks in Africa manage earnings and whether the
managers of these banks use either particular funding source or specifc activity mix or
both.
4.2 Evaluation of earnings management: funding and diversifcation strategies
In this subsection, we present the results for the abnormal LLP test. A two-stage
approach is used to investigate the impact of diversifcation and funding strategy on
abnormal LLP. We begin with the estimation of non-discretionary component of LLP.
The result of the frst-stage regression is presented in Table III. As expected, the LLAis
positively and signifcantly related to LLP, as a lower initial LLA will require a higher
LLP in the current period. Consistent with earlier studies, net charge-off, growth in loan
and loan outstanding have positive association with LLP (Adams et al., 2009;
Kanagaretnam et al., 2010). These results means that an increase in current LLP is as a
result of a corresponding increase in the net charge-off loans, the growth in loans and an
increase in the outstanding bank loans. The managers of banks in Africa thus will
manage their earnings by manipulating the net charge-off of loans and extending loans
without thorough screening and monitoring of the borrowers. A case of interest here is
that of the Lehman Brothers strategy of advancing loans and selling their assets just to
meet the regulatory capital requirements.
Next, we investigate whether the adoption of a particular activity mix or using a
particular funding strategy enables banks managers’ to engage in earnings
management. Table IV presents the results of the second-stage regression and that has
DLLP as a dependent variable. The different columns reported relate to different
empirical approaches to diversifcation strategy (DIV
( REV)
and DIV
( NON)
) and varieties of
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funding modes (DEPOSIT, NON-DEPOSIT and IGF). Following Adams et al. (2009),
equation (4) is estimated using country and time fxed effects and clustering at the bank
level. Fixed effects are used to control for other bank-specifc characteristics that remain
relatively stable over the sample period. Again, of the most interest to us are the
Figure 2
(a) Northern region of
Africa; (b) Central
region of Africa; (c)
Southern region of
Africa
185
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coeffcients on the diversifcation strategy and the funding sources (i.e. ?
1
and ?
2
,
respectively). The positive sign for ?
1
and the negative sign on that of funding modes
suggest less engagement in earnings management. More so, as absolute values are used,
smaller values of DLLP will also indicate less engagement in earnings management.
Consistent with our expectation, the coeffcient on ?
1
in equation (4), where revenue
diversifcation strategy is measured by the Herfndahl – Hirschman Index (DIV
(REV)
), is
positive and statistically signifcant at 5 per cent level. This result supports our
argument that banks that diversify across interest income tend to engage in less
earnings management. This means that managers who adopt a strategy to diversify
their operations across interest-generating activities are less likely to manage their
earnings either to mislead some stakeholders about the underlying economic
performance of their bank or to infuence any contractual agreement. However, the
results on revenue diversifcation within non-interest income (DIV
[NON])
is insignifcant.
That is, diversifcation within non-interest-generating activities does not provide any
incentive for the managers to dwell in earnings management. On the funding sources,
the results suggest that banks that fnance their assets using deposits engage less in
Table III.
Stage-one regression
in estimating
abnormal loan loss
provisions
Explanatory variables Coeffcient Standard error
Intercept ?0.00846*** 0.00260
LLA 0.05175*** 0.00674
CHGOFF 0.01250*** 0.00439
GLOAN 0.00850*** 0.00276
?LOAN ?0.01567** 0.00766
LOAN 0.01397*** 0.00302
DNPL ?0.00783*** 0.00228
MKTS ?0.00475 0.00406
EBTP 0.07784*** 0.01372
GDP per capita ?0.15563** 0.07011
GDP growth 0.14241** 0.06622
INFL ?0.00008** 0.00003
Diagnostics tests
Obs 614
R
2
47.9
Fixed effect within N
Year dummy Y
Country dummy N
Wald (p-value) 161.77**
Notes: The dependent variable is LLP which is the non-discretionary component of loan loss
provision; this is regressed against LLA, the non-discretionary component of loan loss reserves;
CHGOFFis the ratio of net charge-offs to average loans during the period; GLOANis the growth in loan;
?LOAN is the change in total loan outstanding; LOAN is the loan portfolio of bank scaled by total
assets; DNPL is an indicator variable that equals to one if non-performing loan is missing and zero if
otherwise; MKTS is the market share of the respective bank; EBTP is earnings before tax and
provisions, and GDP per capita, GDP growth and INFL are the macroeconomic variables representing
GDPper capita, GDPgrowth and infation respectively; parameter estimates are reported with the small
sample adjustedstandarderrors; ***, ** and indicates statistical signifcance at the 1%, and5%level
respectively
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DLLPs. This result is consistent with the argument that managers normally tend to
manage earnings to meet the contractual obligation of wholesale funds. Furthermore,
even though the results is insignifcant, the positive relationship between DLLP and
non-deposit fund and IGFs give credence that bank managers are motivated to manage
Table IV.
