The Determinants of Net Interest Margins of Commercial Banks:
Panel Evidence from China, India and Japan
Md. Shahidul Islam
Shin-Ichi Nishiyama
August, 2020
Discussion Paper No.2014

GRADUATE SCHOOL OF ECONOMICS
KOBE UNIVERSITYROKKO, KOBE, JAPANThe Determinants of Net Interest Margins of Commercial Banks: Panel
Evidence from China, India and Japan
Md. Shahidul Islama,1 , Shin-Ichi NishiyamabaDepartment of Banking and Insurance, University of Dhaka, BangladeshbGraduate School of Economics, Kobe University, Japan

Abstract
The study examines the determinants of net interest margins of commercial banks of China, India and Japan. Using
the cross-country panel data of 418 commercial banks during the period of 2011 to 2017 (practically the post global
financial crisis period); we classified the determinants as bank-specific, industry specific and macroeconomicspecific variables. In line of the ‘dealership model of margin determination’ of Ho and Saunders (1981) and its later
extensions, we viewed net interest margins or spread as a function of liquidity, profitability, credit risk, risk
aversion, size, business growth, regulatory response and off- balance sheet income in the bank-specific cluster
whereas the industry concentration in industry specific cluster and call money rate, inflation and GDP growth rate inmacroeconomic clusters. We found evidence that liquidity, profitability, risk aversion, required reserve ratio, yieldspread and GDP affect interest margins positively but size and rate of inflation negatively. Our empirical findings
support the traditional structure-conduct-performance (SCP) hypothesis of Bain (1956) for the banking markets ofChina, India and Japan for the post global financial crisis (GFC) era.

Key Words: Net Interest Margins (NIM), GFC, HHI, Efficiency, Market Structure
JEL classification:
G21, C23

1. Introduction
There are two units in the economy- surplus unit (SU) and deficit unit (DU). Economic agents
whose current level of income (Y) is greater than the current level of consumption (C) fall in thesurplus unit and the economic agents whose current level of income (Y) is less than the current
level of consumption (C) are in the deficit units. These economic agents include the households
1

Corresponding Author. E-mail: sizahid2000@gmail.com

1(h), business entities (b), government (g) and foreign and others (f). In the broader framework offinancial intermediation, the depository financial institutions (DFIs) play a pivotal role to collectfunds from the surplus units in the form of ‘deposits’ and channeling those to the deficit units.
Commercial banks are the major type of depository financial institutions and their fundamental
functions are to collect deposit and extend those in the form of ‘loans and advances’ to the deficit
units. These financial intermediation services by the commercial banks contribute to theeconomic development of a country. The capacity to charge different rates of return on different
financial intermediary functions by the commercial banks is obvious. Economics of existence of
depository financial intermediaries lies in the charging capacity of higher interest rate (R) on
their lending functions and lower interest rates (r) on the borrowing functions. In the literature of
the microeconomics of banking, the difference between the lending rate (RL) and the borrowing
rate (rD) is termed as the spread (S) or the net interest margins (NIM) (see Islam- Nishiyama,
2016a). Allen (1988) termed spread or bankers’ mark-up as the difference between the weighted
average of yields on assets (interest revenue) and liabilities (interest expense).
Although, maximization of the net interest margin is one of the most important objectivefunctions of the commercial banks but a very high or low and volatile net interest margin cancause severe bank management problems and can create distrust among the stakeholders of thebanking business. Banking is a highly regulated business unit where different stakeholders likethe shareholders, the management, depositors, clients and customers, regulators and the society
as a whole have diversified and vested interest. Higher net interest margins may deteriorate thebusiness environment by promulgating the state of default risk and a reduction of social welfare
by increasing the dead-weight loss. As the wealth maximizing agents, commercial banks have2

strong arguments for charging higher margins. We, thus, judge that commercial banks should
charge the optimum level of net interest margins.
In line of the Ho and Saunders’ (1981) dealership model of commercial banking where the banksreceive deposits funds at random intervals, and subsequently, utilize these funds to satisfy
stochastically received loan request has been studied for many years by different scholars
surrounding the globe in extended formats. Saunders and Schumacher (2000) decomposed thedeterminants of bank interest margins as regulatory components, market structure components
and a risk premium component. Angbazo (1997) studied the dealership model in single-stage
linear form with particular emphasize on the credit risk and the interest rate risk. Bernanke,
Gertler and Gilchrist (BGG, 1999) in their dynamic general equilibrium model to clear the credit
market frictions in business fluctuation and Gertler and Kiyotaki (GK, 2011) in theirmacroeconomic paper on financial intermediation and credit policy in business cycle analysis
discussed about the net worth of banking firms and default probability and their impact on
margin determination.
In this paper, we empirically tested the dealership model and its later extensions of bank netinterest margin determinants in case of three Asian countries i.e. China, India and Japan, using
fixed effect panel data of 418 commercial banks during the period of 2011 to 2017; notably thepost Global Financial Crisis (GFC) period. Among 418, there are 214 Chinese, 71 Indian and
133 Japanese commercial banks. In fact, Japan, India and China are the top three of the fourworld economies right now. According to the International Monetary Fund World Economic
Outlook (October, 2018), the projected GDP ranking from 2019 to 2023, Japan, India and China
will remain among the top five largest global economies along with the Unites States of America
and Germany. By the way, our motivation to study particularly the JIC’s commercial banks and3their net interest margins determinants having many objectives. Those are: Japan is one of thelargest OECD countries and they maintained a sustainable economic stance in the global stage
and top ranked economy in Asia in terms of GDP per capita. China is right now the second
largest economy and their economic growth were no less than 6 percent in recent past years.
India achieving a super normal growth rate of 7 percent or more realistically. Japan is in true
sense a capitalist economy, China officially a communist country and India having thecharacteristics of both; a mixed type of economy. Thus, we motivated to study these three
countries’ commercial banks together and to find out the similarities and dissimilarities (if any)
and the post GFC period determinants of net interest margins.
We studied the determinants of the net interest margins of Japan, India and China (JIC) alltogether as they are the top three Asian economies but following different types of economic
spectrums. We viewed the determinants of net interest margins as the bank specific factors,
industry specific factors and some macroeconomic specific factors where each firm within theeconomy compete among themselves to optimize their spread income. Using the fixed effectpanel data, we ran the regressions of the JIC to test the theoretical models empirically. We also
ran the country-wise fixed-effect panel regressions and presented in this paper along with thebase-line regression results. In near past literature, we found similar studies2 on developed anddeveloping countries of USA, Europe, Australia, South Asia and Latin America but in case ofJapan, India and China all together, this study is a unique addition to the literature ofdeterminants of bank net interest margins.

2

Kunt and Huizinga (199) studied 80 developed and developing countries, Saunders and Schumacher’s (2000)
studies included USA and six EU countries, Maudos and Fernandez de Guevara (2004) studied 5 European
countries. Also Williams (2007), Islam et al. (2016) and Bouzgarrou et al. (2018) studied determinants of net interest
margins on Australian, South Asian and French commercial banks respectively among others.

4

The rest of the paper has been arranged as follows: in section two, a review of literature ispresented. In section three, we have developed the econometric methodology of the study, in
section four, the sample and data have been presented. We have outlined the empirical result insection five followed by conclusion and policy implications in section six and appendix andreferences finally.

2. The Literature on Determinants of Net Interest Margins
The literature and the theoretical motivation of the studies on the determinants of net interest
margins (NIM) underwent much development since the famous ‘dealership model’ of spread
determination of the commercial banks by Ho and Saunders (1981). Ho and Saunders’ (1981)
dealership model is basically the extension of the hedging hypothesis and the theory of expectedutility approach.

In their landmark initiative, they proposed the two stage model of interest

margin determination. In stage one; they argued the existence of ‘pure spread’ as the price ofproviding immediacy of services in face of the uncertainty generated by asynchronous deposit
supplies and loan demands. In stage two; they attempted to measure the amount of pure spreadby considering number of imperfections and regulatory restrictions. According to the model,
pure spread is the difference between the bank lending rate (RL) and the deposit rate (RD). As
there exist transaction uncertainty, banks set their interest rates as a margin relative to the interest
rate of the money market (𝑝). These happens as,𝑅𝒑𝒂 and 𝑅𝒑𝒃

Where a and b are the margins for the provision of immediacy services. Thus, as the risk averse,
utility maximizer, bank’s pure spread (S) could be determined as follows:

5

𝑺

𝑅

𝑅

𝒂

𝒃

....................................................... Equation 1

Charging interest margins or spread by the commercial banks can be justified in many ways. As
the wealth maximizing agent in the economy, they compete in the loan (L) and the deposit (D)market simultaneously. But bank itself cannot determine the volume of deposit as it is strictly
exogenous. Whenever they maintain the credit inventory (I) but this variable is sensitive to themarket interest rate (i). So, their position 3 in the money market also could be considered indetermining their interest margins.
Lerner (1981) criticized the dealership model that it failed to recognize the bank as a firm having
a certain production function associated with provision of the intermediation services. The
presence of cost inefficiencies associated with the production process across banks can have a
distortionary effect on the margin. The extension of the basic dealership model by Maudos andGuevara (2004) responded to this criticism by explicitly incorporating the role of operating costs
and providing a detailed description of the link between riskiness and the margin in their one
stage model of interest margin determinants which covered the data of 5 European countries toprove the model empirically.
Allen (1988) expanded the dealership model from a structure with one kind of loan and deposit
to loans and deposits with many maturities that is the ‘portfolio effect’ apparent to margin
determination. Angbazo (1997) studied the net interest margins of commercial banks reflect bothdefault and interest rate risk premia. The study also showed that banks with more risky loans
and higher interest rate risk exposure would select loan and deposit rates to achieve higher net
interest margins and cross sectional differences in interest rate risk and liquidity risk are related
to differences in off-balance sheet exposure.
Money market position of a commercial bank may take any of the following two forms. That is (i) Short and (ii)long. Short position = Ir > Ia and long position = Ir < Ia, where Ir is the required credit inventory and Ia is actual credit
inventory.
3

