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CHAPTER 4
You manage what you measure. Unfortunately, performance assessment systems seldom evolve as fast as businesses do. - Andrew Likierman, Dean, London Business School (Harvard
Business Review, October 2009)
103
4. Data Analysis and Interpretation
The present study attempts to analyze the financial performance of
sample commercial banks involved in mergers during the period 1994 to
2009. To evaluate the financial performance, statistical tools like ratio
analysis, mean, standard deviation and t-test have been employed.
4.1 Evaluation of post-merger Performance of select
commercial banks in India employing ratio analysis
approach
The financial performance of the 11 acquiring commercial banks
(constituting the sample) before and after the merger has been analyzed
below with the help of various financial ratios(Please refer to Table 4.1)
which characterize a commercial bank’s performance.
In order to test the validity of null hypotheses stated in chapter 1, the
following parameters/ratios have been selected to test the results of pre
and post -merger periods (Average of three years).
104
Table 4.1 Classification of financial ratios
Class Code Variable/Parameter
Business Parameters V1 Aggregate deposits
V2 Average working funds (AWF)
V3 Operating profits
V4 Net profits(NI)
Operational Parameters
V5 Total Debt to Net worth
V6 Interest income to AWF
V7 Net interest income to AWF
V8 Operating expenses to AWF
V9 Capital adequacy ratio
V10 Net interest Income to Average assets
V11 Operating expenses to total expenses
V12 Efficiency Ratio
Profitability
Parameters
V13 Operating profit to AWF
V14 Net profit to AWF
V15 Net Profit to average net worth
V16 Operating profit to average net worth
V17 Asset utilization(AU)
V18 Equity multiplier(EM)
V19 Net Interest Margin (NIM)
V20 Burden ratio
V21 Earnings per share(EPS)
V22 Price-Earnings(PE) ratio
Productivity
Parameters
V23 Business per employee
V24 Business per branch
V25 Operating profit per branch
V26 Operating profit per employee
V27 Assets per employee
V28 Loans and Advances per employee
V29 Net income per employee
Source: Author’s perspective
105
Table 4.2
Business Parameters (Rs in Crores)
Business Parameter Analysis: Pre-Merger and Post-Merger Mean
Parameter for acquiring banks
Pre-
Merger
(3-year avg)
Post-
Merger
(3-year avg)
t-statistic
(0.05 significance)
p- values
Aggregate Deposits 29759.265 62499.693 -4.548 0.000(s)
Average Working Funds(AWF) 37715.340 76153.234 -4.686 0.000(s)
Operating Profit 734.746 878.520 -0.347 0.262
Net Profit 270.958 655.168 -4.056 0.004(s)
Source: Results of data analysis
The significance of the each parameter/ratio3 is explained below by
plotting a graph of the mean parameter/ratio (on vertical axis) and the
relative time (in years) on horizontal axis.
Aggregate Deposits (AD):
Aggregate deposits include deposits from public (fixed, savings and
current) and deposits from banks (fixed and current). From a different
angle, aggregate deposits equal the total of all demand and time deposits.
A high deposit figure signifies a bank’s brand equity, branch network and
deposit mobilization strength.
3 All the parameter/ratio values used for plotting graphs (4.1 to 4.27) are averages over
three years before and after the merger year (financial year).
106
0
10000
20000
30000
40000
50000
60000
70000
80000
T-3 T-2 T-1 T0 T+1 T+2 T+3
Ave
rage
De
po
sits
(R
s. c
rore
s)
Relative Time (Yrs.)
Graph 4.1
Aggregate Deposits versus Relative Time
.
Source: Processed Data
Average Working Funds (AWF):
The average of the working funds at the beginning and at the close of an
accounting year. Working funds are total resources (total liabilities or
total assets) of a bank on a particular date. Total resources include
capital, reserves and surplus, deposits, borrowings, other liabilities and
provisions. A higher AWF shows a bank’s total resource strength. This
definition of working funds is in line with capital adequacy calculations
to include all resources, not just deposits and borrowings and is more
pragmatic.
107
Graph 4.2
Average Working Funds versus Relative Time
Source: Processed Data
0
10000
20000
30000
40000
50000
60000
70000
80000
90000
100000
T-3 T-2 T-1 T0 T+1 T+2 T+3
AW
F (R
s. C
rs.)
Relative Time (Yrs.)
108
0
200
400
600
800
1000
1200
1400
T-3 T-2 T-1 T0 T+1 T+2 T+3
Ave
rage
Op
era
tin
g P
rofi
t (R
s. C
rs.)
Relative Time(Yrs.)
Operating Profit (OP):
It is Net profit before provisions and contingencies. This is an indicator of
a bank’s profitability at the operating level. In other words, Operating
Profit is a measure of a bank’s operating efficiency.
Graph 4.3
Operating Profit versus Relative Time
Source: Processed Data
109
Net Profit (NP):
This is profit net of provisions, amortization and taxes. Net Profit is the
basic indicator of a bank’s profitability.
Graph 4.4
Net Profit versus Relative Time
Source: Processed Data
4.1.1 Analysis of Business Parameters:
It would be observed that there is significant difference between average
pre- and post-merger figures of Aggregate Deposits, Average Working
Funds (AWF) and Net Profits at 5% level of significance, while it is not so
in respect of Operating Profits(p-value=0.262). While the percentage
growth between average pre and post merger aggregate deposits, average
0
100
200
300
400
500
600
700
800
900
T-3 T-2 T-1 T0 T+1 T+2 T+3
Net
Pro
fit
(Rs.
Crs
.)
Relative Time (Yrs.)
110
working funds and Net profits is 110%, 102% and 142% respectively, the
corresponding growth rate for Operating profit is only 19%, justified by p-
value of 0.262. In today’s intensely competitive and increasingly
deregulated financial markets, both the cost and amount of deposits with
the banks are crucial in maintaining a sustainable competitive
advantage.
The financial management implication of the two features of the deposits-
stability and low cost source of funds- makes them the preferred source
of funds by banks. All else being equal, banks with stronger deposit base
are more valuable than those with a weak deposit base. The above
advantages are reflected in the Net profit that has grown significantly
though the Operating profit has not shown such a high growth rate.
111
Table 4.3
OPERATIONAL PARAMETERS
Source: Results of data analysis; * denotes that the variable in question is significant
The significance of the each ratio is explained below by plotting a
graph between the average ratio (on vertical axis) and the relative time (in
years) on horizontal axis.
Total debt to Net worth:
This ratio is expressed as a number. The corresponding ratio in a
manufacturing company is termed as debt- equity ratio. A higher ratio is
a proof of bank’s ability to leverage its net worth effectively. Debt-Equity
Ratio is arrived at by dividing the total borrowings and deposits by
shareholders’ net worth, which includes equity capital and reserves and
surpluses less revaluation reserves and miscellaneous expenses not
Operational Parameter Analysis: Pre- Merger and Post Merger Mean Ratio for
acquiring banks
Pre-
Merger
(3 years
avg. %)
Post-
Merger
(3 year
avg. %)
t-statistic
(0.05
significance) p-values
Total Debt to Net Worth 91.682 112.272 -0.442 0.850
Interest Income to AWF 8.584 8.157 0.702 0.206
Net Interest Income to AWF 1.754 2.383 -0.898 0.924
Operating Expenses to AWF 3.695 3.387 1.842 0.005*
Capital Adequacy
Ratio(CAR) 9.550 11.362 -0.955 0.272
Net Interest Income to Assets 1.754 2.383 -0.898 0.924
Operating expenses to total
expenses 35.934 37.185 -0.545 0.049*
Efficiency Ratio 79.921 77.757 0.697 0.008*
112
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
T-3 T-2 T-1 T0 T+1 T+2 T+3
Tota
l Deb
t to
Net
wo
rth
Rat
io
(tim
es)
Relative Time (Yrs.)
written off. This is one of the measures of capital adequacy under the
highly popular CAMEL Model, a world-renowned model for evaluating the
financial health of a bank.
Graph 4.5
Total Debt to Net worth versus Relative Time
Source: Processed Data
113
Interest Income to AWF:
Expressed as a percentage, this ratio shows bank’s ability to leverage its
average total resources in enhancing its main stream operational interest
income.
Graph 4.6
Interest Income to Average Working Funds versus Relative Time
Source: Processed Data
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
0.18
0.2
T-3 T-2 T-1 T0 T+1 T+2 T+3
Inte
rest
Inco
me
to A
WF
Relative Time (Yrs.)
114
Net Interest Income to AWF:
It is a measure of bank’s operational profitability as a percentage of
average working funds.
Graph 4.7
Net Interest Income to Average Working Funds versus Relative Time
Source: Processed Data
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
T-3 T-2 T-1 T0 T+1 T+2 T+3
Ne
t In
tere
st In
com
e t
o A
WF
Relative Time (Yrs.)
115
Operating expenses to AWF:
The operating expense to AWF ratio explains the overall operational
efficiency of a bank. In fact, this ratio is one of the indicators of the
operating profitability of a bank.
Graph 4.8
Operating Expenses to Average Working Funds versus Relative Time
Source: Processed Data
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
T-3 T-2 T-1 T0 T+1 T+2 T+3
Op
era
tin
g Ex
pen
ses
to A
WF
Relative Time (Yrs.)
116
Capital adequacy Ratio (CAR):
This ratio relates a bank’s core net worth to its risk weighted assets. This
ratio is an internationally accepted risk- driven measure of a bank’s
degree of capitalization. This ratio indicates the risk exposure of the
bank, the quality of assets and the capacity of the bank’s capital to
sustain the risk level. A higher ratio indicates that a bank is well
capitalized vis-à-vis its perceived risks. It is an excellent indicator of a
bank’s long term solvency. The minimum CAR prescribed by the RBI is
9%.
Graph 4.9
Capital Adequacy Ratio versus Relative Time
Source: Processed Data
0
2
4
6
8
10
12
14
T-3 T-2 T-1 T0 T+1 T+2 T+3
Cap
ita
l Ad
eq
ua
cy R
ati
o %
Relative Time (Yrs.)
117
Net Interest Income (NII) to Assets:
Net interest income is equal to the interest received minus the
interest paid. The NII when expressed as a percentage of earning assets
gives the NIM (Net interest margin) of the bank. This is an extremely
important measure in evaluating a bank’s ability to manage interest rate
risk.
Graph 4.10
Net Interest Income to Avg.Total Assets versus Relative Time
Source: Processed Data
0
0.005
0.01
0.015
0.02
0.025
0.03
0.035
0.04
0.045
T-3 T-2 T-1 T0 T+1 T+2 T+3
Net
Inte
rest
Inco
me
to A
vg.T
ota
l Ass
ets
Relative time (yrs.)
118
Operating expenses to total Expenses:
Operating expenses equals non-interest expenses. It is also called
overhead expense. This can be decomposed into components like
establishment expenditure etc which as a percentage of total overhead
expense indicate where cost efficiencies are being realized or where a
bank has a comparative disadvantage. Non-interest expenses vary
between banks and are a function of the composition of liabilities.
(Timothy W. Koch & S. Scott Macdonald, 2003)
Graph 4.11
Operating Expenses to Total Expenses versus Relative Time
Source: Processed Data
0.27
0.28
0.29
0.3
0.31
0.32
0.33
0.34
0.35
0.36
0.37
T-3 T-2 T-1 T0 T+1 T+2 T+3
Op
era
tin
g ex
pen
ses
to T
ota
l Exp
ense
s
Relative Time (Yrs.)
119
Efficiency Ratio:
Efficiency ratio measures a bank’s ability to control non-interest expense
relative to adjusted operating income. This is given by the formula
Efficiency Ratio = Non-interest expense/ (NII+Non-interest income)
Banks use this ratio to measure the success of efforts to control non-
interest expense while supplementing earnings from increasing fees. The
smaller the efficiency ratio, the more profitable is the bank, all other
factors being equal (Timothy W. Koch & S. Scott MacDonald, 2003).
