Discussion Paper No.338
The determinants of bank profitability: dynamic
panel evidence from South Asian countries
Shahidul Islam
Shin-Ichi Nishiyama
May 7, 2015

TOHOKU ECONOMICS RESEARCH GROUPGRADUATE SCHOOL OF ECONOMICS AND

MANAGEMENT TOHOKU UNIVERSITY

27-1 KAWAUCHI, AOBA-KU, SENDAI,
980-8576 JAPANThe determinants of bank profitability: dynamic panel evidence
from South Asian countries
Shahidul Islam a, b,1 Shin-Ichi Nishiyama aaGraduate School of Economics and Management, Tohoku University, Japanb
Department of Banking and Insurance, University of Dhaka, Bangladesh

Abstract:
Using the GMM estimator, this paper empirically studies the bank-specific, industry specific andmacroeconomics specific determinants of bank profitability of 259 commercial banks in the South Asian
countries (Bangladesh, India, Nepal and Pakistan) in the period of 1997-2012. Empirical results show a low
level of profit persistency and a late-hit of the global financial crisis in the banking sector in the region. We
found no evidence for the traditional SCP hypothesis in relation to banking profit but financial solvency andmanagerial excellence have positive affiliation. Cost of fund, liquidity, funding gap, term structure of interest
rate and economic growth rate found negative influence while rate of inflation positively affect bank profit.
Also to report that South Asian banks are operating with ‘inefficient’ manpower.

Key words: Bank profitability, Term structure of interest rate, Dynamic panel
JEL classification: C23, G21, L2

1. Introduction:
Due to increased pressure of globalization, deregulation, parallel competition from the nonbanking financial institutions and volatile market dynamics, commercial banks constantly seek
ways to remain profitable. Profitable banks can diversify their business, effectively can hedge
against adverse effects and can reward its stakeholders in many ways. So, understanding andregularly updating knowledge regarding the determinants of banking profitability is very
important to the excellent bank management for the existence and stability of banking firm as afinancial intermediary and an importance contributor to the economic development of a country.
1

Corresponding author, E-mail: sizahid2000@gmail.com. 27-1 Kawauchi, Aoba-ku, Sendai 980-8576, Japan1

Thus, the research on the determinants of banking profitability seems Greenfield to theresearchers, bank management, financial market analysts and the regulators in the past and also
will be equally attracted in the future.
Past research on the determinants of banking profitability focused on both the bank specific andindustry and macroeconomic specific variables. Following Short (1979) and Bourke (1989) anumber of researchers studied banking profitability determinants using single linear model of
either cross country or on country specific banking data. Among others, Molyneux and Thornton(1992) examined the determinants of banks profitability operating in 18 European countries overthe period 1986-1989 and Pasiouras et al. (2007) studied that of 15 EU countries. On the other
hand, panel studies of Athanasoglu et al. (2008) and Dietrich et al. (2011) are on the banking
profitability of Greek and Switzerland respectively. However, no single study was out ofcriticism due to insufficiently selection of variables or failure to implement the appropriate
econometric methodology counting for profit persistency of banks (Athanasoglu, 2008).
In this paper, we empirically studied the determinants of banking profitability of South Asian
countries that is Bangladesh, India, Nepal and Pakistan, using dynamic panel of 259 banks data
for the period of 1997-2012. We viewed each country’s banking sector in terms of a single
representative agent and interested in profit determination in national basis. We studied the
explanatory variables of banking profit determinants in terms of bank specific, industry specificand macroeconomic specific and incorporated new bank specific determinant-recurring earning
power of bank and found that it positively and significantly affect banking profit.
Selection of our sample was also notable on the ground that most of our sample countries(Bangladesh, India and Pakistan) were under the rule of British colony for around two hundred2

years. We got the opportunity to study those countries’ banking systems all –together
considering likely regulatory, social and economic environments. In the near past we foundsimilar studies on developed and developing countries of America, Europe, and Asia but in caseof South Asia, this study is a unique addition to the literature of the determinants of banking
profitability.
The rest of the paper has been organized as follows: in section 2, we presented relevant literatureon the determinants of bank profitability. In section 3 the empirical approach of our study and insection 4 the sample description has been outlined. In section 5 the result and finally in section 6
we presented the conclusion and policy implications of our study.

2. The literature on the determinants of bank profitability

Following Short (1979), Smirlock (1985) and Bourke (1989) previous literature viewed the bankprofitability as a function of bank specific, industry specific and macroeconomic specificdeterminants. The bank specific variables may be termed as the microeconomic variables andcan be directly found in the financial statements of a bank. On the other hand, the industry andthe macroeconomic variables are the overall industry condition, regulatory and legal
environment and the country specific conditions within which a bank operates its business.
Explanatory variables used in the studies of banking profitability determinants found either to be
categorical or related to the purpose of the study and the empirical researches focused on boththe cross country studies and the studies on country specific data.
Studies by Molyneux and Thornton (1992), Demirguc-Kunt and Huizinga (1999), Abreu and
Mendez (2002), Staikouras and Wood (2004), Goddard et al. (2004), Pasiouras and Kosmidou3

(2007) on bank profitability determinants investigated the cross country panel. On the other handrecent studies by Berger (1995), Naceur and Goaied (2008), Athanasoglu et al. (2008), Dietrich
et al. (2011) were among others on the single country’s banking profitability determinants.
Studies of Flannery (1981, 1983), Hanweck et al. (1984), Fraser et al. (2002), among others
focused on the relationship between the volatility of market interest rates and the banking
profitability. Outcome of the previous studies vary in terms of data set, type of data, period ofstudy, set of explanatory variables and countries or region but have some commons as well.
Empirical studies on the determinants of banking profitability focus on the size, capital holdingsor the equity to total assets ratio, credit risk, liquidity position and other operational efficiency
indicators as the microeconomic determinants and the ownership structure, concentration indices,
inflation, economic growth, regulatory policy rate, market interest rates as the industry andmacroeconomic determinants.
Short (1979) in his paper examines how industry specific and the macroeconomic determinants
like ownership structure, industry composition, monetary policy rate, interest rate along withbank specific asset growth significantly affect banking profit.