6

Saunders and Schumacher (2000) conducted an international study on USA and European banks
for the net interest margin determination for 1988-1995 period and found that implicit interest
payments, opportunity cost, capital to asset ratio, market power and interest rate volatility affectnet interest margins positively but they omitted any proxy variable for risk aversion and size oftransaction in their model. Afanasieff et al. (2002) used a panel regression of 142 Brazilian banks
and found that size of bank, opportunity cost and operating cost are positively related to interest
margins but a set of macroeconomic variables such as the market interest rate, the volatility ofmarket interest rate, inflation rate and output growth heavily affect margins as well.
Valverde and Fernandez (2007) applied Ho and Saunders dealership model to a multi-output
framework and showed that the relationship between bank margins and market power varies
significantly across bank specializations. They conducted empirical studies for a sample of
19,322 European banks from Germany, Spain, France, the Netherlands, Italy, the UK and
Sweden and found that market power increases as output becomes more diversified towards nontraditional activities. Hawtrey and Liang (2008) studied the determinants of bank interest
margins using panel data covering the banking sector of fourteen OECD countries and found thatnational banking industry interest margins are influenced by market power, operational cost, risk
aversion, interest rate volatility, and credit risk, volume of loans, implicit interest payments andalso quality of management. With a particular emphasis on the bank ownership structure,
Fungacova and Poghosyan (2011) conducted empirical studies using panel data on the interest
margin determinants in the Russian banking. They found that the impact of a number ofcommonly used determinants such as market structure, credit risk, liquidity risk and size ofoperations differs across ownership structure of banks but influence of operational cost and risk
aversion are homogeneous. Among others, Tarus et al. (2012) studied the determinants of net
7

interest margins of commercial banks in Kenya using pooled and fixed effect panel covering theperiod of 2000-2009. They found that operating expenses, credit risk and inflation are positively
and market concentration and economic growth are negatively related to the net interest margins.
Pasiouras et al. (2007) studied the banking margins of 15 EU countries while using DEA
approach, Stewart et. al. (2016) studied the efficiency of Vietnamese banking and found that thelarge and private banks are more efficient than the small and medium sized and the state ownedbanks. In 2008, Kaigo Zhou and Michael C.S. Wong conducted studies on the determinants of
net interest margins of commercial banks in mainland China and found that the firmcompetitiveness, average operating costs, degree of risk aversion, transaction size, implicit
interest payments, opportunity cost of reserve and management efficiency significantly affect the
net interest margins. Michel R. King (2013) studied the Basel III net stable funding ratio and its
effect on the bank net interest margins. His study includes 15 developed economies of the world
including USA and Japan and found that cost effective strategies and diversified funding sources
have opposite impacts on the interest margins.
In the literature of the determinants of interest margins of banks, we also recognize the studies ofBernanke, Gertler and Gilchrist (BGG, 1999) that in their famous business acceleration model ofcredit market imperfections, discussed about the firms’ balance sheet condition should haveeffect on the lending rate that is determined by the commercial banks. Another seminal study
was conducted by Gertler and Kiyotaki (2011) where their macroeconomic viewpoint on theinterest margin is that so called risk aversion or the net-worth position of banks negatively andthe default risk positively influences the net interest margins of banks.
The studies considering the global financial crisis (GFC) includes Bouzgarrou et al., (2018) who
studied the bank profitability of French commercial banking market for the period of 2000 to8

2012 considering net interest margins as a profitability measure and found that the foreign banks
were more profitable than the domestic banks during the global financial crisis period. In 2017
Ozcan Isik et al., attempted the study of the determinants of net interest margins of Turkish listedbanks with particular focus of post crisis era.
In recent years, Dwumfour (2018) under took studies on the banking spread determinants ofdifferent countries in Asia, Africa, Latin American countries. Barik et al., (2019) studied the netinterest margins of banks in India and found that asset quality, deposit mix and operating costamong many significantly affect the interest margins of the banks in India. Qi et al., (2018)studied the determinants of interest margins of Chinese banks and found liquidity and credit risk
having positive impact on bank spread. Among many, Young Tan (2019) carried out research on
the competition and profitability of Chinese banking industry with particular focus on theownership types.
We get the motivation for our present study by the inconclusive nature of the empirical literatureof the determinants of net interest margins of commercial banks. We want to focus on thedeterminants of net interest margins by incorporating explanatory variables both frommicroeconomic specific and macroeconomic specific in our contemporary cross-country study.
A prior cross country study was conducted by Kunt and Huizinga (1999) using global 80
countries’ data over the period 1988 to 1995, however their particular concern was exogenous
influences such as macroeconomic indicators, tax rates and the degree of international
ownership, all of which found significantly related to interest margins. Complementing theirworks, our study results, reported here focus on both microeconomic and macroeconomic
determinants of the net interest margins of banks.

9

3. Empirical Approach
The present study viewed the determinants of the commercial banks net interest margins as afunction of some (a) bank specific factors, (b) industry specific factors and (c) macroeconomic
factors those are also summarized in table 1 below. In total we used 14 explanatory variables ofwhich 9 are bank specific, 1 industry specific and 4 macroeconomic specific. Regarding thevariable selection, we were much more intuitive. We think that among the bank specificvariables, liquidity, profitability, credit risk exposure, size of the firm, solvency, opportunity to
grasp the business expansion, regulatory response, diversified income or off-balance sheet risk
and finally the efficiency of the management will have significant deterministic role on the netinterest margins of a commercial bank. We included the industry specific variable, Hirschman
and Herfindahl Index (HHI) to capture the industry influence on the spread of a bank. Whereas,
we included the macroeconomic variables such the money market interest rate, inflation and
GDP growth rate to see the impact of these variables on the interest margins as these variables
also have been studied by the researchers in previous literature.

10

Table-1: Description of variables used in the study
Variables

Description

Dependent variable:
Difference between interest income and interest expense over
Net interest margins (NIM)
Total Assets
Independent variables(a). Bank-specific variablesi.
Liquidity
Liquid Assets to Total Assets Ratio
ii.
Profitability
Earnings Available to Common Stockholders to Total Equityiii.
Credit Risk
Impaired Loan to Total Loan Ratio
iv.
Size
Logarithm of Total Assetv.
Capitalization
Equity to Total Assets Ratio
vi.
Business Expansion
Growth Rate of Loanvii.
Regulatory Response
Required Reserve to Total Assets Ratio
viii.
Diversified Income
Net Non-interest (Off Balance Sheet)Income Ratioix.
Efficiency
Operating Expenses over Total Assets Ratio(b). Industry-specific variablesx.
Hirschman-Herfindahl
Sum of square of market share in terms of Total Asset is a
Index (HHI)
proxy for market structure variable(c). Macroeconomic-specific variables
xi.
Standard Deviation of
Short Term Interest Rate
Annualized standard deviation of monthly average of daily(SDINT)
call money rates
xii.
Rate of inflation
Annual Rate of Inflation (%)
Real Economic Growth Rate as a Percentage (%) change inxiii.
Growth rate of GDP
Gross Domestic Product (GDP)

Expected
effect

+
+
+
+/+/+
+
+
+/+
+

3.1. Variables
3.1.1. Dependent Variable
Net interest margins (NIM)
In this empirical study of the determinants of net interest margins or spread, net interest margins(NIM) is the dependent variable. Spread is the difference between the weighted average of yields
on assets (interest revenue) and liabilities (interest expense) - also called the bankers’ mark-up(Allen, 1988). We defined net interest margins of a bank as the ratio of the difference between
interest income and interest expense and total assets.

11

3.1.2.

Explanatory Variables(a) Bank Specific Explanatory Variables(i)

Liquid asset to total asset ratio

Following Angbazo (1997), we used the variable liquid asset but calculated differently for our
purpose. Angbazo (1997) used liquid asset to liabilities to proxy for the liquidity risk as thedeterminant of net interest margins. However, in our model, liquid asset (extracted from the
Bank Focus database as the total earning assets) to total asset is a proxy for the liquidity positionof the bank. We argue that banks are not only provider of liquidity on demand to the liability side
but also on the asset side in the form of providing loan commitments and so on. Liquidity shortor long both are unexpected by the bank. There exist the opportunity cost of holding excess
liquidity and at the same time, liquidity crisis will lead the bank to the ultimate risk of ‘bank run’and ‘bank fail’. Banks whose liquidity position is better than others may charge extra margins
on the loans they provide. For this, ceteris paribas, a positive sign is expected.
(ii)

Return on Equity

In this study, return on average equity (ROE) has been used as the proxy measure of profitability.
Yong Tan (2019) used return on asset (ROA) as the proxy for the profitability and studied its
impact on the net interest margins in the Chinese banking industry. Instead, we defined ROE as
the amount of net income as a percentage of shareholders equity. We considered ROE as the key
determinant of bank profitability also relied on the average assets value to capture the changes
during the fiscal year if any.