Graph 4.12
Efficiency Ratio versus Relative Time
Source: Processed Data
4.1.2 Analysis of Operational Parameters:
Of the eight operational parameters explained above, a significant
difference has been observed only in respect of three i.e average i)
Operating Expenses to AWF ii) Operating expenses to total expenses and
0.65
0.7
0.75
0.8
0.85
0.9
0.95
T-3 T-2 T-1 T0 T+1 T+2 T+3
Effi
cien
cy R
atio
(t
imes
)
Relative Time (Yrs.)
120
iii) Efficiency ratio. It may be further observed that average operating
expenses to AWF ratio has declined from 3.67% to 3.39% (t=1.842,
p=0.005) in the post-merger situation. The other operating performance
ratio that has registered a marginal improvement is the efficiency ratio
which has declined from 79.92% to 77.76% (t=0.697, p=0.008) in the
post-merger situation. However, the average operating expenses to total
expenses ratio has slightly increased (from 35.93% to 37.18%) in post
merger period (t=-0.545, p=0.049). There is no significant difference in
respect of other parameters i.e. average i) Total Debt to Net worth ii)
Interest income to AWF iii)Net Interest income to AWF iv) Net interest
income to assets and v) Capital adequacy ratio, at 5% level of
significance.
These results suggest that commercial bank mergers in India have, on
balance, resulted in a slight decline in operating efficiency. The bank
mergers have also not significantly impacted the Interest and Net interest
income to Average Working Funds ratios. Net-interest income (NII) =
Interest income minus interest expense, highlights a few basic risks in
banking. It maps into interest rate risk, liquidity risk and prepayment
risk. (Joseph.S.Sinkey Jr, 2002). The efficiency ratio is quite popular and
measures a bank’s ability to control non-interest expense relative to
adjusted operating income [Non-interest expense/(NII+ Non-interest
income)]. Conceptually, it indicates how much a bank pays in non-
interest expense for one rupee of operating income. The smaller the
121
efficiency ratio, the more profitable is the bank, all other factors being
equal. (Timothy W. Koch & S. Scott MacDonald, 2003)
Table 4.4
PROFITABILITY PARAMETERS
Profitability Parameter Analysis : Pre-Merger and Post-Merger Mean
Ratio for acquiring banks
Pre- Merger
(3 year avg. %)#
Post- Merger (3
year avg.
%)#
t-statistic
(0.05
sig)
p-values
Operating Profit to
AWF 1.8 1.6 1.192 0.158
Net Profit to
AWF(ROA) 1 0.9 0.437 0.036*
Net Profit to Avg Net
Worth(ROE) 13.7 15.8 0.539 0.978
Operating Profit to
average Net Worth 30.7 27.7 0.685 0.495
Asset
Utilization(times) 10.1 9.1 2.07 0.211
Equity Multiplier(Times)
13.7 17.56 0.465 0.007*
Net Interest
Margin(NIM) 1.9 2.3 0.711 0.983
Burden ratio 0.8 1.6 1.217 0.64
Earnings per
Share(EPS)(Rs.) 9.23 19.65 2.47 0.036*
PE ratio** 7.26 9.83 1.80 0.105
Source: Results of data analysis; ** Valuation ratio; #unless stated
otherwise; * denotes the variable in question is significant.
The significance of the each ratio is explained below by plotting a
graph between the average ratio (on vertical axis) and the relative time (in
years) on horizontal axis.
122
Operating Profit to AWF:
Operating profit is net profit before provisions and contingencies. This
ratio is a measure of a bank’s operating efficiency. Profitability of the
bank and also its ability to earn consistently can be easily determined by
its earning quality measures. The ratio PBDITATA (OP/AWF) measures
the effectiveness of the bank in employing its working funds to generate
profits. This measure also finds place in the world-renowned CAMEL
model generally adopted to evaluate the financial performance of the
commercial banks. CAMEL stands for Capital Adequacy, Asset Quality,
Management, Earnings Quality and Liquidity. Working funds is
computed as the average of total assets during the year. (For Indian
Bank this ratio was 2.61%, topping the list among PSBs in 2006-2007).
Graph 4.13 Operating Profit to AWF versus Relative Time
Source: Processed Data
0.000
0.005
0.010
0.015
0.020
0.025
T-3 T-2 T-1 T0 T+1 T+2 T+3
Op
. Pro
fit
to A
WF
RELATIVE TIME (YRS)
123
Net Profit to AWF:
This ratio is a foolproof indicator of excellent utilization of resources and
optimum leveraging of funds.
Graph 4.14
Net Profit to AWF versus Relative Time
Source: Processed Data
0.000
0.002
0.004
0.006
0.008
0.010
0.012
T-3 T-2 T-1 T0 T+1 T+2 T+3
NP
to
AW
F
RELATIVE TIME (YRS)
124
Net Profit to Average Net worth:
This ratio is the equivalent of the return on net worth ratio used in other
industries. It is a good indicator of profitability and return on
shareholder’s funds.
Graph 4.15
Net Profit to Average NW versus Relative Time
Source: Processed Data
0.000
0.050
0.100
0.150
0.200
0.250
T-3 T-2 T-1 T0 T+1 T+2 T+3
NP
to
Avg
. N
W
RELATIVE TIME (YRS)
125
Operating Profit to Net worth:
This ratio is corollary to the NP/ANW ratio and another indicator of the
shareholder’s returns.
Graph 4.16
Operating Profit to ANW versus Relative Time
Source: Processed Data
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
T-3 T-2 T-1 T0 T+1 T+2 T+3
Op
. Pro
fit
to A
NW
RELATIVE TIME (YRS)
126
Asset Utilization:
A bank’s ROA is composed of asset utilization (AU), the expense
ratio(ER) and the tax ratio. ROA = AU – ER—TAX where AU= Total
Revenue /Average total assets. The greater the AU and lower are ER and
TAX, the higher is the ROA.
Graph 4.17
Asset Utilization versus Relative Time
Source: Processed Data
Equity Multiplier:
We have ROE= ROA * EM. A bank’s equity multiplier compares assets
with equity such that large values indicate a large amount of debt
financing relative to stockholders’ equity. EM thus measures financial
leverage and represents both a profit and risk measure. EM influences a
bank’s profits as it has a multiplier effect on ROA in determining a
0.000
0.020
0.040
0.060
0.080
0.100
0.120
T-3 T-2 T-1 T0 T+1 T+2 T+3
Ass
et U
tiliz
atio
n
RELATIVE TIME (YRS)
127
bank’s ROE. Financial leverage works in bank’s favor when the earnings
are positive, but the other side is that it also magnifies the negative
impact of losses. EM is also a risk measure because it reflects how many
assets can go into default before a bank becomes insolvent. A high EM
raises ROE when net income is positive but also implies a high solvency
or capital risk.
Graph 4.18
Equity Multiplier versus Relative Time
Source: Processed Data
0
5
10
15
20
25
30
35
T-3 T-2 T-1 T0 T+1 T+2 T+3
Equ
ity
Mu
ltip
lier
(tim
es)
Relative Time (Yrs.)
128
Net interest margin (NIM):
NIM is a summary measure of the net interest return on income
producing assets. Spread, which equals the average yield on earnings
assets minus the average cost of interest bearing liabilities, is a
measure of the rate spread or funding differential. These two measures
are extremely crucial in evaluating a bank’s ability to manage interest-
rate risk.
Graph 4.19 NIM versus Relative Time
Source: Processed Data
Burden Ratio:
NIM and spread must be large enough to cover burden, loan loss
provisions, securities losses and taxes for a bank to grow profitably. The
burden ratio measures the amount of non-interest expense covered by
fees, service charges, securities gains, and other income as a fraction of
129
average total assets. The greater is this ratio, the greater the non-interest
expense exceeds non-interest income for the bank’s balance sheet size. A
bank is obviously better off with a smaller burden ratio, ceteris paribus.
Graph 4.20
Burden Ratio versus Relative Time
Source: Processed Data
Earnings per share (EPS) and PE ratio
While the EPS has increased from Rs.9.22 to Rs.19.65 (over 100%) post-
merger and the rise is statistically significant, the Price-Earnings ratio
has increased marginally from 7.26 to 9.83 and the change is
statistically not significant.
4.1.3 Analysis of Profitability Parameters
It is observed that while three profitability ratios are significant, the
remaining seven are not at 5% level of significance. The ratios which
show significant difference in performance between pre and post-merger
130
situations are i)Net profit to AWF ii) Equity Multiplier and iii)the EPS.
While the ratio of Net profit to AWF (ROA) has shown a 10% decline from
1% to 0.90%, the Equity multiplier (EM) (Total assets/Total equity) and
the EPS have increased from 13.7 to 17.56. (t= -0.465, p=0.007) and
from Rs 9.23 to Rs 19.65 respecively. The increase in EM is psioitive and
significant. A high EM increases ROE when net income is positive but is
also indicative of a high solvency or capital risk.As regards EPS, it is
observed that while the EPS has risen by over 100% post-merger, the
valuation ratio PE has not kept pace with it in as much as it has
increased by only Rs.2.57 post-merger and the change is not significant.
The interest spread to AWF ratio , which shows how well a bank is
managing and matching its interest income and interest expenditure
effectively has increased only slightly by 0.40% (1.90% to 2.30%,
p=0.983). Spread management is critical in successful bank management
because of its impact on the bottom-line. Similarly, the operating profit
to AWF has also come down from 1.8% to 1.60%. The change in average
net profit to average net worth (ROE)(increase by about 2%) is also not
significant( p=0.978) as also the Operating profit to average net
worth.(p=0.495). The change in AU ratio is not significant in as much as
it has come down by just 1% (p=0.211). The changes in NIM and burden
ratios are not significant. While the Net interest margin (NIM), which is a
summary measure of the net interest return on income producing assets,
has not changed significantly (p=0.983), the burden ratio, has increased
131
from 0.8 to 1.6 (p=0.640) not indicating clearly towards improved
performance in the post-merger scenario.
Table 4.5
PRODUCTIVITY PARAMETERS
(Rs. in Crores)
Productivity Parameter Analysis: Pre-Merger and Post-Merger Mean for acquiring banks
Pre Merger
(3 years avg.)
Post
Merger
(3 year avg.)
t-statistic
(0.05 significance) p-values
Business Per
employee 2.750 4.828 -2.952 0.014*
Business per branch 48.085 76.351 -1.355 0.205
Operating Profit per
branch 2.581 2.502 0.120 0.907
Operating profit per
employee 0.142 0.179 -3.964 0.003*
assets per employee 2.254 5.199 -2.740 0.021*
Loans per employee** 0.937 2.209 -2.235 0.049*
Net income per employee .027 .034 -1.476 0.171
Source: Results of data analysis; **Loans & Advances per employee;
*denotes that the variable in question is significant.
The significance of the each ratio is explained below by means of a
graph between the average ratio (on vertical axis) and the relative time (in
years) on horizontal axis.
132
Business per Employee:
This ratio indicates the degree of labor (employee) productivity of banks.
This reflects the contribution of employees towards the business growth
which in turn impacts the organizational growth.
Graph 4.21 Business per Employee versus Relative Time
Source: Processed Data
0
0.5
1
1.5
2
2.5
3
3.5
T-1 T-2 T-3 0 T+1 T+2 T+3
Bu
sin
ess
Pe
r e
mp
loye
e
RELATIVE TIME (YRS)
133
Business per Branch:
This ratio indicates how well a bank’s branches are being managed and
reflects the degree of branch productivity of banks. The commercial
banks over the years have been mainly concentrating on deposit
mobilization and credit deployment activities. Of late there has been a
marked shift towards non-fund based activities to supplement the
income streams.
Graph 4.22
Business per Branch versus Relative Time
Source: Processed Data
0
50
100
150
200
250
T-1 T-2 T-3 0 T+1 T+2 T+3
Bu
sin
ess
pe
r b
ran
ch
RELATIVE TIME (YRS)
134
Operating Profit per Branch:
This ratio indicates how well a bank’s branches are being managed and
reflects the degree of branch productivity of banks.