Bourke (1989) study did notconfirm the findings of Short (1979) but found evidence to support the Edward-HeggestedMingo hypothesis 2.
Flannery (1981, 1983) found that large banks are well hedged against the interest rate volatility
that means when market rates change, their revenues and costs adjust equally quickly,
leaving net current operating earnings largely un- affected. However, Hanweck et al. (1984)

2

The Edward-Heggested-Mingo theory [Edward and Heggested (1973); Heggested and Mingo (1976)]
that higher concentration in banking markets encourages banks to hold less risky assets and to modify
their behavior in other ways.
4

evidenced those small commercial banks as a group has experienced increases profitability bothabsolute and relative to large banks in periods of rising interest rates.
The studies of Molyneux and Thornton (1992) in their cross country studies on European
banking found positive relationship between the bank profit and the level of interest rates, bankconcentration, ownership and the expense preference hypothesis 3.
In their seminal paper on commercial bank margin and profitability determinants, DemirgucKunt and Huizingla (1999) shows that the level of equity holdings, foreign ownership, GDP per
capita, real interest rate, tax rate affect bank profit positively and significantly while the loans tototal assets ratio, off-balance sheet income, customer and short term funding to total assets,
overhead expenses and taxation reserves have significant inverse relationship with banking profit
and the results also vary in developed and developing countries. Abreu and Mendez (2002)
studied the profitability determinants of European banking and found that loan to assets andequity to assets ratio have positive impact on bank profit while unemployment affect negatively.
Staikouras and Wood (2003) also studied the European banking profitability and their results
show that among bank specific determinants loan to assets ratio, the loan loss provisions haveinverse but the level of equity and funding gap positively affect bank profit. They found noevidence for the SCP hypothesis but the macroeconomic variables like interest rate variability
and GDP growth rate affect banking profit negatively but market interest rate positively.
Goddard et al. (2004) also studied the bank profitability on European banking profitability andfound no evidence for size-profitability relationship but positive effect of capital assets ratio onbank profit. Pasiouras et al. (2007) found significant positive relationship between banking profit
3

The theory of expense preference hypothesis suggests that high profits earned by firms in a regulatedindustry may be appropriated in the form of higher payroll expenditures [see Molyneux andThornton(1992) for further explanation].
5and equity level, liquidity position, concentration, inflation and GDP growth rate but significant
negative relationship between the banking profit and cost of fund and size variables in theirbanking profitability studies on 15 EU countries.
Athanasoglu et al. (2008) studied the banking profitability determinants on Greek banking andfound that equity level, productivity inflation and cyclical output have significant positive
relationship with bank profitability while that with loan loss provision and operating expenses issignificantly negative. Their study also accounted no bank size-profitability relationship of the
traditional SCP hypothesis.
In a recent study of Dietrich et al. (2011) on the Swiss banking profitability found equity to total
assets ratio, cost-income ratio, deposit growth rate, funding cost, interest income share, effective
tax rate and ownership structure negatively affect banking profit. On the other hand, prolonged
banking experience, small banking over large one, GDP growth, and term structure of interest
rate found positive relationship. They also accounted for the particular focus on the crisis andpre-crisis of the global finance. Albertazzi et al. (2009, 2010) studied the bank profitability withparticular importance to the business cycle changes and the taxation effect.
Previous literature on the determinants of banking profitability studied extensively on the
microeconomic determinants; sources of which are the financial statements of the banks.
Investigative results also found on the traditional structure-conduct-performance hypothesis andthe macroeconomic determinants of the bank profitability.

However we did not find any

conclusive deterministic role of the determinants whether bank, industry or macroeconomic
specific.

6

We found that the previous literature ignored the importance of the recurring earning power
which is actually the ability of the excellent management of a bank to generate consistent profit.
We extended the literature of bank profitability studies by incorporating this bank specificvariable in our empirical study. The study considered the sample of the South Asian countries
banking markets as a whole that is also new because no evidence of such study found in the past
literature. Furthermore, the panel data of 259 commercial banks for the period of 1997-2012
which is relatively large that we studied empirically will allow the better insight into the factors
determining the banking profitability.

3. Empirical Approach
3.1. Econometric Model
The general model to be estimated for profitability determinants of banks is of the following
linear form:𝐉𝐣𝐦
𝚷𝐢𝐭𝐤 = 𝐜 + ∑𝐣=𝟎 𝛃𝐣 𝐗 𝐢𝐭 + ∑𝐋𝐥=𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭 + ∑𝐌
𝐦=𝟎 𝛃𝐦 𝐗 𝐢𝐭 + 𝛆𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭 = 𝛎𝐢 + 𝛖𝐢𝐭 ......Equation (1)Where, 𝛱𝑖𝑡𝑘 is the profitability of bank i at time t and measured at parameter k (k = 𝑅𝑂𝐴𝑖𝑡 and

𝑅𝑂𝐸𝑖𝑡 ) with i = 1,.....,N, t = 1,....., T and c is a constant term. The superscripts j, l and m of Xit

denote the bank-specific, industry specific and macroeconomic specific determinants
respectively. εit is the disturbance with νi the unobserved bank-specific effect and υit theidiosyncratic error. The error components of the regression model also distributed as νi ~IIN (0,σ2ν ) and independent of υit ~ IIN (0, σ2υ ).