12

(iii)

Impaired loan to total loan ratio (IL)Impaired loan to total loan, which actually refers to the default risk of a bank, has been used as
the explanatory variable in our model. Many of the past literature expressed this variable ascredit risk or default risk and calculated differently. For example, Ho and Saunders (1981) used
default premium (DPi) as the ratio of net loan charge-offs to total earning assets in their twostage model of determining net interest margins of banks. Moudos and Guevara (2004) usedloans to total assets ratio as the proxy for credit risk. According to the Bank for International
Settlements (BIS, 1999) loans are the largest and most obvious source of credit risk for mostbanks but we think this approach is too pessimistic to measure the credit risk for banks.
However, this is natural for banks to charge additional on the initial margins on lending if theborrower’s probability of being default is high. We expect ceteris paribas, a positive relationshipbetween non-performing loan and the net interest margins.
(iv)

Log (Total Asset)

Size of the bank is used as the proxy variable of information asymmetry in our econometricmodel of determinants of net interest margins. We want to see how information asymmetry
affects the net interest margins of banks. But it is hard to find the information asymmetry
variables from the disclosed chapters of the bank information from their financial statements.
Then we aimed to characterize banks as small and large (relative size) in terms of their holdingof assets. We calculated the size of the bank as the logarithm of total asset of that bank. Relative
size of the bank in our model as explanatory variable, answers the question of whether the largebanks are charging more or less in relation to the small banks or not. Our hypothesis is that,13

holding others remaining constant, the relative size of the bank affect the net interest margin
inversely.
(v)

Equity to total asset ratio

The variable measures the capitalization of a bank considering the regulatory requirements
regarding the minimum equity holdings. Following McShane and Sharpe (1985), we also usedequity to total asset ratio as the proxy of degree of risk aversion of banks. Among others,
Saunders and Schumacher (2000), Maudos and Guevara (2004) used the variable in the same
way but Hawtrey and Liang (2008) used different approach to calculate the risk averseness of thebanks by dividing securities plus other assets by volume of loans.
Regarding the relationship between equity to total asset ratio and the net interest margins ofbanks, we found contrast outcome in the past literature. Macroeconomic stream like Gertler andKiyotaki (2011) framework argued that the rise in net worth (equity) relative to the capital stock
reduces the expected default probability, holding other things remaining constant. The
underlying economics of that proposition is that banks can charge lower margins where default
probability is lower.
On the other hand, mainstream banking and finance literature expect positive sign arguing that
customers are willing to bear (pay for) bank regulatory taxes in return for the positive externality
related to bank monitoring. That is ‘banks are special’ and customers are willing to pay for thatspecialness (as cited by Saunders and Schumacher, 2000). To address the systematic risk andother regulatory reasons, banks maintain certain equity level. So, everything remains constant,the risk averse bank tends to charge more as the interest margins. Hence, we expect the positive
relationship between equity to total asset ratio and the net interest margin.
14

vi.

Growth of loan

Ho and Saunders (1981) showed that size of transaction and banks’ interest margins are
positively related. Maudos and Guevara (2004) also stated that banks apply large margins on
sizable volume of loans. But the reality is different. We see big-loan-customers enjoy special
discounted rate (prime rate) on their borrowings. Also, banks enjoy the economies of scale
benefit of processing and maintenance cost of big loans which reduces banks operating cost to
revenue. As Hawtrey and Liang (2008) expected negative relationship between log (loan) andinterest margins arguing that increased volume of loans should result in a reduction of unit costs,
which achieves economies of scale and results in narrower margins. Again, McShane and Sharpe(1985) assumed that size of the transaction is invariant across trading banks and time. We used
the growth of total loan as the proxy for size of operation or so called scale effect in our modeland expect positive relationship between loan growth and net interest margin.
vii.

Required reserve to total asset ratio

Portion of deposits that a bank must hold but cannot lend out is the required reserve. This is oneof the regulatory variables of central banks to conduct monetary policy. Although, thissafeguards the depositors against sudden run out of banks but for banks, holding every penny
from the deposit amount reduces the loanable funds so as reduce the probability of earning moreinterest income on loans or from investing in the financial markets. The larger the volume ofrequired reserve, the greater will be the opportunity cost. In our econometric model ofdeterminants of net interest margins of banks, we used cash and due from banks from bank focus
database as the proxy measure for required reserve. Our hypothesis is, holding other things15remaining constant, the larger the volume of required reserve of a bank, the more will be the netinterest margin.
viii.

Net non-interest income

Modern banking and their services have been expanded in many folds so their sources of income
and heads of expenditures. A bank incurs cost of deposits as the largest volume of interest
expenses and the interest income on loans as the largest volume of interest income. Other than
the interest income that is the non-interest income may include service and penalty charges,
capital gain on assets sales, property leasing etc. On the other hand, expenses not included in thecost of deposits like almost all operating and overhead costs will be included as non-interest
expenses.
Net non-interest income as the proxy for implicit interest payment and an explanatory variable todetermine the net interest margin of banks can be found in many previous literature. Amongothers, Ho and Saunders (1981), Angbazo (1997), Saunders and Schumacher (2000) calculated
this as the net non-interest income (non-interest expense less non-interest revenue) divided byearning assets whether Maudos and Guevara (2004) expressed the same as the percentage of total
assets. We followed the later approach and expected that, everything else remaining constant, an
increase in the net non-interest income will affect a bank’s net interest margin positively.

ix.

Operating expenses to total assets ratio

Operating expenses of a bank includes non-interest expenses like overhead and administrative
cost, maintenance of properties and others. Controlling for these costs promotes efficiency of the16bank and enhances the competitiveness as well. Among others, Kunt et. all. (1999), Maudos andGuevara (2004), Hawtrey and Liang (2008) studied the effect of overhead expenses ondetermining interest margins of banks. We also employed the operating expenses to total asset
ratio in our model to see how this variable affect the net interest margins of a bank. Principally, if
a bank’s unit operating expenses is relatively higher (less efficient), that bank will try tocompensate that by charging additional on the regular margins. So, a positive sign has been
expected.
x.

Operating expenses to gross income ratio

Operating expenses to gross income ratio captures the bank management’s efficiency showing
the amount of expenses for every one dollar of income generated to the firm. According to
Angbanzo (1997) as well as Maudos and Guevara (2004) the higher the quality of managementof a bank, the higher the interest margins that will be imposed by the bank, on the ground that ahigh quality of administration implies a high yield and low cost composition of assets andliabilities. On the other hand Gischer and Juttner (2002) argued inverse relationship between thequality of management and the net interest margins of banks but failed to prove their hypothesis
empirically. In our empirical model of determinants of net interest margins, our hypothesis is thatbank may charge higher margins for their excellent service offered to the customers.

(b) Industry Specific Explanatory Variable
xi.

Hirschman Herfindahl index (HHI)

We used the Hirschman Herfindahl Index (HHI) in our econometric model of determinants of net
interest margins of banks to find out the relationship between market concentration and the net
17interest margins in the sample countries. Hirschman Herfindahl index has been defined as thesum of squares of individual bank asset shares to the industry and the formula as follows:

𝐻𝐻𝐼

∑

𝑆

Where, Si = Share (.) of individual bank to the industry.

This is a common and widely used measure of market concentration where higher market
concentration means lower competition and vice versa. Past literature has contrasting views
regarding the relationship between market concentration and interest margins of banks. Most of
the recent studies4 on the developed countries found positive sign coefficient for the variable.
But Hesse (2007) and Fungacova and Poghosyan (2011) who studied Kenyan and Russianbanking respectively found negative relationship between market structure and the net interest
margins of banks. We expected positive relationship between the market concentration and net
interest margins of banks supportive to the Ho and Saunders’ (1981) proposition that, if a bankfaces relatively inelastic demand and supply functions in the markets in which it operates, it may
be able to exercise monopoly power by demanding a greater spread than it could get if banking
markets were competitive.

(c) Macroeconomics specific explanatory variables
xii.

Standard deviation of short term interest rate (SDint)

Past literature used variety of measures to proxy for the volatility of interest rates and its impact
on the net interest margins of banks, including rates on short term money market rates to medium
and long term capital market rates. For our model of determinants of net interest margins ofbanks, we used the annualized standard deviation of monthly average of daily call money rates.
4

Angbanzo (1997), Saunders and Schumacher (2000), Maudos and Guevara (2004), Williams (2007) for reference.

18

Based on the monthly average of the daily call money rates, we have calculated the annual
standard deviation. We also assumed that there will be positive impact of the SDINT on net
interest margins of banks.
xiii.

Rate of inflation

Although there is no empirical consensus on the effects of inflation on interest margins, highinflation rates are generally associated with high interest rates and therefore, higher interest
margins. Even if inflation is not anticipated by banks, in the short term interest rates may notreflect the increased inflation, but in the medium and long term, banks will adjust their interest
rates to compensate for the inflation premium and will increase the interest margins (Tarus et al.,2012). Kunt et al. (1999) found a positive relationship between inflation and the net interest
margins in their study with global evidences from 80 countries. On the other hand, Abreu and
Mendes (2003) found inverse relationship between rate of inflation and net interest margins ofbanks on a cross country study of Portugal, Spain, France and Germany. Our hypothesis is rateof inflation affect net interest margins positively. We expect a ceteris paribas negative
relationship.

xiv.

Growth rate of gross domestic product (GDP)Growth rate of GDP or economic growth rate of a country is important variable to influence thedeterminants of net interest margins of commercial banks. Growth rate of GDP affect directly thedemand and supply of deposits and loans and thus the banking activities. Kunt et al. (1999),
Tarus et al. (2012) studied the importance of economic growth rate to determine bank interest
margins and found inverse relationship. We also argue that economic growth brings prosperity to19the economy. Investors find various scope of investment and create opportunities for the banks
for investment. Hence, our hypothesis is that growth rate of GDP will have significant andpositive relationship with net interest margins.