Graph 4.23
Operating Profit per Branch versus Relative Time
Source: Processed Data
0
1
2
3
4
5
6
7
T-1 T-2 T-3 0 T+1 T+2 T+3
Op
era
tin
g P
rofi
t p
er
bra
nch
RELATIVE TIME (YRS)
135
Operating Profit per Employee:
This ratio indicates the degree of labor (employee) productivity of banks.
In a service industry like banking, human resources play a crucial role in
extending quality services needed for overall development and making
the banking profitable and enduring.
Graph 4.24
Operating Profit per Employee versus Relative Time
Source: Processed Data
0
1
2
3
4
5
6
7
T-1 T-2 T-3 0 T+1 T+2 T+3
Op
era
tin
g p
rofi
t p
er
em
plo
yee
RELATIVE TIME (YRS)
136
Assets per Employee: (Self Explanatory)
Graph 4.25
Assets per Employee versus Relative Time
Source: Processed Data
0
0.1
0.2
0.3
0.4
0.5
0.6
T-1 T-2 T-3 0 T+1 T+2 T+3
Ass
ets
pe
r e
mp
loye
e
RELATIVE TIME (YRS)
137
Loans and advances per employee: (Self Explanatory)
Graph 4.26
Loans& Advances per Employee versus Relative Time
Source: Processed Data
Net Income per Employee (Self Explanatory)
Graph 4.27
Net Income per Employee versus Relative Time
Source: Processed Data
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
T-1 T-2 T-3 0 T+1 T+2 T+3 Loan
s p
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plo
yee
RELATIVE TIME (YRS)
0
10
20
30
40
50
60
T-1 T-2 T-3 0 T+1 T+2 T+3
Net
inco
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mp
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RELATIVE TIME (YRS)
138
4.1.4 Analysis of productivity parameters:
Out of the seven productivity parameters that have been considered for
analysis, the improvement in four of them has been found to be
statistically significant, post- merger. These are i) Business per employee
ii) Operating profit per employee iii) Assets per employee and iv) Loans
and advances per employee. The business per employee ratio has
increased from Rs.2.75 crores to Rs.4.828 crores (t = -2.952, p= 0.014)
i.e by about 75.55 % which is quite impressive and indicative of the
significant contribution made by the employees of the banks towards
business growth in the post-merger period. While the operating profit per
employee has grown from Rs. 0.142 crores to Rs. 0.179 crores (t= -3.964,
p=0.003) (by about 26.05%), the Assets per employee ratio has grown
from Rs. 2.254 crores Rs. 5.199 crores (by about 130.65% which is quite
overwhelming) and the increase is statistically signifcant. The Loans per
employee ratio has registered a massive increase of over 135% (from
Rs.0.937 to Rs. 2.209 crores) (t=-2.235,p=0.049). The other two
productivity ratios which have shown an increase are i) Business per
branch(BPB) and ii) Net income per employee. The increase in BPB is not
significant possibly because of the speed of branch expansion to meet the
competition and enhance the reach & the need to meet regulatory
requirements of the RBI in the post liberalization/merger period. The
marginal decline in operating profit per branch can also be attributed to
these factors. The net income per employee ratio has increased
139
marginally from 0.027 to 0.034 possibly due to the fact that growth in NI
(PAT) has not kept pace with the increase in the number of employees.
On balance it can be concluded, that the productivity of commercial
banks has shown a healthy increase in the post-merger period.
4.2 Evaluation of post-merger efficiencies of select commercial banks in India using Data Envelopment
Analysis (DEA) approach
The impact of mergers on the Technical (TE= crste), Pure Technical
(PTE= vrste) Scale (SE-se), Cost(X-or CE) and Profit (PE)
efficiencies(Annexure-B) of the acquiring Indian commercial banks is
investigated below, merger-wise. The tables 4.6 to 4.21 summarize DEA
TE, PTE, SE, CE and PE scores for 6 public sector and 2 private sector
commercial banks as acquiring banks in the respective commercial bank
mergers constituting the sample. This could help shed some light on the
sources of inefficiency of the Indian banking sector in general as well as
to differentiate between the public and private sector banks in terms of
their relative efficiencies. DEA analysis has been conducted using the
computer program (DEAP version 2.1) written by Professor Tim Coelli
(1996). This program has been used to construct DEA frontiers for the
calculation of various efficiency scores and also for the calculation of
Malmquist Total factor productivity (TFP) Indices.
140
DEA data analysis
Table 4.6 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger
(Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency
1995 1996 1997 1998 1999 2000 2001
Technical Efficiency MODEL1
TE 0.764 0.981 0.873 1.000 1.000 1.000 0.987 0.873 0.996
PTE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
SE 0.764 0.981 0.873 1.000 1.000 1.000 0.987 0.873 0.996
Technical Efficiency MODEL 2
TE 0.422 0.598 0.739 0.728 0.570 0.621 0.725 0.586 0.639
PTE 0.885 0.935 1.000 1.000 1.000 1.000 0.863 0.940 0.954
SE 0.477 0.639 0.739 0.728 0.570 0.621 0.840 0.618 0.677
Source: Appendix A, Tables A1, A2
Table 4.7 Oriental Bank of Commerce (OBC) -Bari Doab Bank (BDB) Merger
(Cost & Profit Efficiencies)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency
1995 1996 1997 1998 1999 2000 2001
Cost(X-) Efficiency CE
0.884 1.000 1.000 1.000 1.000 1.000 0.960 0.961 0.987
Profit Efficiency PE
0.890 1.000 1.000 1.000 1.000 1.000 0.951 0.963 0.984
Source: Appendix A, Tables A3, A4
DEA model decomposes Technical Efficiency (TE) in two parts, one due to
Pure technical efficiency (PTE) and the other due to Scale efficiency (SE).
Pure technical efficiency refers to the firm’s (bank’s) ability to avoid waste
by producing as much output as input usage allows, or by using as little
141
input as output production allows. Scale efficiency refers to the ability of
the firm to operate at its optimal scale.(Coelli, 1998).
The above results indicate that the Oriental Bank of Commerce-Bari
Doab Bank Ltd merger led to an increase in mean post-merger TE and
SE of the acquiring bank (OBC) under both the Models, 1 and 2(see table
4.6). A similar improvement has been observed post-merger in regard to
the CE and PE also (see table 4.7). While the PTE has remained at 100%
all along under the Model1, it has gone up from 94.00% to 95.40% under
model 2 post-merger. The scale efficiency has increased from 87.30% to
99.60% under Model, 1 and from 61.80% to 67.70% under Model, 2. The
cost and profit efficiencies of the acquiring bank (OBC) have improved
from 96.10% to 98.70% and 96.30% to 98.40% respectively. The results
suggest that the acquiring bank has performed relatively well in
transforming expenditure into income under Model 1.This follows from
the mean TE scores under Model 1,which are 87.30% pre-merger and
99.60% post-merger. This also indicates that the acquiring bank has
reduced the input waste by 12.30% post-merger. The results compare
favorably with Chu and Lim (1998) where the average overall efficiency of
Singapore banks was found to be 95.30% during the period 1992-
1996.The results also compare well with the14%-25% average input
wastes exhibited by Indian commercial banks (Bhattacharyya et al, 1997)
and the study of Fukuyama (1993) on Japanese banks (14%). However,
142
under Model 2, the mean technical efficiency of the acquiring bank has
gone up by only 5.30% post-merger. While the PTE hovered around 95%
during the period under consideration, the mean SE which was 61.80%
per-merger had gone up to only 67.70% post-merger. It may therefore be
concluded that the primary cause of marginal increase in mean TE under
Model 2 was SE only and the acquiring bank was pure-technically fairly
efficient (95%) during the period under consideration as could be seen
from the above table. The merger has also resulted in a post-merger
increase of 2%- 2.50 %( from 96% to 98.5%) in both mean Cost(X-) and
Profit efficiencies for the acquiring bank(Models 3 and 4 respectively).
Large banks may be more X-efficient than small banks if they are better
able to attract and retain capable managers, and because they tend to be
located in highly competitive metropolitan areas where competitive
pressures are higher (Robert De Young, 1997). OBC’s X-efficiency at a
very high level of 98.50% post-merger, is somewhat in line with this
proposition. The mean profit efficiency of OBC (which was 96.30% before
merger) had gone up by about 2.50% to 98.40%, which is quite
impressive. However the marginal increase in PE of OBC may be
attributed to the very small size of BDB in comparison with that of OBC.
Hence the impact of merger on cost and profit efficiencies of the
acquiring bank OBC does not appear to be significant.
143
Table 4.8
Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB) Merger (Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2001 2002 2003 2004 2005 2006 2007
Technical Efficiency MODEL1
TE 0.987 0.975 1.000 0.995 1.000 1.000 0.961 0.987 0.987
PTE 1.000 0.984 1.000 1.000 1.000 1.000 0.961 0.995 0.987
SE 0.987 0.990 1.000 0.995 1.000 1.000 1.000 0.992 1.000
Technical Efficiency MODEL 2
TE 0.340 0.420 0.613 0.591 0.744 0.804 0.915 0.458 0.821
PTE 0.484 0.461 0.638 0.600 0.746 0.820 0.921 0.528 0.829
SE 0.703 0.911 0.960 0.985 0.997 0.981 0.994 0.858 0.991
Source: Appendix A, Tables A1, A2
Table 4.9 Oriental Bank Of Commerce (OBC)-Global Trust Bank (GTB)Merger
(Cost & Profit Efficiencies )
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2001 2002 2003 2004 2005 2006 2007
Cost(X-) Efficiency CE
0.985 0.976 0.968 0.881 0.984 0.952 0.855 0.976 0.930
Profit Efficiency PE
0.978 0.980 1.000 1.000 0.994 0.912 0.867 0.986 0.924
Source: Appendix A, Tables A3, A4
The table 4.8 clearly indicates that the mean TE of the acquiring bank
(OBC) under Model 1 has remained stable at 98.70% even after the
merger. The mean PTE has declined slightly from 99.50% to 98.70%
post-merger despite the increase in mean SE from 99.20% to 100%.
Under Model 2, there is a quantum jump in mean TE and mean PTEs of
the acquiring bank from 45.80% to 82.10% & from 52.80% to 82.90%
respectively post-merger. The mean SE of the acquiring bank OBC has
jumped from 85.80% to 99.10% post-merger under the Model 2.
144
The mean cost and profit efficiencies have declined by 4.60% and 6.20%
respectively. Hence the increased mean PTE has a significant role in
enhancing the mean TE under Model 2. The Cost and profit
efficiencies(Models 3 and 4 respectively) at a considerably high level of
around 98% before merger and at around 93% post-merger (the first two
years average being 96.50%) (See table 4.9) point to the superior
managerial capabilities displayed in running the organization. However,
the merger does not seem to have helped the acquiring bank (OBC) in
improving its mean X-efficiency or Profit efficiency.
OBC had very strong fundamentals. As on March 31, 2004 it had a
deposit base of Rs.35, 674 crore, advances amounting to Rs.19,681
crore, and total assets worth Rs.41,701 crore. Its gross non-performing
assets (NPAs) were Rs.1, 211 crore and it had no net NPAs. Its operating
profit was Rs.1, 533 crore. Its net profit for the said period was Rs.686
crore. Its investment to deposit ratio worked out to 47.08% and spread to
assets ratio stood at 3.55%. Both its business per employee at Rs.4.16
crore and profit per employee at Rs.5.10 crore were quite impressive.
Based on these statistics, one could have forecasted that OBC’s
operational efficiency would not be unduly affected post-merger. In fact it
consolidated its position in the southern and western parts of the
country by leveraging on the GTB’s branch net work, strong ATM base
and excellent customer service synergies. Further the merger added one
145
million retail deposit holders to OBC’s tally. While the profitability of the
acquiring bank (OBC) could have taken a hit to some extent on account
of additional NPA provisioning, the increase in technical efficiency could
be attributed to the fact that both banks had the same technology
platform, Finacle from Infosys, which facilitated the smooth transition
and integration of operations in good time.