Evaluation of
earnings
management: funding
and diversifcation
strategies
Explanatory variables 1 2 3 4 5 6
LEV 0.0840*** 0.0906*** 0.1029*** 0.0855*** 0.0849*** 0.1719***
(0.0173) (0.0189) (0.0149) (0.0178) (0.0147) (0.0264)
LOAN 0.0124* 0.0161** 0.0080 0.0114 0.0151** 0.0317***
(0.0071) (0.0076) (0.0060) (0.0070) (0.0073) (0.0109)
DNPL 0.0104*** 0.0097*** 0.0101*** 0.0101*** 0.0103*** 0.0099***
(0.0019) (0.0021) (0.0018) (0.0019) (0.0017) (0.0024)
SIZE 0.0009 0.00005 0.0003 0.0002 0.00007 0.00008
(0.0016) (0.0018) (0.0014) (0.0017) (0.0015) (0.0024)
MKTS ?0.0047 ?0.0031 ?0.0084 ?0.0039 ?0.0045 ?0.0215
(0.0106) (0.0108) (0.0096) (0.0117) (0.0098) (0.0146)
COM. INCOME
2
0.0042 0.0087
(0.0069) (0.0079)
DIV
(REV)
0.0188** 0.0356***
(0.0095) (0.0123)
DIV
(NON)
0.0066 0.0054
(0.0069) (0.0077)
DEPOSIT ?0.0304*** ?0.0781***
(0.0062) (0.0143)
NON-DEPOSIT 0.0096 ?0.0328**
(0.0062) (0.0153)
IGF 0.0244 0.0378
(0.0164) (0.0258)
EBTP 0.0245 0.0296 0.0815** 0.0526* 0.0298*** 0.0595
(0.0294) (0.0302) (0.0328) 0.0314 (0.0510) (0.0897)
GDP per capita ?0.9966** ?0.9880** ?1.2977*** ?1.1559** ?1.0288** ?1.5332**
(0.4608) (0.4953) (0.4004) (0.4754) (0.3896) (0.6214)
GDP growth 0.9212** 0.9114* 1.2228*** 1.0912** 0.9670** 1.4626**
(0.4516) (0.4852) (0.3921) (0.4663) (0.3815) (0.6062)
INFL 0.00004 0.00005 ?0.00003 ?0.0007** 0.0010 ?0.0045
(0.00006) (0.00006) (0.00007) (0.00008) (0.00198) (0.0074)
Obs 1,711 1,597 1,841 1,653 1,910 1,321
R
2
7.1 6.7 9.13 6.88 7.25 11.91
Fixed effect Yes Yes Yes Yes Yes Yes
Year dummy Yes Yes Yes Yes Yes Yes
F-test 5.88*** 5.07*** 8.93*** 5.83*** 7.24*** 6.60***
Notes: The dependent variable is discretionary loan loss provisions, DLLP; this is regressed against
leverage LEV; bank loan outstanding LOAN; an indicator of non-performing loan DNPL; the size of the
bank SIZE; the market share of the respective banks MKTS; the square of commission income COM.