7

Bank profits show a tendency to persist over time, reflecting impediments to market competition,
informational opacity and/or sensitivity to regional/macroeconomic shocks to the extent thatthese are serially correlated (Berger et al., 2000). Hence, we adopted a dynamic specification of amodel that includes a lagged dependent variable among the regressors. The dynamic
specification model of the profitability determinants is:𝐉𝐣𝐦𝚷𝐢𝐭𝐤 = 𝐜 + 𝛅𝚷𝐢,𝐭−𝟏,𝐤 + ∑𝐣=𝟎 𝛃𝐣 𝐗 𝐢𝐭 + ∑𝐋𝐥=𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭 + ∑𝐌
𝐦=𝟎 𝛃𝐦 𝐗 𝐢𝐭 + 𝛆𝐢𝐭..Equation (2)Where, Πi,t−1 is the one-period lagged profitability at k parameter and δ is the speed ofadjustment to the equilibrium. A value of 0 < δ < 1 implies the persistence of profitability in theindustry but tends to return to the normality level. δ ~ 0 with high speed in a fairly competitivemarket and δ ~1 (slow adjustment) implies a less competitive market.

Literature usually applies the fixed effects (FE) or the random effects (RE) modeling in static
type of relationships but in dynamic relationships these models produce biased (especially whentime dimension T gets smaller) and inconsistent estimates (see Baltagi, 2001).
Following Athanasoglu et al. (2008) we precede the following five step issues for the
econometric model of profitability determinants.
Firstly, we tested our data for non- stationarity using the Fisher test which does not require apanel to be balanced. This test is a question when the use of a relatively large T in a model of8bank profitability may be criticized on grounds of non-stationarity. The null of non-stationarity
has been rejected at 1% level 4.
Secondly, we examined whether the individual effects are fixed or random. The relevant
Hausman test on model (2) confirms the evidence in favor of a FE modeling 5 . Also theestimation result confirms the existence of individual effect since the F-statistics is significant (F(81, 204) = 2.49, Prob > F = 0.0000). However, the least square (within) estimator of the FEmodel in the presence of a lagged dependent variable among regressors is both biased andinconsistent 6.
Thirdly, we proceed with the estimation of our model using the one step generalized methods ofmoments (GMM) estimator of Arellano and Bond (1991) paradigm which suggest thatconsistency and efficiency gains can be obtained by using all available lagged values of thedependent variable along with the exogenous regressors as instruments.
Fourthly, we dealt with the problem of endogenuity with estimation of bank profitability. The
question is whether capital variable (E/TA) and the credit risk variable (NPL/TL) areendogenous and predetermined or not. Theory suggest that capital and risk variables should betreated as endogenous and predetermined respectively when we measure profitability with ROE
as dependent variable. To confirm such, we ran the same model twice separately in case of ROA
and ROE respectively. First time we treated both variables as strictly exogenous and second time

The relevant chi-squared( 𝜒 2 , 296 ) -value for ROA = 1056.92 with ρ = 0.0000 and ROE = 980.83with
ρ = 0.0000
45

6

The relevant Hausman test chi-squared( 𝜒 2 , 13 ) –value = 496.72 with prob>chi2 = 0.0000

The Monte Carlo studies that measured the corresponding bias in the coefficients of the lagged
dependent variables have found that the bias is significant for small values of T but goes to zero as T
increases (see Judson and Owen, 1999).
9

treated capital as endogenous and risk variable predetermined. Sargan test 7 for over-identifying
restrictions indicates that no endogenuity and pre-deterministic assumptions are valid for ROA as
dependent variable but opposite for ROE. That means we treated capital and risk variable
exogenous in ROA model but capital variable as endogenous and risk variable pre-determined in
ROE model.
Finally, we addressed the unobserved time effects in the error components of our model asfollows:𝐣𝐉𝐦𝚷𝐢𝐭𝐤 = 𝐜 + 𝛅𝚷𝐢,𝐭−𝟏,𝐤 + ∑𝐣=𝟎 𝛃𝐣 𝐗 𝐢𝐭 + ∑𝐋𝐥=𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭 + ∑𝐌
𝐦=𝟎 𝛃𝐦 𝐗 𝐢𝐭 + 𝛆𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭 = 𝛎𝐢 + 𝛌𝐭 + 𝛖𝐢𝐭 ......Equation (3)Where λt is the unobservable time effect and we tested the joint significance of time effects as

𝑯𝟎 = 𝝀𝟐 = 𝝀𝟑 = 𝝀𝑻 = 𝟎. The relevant LM test 8 approves the inclusion of time dummies. We
experimented for time dummies for all years jointly and separately but found the year dummies

2009 is significant (implying the late hit of the global recession in the sample region).
Considering all these, we estimated the profitability determinants by the following dynamic
equation:𝐉𝐣𝐦𝚷𝐢𝐭𝐤 = 𝐜 + 𝛅𝚷𝐢,𝐭−𝟏,𝐤 + ∑𝐣=𝟎 𝛃𝐣 𝐗 𝐢𝐭 + ∑𝐋𝐥=𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭 + ∑𝐌
𝐦=𝟎 𝛃𝐦 𝐗 𝐢𝐭 + 𝛄𝐃𝟎𝟗 + 𝛆𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭 = 𝛎𝐢 + 𝛖𝐢𝐭 ......Equation (4)When we modeled E/TA and NPL/TL as exogenous variables, the ρ = 0.00 for both the models. In
contrast, when we assumed E/TA as endogenous and NPL/TL as pre-determined, the ρ = 0.00 in ROA
model but ρ = 0.19 in ROE model that means the use of instruments for these two variables are notacceptable in ROA model but acceptable in ROE model.
8chi2( 12) = 24.52 and Prob > chi2 = 0.0173
7