Table 4.10
Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger
(Technical Efficiency)
Total
Sample Year
Pre- merger
Merger
Year Post -merger
Mean
pre-
merger
efficiency
Mean
post-
merger
efficiency 1996 1997 1998 1999 2000 2001 2002
Technical
Efficiency
MODEL1
TE 0.824 0.882 0.939 0.93 0.969 0.938 0.951 0.882 0.953
PTE 1 1 1 1 1 0.988 0.991 1 0.993
SE 0.824 0.882 0.939 0.93 0.969 0.95 0.959 0.882 0.959
Technical
Efficiency
MODEL
2
TE 0.788 0.779 0.602 0.643 0.8 0.25 0.352 0.723 0.467
PTE 1 1 1 1 0.954 0.483 0.484 1 0.64
SE 0.788 0.779 0.602 0.643 0.838 0.518 0.727 0.723 0.694
Source: Appendix A, Tables A1, A2
Table 4.11
Bank Of Baroda (BOB)-Bareilly Corporation Bank Ltd (BCB) Merger
(Cost & Profit Efficiencies)
Total Sample
Year end
Pre merger Merger Year Post merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1996 1997 1998 1999 2000 2001 2002
Cost(X-) Efficiency CE
0.945 0.974 0.967 0.977 1.000 1.000 0.958 0.962 0.986
Profit Efficiency PE
0.945 0.935 0.948 0.962 0.990 0.999 0.950 0.943 0.980
Source: Appendix A, Tables A3, A4
146
The Bareilly- headquartered bank(BCB) established in 1927, had a Rs
307 crore deposit base and a Rs.344 crore asset base at the time of
merger(i.e for the financial year 1997-98). The bank’s net profit was
Rs.94.05 lakhs in 1997-98 as against Rs.25 lakh in the year before. The
bank despite two successive profit years, had recorded an accumulated
loss of Rs 3 crore. The rationale given by a senior executive of BOB for
the merger of BCB with BOB was that it (BCB) was not viable as an
independent unit. BCB’s Capital adequacy ratio (CAR) was as low as 3%
against the RBI stipulated CAR of 8%.
Post the merger, the mean TE and mean SE of the acquiring bank BOB
have increased by 7.10% and 7.70% respectively under Model 1.However
the PTE has declined by a marginal 0.70%. Under Model 2, there is a
steep decline in mean TE and PTE levels i.e of the order of 25.60% and
36% ( see table 4.10). The results therefore do not show convincingly that
the merger has resulted in improvements in TE and PTE scores of the
acquiring bank (BOB). However, the mean X-efficiency and Profit
efficiency scores have registered a slender increase in the range of 2%-4%
(see table 4.11). One way of looking at the results is that the target bank
is very small in size (in terms of assets and deposits) to make a
significant impact on the efficiency of the acquiring bank BOB.
147
Table 4.12
Bank Of Baroda (BOB)-Banaras State Bank(BSB) Merger (Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2000 2001 2002 2003 2004 2005 2006
Technical Efficiency MODEL1
TE 0.969 0.938 0.951 0.964 0.915 0.917 0.933 0.953 0.922
PTE 1.000 0.988 0.991 0.983 0.993 0.970 0.954 0.993 0.972
SE 0.969 0.950 0.959 0.980 0.921 0.945 0.978 0.959 0.948
Technical Efficiency MODEL 2
TE 0.643 0.800 0.250 0.352 0.558 0.574 0.690 0.564 0.607
PTE 1.000 0.954 0.483 0.484 0.564 0.597 0.718 0.812 0.626
SE 0.643 0.838 0.518 0.727 0.989 0.961 0.961 0.666 0.970
Source Appendix A, Tables A1, A2
Table 4.13
Bank Of Baroda (BOB)-Banaras State Bank (BSB) Merger
(Cost & Profit Efficiencies)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2000 2001 2002 2003 2004 2005 2006
Cost(X-) Efficiency CE
0.977 1.000 1.000 0.958 1.000 0.913 0.934 0.992 0.949
Profit Efficiency PE
0.962 0.990 0.999 0.950 1.000 0.899 0.873 0.984 0.924
Source: Appendix A, Tables A3, A4
Banaras State Bank was the second beleaguered UP-based bank to be
merged with BOB, the first being the Bareilly Corporation Bank, in
accordance with the scheme of amalgamation drawn up by RBI under
Section 45 of the Banking Regulation Act. BOB gained 105 branches
across the country following the merger, taking its branch network to
over 2,500. As on March 31, 2001 BOB’s deposits accounted for Rs
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53,985 crore, it had an advance portfolio of Rs.27, 420 crore and an
investment portfolio of Rs.19, 857 crore.
The asset base of BOB stood at Rs.62, 462 crore as on 31st March, 2001.
In contrast, BSB had assets worth Rs.1, 134 crore and its deposits,
advances and investments amounted to Rs.1, 031 crore, Rs.230 crore
and Rs.631 crore respectively. BSB had posted a net loss of Rs.13.38
crore as on March 31, 2001. As on the date of amalgamation 19th june
2002, BSB’s deposits were Rs. 1096 Crore and advances Rs. 151 Crore.
The bank had a total branch network of 105 of which 91 were located in
UP and Uttaranchal while that of BOB’s strength in those two states was
554 branches before the merger approval in June 2002.
DEA analysis(see table 4.12 ) indicates that under Model 1, the mean TE
of the acquirer had declined by 3.10% due to around 1%-2% decline in
mean PTE and SE. Under Model 2 (table 4.12), the mean TE of the
acquiring bank had improved by about 4.30% despite the decline in
mean PTE by 18.60% due to a massive increase in mean SE by over 30%.
This lends credence to the hypothesis that mergers can result in scale
and scope economies. While the mean cost efficiency declined post-
merger by about 4%, the mean profit efficiency declined by 6% (see table
4.13). This could be explained by the fact that the merger was more in
the nature of a rescue exercise under the mandate of the RBI, to salvage
an ailing bank and it is possible that the positive effects of merger from
the marginally increased size and reach would have been experienced by
149
the merged bank only in the long run in terms of increase in cost and
profit efficiencies.
Table 4.14 Union Bank Of India (UBI)-Sikkim Bank (SB) Merger
(Technical Efficiency)
Total Sample
Year Pre -merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1997 1998 1999 2000 2001 2002 2003
Technical Efficiency MODEL1
TE 0.949 0.946 0.930 0.943 0.973 0.988 0.979 0.942 0.980
PTE 0.994 1.000 1.000 0.962 1.000 0.997 0.987 0.998 0.995
SE 0.955 0.946 0.930 0.980 0.973 0.991 0.992 0.944 0.985
Technical Efficiency MODEL 2
TE 0.694 0.673 0.484 0.636 0.79 0.246 0.397 0.617 0.478
PTE 0.929 0.883 0.857 0.854 0.829 0.413 0.513 0.890 0.585
SE 0.748 0.762 0.565 0.744 0.953 0.596 0.775 0.692 0.775
Source: Appendix A, Tables A1, A2
Table 4.15 Union Bank Of India (UBI)-Sikkim Bank(SB) Merger
(Cost & Profit Efficiencies)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1997 1998 1999 2000 2001 2002 2003
Cost(X-) Efficiency CE
0.968 0.977 0.951 0.931 0.973 0.975 0.970 0.965 0.973
Profit Efficiency PE
0.968 0.963 0.948 0.925 0.967 0.967 0.962 0.960 0.965
Source: Appendix A, Tables A3, A4
Under the merger scheme Union Bank of India (UBI) was required to
absorb the accumulated losses of Sikkim Bank (SB) as well as their
total staff. SB’s entire loan outstandings of Rs.60 crore had turned bad.
Its net worth was negative at Rs.-40.11 crore. The only attraction to UBI
150
in the merger proposition was that Sikkim bank had 8 branches in the
North-East and this could give UBI the needed foothold in the North
Eastern region where it did not have a significant presence. On the other
hand, UBI was among the top public sector banks in India in terms of
business mix and customer profile, with a net profit of Rs.250.10 crore
for the financial year ended 1997-98.
It may be observed from the above table, that under Model 1(see table
4.14), the mean TE has increased by 3.8% which is accounted for by a
marginal increase of 4.10% in mean SE. The mean PTE remained high all
along at around 99.50%, an impressive feature in its own right. But
under Model 2 (see table 4.14), (inputs: Deposits and Employee
compensation and outputs: Loans and Advances & Non-interest income),
the mean Technical Efficiency (TE) had received a major hit, declining as
it did, by about 14% prompted by the decline in mean PTE by a
whopping 30.50%. However, the mean SE under Model 2, had gone up
by 8.30% and under the Model 1, it had increased by 4.10%, which may
be attributed to the impact of merger. The pre- and post-merger Cost and
profit efficiencies had remained stable at around 97%. Though the figure
appears to be healthy in itself, the absence of any increase in this regard
might be attributed to the fact that the target bank was a small and
ailing bank with just 8 branches that too in the North Eastern region of
India besides having accumulated losses leading to a negative net worth.
151
Table 4.16
Punjab National Bank (PNB)-Nedungadi Bank Ltd (NB) Merger (Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2000 2001 2002 2003 2004 2005 2006
Technical Efficiency MODEL1
TE 0.944 0.949 0.958 0.987 0.921 0.953 0.974 0.950 0.949
PTE 0.983 1.000 1.000 1.000 1.000 1.000 1.000 0.994 1.000
SE 0.960 0.949 0.958 0.987 0.921 0.953 0.974 0.956 0.949
Technical Efficiency MODEL 2
TE 0.541 0.777 0.226 0.325 0.597 0.627 0.669 0.515 0.631
PTE 0.789 0.807 0.475 0.484 0.600 0.656 0.702 0.690 0.653
SE 0.686 0.963 0.475 0.671 0.994 0.955 0.954 0.708 0.968
Source: Appendix A, Tables A1, A2
Table 4.17
Punjab National Bank(PNB)-Nedungadi Bank Ltd(NB) Merger (Cost & Profit Efficiencies)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 2000 2001 2002 2003 2004 2005 2006
Cost(X-) Efficiency CE
0.969 0.976 0.950 0.996 0.962 1.000 0.965 0.965 0.976
Profit Efficiency PE
0.961 0.976 0.950 0.999 0.963 1.000 0.939 0.962 0.967
Source: Appendix A, Tables A3, A4
Public sector Punjab National Bank (PNB) took over Kozhicode (Kerala)
based troubled Nedungadi Bank Ltd (NB) the oldest private sector bank
in Kerala, along with its 1,619 employees under a scheme of
amalgamation prepared by the RBI in the year 2003. The merger added
173 additional branches to PNB’s branch network taking it to around
4,000. Of the 173 branches of Nedungadi bank, 110 branches were in
152
Kerala with a pool of NRI accounts. Nedungadi Bank (NB) had a deposit
base of about Rs.1, 400 crore and advances of over Rs.750 crore as on
March 31, 2002. On the other hand, PNB’s deposits and advances figures
stood at Rs.66, 680 crore and Rs.34, 450 crore respectively. The merger
added only 2.24% to PNB’s business which was over Rs.1, 00,000 crore
at the time of merger.
The meagre addition of 2.24% to the acquiring bank (PNB)’s business
from the merger explains the around 1 % (relatively small) change in
mean cost and profit efficiencies of PNB post-merger. Under Model 1 (see
table 4.16), while the mean TE had not changed much, the mean PTE
change had placed the bank on the efficient frontier post-merger.
However, there was an insignificant decline in the mean SE of PNB to the
extent of 0.07%. Referring to Model 2 (see table 4.16), we find that the
mean SE had increased by a massive 26%, which would speak well of the
scale economies that are theoretically expected to result from the merger.