3.2. Econometric Model

To study the determinants of the net interest margins of the commercial banks in China, India
and Japan empirically, we viewed net interest margins (NIM) of a bank is a function of bankspecific variables (BSV), industry specific variables(ISV) and macroeconomic specific variables(MSV). That is,(NIMit) = f [{BSV (.)}, {ISV (.)}, {MSV (.)}].......... Equation (2)The general model to be estimated for the determinants of net interest margins (NIMs) of banks
in China, India and Japan is of the following linear form:
𝐍𝐈𝐌𝐢𝐭

𝐜

∑𝐉𝐣 𝟎 𝛃𝐣 𝐗 𝐣𝐢𝐭∑𝐋𝐥 𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭𝐦∑𝐌𝐦 𝟎 𝛃𝐦 𝐗 𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭𝛎𝐢𝛆𝐢𝐭𝛖𝐢𝐭 ......Equation (3)Where, 𝑁𝐼𝑀 is the net interest margin of bank i at time t where i = 1,.....,N, t = 1,....., T and c isa constant term. The superscripts j, l and m of X

denote the bank-specific, industry specific andmacroeconomic specific determinants respectively. ε is the disturbance with ν the unobservedbank-specific effect and υ the idiosyncratic error. The error components of the regression
model also distributed as ν ~IIN (0, σ ) and independent of υ ~ IIN (0, σ ) where IIN denotesthat independent, identical and normal (IIN).

20

We precede the following four step issues for the econometric model of NIM determinants.
Firstly, we tested the variables included in our empirical model for the multicollinearity
problems. We tested the issue of multicollinearity using the correlation matrix and varianceinflation factor (VIF) (see appendix table A1 and A2 respectively). Thus we included thosevariables having no multicollinearity problem.
Secondly, we tested our model for the heteroskedasticity issue. Using the LM heteroskedasticity
test (xttest 3 for stata)5 we detected the problem and allowed the robust standard error to get ridof the heteroskedasticity problem.
Thirdly, we examined whether the individual effects are fixed or random. The relevant Hausman
test (the null hypothesis that the individual effect and the explanatory variables are uncorrelated)for equation 3 confirms the evidence in favor of a FE modeling6. A fixed effect or the withingroup estimator model refers to a regression model in which the group means are fixed (nonrandom) as opposed to a random effects model in which the group means are a random sample
from a population (Ramsey et. el., 2002). Although fixed effect estimator has been criticized in
the ground of biasness but we used the fixed effect panel estimator as it is the efficient one.
Finally, we addressed the unobserved time effects in the error components of our model asfollows:
𝐍𝐈𝐌𝐢𝐭𝐜∑𝐉𝐣 𝟎 𝛃𝐣 𝐗 𝐣𝐢𝐭∑𝐋𝐥 𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭𝐦∑𝐌𝐦 𝟎 𝛃𝐦 𝐗 𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭5𝛌𝐭

𝛖𝐢𝐭 ......Equation (4)H0: sigma (i)^2 = sigma^2 for all and the value derived was 𝜒 (107) = 8.80E+33 with p-value= 0.0000.

The relevant Hausman test specification is 𝜒 .
𝑏 𝐵𝑣
chi-squared statistics was 𝜒 12 ) = 134.11 with p-value is 0.0000.
6

𝛎𝐢

𝛆𝐢𝐭

𝑣

𝑏

𝐵 and our calculated21

Where λ is the unobservable time effect and we tested the joint significance of time effects as
𝑯𝟎

𝝀𝟐

𝝀𝟑

𝝀𝑻

𝟎. The relevant LM test7 approves the inclusion of time dummies. We

experiment the model estimator for time dummies for all years jointly and separately.
Considering all these, we estimated the determinants of net interest margins by the following
fixed effect panel equation:
𝐍𝐈𝐌𝐢𝐭

𝐜

∑𝐉𝐣 𝟎 𝛃𝐣 𝐗 𝐣𝐢𝐭∑𝐋𝐥 𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭𝐦∑𝐌𝐦 𝟎 𝛃𝐦 𝐗 𝐢𝐭

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭

𝛄

𝛎𝐢

𝛆𝐢𝐭

𝛖𝐢𝐭 ......Equation (5)4. Sample and Data Description

In this current study of the determinants of the net interest margins of commercial banks of
China, India and Japan, we studied 418 active commercial banks data in between 2011 and 2017.
By country, we studied the panel data set of 214 Chinese, 71 Indian and 133 Japanesecommercial banks respectively.

We defined banks as the financial intermediary who takes

deposits and provide loans and advances in the ordinary courses of business. For our analyses,
we collected data from various sources. The dependent variable and the bank specificexplanatory variables, we collected data from the Bureau Van Dijk’s Bank Focus database (BankFocus 2018) using the universal model of banking database. We took the primary data set fromthe Bank Focus but calculated by our own to get the Hirschman Herfindahl Index (HHI). Finally,
for macroeconomic specific variables, we collected data from the Federal Reserve Economic
7

The relevant LM test chi-squared statistics was 𝜒 (6) = 29.89 with p-value= 0.0000.

22

Data (FRED, 2018). We collected the data regarding short term interest rate volatility of therespective countries and the monthly averages of the daily call money rates and later calculated
the annual standard deviation at our own. From FRED (2018), we also collected yearly data ofrate of inflation and the growth rate of gross domestic product (GDP).
Table-2 in the following presents the summary statistics of the variables we included to study net
interest margins of commercial banks of China, India and Japan. Mean net interest margins of
the sample countries is 1.99% with the standard deviation of 0.0477 during the sample period.
Among the bank specific independent variables, liquid asset to total asset ratio was 17.77%.
Profitability of the banks in terms of return on equity was 8.74%. Non-performing loan ration
was slightly higher than 3%. On an average, commercial banks in the sample countries
maintained around 8% equity to total asset ratio whereas their loan growth was more than 12%
per annum. Required reserve ration mean value was 14.18% and their off-balance sheet income
ratio was 47.71%. Finally, their operating expense ratio was 17.20%.
We found the banking market in the sample countries is quiet competitive. Hirschman
Herfindahl Index value 536 (= 0.0536*10,000) indicates the industry structure is already overcompetitive 8 or un-concentrated market. Mean standard deviation of short term interest rate
(SDINT) is 0.0761. Average rate of inflation was 2.8% whereas the mean GDP growth rate of
the countries was 5.4% during the sample period.

8

As Lovett (1988) states that An HHI below 0.01 (or 100) indicates a highly competitive industry, An HHI below
0.15 (or 1,500) indicates an un-concentrated industry. An HHI between 0.15 to 0.25 (or 1,500 to 2,500) indicatesmoderate concentration and An HHI above 0.25 (above 2,500) indicates high concentration.

23

Table-2: Summary Statistics of the Variables Used in the Study
Variables
Dependent Variable: Net Interest Margins(NIM)
Independent Variables
Bank Specific

Number of
Observations
2,174

0.01993

Standard
Deviation
0.0477

2,174
2,175
1,835
2,175
2,172
1,865
1,282
2,162
2,174

0.17770.08742.693916.85
0.07990.12020.14180.47710.17200.15590.07754.42631.654
0.22780.34730.55870.00199
0.00662,926

0.05360.01812,926

0.0761

0.186

2,926
2,926

2.802
5.401

2.571
3.064

Liquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio
Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index (HHI)
Macroeconomics SpecificStandard Deviation of Short Term
Interest Rate (SDINT)
Rate of Inflation
Growth Rate of GDP

Mean

Table-3, we compared the net interest margins of the sample countries separately. Among the 3
countries, mean net interest margins in China were the highest (2.56%) and in Japan the lowest(1.29%). In case of India the mean spread (1.99%) was in between of China and Japan.

Table-3: Comparative Summary Statistics of Net Interest Margins (NIM) of JIC during the
Sample period of 2011 to 2017
Net Interest
Margins (NIM)
AverageStandard
Deviation
Number of
Banks Studied

Japan

India

China

JIC

1.269
0.404

1.986
11.11

2.557
1.166

1.993
4.772

133

71

214

418

24

5. Results

In this paper, we studied the determinants of the net interest margins of commercial banks of
China, India and Japan. This empirical study includes the panel data of 418 commercial banks of
3 sample countries for the period of 2011 to 2017; notably the post global financial crisis era. We
used fixed effect (FE) model to capture the specific characteristics of each group, using thewithin-group estimator. Firstly, we presented our baseline model result of the pooled estimation
using the baseline econometric model in equation- 5. And then we presented the output ofrobustness check of our baseline model and finally demonstrated the country-wise regression
results. In all the cases, we allowed for individual heterogeneity and used robust standard error9.

5.1. Baseline Result

Result of our baseline model of fixed effect regression, where we assumed each country’s
banking sector as a single representative firm, has been presented in table -4. The first column of
the table present the list of the dependent and independent variables but the independent
variables have been presented in quite classification of bank specific, industry specific andmacroeconomic specific variables respectively.
Another feature of the presentation of table -4 is that there are three models- model 1 is the baseline fixed effect estimation output. Model 2 and model 3 present the Random effect (RE) and
Ordinary Least Square (OLS) as the alternative estimation outputs. R-squared value for FE
estimation is 0.4571 whereas that of RE is 0.6755 and of OLS is 0.7276. The adjusted R-squarevalue of the OLS estimation is 0.7241.