10

3.2. Empirical determinants of bank profitability
We empirically studied the econometric model of bank profitability determinants developed in
section 3.1 using 3 categories of proxy variables namely (a) firm specific, (b) industry specificand (c) macroeconomic specific (see table-1 for a summary of these variables).
3.2.1. The dependent variables
We used return on average assets (ROA) as the key profitability determinant of banks. ROA has
emerged as the key ratio for the evaluation of bank profitability and has become the mostcommon measure of bank profitability in the literature (Golin, 2001). ROA is an indicator of
how profitable a company is relative to its total assets and gives an idea as to how efficientmanagement is at using its assets to generate earnings. We defined ROA as the ratio of net
income over average total assets expressed in percentage.
Return on average equity (ROE) is the second measure of profitability in our empirical study.
We defined ROE as the amount of net income as a percentage of shareholders equity. ROE
equals ROA time assets-equity ratio, often termed as equity multiplier or financial leverage.
Problems of considering ROE as the profitability measure is authority often regulates theleverage position of a bank and also for accounting identity fact banks with lower leverage ratio
generally report higher ROA but lower ROE. So, we considered ROA as the key determinant ofbank profitability also relied on the average assets value to capture the changes during the fiscal
year if any.
<Table 1>113.2.2. The explanatory variables(a) Bank-specific explanatory variables(i) Equity to Total Assets ratio
Equity to total assets ratio measures the capitalization strength of a bank considering theregulatory requirements regarding the minimum equity holdings (Islam et. al., 2015).
Anticipating impact of this variable on bank profitability is complex. The traditional risk-return
hypothesis (invested money can render higher profits only if it is subject to the possibility of
being lost) imply a negative relationship between bank capital and profitability because bankswith higher equity to asset ratios are relatively safer in the event of loss or liquidation. Also
considering the Berger (1995) model of one-period perfect capital markets with symmetric
information where a negative relationship between equity and profitability exists, capital variable
should be modeled as endogenous. On the other hand, better capitalized banks can effectivelytransform their creditworthiness into lowering their cost of fund and generating higher
profitability. This assumption gets solid ground considering the recent trend of merger andacquisition also the ace of financial liberalization. Finally we hypothesized a significantly
positive relationship between equity and ROA but significantly negative relationship between
equity and ROE.

(ii)

Non-performing loan ratio

The ratio of nonperforming loan to total loan (NPL/TL) is the proxy variable for the credit risk
exposure to a bank. Facing the high regulations from the regulatory bodies and maintaining thequality of assets (loan is the largest head of a bank balance sheet), banks focus to keep a lower
non-performing loan ratio. Following this standard controlling nature, some literature term NPL
12

is a pre-deterministic variable (see Athanasoglu, 2008). However, we expect negative
relationship between non-performing loan and profitability.

(iii)

Liquidity ratio

Maintaining a sound liquidity position to safeguard against the liquidity risk is a vital policy of acommercial bank. We calculated the liquidity ratio (LA/D&STF) as the liquid assets of a bankover the deposits and short term funding in percentage form. Although a higher liquidity ratio
reduces the liquidity risk but at the same time reduces the loanable fund of a bank which in turns
reduces the banks’ earning potential. Thus we expect liquidity position of a bank and its
profitability negatively related.

(iv)

Cost of fund ratio

Total interest expenses over total deposit (IE/TD) is a proxy for funding cost and used tomeasure the impact of bank managements’ efficiency over banks profitability. A bank with its
excellent managerial efficiency will be able to collect low cost fund in a competitive but
unstructured savings of the depositors providing sound bank profitability. A negative andstatistically relationship is expected.

(v)

Productivity ratio

In a world of increased globalization and deregulations, banks must increase the productivity (i.e.
the input-output ratio) for a stable earning and sustainable growth. It is possible to linearize theproductivity growth (δπ)

9in a capital augmented production function but difficult whenIn the Cobb-Douglas production function, 𝑌 = 𝐴𝐿𝛽 𝐾 𝛼 where L = labor, K = capital, A = total factor
productivity, α and β are the output elasticities of capital and labor respectively, α + β < 1 indicates

9

13

production function is labor augmented or both due to inefficiency of the workforce. Although it
is a question whether bank performance (e.g. profitability, π) is capital or labor augmented, we
expect positive relationship between productivity and profitability. We used the ratio of
operating profit per employee as a proxy for productivity.

(vi)

Recurring earning power

We introduced the ratio of recurring earning power (REP) 10 of a bank in our econometric modelof profitability determinants as a proxy for the stability of its earnings and sustainable managerial
efficiency. REP is defined as the adjusted ratio of stable net income (profit before taxes plus loan loss
provisions less income from associates and extraordinary sources over total assets). We found no
significant evidence of studies on the relationship between the REP and bank profitability in the previous
literature. We expect that managerial excellence and profitability are positively related.

(vii)

Growth rate of total deposit

As a financial intermediary, bank always eager to expand its market share of deposit in thedeposit market in order to expand its loan operation. So, the impact of growth in deposit does notnecessarily ensure the bank profitability. To crop up the advantage of higher deposit growth isrelated to the quality of credit management. Hence, the impact of this variable on bankprofitability is not clearly anticipated in the present study.

decreasing return to scale. But in a perfectly competitive market, α + β = 1 meaning constant return toscale.
10
This variable is different from the dependent variables ROA and ROE. We found ρ (ROA, REP) = 0.78
and ρ (ROE, REP) = 0.25 only (see table-5A of correlation matrix in the appendix).
14

(viii) Bank size
We measured the bank size in terms of natural logarithm of its total assets. Although Smirlock(1985) argued that a growing bank size is positively related to bank profitability on the ground of
economies of scale benefit but extremely large banks might become operationally inefficient due
to bureaucratic complexity and ‘too big to fail’ reasons (Pasioras et. al., 2007). So, this sizeprofitability relationship is still unpredictable also in our study.
(ix)

Loan to deposit ratio

We introduced the loan to deposit ratio (TL/TD) in our bank profitability determinants model to
see the impact of asset-liability management on profitability of a bank. Loan to deposit ratio
components are also interest rate sensitive meaning these balance sheet components are also
affected by the interest rate risk literally called the duration gap (difference between ratesensitive assets and rate sensitive liabilities). Higher the ratio indicates the bank is effectively
utilizing its fund to generate higher profit although possible bank run problem is associated withthis scenario. On the other hand, a lower TL/TD means banks have excess liquidity and underperforming their asset-liability management. In this scenario, banks will incur the excess
liquidity cost burden in addition to the cost of fund that will result a state of negative profitability.
Entrop, O., et al. (2015) studied the relationship between duration gap and interest margin but therelationship between rate sensitive assets and liabilities with a bank’s overall profitability (ROAor ROE) seems first we included in our present study. We expect a negative and significant
relationship under the assumption of underperformance of asset-liability management.