This had in turn resulted in an increase of mean TE of the acquiring
bank (PNB) by 11.60% despite a drop in mean PTE of PNB by 3.70%. The
marginal decline in mean PTE post-merger reflects the inability of the
merged bank in converting the deposits and employee potential into
Loans and Advances and Non-interest income (fee-based income) on a
substantial basis. It is observed from the table 4.17 that the cost and
profit efficiencies have increased from 96.5% and 96.2% respectively to
97.6% and 96.7% respectively post-merger indicating a marginal, though
153
positive, impact of the merger on the cost and profit efficiencies of the
acquiring bank(PNB).
Table 4.18 ICICI Bank (ICICIB)-Bank Of Madura (BOM) Merger
(Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1998 1999 2000 2001 2002 2003 2004
Technical Efficiency MODEL1
TE 1.000 1.000 1.000 1.000 0.998 0.971 1.000 1.000 0.990
PTE 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
SE 1.000 1.000 1.000 1.000 0.998 0.971 1.000 1.000 0.990
Technical Efficiency MODEL 2
TE 0.732 1.000 0.596 0.714 1.000 1.000 1.000 0.776 1.000
PTE 0.734 1.000 0.799 1.000 1.000 1.000 1.000 0.844 1.000
SE 0.996 1.000 0.746 0.714 1.000 1.000 1.000 0.914 1.000
Source: Appendix A, Tables A1, A2
Table 4.19 ICICI Bank(ICICIB)-Bank Of Madura(BOM) Merger
(Cost & Profit Efficiencies)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1998 1999 2000 2001 2002 2003 2004
Cost(X-) Efficiency CE
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Profit Efficiency PE
1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Source: Appendix A, Tables A3, A4
The merger between ICICI Bank and Bank of Madura (BOM) was a
remarkable one. This merger was the first between an old generation
private sector bank and a new generation private sector bank.
154
Pre–merger status of ICICI Bank: Assets: Rs.12, 063 crore; Deposits:
Rs.9, 728 crore; Equity market capitalization: Rs.2, 466 crore; Capital
adequacy ratio: 17.59%; Branch network and extension counters: 106;
Net worth: Rs.1, 219 crore; Number of employees: 1,700; one of the most
tech-savvy and fastest growing private sector banks in the country.
Pre-merger status of Bank of Madura (BOM): Assets: Rs.3, 988 crore;
Deposits: Rs.3, 395 crore; capital adequacy ratio: 15.80%; Equity market
capitalization: Rs.100 crore; Branch net work: 263; Number of
employees:2,700;A 57-year old South India based private sector
commercial bank.
Synergies expected from the merger: ICICI Bank was looking at a
branch network of 350-400, which would have taken 4 to 5 years to
achieve, given the pace of branch expansion. This merger provided the
much needed network immediately and also provided opportunities to
ICICI Bank to spread its network to several other states. BOM had a
customer base of 1.20 million. Hence the merger enabled ICICI Bank to
have an aggregate customer base of 2.70 million on an asset base of
Rs.16,000 crore(providing the needed economies of scale and scope) in
addition to cross selling opportunities for assets and other products &
services, like cash management services. The merger was also expected
to be favorable to BOM shareholders in term of value creation besides
providing technology based and sophisticated banking services to the
customers.BOM looked at the merger favorably because size was a major
155
consideration in the highly competitive banking scenario emerging in
India in the aftermath of economic reforms launched by the Government
of India. Size (critical mass) was a necessity in the context of compliance
with the capital adequacy norms stipulated by the RBI and the risk
management measures to be put in place by the commercial banks as
mandated by the Basel committee.
It is observed from the above table that under Model 1(see table 4.18),
the mean TE came down by 1% due to the decline in mean SE by
1%.However, post the merger, while the mean TE came down by just 1%,
the PTE continued to remain at 100%, an impressive performance in
deed. Even under Model 2(see table 4.18), the mean TE, PTE and SE
remained at 100% level post the merger as was the case before merger.
Even the cost(X-) and profit efficiencies, remained at the level of 100%
(see table 4.19) post-merger. These facts clearly show that ICICIB was an
efficient bank (it was on the cost and profit frontiers) and could gainfully
exploit the synergies predicted before the BOM’s merger with itself.
156
Table 4.20
HDFC Bank (HDFCB)-Times Bank (TB) Merger (Technical Efficiency)
Total Sample
Year Pre-merger
Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1997 1998 1999 2000 2001 2002 2003
Technical Efficiency MODEL1
TE 0.976 0.970 0.957 0.961 0.986 0.925 0.971 0.968 0.961
PTE 1.000 1.000 1.000 1.000 1.000 0.976 1.000 1.000 0.992
SE 0.976 0.970 0.957 0.961 0.986 0.948 0.971 0.968 0.968
Technical Efficiency MODEL 2
TE 0.700 0.645 0.668 0.643 0.654 0.508 0.556 0.671 0.573
PTE 0.731 0.645 0.671 0.701 0.722 0.590 0.567 0.682 0.626
SE 0.958 0.999 0.996 0.917 0.906 0.861 0.981 0.984 0.916
Source: Appendix A, Tables A1, A2
Table 4.21
HDFC Bank (HDFCB)-Times Bank (TB) Merger (Cost & Profit Efficiencies)
HDFC BANK(HDFCB)-TIMES BANK(TB) MERGER
Total Sample
Year end
Pre-merger Merger Year Post-merger
Mean pre-
merger efficiency
Mean post-
merger efficiency 1997 1998 1999 2000 2001 2002 2003
Cost(X-) Efficiency CE
* 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Profit Efficiency PE
* 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
Source: Appendix A, Tables A3, A4
*Efficiency could not be calculated for the values of input or output for the said period.
(Please refer limitations).
The takeover of the Times Bank (TB) by HDFC Bank (HDFCB) was
unique in the sense that it was the first merger deal between two new
generation private sector banks. In a milestone transaction in the Indian
157
banking sector, Times Bank Ltd promoted by Bennett, Coleman &Co
(Times Group) was merged with HDFC Bank effective February 26,
2000.The shareholders of the Times Bank received 1 share of HDFC
Bank for every 5.75 shares of Times Bank.
The merger with Times Bank had catapulted HDFC Bank into a different
league, providing it with greater muscle in terms of retail client base as
well as mid-market corporate clientele. The bank had nearly 8.5 lakh
retail accounts post-merger. While the lending focus continued to be on
top-end corporate clientele, it had an added advantage (diversification
benefits) of serving the mid-market clientele that came as a part of the
Times Bank baggage.
Times Bank had an asset base of Rs.3,274.46 crore;deposits:Rs.3011.18
crore, Capital adequacy ratio:9.97;Advances: Rs.1,311.90 crore; Fee
based income to total income ratio:24.58% and Credit-deposit
ratio:44%;Investment-deposit ratio:35% as on 31.3.1999.
HDFC Bank: The bank’s total assets increased almost three fold post-
merger to Rs.11, 656.14 crore. Pre-merger investment/deposit ratio:
58.23%; Assets: Rs.4349.96crore Deposits: Rs.2915.51crore
Advances: Rs 1400.56 crore; Paid-up capital: Rs.424.60 crore.
Synergies expected from the merger: As per the scheme of amalgamation
issued by the HDFC Bank to its shareholders, the following synergies
were expected to be realized from the deal:
158
Branch network to increase by over 50%
Increase geographical coverage and ATM numbers which allow multi-
branch access to retail clients.
Increase in retail customer base and improvement in product portfolio
Increase in shareholders’ wealth
Cost savings from centralized processing and scale and scope economies
Complementary business practices
Improved infrastructure facilities
While the mean TE under Model 1 (see table 4.20),declined slightly by
0.70% due to decline in mean PTE by 0.80%,the mean SE remained
steady at 96.80%( a healthy figure) post-merger. Under Model 2 (see
table 4.20), the mean TE, PTE and SE dropped by 9.80%, 5.60% and
6.80% respectively. Hence it would appear that the merger had not
improved the PTE and SE under Model 2, which involved conversion of
deposits &compensation to employees into Advances and Non-interest
income. Hence the merger could not leverage the resource base available
in terms of employee potential and deposits for the acquiring bank,
HDFCB. Coming to the Cost and Profit efficiencies (see table 4.21), they
had remained at 100% both pre and post merger which could be
construed as the hallmark of efficiency. The ability to sustain the cost
and profit efficiency post-merger could be attributed to the three fold
increase in size, increase in geographical and improved access to retail
clients through increased ATM numbers.
159
4.2.1 Analysis of Technological and Technical Efficiency changes
post-merger employing DEA Malmquist Productivity Index
Malmquist index of total factor productivity (TFPCH) examines whether
firms (banks) are using the resources efficiently to produce goods and
services, and if they are using the existing technology to produce goods
and services. Values greater than one means increases in productivity,
while values less than one indicate decreases in productivity over time.
Farrell et al (1992) decomposed this index into sub indexes measuring
changes in technical efficiency and changes in technology:
TFPCH= TEFFCH * TECHCH
The first term on the right hand side of the above equation represents the
change in technical efficiency (TEFFCH); and the second term is the
change in technology (TECHCH). A value greater than one means
increases in output technical efficiency, value less than one means
decrease and a value of one indicates no change. The second term
represents the technological change.
Using the data envelopment analysis computer program written by Coelli
(1996), the input oriented Malmquist Total Factor Productivity Change
(TFPCH) index has been computed.
160
The table 4.22 provides the summary of efficiency scores of mean
TEFFCH, TECHCH and TFPCH before and after merger of the merged
banks under Model 1.
Table 4.22
DEA Malmquist Productivity Index (TFPCH) (Model 1)
Merged banks(Acquiring bank
in brackets)
Mean pre-merger efficiency change
Mean post-merger efficiency change
TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH
OBC-BDB(OBC) 1.096 0.867 0.943 0.996 0.983 0.979
OBC-GTB(OBC) 1.000 0.968 0.969 0.989 1.020 1.008
BOB-BCB(BOB) 1.060 0.933 0.990 1.008 0.987 0.995
BOB-BSB(BOB) 1.008 0.987 0.995 0.990 0.999 0.988
UBI-SB(UBI) 1.041 0.978 1.018 1.013 0.985 0.997
PNB-NB(PNB) 1.003 0.986 0.989 0.996 1.013 1.007
ICICIB-BOM(ICICIB) 0.998 0.987 0.985 1.011 1.010 1.021
HDFCB –TB(HDFCB) 1.000 0.992 0.992 1.000 0.980 0.980
Source: Data processed
It is observed from the table 4.22 that the Malmquist Productivity Index
(MPI) of the acquiring bank post-merger has increased substantially in
respect of to five out of eight bank mergers listed above under Model 1.
On further examination by decomposing the MPI into its components,
Technical efficiency change (TEFFCH) and Technological change
(TECHCH)(also known as Frontier Shift), it follows that the technical
efficiency change has declined post-merger in six out of eight cases and
in one case, it has remained stable. However, the rate of technological
change has increased in seven out of eight cases. Fare et al.(1992) define
that MPI>1indicates productivity gain; MPI<1 indicates productivity loss;
and MPI=1 means no change in productivity from time t to t+1. They also
161
state that a value of TECHCH greater than one indicates a positive shift
or technical progress where as a value of TECHCH less than one
indicates a negative shift or technical regress, and a value of TECHCH
which equals is indicative of no shift in technology frontier. From this
perspective, it may be inferred that the total factor productivity index
(TFPCH) has increased in five out of eight cases. Hence, on an average, it
is found that there has been an increase in MPI post-merger prompted
more by a technological frontier shift rather than technical efficiency
change.
The table 4.23 provides the summary of efficiency scores of mean
TEFFCH, TECHCH and TFPCH before and after merger of the merged
banks under Model 2.