9

Confirmed by LM Heteroskedasticity test and the test statistics (p-value) are presented in the respective tables.

25

The independent variables, coefficients of the independent variables and the robust standard
errors of the coefficients have been presented according to the initial hypotheses developed in theempirical approach section.
The liquidity risk was our center of intuition and we found that liquidity risk has significant
effect on the net interest margins of the commercial banks in China, India and Japan during thepost global financial crisis period. This is indeed supportive to the findings of Ming Qi (2018)
who found liquidity risk has positive impact on banking spread in Chinese banking industry.
We found the managerial motivation to maximize the shareholders’ wealth has significant impact
on the commercial banks’ spread. The estimation output states that for every 1.23% increase inreturn on equity, net interest margins will increase by 1%. This outcome supports the findings ofYong Tang (2019) who studied the spread determinants of the Chinese banks.
In this present study, we found positive, although statistically insignificant coefficient of the nonperforming loans (NPL), which also captures the so called credit risk exposure of the banking
firm. To remember, in the past, Ho and Saunders (1981), Angbanzo (1997), Saunders andSchumacher (2000), Maudos and Guevara (2004), Hawtrey and Liang (2008) studies, mostly on
the developed countries, found positive and statistically significant relationship between credit
risk and the spread. In counter, Williams (2007), Hesse (2007) and Fungacova and Poghosyan(2011), Kutan (2012) and Islam –Nishiyama (2016a) who studied on Australian banking,Kenyan, Russian, 36 dollarized banking system and South Asian banking respectively, foundcredit risk is negatively related to net interest margins. But current study result could not support
the views in the past banking literature.

26

Table - 4: Determinants of net interest margins of banks in China, India and Japan, 2011-2017,
total sample
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-squared (R2)
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β)(Robust Standard
Error, σ)Random Effect (RE)OLS Estimation
Estimation
Coefficient (β) (Robust Coefficient (β) (RobustStandard Error, σ)Standard Error, σ)

0.00499**(0.00249)
0.0123**(0.00557)
0.000380(0.0133)-0.656***(0.197)0.0444*(0.0253)-0.00485(0.00361)
0.0601***(0.0200)-0.0620(0.0483)0.00147(0.00232)
0.0191***(0.00569)
0.0151(0.0124)-0.193***(0.0478)0.0503***(0.0148)-0.00370(0.00328)
0.0788***(0.0196)-0.0673(0.0465)-0.0115***(0.00167)
0.0554***(0.00416)
0.0566***(0.00874)-0.185***(0.0190)0.0581***(0.00580)
0.00424**(0.00175)
0.0983***(0.0134)-0.0358*(0.0215)-0.228***(0.0814)-0.305***(0.0660)-0.0872(0.0880)

0.832***

1.019***

1.103***(0.160)-0.0633***(0.0171)0.210***(0.0243)0.316***(0.0414)10.37***(3.651)1,034(0.154)-0.0447***(0.0142)0.245***(0.0143)0.360***(0.0416)
1.621*(0.906)
1,034(0.238)-0.0623***(0.0207)0.203***(0.0100)
0.382***(0.0647)
1.913***(0.428)
1,034

F (13,243) = 31.44(0.0000)
0.4571134.11(0.0000)
4.7e+31(0.0000)

F(13)= 576.63(0.0000)
0.6755-F(13, 1020)=209.59(0.0000)
0.72760.7241-

Note: The table reports the regression output from Fixed Effect estimation (Model 1) of the net interest margins
determinants. as the baseline estimator. The Coefficients that are significantly different from zero at the 1%, 5% and
10% level are marked with ***, **, and * respectively. Wald test is the test for the goodness of fit of the model
while Hausman test confirm the justification of using fixed effect estimator and LM heteroskedasticity test justify
the use of robust standard error for the models.

27

In this study, we found the size of the firm and the interest margins is negatively related. The
coefficient of log (Total Asset) is statistically significant and depicts that for every 65.6 basis
point increase in size variable will lead 100 basis point decreases in spread income. This finding
supports the studies of Islam and Nishiyama (2016a) and Smirlock (1985) who found significant
and negative impact of size on bank net interest margins.
Unexpectedly, we found no significant impact of equity variable on the net interest margins ofcommercial banks. Saunders and Schumacher (2000) found positive relationship whereas Gertler
and Kiyotaki (2011) expect negative relationship between the equity position and the interest
spread of commercial banks. Among other bank specific variables, we found only required
reserve ratio has significant impact on interest margins though the impact is minimum. The result
is also supportive to the baseline dealership model of net interest margins determination of Hoand Saunders (1981) and also the subsequent expansion and their studies of the model so as our
initial hypothesis that everything else constant, increase in cost of reserve will cause increase ininterest margins of banks. That is if the bank’s cost of holding regulatory reserve increases,banks subsequently shift that cost burden to the customers.
Interestingly, our empirical study found statistically significant and negative coefficient of theindustry specific variable, Hirschman Herfindahl Index (HHI).

This means in the highly

concentrated banking industry of a market lead to the exercise of the monopolistic behavior of
the large banks. Apparently, this result depicts that large firms are charging lower margins in thesample countries banking markets.
Among the macroeconomics specific variables, we found standard deviation of short terminterest rate (SDINT) have significant positive relationship in determining the net interest

28

margins of banks. This means the term structure of the interest rates or the yield spread of theeconomy positively affect the commercial banks spread. As result shows, in the sample period,for every 83.2 basis point change in the yield spread, net interest margins of commercial banks
increased by 1 percent. We found statistically significant negative coefficient of inflation for the
sample countries. This means when due to inflation, the purchasing power of the borrowers
deteriorate, they cannot pay higher interest margins to the banks. Thus we cannot accept thefindings of Yong Tan (2019) who found positive relationship between inflation and interest
margins in Chinese banking industry.
The coefficient of the growth rate of Gross Domestic Product (GDP) and net interest margins are
statistically significant and positively related. The underlying principles of that, when GDP
grows, economic expansion also becomes visible. In that expansionary economy bank can chargehigher from their customers. The estimation output shows that for every 21 basis point increase
in GDP, net interest margins will increase by 1 percentage point. Barik and Raje (2019) studiesof the net interest margins of banks in India also support our findings.
Finally we found the year dummies are significant in our baseline estimation meaning there istime specific heterogeneity after the global financial crisis period in commercial banking markets
of China, India and Japan.

5.2. Robustness Check

Table -5 presents the Robustness check for the determinants of net interest margins of banks in
China, India and Japan, 2011-2017, total sample (using Total Operating Expense Ratio as29

alternative of Net-Non-interest Income Ratio). Operating expense to total asset ratio has beenused as the efficiency variable and included in many of the past literature of interest margins
determination (see Richard Dwumfour, 2018).
Instead of using net non-interest income ratio (proxy for off-balance sheet income), we used
operating expense ratio in our regression equation to see the impact of efficiency on net interest
margins of commercial banks. Due to the inclusion of this alternative variable, we found no
significant change in the R-squared values of different models. We also found no significant
change in the sign of the base line estimator and their level of significance in the robustness
check output. Thus we claim that our baseline estimation output is robust and researchers andpolicy makers can use the baseline coefficients with confidence.

30

Table – 5: Robustness check for the determinants of net interest margins of banks in China, India andJapan, 2011-2017, total sample (using Total Operating Expense Ratio as alternative of Net-Non-interest
Income Ratio)
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-Squared R2
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β) (RobustStandard Error, σ)Random Effect(RE) Estimation
Coefficient (β)(Robust Standard
Error, σ)

OLS Estimation

0.00525**(0.00243)
0.0118**(0.00491)-0.00979(0.0133)
0.00715(0.158)0.0392(0.0251)-0.00500(0.00361)
0.0301**(0.0150)
1.125***(0.151)0.00113(0.00238)
0.0183***(0.00531)
0.00345(0.0131)-0.0592*(0.0322)0.0377***(0.0115)-0.00395(0.00356)
0.0227(0.0144)0.924***(0.118)-0.00627***(0.00154)
0.0569***(0.00407)
0.0451***(0.00860)-0.147***(0.0180)
0.0366***(0.00570)
0.000824(0.00171)
0.0678***(0.0137)
0.563***(0.0554)-0.158**(0.0764)-0.134**(0.0661)-0.00279(0.0850)

0.436***

0.638***

0.775***(0.150)-0.0137(0.0141)0.128***(0.0235)0.201***(0.0377)5.186*(2.828)(0.140)-0.0131(0.0120)
0.197***(0.0157)
0.247***(0.0360)
5.275***(0.835)(0.236)-0.0611***(0.0204)
0.201***(0.00979)
0.321***(0.0640)
4.758***(0.375)1079
F (13, 250) =38.22(0.0000)
0.5208113.32(0.0000)
7.0e+31(0.0000)

1079
F(13)= 702.26(0.0000)
0.6732-1079
F(13,1065)=217.36(0.0000)
0.72630.7229-Coefficient (β) (RobustStandard Error, σ)Note: The table reports the regression output from Fixed Effect estimation (Model 1) of the net interest margins
determinants. Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with***, **, and * respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm thejustification of using fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error forthe models.