15

(x)

Total interest income ratio

Total interest income over total loan (TII/TL) ratio indicates the loan pricing behavior of a bank.
Certainly a commercial bank will try to charge higher on its loans and advances to optimize
profit. Higher interest income will represents the higher profitability of a bank.

(xi)

Off-balance sheet income

Now a day banking business model has been diversified in many folds. Following Angbazo(1997), we examined the effect of off-balance sheet income on the banking profitability. In thename of loan commitments, standby letter of credit, commercial letter of credit, securities
lending and trading, futures and forwards contracts, options, swaps, cards, service and penalty
charges, capital gain on assets, property leasing etc. and other fee income, banks generate sizable
portion of their total income. On the other hand, banks incur handsome operating and overhead
expenses to generate off-balance sheet activities (Islam et. al. 2015). We calculated off-balance
sheet income ratio as the net non-interest income (non interest expense less non-interest income)over total assets (NNII/TA) and expect positive impact of this variable on banking profitability.

(b). Industry-specific variables(xii)

Hirschman-Herfindahl index

Hirschman-Herfindahl index (HHI) is the proxy variable for the market concentration and its
impact on bank profitability in our empirical study. This is a common and widely used measureof market concentration where higher concentration means lower competition and vice versa andcalculated as the sum of square of market share (𝐻𝐻𝐼 = ∑𝒔𝟐𝒊 .where 𝐬𝐢 is the share of total

16

industry assets of each bank as calculated in our study). According to the structure-conductperformance (SCP) hypothesis, banks in highly concentrated markets earn monopoly rents,
because they tend to collude (Gilbert, 1984). This state of collusion may direct opposite scene
also where smaller banks face tougher competition that result overall negative profitability. So,
the theoretical relationship between concentration and bank performance is yet indeterminate andto be answered empirically.
(c). Macroeconomic-specific variables(xiii) Term structure of interest rate
We used the difference of yield spread of 10 year and 5 year treasury bonds as the proxy of term
structure of interest rate (R) and its impact on the bank profitability. Maturity gap (borrowing
short- lending long and vice versa) management is an important aspect of bank management
because of interest rate sensitivity. Banks’ revenues and costs will be adjusted with different
speeds that will generate either profit or loss for the bank (Flannery, 1984). In a perfectlycompetitive capital market, where the banks also compete with the government to collect funds
may expect inverse relationship with the term structure of interest rate and its profitability.
(xiv)

Rate of inflation

Although there is no empirical consensus on the effect of inflation on the bank profitability, highinflation is generally associated with high interest rates and consequently increases bankprofitability. In this study, we expect positive relationship with inflation and bank profit.
(xv)

GDP growth rate

Gross domestic product (GDP) growth rate affect the demand and supply of loans and deposits
directly and thus influence the banking business. We assume that sound GDP growth ensures the17stability of the economy and in that stable economic environment a bank’s business risk reduces
significantly. Following that risk return trade off banks profitability may reduce. Hence, we
expect inverse relationship with GDP growth and bank profitability.

4. Data and sample description
To prove the econometric model of bank profitability determinants (equation 4) empirically westudied the unbalanced panel data of 259 11 South Asian banks over the period of 1997-2012. We
defined banks as the financial intermediary who takes deposits and provide loans and advancesin the ordinary courses of business. We excluded the data of Islamic banks from our sample as inIndia and Nepal there is no or very limited Islamic banking operation. For our analyses, wecollected data from various sources. The dependent variable and the bank specific explanatory
variables, we collected data from the Bureau Van Dijk’s Bank Scope database (Bank Scope2013) using the universal model of banking database. We took the primary data set from the
Bank Scope but calculated by our own to get the Hirschman-Herfindahl index which we used as
the industry specific explanatory variables. Finally, for macroeconomics specific variables, wecollected data from two sources. We collected the data regarding the term structure of interest
rate 12 from the central banks websites of the respective countries included in our study. From
International Financial Statistics (IFS) database (IFS 2014), we collected yearly data of rate of
inflation and the growth rate of gross domestic product (GDP).

11

By countries, India represents 60% banks in our total sample while Bangladesh, Nepal and Pakistan
represent 12%, 10% and 18% respectively.
12
Term structure of interest rate is proxied as the difference between the yields spread of 10 year and 5
year Treasury bonds (T-bond). Also for Nepal, we sampled the development bond yield as the equivalent
to 5 year T-bond and the national savings certificates yield as equivalent to 10 year T-bond as they haveno such classified maturity bonds.
18

Table-2 in the following presents the descriptive statistics of the empirical variables used in thepresent study. We see in South Asia, banks earned around 1% ROA while ROE was more than
14%. Among other key indicators, the non-performing loan to total loan ratio was quiet high(more than 8%) and the cost of fund averaged a slightly higher than 6.6%. Off balance sheet
income was sound good meaning well diversified banking activities in the region. HHI over 13%
means a fairly competitive banking industry.
<Table 2>During the sample period rate of inflation was in single digit (7%) seems satisfactory and theregional average GDP growth rate was more than 6%. Figure-1 in the following presents theaverage yield spread of long term T-bond and that was a slightly higher than 1%.
<Figure 1>

5. Empirical results
Table 3 in the following presents the regression output of equation 4 of the key bank profitability
determinants (ROA) for the total sample period of 1997-2912. The first column of the table
presents the list of the dependent and the deterministic variables while each column of model 1
and 2 represents the coefficient and standard error respectively. To see the stability and thesignificant of the coefficients, in model 1 we included all of the determinants while in model 2
only the bank-specific variables.
The Wald-test confirms the fine goodness of fit of our panel data set and the Sargan-test showsno evidence of over-identifying restrictions. According to the results of AB (AR1) test a negative