Table 4.23
DEA Malmquist Productivity Index (TFPCH)(Model 2)
Merged banks(Acquiring bank in brackets)
Mean pre-merger efficiency change
Mean post-merger efficiency change
TEFFCH TECHCH TFPCH TEFFCH TECHCH TFPCH
OBC-BDB(OBC) 1.218 0.756 0.919 0.950 1.055 0.989
OBC-GTB(OBC) 0.882 1.651 1.059 1.276 0.881 1.067
BOB-BCB(BOB) 1.142 0.840 0.945 1.026 0.990 1.016
BOB-BSB(BOB) 1.026 0.990 1.016 1.158 0.993 1.140
UBI-SB(UBI) 1.044 0.963 0.938 0.817 1.803 0.992
PNB-NB(PNB) 1.186 1.001 1.177 1.288 0.855 1.075
ICICIB-BOM(ICICIB) 0.924 0.927 0.820 1.844 0.788 1.376
HDFCB –TB(HDFCB) 1.041 0.963 0.989 0.878 1.713 1.068
Source: Data processed
162
It is be observed from the table 4.23 that the total factor productivity
change (TFPCH) has increased post-merger in seven out of eight mergers
listed above. While the TEFFCH has increased in four cases, it has
declined in the remaining four cases. The technological frontier has
shifted positively in five out of eight cases. Hence the change in MPI post-
merger cannot be solely attributed to either technical efficiency change
increase or positive frontier shift change. Both have played their role.
4.2.2 Tobit Analysis
Model specification
To determine the influence of different factors on the efficiency estimated
using Data Envelopment Analysis (DEA), Tobit model has been employed.
Important variables considered for the analysis (based on the literature
review) exclude those considered as input and output variables for
determining the respective efficiencies i.e. Technical Efficiency, Cost
Efficiency(X-Efficiency) and Profit Efficiency using DEA methodology. The
variables chosen are both qualitative and quantitative in nature.
Qualitative variables are included to capture the effect of
merger/likelihood of merger and the ownership (the bank in question is a
public sector bank or a private sector bank).
163
Table 4.24
Selected Quantitative and Qualitative Variables
Predictor Symbol Description
Size(Market share of
business)
SIZE ln (Average total assets)
Return on net worth RONW Net income/Average total equity
Return on capital
employed
ROCE Net income/Average capital
employed
Capitalization SHFATA Shareholders fund / Average total
assets
Employee(Staff) cost COE ln(Compensation to employees)
Level of fee-based
activity
NIITI Non- interest income / Total
income Proxy for fee-based
activity
NIINI Non – interest income / Net income
Earning power or
Operating Profitability
PBDITATA Profit before depreciation, interest
and tax / Average total assets
A measure of fund’s cost
( Cost Of funds)
INTEXPTE Interest expenses/Total expenses
Significance of Bank
ownership
DSECTOR Public / Private sector
Significance of the year before merger year
DYBMY Year before merger year
Significance of the year after merger year
DYAMY Year after merger year
Source: Author’s perspective
4.2.2.1 Factors influencing the Technical Efficiency (TE) of
commercial banks in India (Model 1)
The following Tobit model has been employed to develop an average
relationship between the technical efficiency scores obtained under CRS
(Model 1) and the factors affecting it.
Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it + β4 ANWATA it
+β5COE it +β6 NIITI it +β7 PBDITATA it +β8 DSECTOR it + β9
DYBMY it +β10 DYAMYit + ε it
164
Y it (Dependent variable) = Technical efficiency score obtained by i-th
(acquiring) bank in time period t under CRS under Model 1.
SIZE it = Natural logarithm of average total assets of the i-th bank in time
period t.
RONW it = Return on net worth of the i-th bank in time period t.
ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th
bank in time period t. Capital refers to the Tier-I capital of the
commercial bank computed in accordance with the Basel norms
circulated by the RBI.
COEit = Natural logarithm of compensation to employees (salaries,
wages and bonus) paid by the i-th bank in time period t.
NIITI it = Non-interest income to Total income ratio of the i-th bank
in time period t.
PBDITATA it = Profit before depreciation, interest and tax to average
total assets ratio of the i-th bank in time period t.
DSECTOR it= 1 if i-th bank in time period t is a public sector bank
otherwise zero.
DYBMY it = 1 if the time period t represents the year before merger year
for the i-th bank, otherwise zero.
DYAMYit = 1 if the time period t represents the year after merger year for
the i-th bank, otherwise zero.
165
α, β1, β2…………………………………………………….. β10 are the regression parameters to
be estimated by using the Tobit regression model. And ε it is the error
term.
It is expected that all the explanatory variables except dummies for
public sector banks and the year before merger year will have positive
impact on the technical efficiency of the bank.
Results of Tobit Analysis
The results of the Tobit estimation using the STATA software are
presented in the Tables 4.25 to 4.28. In suggested specifications,
nominal values of the variables are used. As inflation has proportionate
impact on the values of input and output of the commercial banks, it is
not necessary to adjust the effect of inflation while computing the
efficiency scores using DEA methodology. For the efficiency estimation of
commercial banks, those banks with negative values of considered input
and output variables have been excluded. Quantitative explanatory
variables which characterize the commercial banks are considered along
with dummies (to capture over time the performance characteristics of
the commercial banks) in the suggested Tobit models. These
quantitative explanatory variables exclude those variables which are
considered as input and output variables in determining the efficiency in
the respective specification(Coelli et al,1998), as otherwise they would be
highly correlated with input and output variables of DEA leading to
biased results. In a similar fashion, in Tobit models, quantitative
166
explanatory variables have been transformed to remove the skewness in
the distribution and to accomplish this objective, logarithmic
transformation is applied on these variables. This transformation is also
intended to compress the difference among the values of these variables.
Four models (TE1, TE2, CE and PE) are presented in the following tables
4.25 to 4.284. For each of the four models used, the Prob > χ2 is zero,
implying that the set of independent variables considered together
satisfactorily explain the variations in the dependent variable.
The results of Tobit regression for Model 1 are presented in Table 4.25.
Table 4.25
Model 1: Technical Efficiency Explanatory
Variables Coefficient Std.
Error z-
statistic P>|z|
SIZE 0.076601 0.015877 4.82 0.0000*
RONW -4.48E-06 1.11E-05 -0.4 0.6870
ROCE 5.36E-05 7.24E-05 0.74 0.4590
COE 0.002341 0.019682 0.12 0.9050
NIITI 0.037467 0.013611 2.75 0.0060*
ANWATA 0.423263 0.110307 3.84 0.0000*
PBDITATA 3.256965 0.310189 10.5 0.0000*
DSECTOR -0.12803 0.03791 -3.38 0.0010*
DYBM 0.011918 0.028082 0.42 0.6710
DYAM 0.019394 0.029444 0.66 0.5100
Constant -0.00195 0.107372 -0.02 0.9860
No. of observations = 24*15 = 360
* Significant at 1% level
Prob > χ2 = 0.00000
Log likelihood = 282.72733
Source: Appendix A, Table A1
4 None of these four models indicate the existence of muti-collinearirty among the independent
variables.
167
A positive regression coefficient implies an efficiency increase whereas a
negative coefficient reflects a decline in efficiency.
Size has a highly significant positive effect on technical efficiency of the
bank indicating that larger banks on an average would be more
technically efficient, possibly because of the scale economies derived
from the bank merger. This is in line with the efficiency theory of Mergers
and Acquisitions (Weston 2000). Capitalization variable (ANWATA)’s
impact is positive and significant in explaining the technical efficiency.
Theoretically, better capitalized banks should enjoy a higher level of
efficiency (Sufian et al, 2007).
The variables (ratios) Non-interest income to total income (NIITI) & PBDIT
to average total assets (PBDITATA) are highly significant and the signs of
their regression coefficients are positive indicating that they have a
positive influence on the technical efficiency of the commercial banks.
The dummy variable, ownership of the bank (DSECTOR) is also highly
significant with its regression coefficient taking a negative sign. This
implies that the impact of owner ship on the technical efficiency of the
bank, though highly significant, is negative. To state differently, private
sector banks are more technically efficient than public sector banks,
which is in line with our earlier findings. The other explanatory variables
are, however, not significant in their contribution to the technical
efficiency of the bank under Model 1.
168
4.2.2.2 Factors influencing the Technical efficiency (TE) of
commercial banks in India (Model 2)
The following Tobit model has been employed to develop an average
relationship between the technical efficiency scores obtained under CRS
(Model 2) and the factors affecting it.
Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5
NIITI it +β6 PBDITATA it +β7 INTEXPTEit +β8 DSECTOR it +β9
DYBMY it +β10 DYAMYit + ε it
Y it (Dependent variable) = Technical efficiency score obtained by i-th
bank in time period t under CRS under Model 2.
SIZE it = Natural logarithm of average total assets of the i-th bank in time
period t.
RONW it = Return on net worth of the i-th bank in time period t.
ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th
bank in time period t. Capital refers to the Tier-I capital of the
commercial bank computed in accordance with the Basel norms
circulated by the RBI.
NIITI it = Non-interest income to Total income ratio of the i-th bank
in time period t.
INTEXPTEit = Interest expenses to total expenses ratio of the i-th bank
in time period t.
PBDITATA it = Profit before depreciation, interest and tax to average
total assets ratio of the i-th bank in time period t.
169
DSECTOR it= 1 if i-th bank in time period t is a public sector bank
otherwise zero.
DYBMY it = 1 if the time period t represents the year before merger year
for the i-th bank, otherwise zero.
DYAMYit = 1 if the time period t represents the year after merger year for
the i-th bank, otherwise zero.
α, β1, β2…………………………………………………….. β10 are the regression parameters to
be estimated by using the Tobit regression model and ε it is the error
term.
Table 4.26
Model 2: Technical Efficiency
Explanatory
Variables Coefficient Std.
Error z-statistic P>|z|
SIZE 0.0718964 0.010502 6.85 0.0000*
RONW -0.0000591 2.62E-05 -2.26 0.0240**
ROCE 0.0003514 0.000179 1.96 0.0499**
INTEXPTE -0.2586989 0.191868 -1.35 0.1780
NIITI 0.0515453 0.032075 1.61 0.1080
ANWATA 0.5792328 0.262388 2.21 0.0270**
PBDITATA 1.805336 0.884924 2.04 0.0410**
DSECTOR -0.3070882 0.045147 -6.8 0.0000*
DYBM -0.1426229 0.06201 -2.3 0.0210**
DYAM 0.0199808 0.062278 0.32 0.7480
Constant 0.0866815 0.18823 0.46 0.6450
No. of observations = 24*15 = 360
* Significant at 1% level;**Significant at 5% level
Prob > χ2 = 0.00000
Log likelihood = 95.350563 Source: Appendix A, Table A2
170
It is expected that all the explanatory variables except dummies for
public sector banks and the year before merger year will have positive
impact on the technical efficiency of the bank.
Analysis of results
Bank size has a positive and highly significant impact on it’s technical
efficiency computed under, Model 2 as was the case under Model 1.While
the Return on net worth (RONW) and Capitalization (ANWATA) ratios
have been found to be significant in determining the technical efficiency
of commercial banks under Model 2, the former has a negative impact
and the latter has a positive impact on the technical efficiency of the
banks as seen from the above table. That a better capitalized bank will
have a higher level of technical efficiency is in line with the theory. The
negative impact of RONW on technical efficiency though, is counter
intuitive; it can be treated as negligible because of the extremely small
value of the corresponding regression coefficient. The negative sign of the
regression coefficient RONW can be explained as under:
ROE=ROA X EM where ROA stands for the Return on Assets and EM
for equity multiplier or the financial leverage. The ROA of the banks is
around 1% which is very low. To earn a decent ROE the banks have to
increase the EM to say, 15% to 20%. According to finance theory,
financial leverage is a double edged sword. In good times it supercharges
the profit and in bad times its effect is just reversed. The profit does not
fall, but plummets. Further in their effort to achieve a healthy ROA if the
171
banks push up the EM, it might result in negative consequences to the
bank if the level of debt is sub-optimal, despite the increase in ROE or
RONW.