INCOME
2
; diversifcation across DIV
(REV)
and DIV
(NON)
interest and non-interest income; DEPOSIT is
deposit sources of funding while NON-DEPOSIT is the wholesale fund and is calculated as all other
debts (except deposits) divided by total assets; IGFis the funds generated internally; earnings before tax
and provisions EBTP; and macroeconomic variables, GDP per capita, GDP growth and INFL; standard
errors are reported in parentheses; ***, ** and * indicates statistical signifcance at the 1, 5 and 10%
level, respectively
187
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earnings to meet the obligation of both debt holders and the shareholders. The positive
coeffcient on leverage (LEV) and statistically signifcant results irrespective of the
funding sources and the activity mix means that highly leverage banks engage in
earnings management.
4.3 Earnings management and change in market share
To provide precise inference on the relationship between earnings management,
funding structure and bank activity mix, we interact market share with these variables.
We control for possible endogeneity of funding choices, activity mix, market share and
earnings management. Banks with high earnings quality may systematically choose
deposit funds to diversify across interest income generating activities. Similarly, banks
with high market power use IGFs to diversify into non-interest generating income
(Amidu, 2013). We therefore use the Heckman (1979) two-stage least square (2sls) to
address this concern. First, we develop a model that relates to funding mode, activity
mix, market share and discretionary earnings management as follows:
DLLP
it
? ?
0
? ?
1
DLLP
it?1
? ?
2
DIV
it
? ?
3
(DIV
it
? ?MKTS
it
) ? ?
4
FS
it
? ?
4
(FS
it
? ?MKTS
it
) ?
?
j?4
k
?
j
M
j
? ?
t
YEARDUMMY
t
? ?
it
(5)
The model given in equation (5) includes interaction terms that are the product of the
change in market share with the activity mix, funding modes and a vector of
bank-specifc and country-level characteristics. DLLP
it,
is the estimated LLP of a bank i
in period t, DLLP
it?1
is the observation on the same bank in the same county in the
previous year, DIV
it
is the revenue diversifcation of a bank i in period t,
(DIV
it
* ?MKTS
it
) is the interactions of activity mix and market share of a bank i in
period t, FS
it
is the funding strategy of bank i in period t, (FS
it
* ?MKTS
it
) is the
interactions of funding modes and market share of a bank i in period t, the variables M
are a set of ?k? variables controlling for the respective countries’ macro-economic
environments and regulatory variables and ?
it
is the error term.
The positive coeffcient on lagged DLLP means that banks in Africa that engage in
discretionary earnings management in current year managed their earnings in the
previous year especially with the banks that diversify into non-interest-generating
activities. Also, the lagged-dependent variable, DLLP among the explanatory variables,
is positive and statistically signifcant, illustrating the importance of accounting for
previous values of the dependent variable. The relationship between market share and
DLLP is positive and statistically signifcant across all the models except Column 4 where
changes in market share interact with wholesale fund. This means that as a bank acquires
more market share, its earnings quality reduces. Thus, the earnings quality reduces when
banks in Africa with higher market share use the IGF to diversify into non-interest income.
However, we could not ascertain in this current study that managers engage in earnings
management tomaintaintheir market share. More so, banks that extendcredit inthe formof
loan delivery have less incentive to engage in earnings management. In Table V, we also
report the interaction of the market share and the funding and diversifcation strategies of
banks. This is to enable us to investigate whether changes in market share sensitivity of
earnings quality depends on the funding strategies or activity mix of the banks. The results
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Table V.