31

5.3 Marginal Analysis

Following Moudos and Guevara (2004), we also conducted the marginal analysis of the variables
included in our present empirical study of the determinants of net interest margins of commercial
banks of China, India and Japan during the post global financial crisis period of 2011 to 2017.
We conducted the marginal analysis in stata econometric software for two different models ofinterest margins determination.
Table-6: Marginal analysis of the determinants of net interest margins of commercial banks in
China, India and Japan; 2011-1017 total sample
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio

Model 1Model 2dy/dx (Standard
Error, σ)dy/dx (Standard Error, σ)

0.00499**(0.00249)
0.0123**(0.00557)
0.000380(0.0133)-0.656***(0.197)0.0444*(0.0253)-0.00485(0.00361)
0.0601***(0.0200)-0.0620(0.0483)0.00525**(0.00243)
0.0118**(0.00491)-0.00979(0.0133)
0.00715(0.158)0.0392(0.0251)-0.00500(0.00361)
0.0301**(0.0150)
1.125***(0.151)

Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate (SDINT)
Rate of Inflation
Growth rate of GDP
Year-0.228***(0.0814)-0.158**(0.0764)0.832***(0.160)-0.0633***(0.0171)0.210***(0.0243)0.316***(0.0414)0.436***(0.150)-0.0137(0.0141)0.128***(0.0235)0.201***(0.0377)32

In model 1 we included all the variables also included in our baseline FE regression and in model
2, we excluded the net non-interest income and included operating expense ratio as an alternative
measure. In fact marginal analysis output, those are presented in table-6, support the outcome ofour baseline regression coefficients.

5.3. Country-wise Regression Result

In this section of our study, we presented the regression outputs of the country-wise regressions.
In section 5.3.1, the regression outputs of China, in 5.3.2, the same for India and finally, in
section 5.3.3. the regression outputs of Japan have been presented. In the case of thedeterminants of the net-interest margins of commercial banks in China, in table -7, the baseline
regression equation estimation has been presented. We also presented the robust outcomes for
Chinese cases in the table-8. Following the same strategy of presentation, in table 9 and 11, thebase line regression outputs of India and Japan have been illustrated whereas, in table 10 and 12,
the robust outputs for India and Japan have been dully presented.
The bottom line of the outputs of the table are that, we found no significant deviation in thecoefficients’ sign and magnitude from our baseline regression output that has been outlined in
the table-4.

33

5.3.1.

China

Table - 7: Determinants of Net Interest Margins of Banks in China 2011-2017, Total Sample
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-squared (R2)
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β)(Robust Standard
Error, σ)Random Effect (RE)OLS Estimation
Estimation
Coefficient (β) (Robust Coefficient (β) (RobustStandard Error, σ)Standard Error, σ)-0.00434(0.00603)
0.0468***(0.0131)0.178**(0.0862)-0.675*(0.359)0.0574(0.0468)0.00197(0.00312)
0.0485**(0.0238)-0.0782(0.0670)-0.0102**(0.00516)
0.0531***(0.0118)
0.193**(0.0838)-0.185***(0.0479)
0.0531*(0.0313)0.00193(0.00291)
0.0440*(0.0229)-0.0756(0.0534)-0.0257***(0.00225)
0.0657***(0.00540)
0.206***(0.0317)-0.153***(0.0180)
0.0823***(0.00933)
0.00231(0.00180)0.0453***(0.0115)-0.0233(0.0204)-0.226(0.294)-0.578***(0.101)-0.644***(0.141)

1.671***

2.008***

2.202***(0.341)-0.0266(0.0290)0.203*(0.113)0.371***(0.0512)3.417(2.544)
1,034(0.298)-0.0246(0.0325)
0.268**(0.106)
0.415***(0.0521)-0.00249(0.00253)
1,034(0.369)0.0722**(0.0327)
0.152*(0.0857)
0.465***(0.0521)-0.000610(0.0128)
1,034

F (13,243) = 26.15(0.0000)
0.674643.51(0.0000)
7.3e+30(0.0000)

F(13)= 2551.28(0.0000)
0.9264-F(13, 1020)=1097.52(0.0000)
0.93330.9324-

Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

34

Table - 8 Robustness Check for the Determinants of Net Interest Margins of Banks in China, 2011-2017,
Total Sample (using Total Operating Expense Ratio as Alternative of Net-Non-interest Income Ratio)
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-Squared R2
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β) (RobustStandard Error, σ)Random Effect(RE) Estimation
Coefficient (β)(Robust Standard
Error, σ)

OLS Estimation

0.00135(0.00590)
0.0306**(0.0121)0.116(0.0738)
0.349(0.301)
0.0676*(0.0406)
0.000108(0.00247)
0.00184(0.0179)
1.669***(0.269)-0.0106**(0.00507)
0.0447***(0.0110)0.208***(0.0801)
0.0354(0.0448)0.0590**(0.0282)0.00176(0.00270)-0.00227(0.0197)0.749***(0.160)-0.0283***(0.00201)
0.0673***(0.00491)
0.207***(0.0287)-0.0935***(0.0154)0.0662***(0.00852)
0.00416**(0.00162)
0.0110(0.0108)
0.824***(0.0582)-0.249(0.220)-0.437***(0.0963)-0.292**(0.129)-0.0378

1.270***

1.761***(0.442)0.0432(0.0272)-0.0395(0.108)0.176***(0.0664)0.778(1.728)(0.326)-0.0626*(0.0327)
0.422***(0.109)0.276***(0.0570)0.00820***(0.00255)(0.335)-0.179***(0.0326)0.860***(0.0780)
0.205***(0.0501)
0.00108(0.0113)1079
F (13, 250) =39.73(0.0000)
0.7285225.38(0.0000)
1.5e+32(0.0000)

1079
F(13)= 2808.20(0.0000)
0.9322-1079
F(13,1065)=1376.63(0.0000)
0.94380.9431-

Coefficient (β) (RobustStandard Error, σ)Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

35

5.2.2. India
Table - 9: Determinants of Net Interest Margins of Banks in India 2011-2017, Total Sample
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-squared (R2)
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β)(Robust Standard
Error, σ)Random Effect (RE)OLS Estimation
Estimation
Coefficient (β) (Robust Coefficient (β) (RobustStandard Error, σ)Standard Error, σ)0.00995(0.0278)0.0110(0.00798)-0.0166(0.0157)-0.876(0.737)0.00777(0.0797)-0.00950(0.00673)
0.311(0.219)0.103(0.309)0.0116(0.0310)0.0178*(0.00910)
0.00498(0.0159)-0.308**(0.156)
0.0617*(0.0367)-0.000971(0.00369)
0.417***(0.157)-0.0673(0.211)-0.0145***(0.00490)
0.0533***(0.00368)
0.0580***(0.00736)-0.526***(0.0429)0.0845***(0.00606)
0.0252***(0.00245)
0.522***(0.0476)
0.0967***(0.0374)-0.0417(0.641)-0.769(0.475)-0.990***(0.283)-0.533-0.276-0.168(0.339)-0.0187(0.0211)-0.0249(0.0832)-0.292(0.180)1.493(1.235)
1,034(0.238)-0.0313(0.0232)
0.114(0.0969)-0.291*(0.152)0.00427*(0.00259)
1,034(0.345)-0.0474(0.0304)
0.388***(0.0542)-0.412**(0.191)0.000520(0.00942)
1,034

F (13,243) = 9.77
(0.0000)
0.2871350.97(0.0000)
9.3e+26(0.0000)

F(13)=617.82(0.0000)
0.8911-F(13, 1020)=1030.27(0.0000)
0.92920.9383-

Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

36

Table -10: Robustness Check for the Determinants of Net Interest Margins of banks in India, 2011-2017,
total sample (using Total Operating Expense Ratio as alternative of Net-Non-interest Income Ratio)
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-Squared R2
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β) (RobustStandard Error, σ)Random Effect(RE) Estimation
Coefficient (β)(Robust Standard
Error, σ)

OLS Estimation

0.0101(0.0275)
0.0129(0.00836)-0.0187(0.0155)-0.596(0.787)0.00277(0.0774)-0.00965(0.00657)
0.304(0.216)0.677(0.632)0.0131(0.0292)
0.0203**(0.0103)
0.00658(0.0162)-0.155(0.124)0.0648**(0.0315)0.00199(0.00443)
0.336**(0.146)0.775*(0.417)0.00187(0.00417)
0.0507***(0.00321)
0.0692***(0.00608)-0.334***(0.0384)0.0708***(0.00467)
0.0216***(0.00214)
0.404***(0.0415)
1.612***(0.0956)-0.309(0.709)-1.354***(0.498)-2.368***(0.240)-0.633*-0.393*-0.268(0.352)-0.0250(0.0225)-0.0528(0.0819)-0.357*(0.193)
1.167(1.233)(0.225)-0.0501*(0.0265)
0.155*(0.0894)-0.408***(0.156)0.00636**(0.00257)(0.298)-0.0810***(0.0264)0.436***(0.0463)-0.562***(0.165)0.000844(0.00802)1079
F (13, 250) =12.08(0.0000)
0.3000504.54(0.0000)
3.4e+29(0.0000)

1079
F(13)= 685.65(0.0000)
0.9116-1079
F(13,1065)=1380.38(0.0000)
0.94400.9433-

Coefficient (β) (RobustStandard Error, σ)Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

37

5.3.3.