The dummy variable DSECTOR has been found to be significant but its
regression coefficient has a negative sign. This implies that public
ownership of a commercial bank though significant, is associated with a
decline in technical efficiency as was observed under Model 1. Though
the last explanatory variable DYBM is significant in explaining technical
efficiency under Model 2, its regression coefficient is negative. This can
be explained by the theories of merger motives which state that
acquisition of complementary resources(synergies) implying the absence
of certain resources which are crucial for continued survival and growth
is inferred from the bank’s characteristics before merger in the year
preceding the year in which the bank merger has taken place. The other
explanatory variables have not been found to be significant in their
contribution to the technical efficiency of the bank under Model 2.
4.2.2.3 Factors influencing the Cost Efficiency (CE) of commercial
banks in India
The following Tobit model has been employed to develop an average
relationship between the Cost efficiency(X-efficiency) scores obtained
under CRS (Model 3) and the factors affecting it.
172
Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5COE
it +β6 NIINI it +β7 PBDITATA it +β8 DSECTOR it +β9 DYBMY it +β10
DYAMYit + ε it
Y it (Dependent variable) = Cost efficiency score obtained by i-th bank in
time period t under CRS under Model 3.
SIZE it = Natural logarithm of average total assets of the i-th bank in time
period t.
RONW it = Return on net worth of the i-th bank in time period t.
ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th
bank in time period t. Capital refers to the Tier-I capital of the
commercial bank computed in accordance with the Basel norms
circulated by the RBI.
COEit = Natural logarithm of Compensation to Employees (COE:
Salaries, wages and bonus) paid by the i-th bank in time period t.
NIINI it = Non-interest income to Total income ratio of the i-th bank
in time period t.
PBDITATA it = Profit before depreciation, interest and tax to average
total assets ratio of the i-th bank in time period t.
DSECTOR it= 1 if i-th bank in time period t is a public sector bank
otherwise zero.
DYBMY it = 1 if the time period t represents the year before merger year
for the i-th bank, otherwise zero.
173
DYAMYit = 1 if the time period t represents the year after merger year for
the i-th bank, otherwise zero.
α, β1, β2…………………………………………………….. β10 are the regression parameters to
be estimated by using the Tobit regression model and ε it is the error
term.
It is expected that all the explanatory variables except dummies for
public sector banks and the year before merger year will have positive
impact on the Cost efficiency of the bank.
Table 4.27
Model 3: Cost Efficiency(X-Efficiency)
Explanatory variables Coefficient Std. Error
z-
statistic P>|z|
SIZE 0.0226994 0.0098182 2.31 0.021**
RONW 0.000044 0.0000115 3.83 0.000*
ROCE 0.0000123 0.0000656 0.19 0.852
COE -0.0081477 0.0098715 -0.83 0.409
NIINI 0.0419832 0.0187763 2.24 0.025**
ANWATA 0.0826686 0.1210511 0.68 0.495
PBDITATA 0.9785573 0.2722773 3.59 0.000*
DSECTOR -0.0494715 0.0213897 -2.31 0.021**
DYBM 0.0313207 0.0253443 1.24 0.217
DYAM 0.0426646 0.0259696 1.64 0.100
Constant 0.7367723 0.0664688 11.08 0.000
No. of observations = 24*15 = 360
* Significant at 1% level;**Significant at 5% level
Prob > χ2 = 0.00000
Log likelihood = 239.91618
Source: Appendix A, Table A3
174
Results analysis
It is observed from the table 4.27 that while SIZE, RONW, NIINI and
PBDITATA have positive and significant influence on the Cost
Efficiency(X-Efficiency) of the banks, RONW and PBDIATA have highly
significant influence on the CE of the banks. The dummy variable
DSECTOR is also significant but the negative sign of the corresponding
regression coefficient indicates that public sector banks are less cost
efficient as compared to the new generation banks in the private sector.
4.2.2.4 Factors influencing the Profit Efficiency (PE) of commercial
banks in India
The following Tobit model has been employed to develop an average
relationship between the Profit efficiency scores obtained under CRS
(Model 4) and the factors affecting it.
Y it = α + β1 SIZE it +β2 RONW it +β3 ROCE it +β4 ANWATA it +β5COE
it +β6 NIITI it +β7 PBDITATA it +β8 DSECTOR it +β9 DYBMY it +β10
DYAMYit + ε it
Y it (Dependent variable) = Profit efficiency score obtained by i-th bank in
time period t under CRS under Model 4.
SIZE it = Natural logarithm of average total assets of the i-th bank in time
period t.
RONW it = Return on net worth of the i-th bank in time period t.
175
ANWATA it = Capitalization (Shareholders equity ratio) of the of the i-th
bank in time period t. Capital refers to the Tier-I capital of the
commercial bank computed in accordance with the Basel norms
circulated by the RBI.
COEit = Natural logarithm of Compensation to Employees (Salaries,
wages and bonus) paid by the i-th bank in time period t.
NIITI it = Non-interest income to Total income ratio of the i-th bank
in time period t.
PBDITATA it = Profit before depreciation, interest and tax to average
total assets ratio of the i-th bank in time period t.
DSECTOR it= 1 if i-th bank in time period t is a public sector bank
otherwise zero.
DYBMY it = 1 if the time period t represents the year before merger year
for the i-th bank, otherwise zero.
DYAMYit = 1 if the time period t represents the year after merger year for
the i-th bank, otherwise zero.
α, β1, β2…………………………………………………….. β10 are the regression parameters to
be estimated by using the Tobit regression model and ε it is the error
term.
It is expected that all the explanatory variables except the dummies for
public sector banks and the year before merger year will have a positive
impact on the Profit efficiency of the bank.
176
Table 4.28
Model 4: Profit Efficiency
Explanatory variables Coefficient
Std. Error
z-
statistic P>|z|
SIZE 0.0164065 0.010074 1.63 0.1030
RONW 0.0000297 9.49E-06 3.13 0.0020*
ROCE 0.0000346 6.52E-05 0.53 0.5960
COE -0.0033713 0.010031 -0.34 0.7370
NIITI -0.0130974 0.012011 -1.09 0.2760
ANWATA 0.0720379 0.121646 0.59 0.5540
PBDITATA 1.0600000 0.269153 3.94 0.0000*
DSECTOR -0.0526502 0.021825 -2.41 0.0160**
DYBM 0.0332491 0.025484 1.3 0.1920
DYAM 0.0431395 0.026157 1.65 0.0990
Constant 0.7833794 0.069471 11.28 0.0000
No. of observations = 24*15 = 360
* Significant at 1% level;**Significant at 5% level
Prob > χ2 = 0.00000
Log likelihood = 237.41821
Source: Appendix A, Table A4
Analysis of the results
Both the explanatory variables RONW and PBDITATA are highly
significant and positive in their impact on profit efficiency. This is of
course intuitive. It is interesting to note that the variable Compensation
paid to Employees (COE) though not significant has a negative impact on
the profit efficiency, which is again intuitive. The last significant
explanatory variable is the dummy variable DSECTOR which has a
negative impact on the profit efficiency of the banks thereby indicating
177
public sector banks are less profit efficient as compared to the new
generation banks in the private sector like HDFC Bank. This is also
borne out of our observations in the context of other efficiencies referred
to above.
4.3 Marketing implications of commercial bank mergers
The data has been analyzed for significant association between the
variables influencing the customer perception of bank mergers (items in
the questionnaire) and the Demographic/Behavioral Variables (DBV) of
the respondents employing Chi-square test.
178
Relationship between Demographic (S.Nos: 1-4)/Behavioral Variables (S.Nos:5&6) (DBV) and customer perception of
Service quality in the face of bank mergers in India
The results of analysis are summarized below, item (variable) wise.
Cohran recommends that, while performing Chi-square test, at least 80%
of the expected cell count be five or more and that no expected cell count be
less than one(Cohran’s criterion)
Table 4.29 Relationship between DBV and customer perception regarding
mergers of commercial banks improving dependability of service
S. NO. DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE
P- VALUE
1 Gender 14.353 0.001(s)
2 Age ( years) 7.720 0.102
3 Educational Qualification 6.81 0.146
4 Yearly Income(Rs. lakhs) 24.190 0.000(s)
5 Association with Bank (years) 29.154 0.000(s)
6 Monthly Transaction Frequency 9.17 0.057
Source: Appendix C, Tables C1 to C6
The table 4.29 provides a summary of broad perception of customer-
respondents on the impact of bank mergers on dependability of customer
service. It is observed that there is highly significant association between
the two sets in terms of three out of six demographic/behavioral
179
variables. These are gender, yearly income and the length of association
with bank (in years). There is however no significant relationship
between the opinions of individuals and their age based on the results of
the Chi-square test (p=0.102).This is in line with the observation of
Urban &Prat (2000) in their studies in US. A similar conclusion follows in
respect of the other variables Educational qualification and Monthly
transaction frequency also.
There is no significant difference between the within group male and
female respondents who are highly optimistic that merged banks serve
better. However, there is significant discrimination among the
respondents who are pessimistic. Only 2.5% of the female respondents
feel that there will be very insignificant change in the service quality of
merged banks. On the other hand, 19% of the male respondents perceive
the same. While among those respondents with an income level of Rs
1.50 to Rs.2.50 lakhs, only 45% opined that the service quality would
improve after merger, respondents from all other income classes
expressed in favor of bank mergers improving their service quality. It is
possible that middle income groups are more conservative in expressing
their views as compared to the younger and older generation
respondents.
Among those respondents who have more than 10 years of association
with the bank, 58.6% feel that the dependability of service of the banks
would improve after merger, whereas 20.7% of the people think otherwise
180
and the same percentage of people remain neutral in their perceptions. A
significant observation is that among the respondents with less than 2
years of association with the bank, 88.2% of the people feel that merged
banks provide more dependable service to the customers. It has also
been found that the frequency of monthly transactions is also a
significant study variable. 59.7% of those with higher number of bank
transactions are optimistic about improvement in dependability of the
banking service with mergers as compared to 14.9% who perceive the
improvement in dependability of service post-merger as insignificant.
These relationships are further investigated below.
Table 4.30
Relationship between DBV and customer perception regarding the
increase in number of banking services provided post-merger
S. NO. DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE P - VALUE
1 Gender 2.645 0.266
2 Age ( years) 12.00 0.017(s)
3 Educational Qualification Not calculated ***
4 Yearly Income( Rs.lakhs) 6.600 0.159
5 Association with Bank (years) 10.9 0.028(s)
6 Monthly Transaction Frequency 12.010 0.017(s)
***Cohran’s criterion not satisfied.
Source: Appendix C, Tables C7 to C11
It is observed from the above table 4.30, that there is significant
association between the respondents’ opinions and their age, length of
their association with the bank and their monthly transaction frequency.
181
It is observed that 42.1% of the customers who have optimistic views in
this regard are of the age group of more than 45 years and 29.3% of the
people are of the age group of 30-44 years. Further, the impact of
customer’s association on their perception is very much on the expected
lines, as customers whose association with the bank is long would
generally be more knowledgeable about the great variety of products and
services offered by the bank and perceive the implications of bank
mergers on customer service equally well. It is however, interesting to
note that while of those with over 10 years association 75% strongly
opined that bank mergers result in increase in the number of services
provided, a much higher percentage i.e. about 83% to 93% of those
customers with an association of 10 years or less agreed with this view
strongly.
85% of the total respondents feel that a merger results in an increase
in the number of services provided, in contrast to the 5.7% of
respondents who think that a merger is unlikely to prompt an increase in
the number of new services. 9.3% of the respondents have a neutral
opinion on the issue. Another interesting observation is that out of the
84.3% of ihe respondents whose length of association influences their
perception as to the increase in number of services provided post-merger;
only 15% have more than 10 years of association with their respective
banks.
182
Table 4.31
Relationship between DBV and customer perception regarding the increase in range of banking products available post-merger
S. NO.
DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)
CHI- SQUARE VALUE
P - VALUE
1 Gender 4.249 0.119
2 Age ( years) Not calculated ***
3 Educational Qualification Not calculated ***
4 Yearly Income( Rs.lakhs) 8.22 0.084
5 Association with Bank (years) Not calculated ***
6
Monthly transaction
Frequency 14.1 0.007(S)
***Cohran’s criterion not satisfied.
Source: Appendix C, Tables C12 to C14
The above table 4.31 indicates significant association between the
respondents’ monthly transaction frequency and in perceiving the
implications of bank mergers on customer service in so far as the range
of products available is concerned.