19

first order autocorrelation exists but does not imply the inconsistency of the estimates.
Inconsistency would imply if there is the second-order autocorrelation (Arellano and Bond,
1991) but is rejected by AB (AR2) test subsequently.
Empirical results show a low degree of profit persistence in banking as the one-period lagged
dependent variable (δ (L1.ROA) = 0.1076) is statistically significant also justify the use of GMM
dynamic panel estimation of our model. This level of profit persistency in the South Asian region
seems similar market competition to the European region as Goddard et al. (2004) foundstatistical evidence of weak profit persistency.
Among the bank-specific determinants, results show that capital plays a strong determinant ofbank profitability. As expected, equity to total assets ratio positively and significantly affect
ROA. Economically speaking for every 100 basis point (BP) increase in this variable will
increase a bank ROA by over 16 BP. Also the rejection of endogenuity of equity variableconfirms the existence of sound one-period perfect capital markets (Berger, 1995) in the region.
We found the expected negative coefficient of credit risk (NPL/TL) variable but statisticallyinsignificant. The negative and highly significant coefficient of liquidity ratio (LA/D&STF)
indicates the banks are in excess liquidity and thus under utilization of assets emerge. As seenbanks forego around 3 BP of ROA for every 1% increase in LA/D&STF. Cost of fund negatively
affects bank profitability as obvious.
One alarming finding of the present study is the statistically significant and negative coefficientof the productivity ratio (OP/NoE) seems the inefficiency of the production inputs particularly
the employees. As we discussed in section 3.2, in a state of decreasing return to scale suchoutcome will occur. Another indication of the result is probably in South Asian banking labor is20

dominating over technology and well behind the banking progress all over the world.
Technological advancement and digitalization of banking may rescue the banks (Athanasoglu etal., 2008) from this poor productivity of the employees of South Asian banking.
We found our expected positive and statistically significant relationship between the recurring
earning power and banking profitability. Recurring earning power fits as a very good proxy for
managerial excellence who generates more profit and pays more taxes, effectively manages theloan loss provisions and generates income from associates. Economically for every 1% increasein the recurring earning power will add around 73 BP on ROA.
The present study found no statistical evidence that deposit growth rate and a bank size affectbank profitability. But rate sensitive assets and rate sensitive liabilities (TL/TD) ratio negatively
and significantly affect the profitability of banking. Although the economic impact seems verysmall, probable explanation for the negative coefficient may be the portfolio managers were lessaware regarding maturity gap and credit quality.
<Table 3>
We split the total income of a bank into interest income and off balance sheet income to check
which portion significantly affects the bank profitability. We found positive coefficient for both
the determinants but only found the off balance sheet income significantly affect the bankprofitability when we considered only the bank-specific determinants (model 2).
Our empirical studies found negative but statistically insignificant coefficient for HirschmanHerfindahl index. Berger (1995) and also other recent studies claim that concentration is usuallynegatively related to profitability once other effects are controlled rejecting the traditional SCP
hypothesis. In this study we found the low degree of profit persistency and size has no significant
21

effect on bank profitability that also support that in the South Asian banking market is fairly
competitive and exist few scope of monopoly rent seeking behavior.
Turning to the macroeconomic determinants of bank profitability, we found the term structure ofinterest rate; rate of inflation and the GDP growth rate all significantly influence the banking
profitability. The significant negative coefficient of term structure of interest rate indicates thatbanks do compete with government to satisfy the depositors to retain their savings. Thishypothesis confronts the recent studies of Albertazzi et al. (2009) but support the findings ofFraser et al. (2002). Economically banks lose approximately 19 BP of profit for every 100 BP
increase in the term structure of interest rates.
As expected, the significant positive influence of inflation on bank profitability indicates that thebank managers effectively anticipated the future upward movement of inflation but remains
unanticipated by the bank clients. This existence of asymmetric information made some way of
profit in South Asian banking.
We also prove our hypothesis that in an expansionary economy banks operate their business in a
relatively ease and less risky environment and thus can charge less from their customers. Hence
we got negative coefficient of GDP growth rate as the determinant of bank profitability.
Finally, one of the important findings of our empirical studies on bank profitability determinants
is the significant reduction of profitability in the South Asian countries as we term as the late hitof the global recession in the region. Our results show that banks approximately lose 26 BB of
ROA in the year 2009.
Table 4 in the following presents the regression output of the return on average equity (ROE) as
the bank profitability measure. Overall we found consistent but relatively inferior coefficients
22and their statistical significant compared to return on average assets (ROA) as the bankprofitability measure. Notably, we found significant negative coefficient of equity to total assets(E/TA) ratio proving our in- deterministic hypothesis discussed in section 3.2.2. That meansincreases in the amount of equity subsequently decrease the ROE. We also confirmed the profit
persistency state in the sample region as the lagged ROE is positive and significant and SCP
hypothesis still in effect in South Asian banking when we considered ROE as the measure ofbank profitability.
Here we also report that our regression estimates are robust as we checked by alternative
variables. For purpose, we used CR313 as alternative to HHI and standard deviation of short terminterest rates 14 as alternative to the term structure of interest rate variables for robustness check.
We found no significant change in the values of the coefficients or their level of significanceduring the robustness check.

<Table 4>

6. Conclusions and implications
Using a comprehensive cross-country panel data set with micro and macro level variables, thispaper presents the empirical results on how bank specific, industry specific and macroeconomics
specific factors affect the bank profitability. We followed the single stage model of profit
determinants for the empirical study that included four South Asian countries’ that is Bangladesh,
13

CR3 is the concentration ratio of the largest 3 banks in the industry in terms of total assets as we used in
the present study.
14
We used the annualized standard deviation of monthly average of daily call money rates and datacollected from the web sites of the central banks of the sample countries included in this study and later
our own calculation.
23