Japan

Table - 11: Determinants of Net Interest Margins of Banks in Japan 2011-2017, Total Sample
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Net Non-interest Income Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-squared (R2)
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β)(Robust Standard
Error, σ)Random Effect (RE)OLS Estimation
Estimation
Coefficient (β) (Robust Coefficient (β) (RobustStandard Error, σ)Standard Error, σ)-0.00231(0.00380)
0.00444(0.00663)
0.0243(0.0390)0.586(0.548)0.130(0.0805)-0.00828(0.00577)
0.0306*(0.0156)-5.74e-05(0.0107)-0.00113(0.00359)
0.0104**(0.00501)
0.0882***(0.0306)-0.0155(0.0261)0.0501***(0.0134)-0.00569(0.00524)-0.00395(0.0235)-0.0943***(0.0257)-0.00226***(0.000768)
0.0224***(0.00315)
0.106***(0.00932)-0.0455***(0.00948)
0.0385***(0.00445)-0.00226**(0.000971)
0.00633(0.00904)-0.109***(0.0101)-0.261***(0.0800)-0.111(0.0867)-0.158***(0.0401)-0.0430

0.459

2.858***(1.202)-0.0238(0.0633)
0.0806**(0.0333)
0.152(0.0949)-6.100(5.667)1034(0.755)-0.0145(0.0283)
0.0576(0.0401)0.0706(0.0679)
0.00405***(0.00104)1034(0.970)0.0258(0.0338)0.126***(0.0364)0.0442(0.0930)
0.00261(0.00891)
1034

F (13, 243)=18.55(0.0000)
0.2841211.41(0.0000)
1.2e+06
0.0000F(13)= 5958.70(0.0000)
0.9113-F(13,1020)=862.02(0.0000)
0.91660.9155-

Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

38

Table – 12: Robustness Check for the Determinants of Net Interest Margins of Banks in Japan, 2011-2017,
Total Sample (using Total Operating Expense Ratio as alternative of Net-Non-interest Income Ratio)
Variables
Dependent Variable: Net Interest Margins (NIM)
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio
Return on Equity
Non-performing Loan (NPL) Ratio
Log (Total Asset)
Equity to Total Asset Ratio
Loan GrowthRequired Reserve Ratio
Total Operating Expense Ratio
Industry Specific
Hirschman Herfindahl Index
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT)
Rate of Inflation
Growth rate of GDP
Year
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-Squared R2
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)

Fixed Effect (FE)
Estimation
Coefficient (β) (RobustStandard Error, σ)Random Effect(RE) Estimation
Coefficient (β)(Robust Standard
Error, σ)

OLS Estimation-0.000877(0.00352)
0.00366(0.00654)
0.0288(0.0364)
0.679(0.485)0.143*(0.0747)-0.00810(0.00576)
0.0256*(0.0148)0.302***(0.0868)

1.12e-05(0.00285)
0.00535(0.00528)
0.0740**(0.0333)
0.00616(0.0298)
0.0617***(0.0136)-0.00742(0.00561)-3.32e-05(0.0190)-0.0256(0.105)0.00201**(0.000993)
0.0187***(0.00412)
0.114***(0.0113)-0.0424***(0.0147)
0.0240***(0.00654)-0.00617***(0.00133)
0.00271(0.0124)0.0551(0.0385)-0.256***(0.0733)-0.141(0.0993)-0.398***(0.0524)

0.330

0.574

7.175***(1.118)0.0133(0.0583)
0.0851***(0.0320)0.0874(0.0915)-6.387(5.131)(0.689)-0.0184(0.0305)
0.0717*(0.0430)
0.0899(0.0658)
0.00846***(0.00179)(1.266)0.128***(0.0451)
0.308***(0.0486)-0.0179(0.125)0.00591(0.0124)

1079
F (13, 250) =22.75(0.0000)
0.2858211.41(0.0000)
1.2e+06(0.0000)

1079
F(13)= 2887.23(0.0000)
0.8335-1079
F(13,1065)=478.59(0.0000)
0.85380.8521-

Coefficient (β) (RobustStandard Error, σ)Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. Wald test is the test for the goodness of fit of the model while Hausman test confirm the justification ofusing fixed effect estimator and LM heteroskedasticity test justify the use of robust standard error for the models.

39

6. Test of Homogeneity of the Coefficients across Three Countries
Regarding the determinants of net interest margins of commercial banks of China, India andJapan in previous section, we estimated the baseline regression equation (equation-5).Although
the country dummy variables were not allowed in fixed effect model, but we did include country
dummy variables in random effect and in pooled OLS estimation We found country-specificheterogeneity regarding the intercepts (α) in the baseline regression equation. However, it should
be noted that in this baseline regression, the coefficients or slope parameters (β) were assumed tobe homogeneous across three countries.

On the other hand, for the country-by-country

estimation of baseline regression equation, we allowed the slope parameters to be different
across the countries. Indeed, we found some differences in the estimates of slope parameters inour country-wise regression that have been presented in section 5.3. Now the question must be
asked. Is it plausible to assume the homogeneity in slope parameters of the commercial banks
across China, India, and Japan? Or should we allow for the heterogeneity in slope parameters of
the commercial banks across three countries? In order to answer this question formally, we
conduct the test of homogeneity in slope parameters across three countries in this section.
6.1. Econometric Methodology
In testing for homogeneity of the coefficients across three countries, the idea is to set thebenchmark country and test whether other country’s coefficients are significantly different fromthe benchmark country’s coefficients. In this paper, we set Japanese banks to be the benchmark.
Recall that our fixed effect regression equation was formulated as follow;
𝐍𝐈𝐌𝐢𝐭

𝐜

∑𝐉𝐣 𝟎 𝛃𝐣 𝐗 𝐣𝐢𝐭∑𝐋𝐥 𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭𝐦∑𝐌𝐦 𝟎 𝛃𝐦 𝐗 𝐢𝐭

𝛄𝐃𝟐𝟎𝟏𝟒

𝐰𝐡𝐞𝐫𝐞, 𝛆𝐢𝐭

𝛎𝐢

𝛆𝐢𝐭

𝛖𝐢𝐭 ......Equation (6)40In order to capture the country-specific effect in slope parameters, we introduce the countryspecific cross-terms to the above equation as follow;
Now let, ∑𝐉𝐣 𝟎 𝛃𝐣 𝐗 𝐣𝐢𝐭∑𝐋𝐥 𝟎 𝛃𝐥 𝐗 𝐥𝐢𝐭𝐦∑𝐌𝐦 𝟎 𝛃𝐦 𝐗 𝐢𝐭

𝛄𝐃𝟐𝟎𝟏𝟒 = 𝜷. 𝑿𝒊𝒕....Equation (7)Then, the equation is,
𝐍𝐈𝐌𝐢𝐭

𝛂𝐢

𝜷. 𝑿𝒊𝒕𝛅𝐜 𝐙𝐢𝐭𝐜

𝛅𝐈 𝐙𝐢𝐭𝐈𝛆𝐢𝐭.......Equation (8)Where, 𝐙𝐢𝐭𝐜 ≡ 𝑿𝒊𝒕 . 𝜤𝒄𝒊 and 𝐙𝐢𝐭𝐈 ≡ 𝑿𝒊𝒕 . 𝜤𝑰𝒊
Where,

𝜤𝒄𝒊

𝟏 if bank 𝒊 is Chinese bank and 𝟎 otherwise and 𝜤𝑰𝒊

𝟏 𝑖𝑓 𝑏𝑎𝑛𝑘 𝒊 𝑖𝑠 𝐼𝑛𝑑𝑖𝑎𝑛 𝑏𝑎𝑛𝑘 𝑎𝑛𝑑 𝟎 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Interpretation:
β will be the benchmark coefficient which is Japanese bank. 𝜷 𝜹𝒄 . 𝜤𝒄𝒊 . 𝑿𝒊𝒕
𝑪𝒉𝒊𝒏𝒆𝒔𝒆 𝒄𝒐𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕 𝒂𝒏𝒅 𝜷 𝜹𝑰 . 𝜤𝑰𝒊 . 𝑿𝒊𝒕 𝜷𝑰 𝑰𝒏𝒅𝒊𝒂𝒏 𝒄𝒐𝒆𝒇𝒇𝒊𝒄𝒊𝒆𝒏𝒕.
And null hypothesis 𝑯𝒐 : 𝜷Or, 𝑯𝒐 : 𝜷𝒄

𝟎, 𝜷𝑰

𝜷𝒄

𝜷𝒄

𝜷𝑰

𝟎

41

6.2. Empirical Result for Test of Homogeneity:
Table – 13: Test of Homogeneity across the sample countries, 2011-2017, Total Sample (nullhypothesis 𝐇𝐨 : 𝜷 𝛃𝐜 𝛃𝐈 or 𝐇𝐨 : 𝛃𝐜 𝟎, 𝛃𝐈 𝟎)
Variables
Dependent Variable: Net Interest Margins (NIM)_CN
Independent Variables
Bank SpecificLiquid Asset to Total Asset Ratio_CN
Return on Equity_CN
Non-performing Loan (NPL) Ratio_CN
Log (Total Asset) _CN
Equity to Total Asset Ratio_CN
Loan Growth_CN
Required Reserve Ratio_CN
Net Non-interest Income Ratio_CN
Industry Specific
Hirschman Herfindahl Index_CN
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT) _CN
Rate of Inflation_CN
Growth rate of GDP_CN
Year_CN
Dependent Variable: Net Interest Margins (NIM)_IN
Independent Variables_IN
Bank Specific_IN
Liquid Asset to Total Asset Ratio_IN
Return on Equity_IN
Non-performing Loan (NPL) Ratio_IN
Log (Total Asset) _IN
Equity to Total Asset Ratio_IN