Earnings
management and
change in market
share
Explanatory variables 1 2 3 4 5
DLLP
?1
0.0997*** 0.0858*** 0.0956*** 0.0888*** 0.0989***
(0.0142) (0.0133) (0.0135) (0.0133) (0.0140)
MKTS 0.0580** 0.0462** 0.0460** 0.0383 0.0583*
(0.0271) (0.0224) (0.0264) (0.0300) (0.0322)
LEV ?0.0085 ?0.0038 0.0097 ?0.0027 ?0.0093
(0.0075) (0.0065) (0.0085) (0.0118) (0.0095)
LOAN ?0.0085** ?0.0075** ?0.0075* ?0.0054 ?0.0094**
(0.0038) (0.0033) (0.0039) (0.0042) (0.0045)
DNPL 0.0115*** 0.0108*** 0.0109*** 0.0117*** 0.0114***
(0.0009) (0.0007) (0.0008) (0.0009) (0.0009)
SIZE ?0.0010** ?0.0007* ?0.0009** ?0.0007 ?0.0009*
(0.0004) (0.0004) (0.0004) (0.0004) (0.0005)
DIV
(REV)
0.00145
(0.0034)
DIV
(NON)
0.0061**
(0.0031)
DEPOSIT ?0.0080***
(0.0024)
NON-DEPOSIT 0.0018
(0.0029)
IGF ?0.00708
(0.0074)
?MKTS ?DIV
(REV)
?0.0342*
(0.0196)
?MKTS ?DIV
(NON)
?0.0307
(0.0217)
?MKTS ?DEPOSIT ?0.0114
(0.0146)
?MKTS ?NON-DEPOSIT 0.0047
(0.0323)
?MKTS ?IGF ?0.0935*
(0.0493)
EBTP ?0.0117 ?0.0007 ?0.0056 ?0.0007 0.0037
(0.0188) (0.0136) (0.0223) (0.0249) (0.0338)
GDP growth 0.0043 0.0069 0.0041 0.0030 0.0057
(0.0104) (0.0102) (0.0103) (0.0102) (0.0112)
INFL ?0.00003 ?0.00005 ?0.00005 ?0.00008 0.0001
(0.00004) (0.00003) (0.00003) (0.00005) (0.0009)
Diagnostics tests
Sargen N * R
2
test 2.886 2.12 2.651 3.9 3.938
Obs 1,589 1,404 1,546 1,402 1,608
R
2
(uncentered) 12.36 17.79 19.96 24.24 10.76
F-test (p-value) 30.15*** 28.61*** 33.48*** 32.00*** 29.79
Wu-Hausman test 4.883** 3.971** 3.072* 1.622 3.768*
Durbin-Wu-Hausman 4.909** 3.997** 3.092* 1.635 3.789*
(continued)
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indicate that the sensitivity of earnings management to revenue diversifcation (DIV
[REV])
decreases, as bank market share increases. Similar results are obtained for IGFs. However,
there is no evidence to suggest that earnings quality increases for banks with market share
and with deposits and non-deposits funding.
5. Conclusion
This paper contributes to empirical literature on banks’ earnings management in
developing countries. We analyse the relationship between bank activity mix and the
funding pattern of banks on one hand and the earnings quality on the other. We use
macro-economic variables to account for differences in economic development. We
sample 330 banks across 29 countries during the eight-year period, 2002-2009. To
account for endogeneity and to provide precise and consistent parameter estimates, we
use 2sls (Section 4.3) in the estimation process, while in Sub-section 2, we include
country and year fxed effects and clustering of the errors at the bank level. Two stage
procedures are used:
(1) the construction of a DLLP as a proxy for the degree of bank earnings
management; and
(2) to use the results to test its relationship with diversifcation and the funding modes.
We fnd that banks’ net income are half-normally distributed for the entire sample
except banks in Angola. These results suggest the possibility of earnings management
of banks in Africa. Consistent with our expectation, our results, support the argument
that banks that diversify across interest income tend to engage in less earnings
management. On the funding sources, the results reveal that banks that fnance their
assets using deposits engage less in DLLPs. These results are consistent with the
argument that managers tend to manage earnings to meet the contractual obligation of
Table V.