India, Nepal and Pakistan banking sector data covering the period of 1997-2012. Our empiricalfindings are consistent with our theoretical analysis.
Among the bank specific determinants, we found that equity level which is the proxy of financial
strength and the recurring earning power of a bank positively affects its profitability. On theother hand, liquidity position of a bank, funding gap, cost of fund and productivity ratio foundnegatively and significantly affect banking profit.
We found no evidence to support for the traditional SCP hypothesis as the proxy variable HHI
was insignificant. Among the macroeconomic determinants we found the term structure ofinterest rate and macroeconomic growth rate of a country negatively influence bank profitability
while inflation affect that positively.
The novel feature of our study is we successfully traced the significant deterministic role ofmanagerial excellence in the name of recurring earning power on which previous literature paid
little attention. Though it is low but significantly positive profit persistency behavior in thesample region justify our use of GMM estimator, an up-to date econometric methodology that
we effectively addressed the issues that profits show a tendency to persist over time, reflecting
impediments

tomarketcompetition,

informational

opacity

and/orsensitivity

toregional/macroeconomic shocks. Our empirical result also shows that a late-hit of the global
financial crisis affected the banking profitability in the South Asian countries.
Regarding the policy implications, we suggest the banks to take appropriate actions so that thecredit risk would have appropriate reflection in banking profit as we found no significant
negative impact of default probability on banking profit that is contradictory to the establishedtheory. Another issue for both the bank management and the regulatory authority to implement
24

the digitalization and through to the state to electronic banking as the productivity ratio shows
negative impact on banking profit indicating inefficient manpower. Hoping these initiatives will
benefit the society as a whole.
In this paper, we comprehensively addressed the question of how microeconomic and macroeconomic
forces affect banking profitability. However, studies on a number of additional explanatory variables like
corporate tax rates, competition among banks and other financial institutions, ownership structure, deposit
insurance, rate of unemployment, information asymmetry, and portfolio effect, those could not be tested
due to limitation of data and the degrees of freedom or for the potential multicollinearty problem, would
be tested as the extension of the model. Implementing contemporary econometric methodology in themodel would be fruitful insight of the literature and apparently an interesting path for future research.

25

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22. Islam, S., Nishiyama, S. 2015. The determinants of bank net interest margins: A panel
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28

Figure 1: Long term average Treasury bond yield spread in South Asia

1.6
1.4
1.2
1
0.8
0.6
0.4
0.2
0
1998

2000

2002

2004

2006

2008

2010

2012

2014

Long term T-bond yield spread(South Asia) . . .

29

Table-1: Description of variables used in the study
Variables
Dependent variables
Profit(Π)
Independent variables(a). Bank-specific variablesi.
Equity to total
assets ratioii.
Non-performing loan
ratio
iii.

Liquidity ratio

iv.
v.
vi.

Cost of fund ratio
Productivity ratio
Recurring earning
power
Growth of total deposit
Bank size

vii.
viii.
ix.
x.

Loan to deposit ratio

Notation

Description

ROA
ROE

Net income over average total assets (%)
Net income over average total equity (%)

Equity to total assets ratio (%) is a measure of
E/TA
capital adequacy of respective bankNon-performing loan (%) over total loan is a proxy
NPL/TL variable for credit quality or credit risk exposed to a bank
LA/D&S Liquid asset to total deposits and short term funding ratio
TF(%) express the liquidity position of a bankTotal interest expenses (%) over total deposit is a proxy
IE/TD
for funding cost
OP/NoE Operating profit per employee
Adjusted ratio of stable net income(net income less non
REPstable earnings and taxes) over total assets
ΔTD
Annual growth rate (%) of depositln(TA)Natural logarithm of total assets of a bank
TL/TDTotal loan over total deposit ratio (%)

Interest income toTotal loan ratio
TII/TLxi.
Off-balance sheet
income ratio
NNII/TA(b). Industry-specific variables
xii.
Hirschman-Herfindahl
index
HHI
(c). Macroeconomic-specific variables
xiii.
Term structure ofinterest rate
R
xiv.
Rate of inflation%Inf
xv.
Economic growth rate %ΔGDP

Expected
effect

+/+
+
+/+/+

Total interest income over total loan (%)+
Net non-interest income over total assets (%)
Sum of square of market share is a proxy for market
structure variable+/-+/Interest rate of 5 year treasury bill (%)
Annual rate of inflation (%)
Real economic growth rate as a % change in GDP

+
-

30

Table-2: Descriptive statistics

Name of the Variables
Return on Asset (ROAA)
Return on Equity (ROAE)
Equity to Total Asset ratio (E/TA)
Non-performing loan ratio (NPL/TL)
Liquidity ratio (LA/D&STF)
Cost of fund ratio (IE/TD)
Productivity ratio (OP/NoE)
Recurring earning power (REP)
Growth rate of total deposit (GTD)
Size (lnTA)
Loan to deposit ratio (TL/TD)
Total interest income ratio (TII/TL)
Off-balance sheet income (NNII/TA)
Hirschman- Herfindahl index (HHI)
Term structure of interest rate (R)Rate of inflation (Inf%)
GDP growth rate (GDP%)

No of
Observation
1558
1557
1565
1166
1552
1543
866
1558
1404
1565
1488
1082
1556
3744
2265
3397
3370

Mean
0.9949936
14.01718
9.988326
8.343208
19.71283
6.600652
1.817132.202445
2.8564906
7.186992
82.38909
72.37875
0.8787875
0.1360505
1.0741197.026147
6.136493

Standard
Deviation
3.374581
52.9610412.38309
10.40563
28.85115
6.895042
17.55766
3.429645
11.30089
2.054729
63.00418
19.44414
3.314843
0.1156652
1.22368
3.411614
2.259228

31

Table-3 15: Determinants of bank profitability (ROA) in South Asia, 1997-2012, total sample