Fixed Effect (FE)
Estimation
Coefficient (β)(Robust Standard
Error, σ)Random Effect (RE)OLS Estimation
Estimation
Coefficient (β)
Coefficient (β) (Robust(Robust Standard
Standard Error, σ)Error, σ)-0.00203(0.00722)
0.0424***(0.0149)
0.154(0.0958)-1.261*(0.664)-0.0724(0.0943)0.0103(0.00665)
0.0179(0.0288)-0.0782(0.0687)-0.00960*(0.00578)
0.0469***(0.0132)
0.122(0.0922)-0.138**(0.0565)-0.00604(0.0365)
0.00942(0.00662)
0.0206(0.0299)-0.0223(0.0559)-0.0234***(0.00380)0.0408***(0.0113)0.0866*(0.0518)-0.0755**(0.0372)
0.0515***(0.0178)0.00419(0.00360)
0.0241(0.0290)0.0610(0.0401)

0.0343(0.309)-0.465***(0.152)-0.657***(0.235)1.714-0.465***-0.657***(1.266)-0.00285(0.0705)
0.122(0.119)0.218**(0.109)(0.152)
3.103***(0.733)0.0706(0.0495)
0.163(0.119)(0.235)1.997(2.686)
0.189*(0.109)-0.0490(0.157)

Coefficient (β)(Robust Standard
Error, σ)

Coefficient (β)(Robust Standard
Error, σ)

Coefficient (β)(Robust Standard
Error, σ)0.0123(0.0285)
0.00654(0.0105)-0.0410(0.0426)-1.461(0.931)-0.122(0.115)0.0140(0.0318)
0.0110(0.0109)-0.0513(0.0416)-0.309*(0.166)0.0109(0.0391)-0.0122(0.00848)
0.0266***(0.0103)-0.0471*(0.0269)-0.465***(0.0764)0.0553***(0.0156)42

Loan Growth_IN
Required Reserve Ratio_IN
Net Non-interest Income Ratio_IN
Industry Specific
Hirschman Herfindahl Index_IN
Macroeconomics SpecificStandard Deviation of Short Term Interest Rate
(SDINT) _IN
Rate of Inflation_IN
Growth rate of GDP_IN
Year_IN
Constant
Number of observations
Wald χ2 , F –Stat (p value)
R-Squared R2
Adjusted R2
Hausman Test, χ2 (12)(p-value)
LM Heteroskedasticity test, χ2 (p-value)-0.00121(0.00898)
0.280(0.223)0.103
0.01230.00815(0.00733)
0.429**(0.173)0.03540.01400.0267***(0.00482)
0.513***(0.0834)
0.193***-0.01220.219(0.654)-0.614(0.491)-0.629(0.492)-0.4900.769-0.221(1.266)0.00509(0.0676)-0.106(0.0907)-0.445**(0.207)-1.190(6.416)(0.704)0.0368(0.0359)
0.102(0.117)-0.438**(0.183)2.030**(1.013)(2.712)0.0218(0.105)0.358***(0.134)-0.431(0.400)1.782***(0.580)1034
F (39, 243) =17.69(0.0000)
0.5654190.76(0.0000)
8.6e+30(0.0000)

1034
F(39)= 1491.96(0.0000)
0.7318-1034
F(39,994)=93.78(0.0000)
0.78630.7779-Note: The table reports the regression output from Fixed Effect estimation of the net interest margins determinants.
Coefficients that are significantly different from zero at the 1%, 5% and 10% level are marked with ***, **, and *respectively. _CN represents Chinese and _IN represents Indian abbreviation respectively. Wald test is the test for
the goodness of fit of the model while Hausman test confirm the justification of using fixed effect estimator and LM
heteroskedasticity test justify the use of robust standard error for the models.

6.

Conclusions and Implications

This study investigates the determinants of net interest margins of commercial banks of China, India andJapan; the top 3 of the 4 largest economies of the world right now. Using a comprehensive cross –country
panel data set with bank-level, industry-level and macroeconomic –level explanatory variables, this paper
presents the empirical results on how bank specific, industry specific and macroeconomics specificfactors affect the interest margins of commercial banks. We started from the dealership model of Ho andSaunders (1981) and later extension by other authors but followed the single stage model of margin
determination. Our panel data covering the period of 2011-2017 carries significance that we particularly
43focused on the post global financial crisis period. Regarding the econometric modeling, we used the fixed
effect panel estimator as the baseline model estimator but also estimated the model using the random
effect and the OLS estimators. In addition, we estimated the marginal effect of the independent variableson the dependent variable.
We found strong empirical evidence that among the bank specific factors, liquidity, profitability,
regulatory capital, size and required reserve ratio have significant impact on net interest margins.
Liquidity, profitability and regulatory capital and required reserve ratio have positive impact whereas size
has negative impact on commercial banks’ spread. The evidence support that the impact of regulatory
capital (proxy by equity to total asset ratio) on interest margins that the views of Ho and Saunders (1981)
and Saunders’ and Schumacher (2000) who viewed that commercial banks should charge higher interest
margins due to their excellence in financial capital. Again in the macroeconomic views of Gertler andKiyotaki (2011) who propose the New Keynesian views in their famous business cycle acceleration incredit market imperfections where lending firm should charge lower whose equity condition is stronger
and apparently our evidence support that.
We also found evidence of the structure conduct performance (SCP) hypothesis of Bain (1956) where theindustry composition should have effect on the profitability of firms. Although the sample countries’banking industry seems over competitive, but still, the relatively larger firms have some monopolistic
behavior in determining the net-interest margins of commercial banks.
Macroeconomic consequences in the economy found to have significant effect on commercial banks’spread. Evidence shows that yield spread, rate of inflation and GDP growth rate have significant impact
on interest margins. We found opposite evidence of Yong Tan (2019) who found positive relationship ofnet interest margins with inflation and negative relationship with GDP growth rate.

Although, we conducted our empirical study using fixed effect, random effect and OLS
estimations but fixed effect model performs better and generates the expected specification.
44

Robust standard errors have been incorporated for all the models, the robustness check and thecountry-wise regression which ensure the correct inferences from the estimation.
Regarding policy implications, we suggest the banks to take appropriate actions so that the default
probability would have proper reflection in determining the interest margins. For the regulatory bodies,
we recommend liberal policy actions for new entrants that could contribute to the improvement of thecompetitive environment in the industry in order to reduce the cost of financial intermediation.
In terms of directions for future research, studies on a number of additional explanatory variables like
corporate tax rates, competition among banks and other financial institutions, ownership structure, deposit
insurance, information asymmetry, portfolio effect would be tested as the extension of the model. Due tolimitation of data and the degrees of freedom or for the potential multicollinearity problem, we could nothave fruitful insight of the literature but doing such could be apparently an interesting path for future
research.

Acknowledgement:
We, hereby, acknowledge the contribution of Masahiko Shibamoto, Agata Wierzbowska and John-Paul
Karani of Kobe University, Japan for their valuable comments at the ‘Rokko Forum’ to improve thequality of the paper. We also, would like to gratefully acknowledge the financial support from JSPS
KAKENHI Grant-in-Aid for Scientific Research (C) Number 18K01687 for this research.

45

Appendix A.

Table A1
Correlation matrix* of the variables studied for the model of bank net interest margins determination of Japan, India and China (JIC)
Variables**

nim
lata
roe
il
log_TAETA
loangrowth
log_reserve
log_nniiTA
ln_opexTA
log_HHISTDV_intrate
Inflation
GDP_growth

nim

lata

1.00
0.04
0.49
0.17
‐0.14
0.44
0.33
‐0.18
0.04
0.36
‐0.26
0.37
0.52
0.73

1.00
0.18
‐0.15
0.06
0.31
0.16
‐0.10
0.17
‐0.05
‐0.07
‐0.04
0.04
0.16

roe

1.00
‐0.45
0.32
0.08
0.48
‐0.07
0.21
‐0.14
‐0.40
0.04
0.28
0.55

il

1.00
‐0.29
0.26
‐0.31
0.05
‐0.09
0.33
0.14
0.27
0.16
0.04

log_TA

1.00
‐0.23
0.08
0.59
0.47
‐0.38
‐0.14
‐0.17
‐0.07
0.08

ETA

loangr~hlog_re~elog_nn~A

ln_ope~A

log_HHISTDV_i~e

Infla~on

GDP_growth

1.00
0.06
‐0.16
0.16
0.35
0.04
0.21
0.23
0.30

1.00
‐0.23
0.16
0.00
‐0.19
0.11
0.24
0.43

1.00
0.31
0.01
0.00
0.04
‐0.01
‐0.25

1.00
0.20
‐0.08
0.14
0.18
0.12

1.00
‐0.02
0.34
0.28
0.04

1.00
‐0.09
‐0.36
‐0.27

1.00
0.72
0.30

1.00
0.51

1.00

*Output of Stata Econometric Software and ** refer to Table 1 of description of variables for elaboration of the names of the variables

46

Table A2
Variance Inflation Factor (VIF) of the variables studied for the model of bank net interest
margins determination of Japan, India and China (JIC)
Variables(a). Bank-specific variablesi.
Liquidityii.
Profitability
iii.
Credit Risk
iv.
Size
v.
Capitalizationvi.
Business Expansionvii.
Regulatory Response
viii.
Diversified Incomeix.
Efficiency(b). Industry-specific variablesx.
Hirschman-Herfindahl Index(HHI)(c). Macroeconomic-specific variables
xi.
Standard Deviation of ShortTerm Interest Rate (SDINT)
xii.
Rate of inflation
xiii.
Growth rate of GDP

Mean VIF

VIF

1/VIF

1.23
2.43
1.85
3.87
1.61
1.55
2.47
1.87
1.89

0.812383
0.411959
0.541807
0.258164
0.622953
0.647230.404526
0.53409
0.528329

1.39

0.720439

2.39
3.04
2.52
2.16

0.418817
0.329416
0.397144

47

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