Explanatory variables 1 2 3 4 5
Anderson canon test 19.144*** 23.074*** 17.174*** 13.481** 13.35**
Cragg-Donald Wald 3.195 3.863 2.863 2.241 2.221
Notes: Table Vreports the second stage of the 2sls regression results; the dependent variable is DLLP
which is the discretionary component of loan loss provisions; this is regressed against the lagged DLLP;
the market share of the respective banks MKTS; the leverage LEV; bank loan outstanding LOAN; an
indicator of non-performing loan DNPL; the size of the bank SIZE; diversifcation across DIV
(REV)
and
DIV
(NON)
interest and non-interest income; deposit funds DEPOSIT; wholesale funds NON-DEPOSIT;
internal generated funds IGF; earnings before tax and provisions, the interaction of market share with
funding structure and activities mix; EBTP; and macroeconomic variables, GDP growth and INFL. All
regressions are conducted using dynamic panel data estimation, 2sls; parameter estimates are reported with
the small sample adjusted standard errors. ***, ** and * indicates statistical signifcance at the 1, 5 and
10% level, respectively; the R
2
measures goodness of ft; the F-test measures the joint signifcance of
coeffcients; the following diagnostic tests are conducted: the Sargan test for over-identifying restrictions
measures instruments exogeneity. The Andersons likelihood ratio test is a test of instrument relevance; the
Durbin-Wu-Hausman (DWH) chi-square test and the Wu-Hausman F-test also measure the effciency of the
2sls over OLS in estimating the model; the Anderson likelihood ratio test suggesting that the instrument set
used is not valid; the dependent variables and year dummies are treated throughout as endogenous; bank
fxed effects are not included in the estimation
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wholesale funds. Our study fnds evidence that bank activity mix and funding modes
explain bank earnings quality. In addition, the study reveals the benefts of revenue
diversifcation, as it contributes to the quality of corporate fnancial reporting through
reduction of earnings management. Overall, our results suggest that earnings
management among banks with higher market share is signifcantly more sensitive to
IGFs than it is with deposits and wholesale funding. The results indicate that the
sensitivity of earnings management to revenue diversifcation across interest income
decreases, as bank market shares increases.
Regarding policy implications, Mayers and Smith (2004) argue that detecting
earnings management in highly regulated industries is important, as it demonstrates
that, to the extent, earnings management is a problem, regulatory oversight may not
prevent self-serving accounting choices by managers. Our results show that even in a
highly regulated industry such as banking, activity mix and funding pattern play an
important role in reducing earnings management. Moreover, in the developing countries
banking sector context, our study can be regarded as documenting an important
internal decision mechanism (activity mix and funding choices) in addition to
previously identifed external factors, bank monitoring and international institutional
factors limit earnings management in banks. Finally, to the extent that the level of loan
and the net charge-off of loans are used by the bank managers to manipulate the
earnings, supervisory authorities in Africa must increase their reporting requirements
for banks. Banks should also be made to adopt IFRS. These measures will prevent the
Lehman Brothers-induced type of banking crisis in Africa.
Notes
1. See A. R. Valukas Chapter 11 Case No. 08-13555 ( JMP) in Re: Lehman Brothers Holding Inc.,
et al. Volume 3, 11 March 2010, available at: http://lehmanreport.jenner.com/
2. Banks focus on manipulation of LLP because they have substantial latitude in determining
the amount of provision. Also, banks with high leverage make them quite vulnerable to
volatility in asset values, prompting adequate LLP, which become banks’ main accrual
(Fronseca and Gonzalez, 2008).
3. In this context, bank divisions, units or sector connote the business of the bank that allowthe
banks to engage in activities that generate non-interest income. That is, a unit of a commercial
bank that participates in securities markets, insurance and real estate activities. It is a
measure used to describe diversifcation (Sanya and Wolfe, 2011).
4. As a large number of NPL observations are missing, we use the modifed zero-order
regression method suggested by Maddala (1977) for estimating equation (4). This method
substitutes a zero for missing value and adds an indicator variable coded one if the
corresponding variable is missing.
5. This is based on the coeffcients from the frst-stage regression in equation (4).
6. Countries included in analysis for northern region of Africa are Algeria, Egypt, Morocco,
Sudan and Tunisia; the central Africa countries included are Benin, Burkina Faso, Cameroon,
Cote d’voire, Ethiopia, Ghana, Kenya, Mali, Mauritania, Nigeria, Rwanda, Senegal, Sierra
Leone and Uganda, while Angola, Botswana, Malawi, Mauritius, Mozambique, Namibia,
South Africa, Swaziland and Zimbabwe are included for southern Africa countries.
7. The results are not reported but available upon request.
191
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Corresponding author
Mohammed Amidu can be contacted at: [email protected]
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