Variables
Dependent variable: Return on
average Assets (ROA)
L1.ROA
Equity to Total Asset ratio
Non-performing loan ratio
Liquidity ratio
Cost of fund ratio
Productivity ratio
Recurring earning power
Growth rate of total deposit
Size
Loan to deposit ratio
Total interest income ratio
Off-balance sheet income
Hirschman-Herfindahl index
Term structure of interest rate
Rate of inflation
GDP growth rate
Year 2009 (Dummy)
Intercept
Wald-test
Sargan-test
AB test AR(1)AB test AR(2)Model-1(all determinants)
Standard
Coefficienterror.10760625*
0.056919.15133647***
0.028000-0.007028
0.019252-.04288614***0.010198-.16775388***
0.052754-.43347373***
0.068875.75176167***
0.118623
0.000046
0.000050
0.1753600.226166-.01439217**
0.006367
0.000035
0.000046
0.112275
0.141199-0.345839
0.074956-.19451958***
0.024386.05226083**
0.043628-.07972443*
0.155230-.25603382*
0.666215
0.259592
1.911693
Chi2(17) = 350.85
Chi2(95)=166.31
z=-1.75
p-value= 0.07
z=-1.1136
p-value=0.2655

Model-2 (only bank-specificdeterminants)
Standard
Coefficienterror.16700547***
0.046182.1339632***
0.023350-0.016082
0.013626-.03681156***
0.008665-0.059857
0.036788-.32035509***
0.051464.73583202***
0.093714
0.000020
0.000042
0.092553
0.150589-.01490682***
0.005026
0.000043
0.000028.21824237**
0.102645-.23035388**-0.214456
Chi2(13)= 457.42
Chi2(104)=253.85
z=-1.89p-value=0.05
z= -1.3114
p-value= 0.1897

0.115331
1.37397815

The table reports the regression output from GMM estimation of the bank profitability determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***,**, and * respectively. Sargan test is the test for over-identifying restrictions in GMM dynamic model
estimation. AB test AR(1) and AR(2) refer to the Arellano-Bond test that average auto covariance inresiduals of order 1 and order 2 is 0 (𝐻0 : no auto correlation).
32

Table-4 16: Determinants of bank profitability (ROE) in South Asia, 1997-2012, total sample

Variables
Dependent variable: Return on
average Assets (ROE)
L1.ROE
Equity to Total Asset ratio
Non-performing loan ratio
Liquidity ratio
Cost of fund ratio
Productivity ratio
Recurring earning power
Growth rate of total deposit
Size
Loan to deposit ratio
Total interest income ratio
Off-balance sheet income
Hirschman-Herfindahl index
Term structure of interest rate
Rate of inflation
GDP growth rate
Year 2009 (Dummy)
Intercept
Wald-test
Sargan-test
AB test AR(1)AB test AR(2)Model-2 (only bank-specific
Model-1(all determinants)
determinants)
Standard
Standard
Coefficienterror
Coefficienterror.52418001***
0.113610.42597704***
0.081668-3.9676741***0.667903-4.1063285***0.519022
0.182756
0.514501-0.318888
0.302206-0.222673
0.220849-0.093891
0.179014-3.0388376**
1.240529-0.808211
0.773543
0.232178
1.596769-0.070620
1.164588
3.633552
2.661615
4.3750678**
2.032838
0.000389
0.001214-0.000337
0.000993
6.868638
4.956729
0.600586
2.792117
0.037761
0.138309
0.028622
0.107174
0.000589
0.001010
0.000406
0.000582
2.188804
3.250615-1.109140
2.242806
28.088533*
1.730980
2.002792
0.560012-0.037337
1.043490-0.807143
3.691096-8.4449765**
15.410420-7.5645774***2.654180-4.430819
41.456540
32.265954
25.317280
Chi2(17) = 135.91
Chi2(13)= 185.76
chi2(179) = 194.36
Chi2(247)=325.91
z=-1.7045
z=-1.82
p-value= 0.0883
p-value=0.068
z=-.69844
z= -.783
p-value=0.4849
p-value= 0.4336

16

The table reports the regression output from GMM estimation of the bank profitability determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***,**, and * respectively. Sargan test is the test for over-identifying restrictions in GMM dynamic model
estimation. AB test AR(1) and AR(2) refer to the Arellano-Bond test that average auto covariance inresiduals of order 1 and order 2 is 0 (𝐻0 : no auto correlation).
33

Appendix
Table-A5: Correlation matrix*

roa
roe
eta
npltl
ladstfcof
opnoem
rep
gtd
lnta
ld
tiitr
nniita
hhi
ltint
inf
gdp

roa
1.00
-0.02
-0.14
-0.39
-0.03
-0.37
0.21
0.78
0.00
-0.03
0.04
-0.06
-0.49
0.10
0.12
-0.17
0.08

roe

eta

npltl

ladstf

1.00
-0.01
-0.52
-0.02
-0.24
0.36
0.25
-0.01
0.11
-0.02
0.00
-0.23
0.08
0.14
-0.18
0.08

1.00
-0.02
0.33
0.36
0.17
-0.03
0.01
-0.23
0.20
-0.10
0.29
-0.02
-0.11
0.25
-0.13

1.00
0.06
0.31
-0.33
-0.44
0.04
-0.04
-0.02
-0.04
0.37
-0.20
-0.15
0.29
-0.31

1.00
-0.24
0.03
-0.05
0.11
-0.56
0.04
-0.12
0.26
0.25
0.25
-0.01
-0.27

cof

1.00
0.02
-0.29
-0.09
0.06
0.11
0.06
0.14
-0.07
-0.38
0.34
-0.11

opnoem

1.00
0.48
-0.05
0.07
-0.02
-0.06
-0.49
0.13
-0.01
0.00
0.03

rep

gtd

lnta

ld

tiitr

1.00
0.01
0.03
0.08
-0.11
-0.68
0.04
0.14
-0.17
0.04

1.00
-0.17
0.01
-0.01
0.04
0.07
0.03
0.04
0.09

1.00
-0.09
0.13
-0.16
-0.40
-0.42
0.19
0.34

1.00
-0.05
-0.03
-0.01
0.01
0.05
-0.05

1.00
0.07
-0.06
-0.10
0.07
0.02

nniita

1.00
0.00
-0.18
0.33
-0.24

hhi

ltint

inf

gdp

1.00
-0.02
-0.01
-0.13

1.00
-0.31
-0.23

1.00
-0.39

1.00

*Output of Stata** Refer to the table -2 of descriptive statistics for elaboration of the names of the variables

34