88.1% of the respondents with a transaction frequency of more than 5
opine that the transaction frequency brings about new product
introductions by the banks in contrast to the 12% of the people who
either think this is unlikely or remain neutral. Also, a larger percentage
of respondents (65.4%) agree with this view compared to the 7.7% of
respondents who do not agree and 26.9% who remain neutral. 35% of
the customers who agree with the above mentioned view fall in the
income slab of up to Rs. 2.5 lakh, 25.7% of the people under Rs. 2.5-5
lakh and 19.3% come under less than Rs. 5 lakh income category. This
183
is an indication that more people from low income groups expect bank
mergers to give rise to new products.
Table 4.32 Relationship between DBV and customer perception regarding the
increased size of bank loan limits post-merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE
P-VALUE
1 Gender 4.036 0.133
2 Age ( years) 5.69 0.0224
3 Educational Qualification 14.800 0.005(S)
4 Yearly Income(Rs. lakhs) 16.519 0.011(S)
5 Association with Bank (years) 14.158 0.028(S)
6 Monthly Transaction Frequency 9.831 0.132
Source: Appendix C, Tables C15 to C20
Mergers are supposed to enhance the ability of the banks to lend
more because of the enhanced financial and other resources of the
merged entity. Again we find(See Table 4.32) that the customers’
educational qualification in addition to their yearly income and length of
association with the bank are significant in influencing their perception
of the merged bank’s ability to offer larger loan limits. It is observed that
only 9.3% of the respondents strongly agreeing with the above view have
an association of more than 10 years with the bank. Further, while 40%
of the professionals strongly agree with the above opinion, 48% of the
post-graduates do not entertain this view. A widely varying view of
perception emerges in terms of variation in annual incomes as well.
184
Table 4.33
Relationship between DBV and customer perception regarding the increased accessibility to conventional bank services (not online)
post-merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE
P- VALUE
1 Gender 14.078 0.001(S)
2 Age ( years)
Not
calculated ***
3 Educational Qualification 21.200 0.000(S)
4 Yearly Income( Rs. lakhs) 36.810 0.000(S)
5 Association with Bank (years) 24.342 0.000(S)
6 Monthly Transaction Frequency 14.144 0.028(S)
Source: Appendix C, Tables C21 to C25
S***Cohran’s criterion not satisfied
In regard to the improvement in the accessibility to conventional
banking (not online) services post-merger, the above table 4.33 indicates
significant association between all the demographic variables except age
and the perception of customers. It is generally expected that
accessibility to conventional banking services(not online) would improve
after merger because of the merged entity’s increased human, financial
and technological resources which if deployed intelligently would enable
it to take it closer to this goal. However, it is found that the perception of
customers’ is influenced significantly by their Gender and other
demographic/behavioral variables.
65% of the male respondents and 55% of female respondents feel that
accessibility to conventional banking services improves significantly after
mergers. However, it is interesting to note that out of the 62.1% people
185
who agreed with this opinion, only 15.7% were female respondents and
46.4% were male respondents. While, a majority of the male respondents
(65%) agreed with the view, only 12% of them were strongly opposed to
this line of thinking. 69% of the respondents who had more than 10
years of association with the bank strongly agreed with the above view
while 10.3% felt otherwise. 61.2% of the respondents who had greater
frequency of transaction also agreed that access to banking services
would improve after merger. Another important observation is that out of
the total respondents, who agreed with the view, 17.1% were
professionals, 38.6% were post graduates and 6.4% were graduates.
62.9% of the respondents with income exceeding Rs. 5 lakhs also opined
in favor of this view.
Table 4.34 Relationship between DBV and customer perception regarding
improvement in Online Banking Services after merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE
P-
VALUE
1 Gender 1.530 0.465
2 Age ( years) 6.000 1.990
3 Educational Qualification 27.800 0.000(S)
4 Yearly Income( Rs. lakhs) 35.494 0.000(S)
5 Association with Bank (years) 15.091 0.020(S)
6 Monthly Transaction Frequency 10.400 0.109
Source: Appendix C, Tables C26 to C31
Online banking services which require deployment of advanced and
latest technology & more financial resources are generally expected to
186
improve after the merger as the combined entities are better off because
of the possible realization of financial and technological synergies.
However, it is observed from the above table 4.34 that there is significant
association between customer’s educational qualification, yearly income
and length of association with the bank, and their perception of the
improvement in merged entity’s ability to provide improved online
banking services.
48.6% of the respondents opine that online banking improves with
mergers. It is observed that among these, 10% have more than 10 years
of association with the banks concerned, 16.4% have 6-10 years of
association and remaining 22.2% have less than 6 years of association. It
is interesting to note that as high as 60% of the respondents whose
transaction frequency is as low as once in a month have strongly
supported the view that online banking services will improve/expand
post-merger.
187
TABLE 4.35
Relationship between DBV and customer perception regarding the reduction in service time of bank after merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P- VALUE
1 Gender 2.826 0.243
2 Age ( years) 13.50 0.009(S)
3 Educational Qualification 26.700 0.000(S)
4 Yearly Income( Rs.lakhs) 13.541 0.035(S)
5 Association with Bank (years) 21.367 0.002(S)
6 Monthly Transaction Frequency 16.914 0.010(S)
Source: Appendix C, Tables C32 to C37
Again strong association has been observed (Table 4.35) between all the
demographic variables except gender and the perception of customers on
the reduction of service time post merger. It is generally expected that the
perception in this regard should not be influenced by the demographic
variables. But the above results of Chi-square test show significant
relationship between the two.
Out of the 29% of people who agree with the statement that service time
is reduced after merger, it is interesting to note that an overwhelmingly
high percentage i.e. 25% of them are in the age group of 18-29.This
shows that the younger generation is more optimistic in their perception.
Significant divergence of opinion has also been observed in terms of the
differences in the length of association, educational qualification,
yearly income and the frequency of transaction.
188
Table: 4.36
Relationship between DBV and customer perception regarding increased safety of deposits after merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P-
VALUE
1 Gender 8.576 0.014(S)
2 Age ( years) 6.370 0.173
3 Educational Qualification 37.200 0.000(S)
4 Yearly Income(Rs. lakhs) 29.320 0.000(S)
5 Association with Bank (years) 16.076 0.013(S)
6 Monthly Transaction Frequency 14.681 0.023(S)
Source: Appendix C, Tables C38 to C43
Safety of deposits is one of the primary factors about which all the bank
customers are concerned. It is normally expected that the safety of the
deposits of the customers will improve post merger because of the
enhanced capital base and other financial resources of the combined
entity. It is however found (Table 4.36) that there is significant
association between all the variables in question except age and the
perception of customers about the increased safety of their deposits post-
merger.
47.1% of the respondents are of the view that safety of deposits increases
after merger. Out of this, it is significant to note, that 37.1% are male
and only 10% are female. It is also interesting to note that 23.6% of the
respondents who share this view are post graduates. While about 67.5%
of the professionals have opined that safety of deposits is enhanced after
189
the merger, an equally high percentage i.e. 63% of the respondents
whose income is in the range of Rs 2.6-5 lakh have strongly veered
around this view. 60% of the respondents with frequency of transaction
as low as once in a month have also exuded optimism in this regard.
Table: 4.37 Relationship between DBV and customer perception regarding bank
mergers resulting in less competitive interest rates
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P-
VALUE
1 Gender 0.809 0.667
2 Age ( years) 11.000 0.027(S)
3 Educational Qualification 36.500 0.000(S)
4 Yearly Income( Rs.lakhs) 20.818 0.002(S)
5 Association with Bank (years) 15.918 0.014(S)
6 Monthly Transaction Frequency 4.004 0.676
Source: Appendix C, Tables C44 to C49
It is hypothesized that bank mergers result in less competitive interest
rates in view of the reduction in number of banks that follows leading to
greater monopolistic tendencies which bring in their wake systemic
rigidities. But the same line of thinking is not visible across the different
demographic groups as seen from the above table 4.37. Educational
qualification, age, association with the banks and yearly income
differences seem to be strongly influencing the perception of customers in
regard to the movement of interest rates of the banks in the post-merger
scenario.
190
68.8% of the people, who are of the age group 45 years and more, opine
that bank mergers result in less competitive interest rates. This shows
that elderly respondents are less positive in their approach. While 78.9%
of the post-graduates and 61.5% of graduates entertain this perception, a
lesser percentage i.e about 45% of the professionals agree with this view.
Further as high as 68.2% of the respondents whose income level is Rs.
1.5-2.5 lakh (middle income groups) are supportive of this view. However,
on taking a comprehensive view, it is observed that only 47.90% of the
total respondents strongly entertain the opinion (52.10% do not
subscribe strongly to this view) that the bank interest rates will become
less competitive post-merger. The rationale for this view is that a larger
bank can better exploit financial synergies post-merger and will be in a
stronger position to raise cheaper funds which in turn will enable it to
quote more competitive rates of interest.
Table: 4.38
Relationship between DBV and customer perception regarding fee
reduction for different banking services post-merger
S. NO. DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)
CHI-
SQUARE
VALUE
P - VALUE
1 Gender 12.700 0.002(S)
2 Age ( years) 5.650 0.227
3 Educational Qualification 11.500 0.021(S)
4 Yearly Income(Rs. lakhs) 21.429 0.002(S)
5 Association with Bank (years) 1.094 0.982
6 Monthly Transaction Frequency 8.040 0.235
Source: Appendix C, Tables C50 to C55
191
Mergers are broadly expected to result in fee reductions following the
merged bank’s ability to achieve economies of scale and scope. It is
however observed that the respondents’ opinion in this regard is shaped
by their demographic segmentation. The gender, educational
qualification and income levels seem to be strongly associated with their
thinking in this regard. More educated respondents (and probably they
belong to higher income groups) might possess deeper understanding of
the implications of mergers and hence are probably in a better position to
look at the issue from other perspectives as well.
It is observed that 60.7% of the respondents think that mergers do not
result in fee reduction for different services. Out of this, 39.3% of them
are males. It is however significant to note that 75% of the female
respondents entertain a similar view.68.2% of the graduates 60% of the l
professionals and 78.4% of the respondents whose income is less than
Rs 1.5 lakh feel that fee reduction for banking services is not likely to
follow bank mergers.
192
Table 4.39
Relationship between DBV and customer perception regarding enhancement in Goodwill of the bank post-merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P-
VALUE
1 Gender 0.067 0.967
2 Age ( years) 10.325 0.262
3 Educational Qualification 34.670 0.000(S)
4 Yearly Income(Rs. lakhs) 28.723 0.000(S)
5 Association with Bank (years) 59.966 0.000(S)
6 Monthly Transaction Frequency 9.600 0.143
Source: Appendix C, Tables C56 to C61
The thinking of the customers in regard to the enhancement of goodwill
post-merger appears to be very strongly influenced by their educational
qualification, annual income and length of association with the bank as
can be seen from the above table 4.39.
68.3% of the respondents who have an association of more than 10 years
opine that the goodwill of the bank is enhanced significantly after the
merger in contrast to only 19.5% of them who think otherwise and 12.2%
who have neutral views. While 67.9% of the post graduates and 50% of
the professionals are very optimistic about the improvement in the
goodwill of the bank, only 23% of the graduates strongly entertain this
view. In terms of the annual income levels also significant divergence in
opinion has been observed.
193
Table: 4.40
Relationship between DBV and customer perception regarding bank’s technological advancement after merger
S. NO. DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P - VALUE
1 Gender 14.706 0.001(S)
2 Age ( years)
Not
calculated ***
3 Educational Qualification 25.500 0.000(S)
4 Yearly Income(Rs. lakhs) 23.983 0.001(S)
5 Association with Bank (years) 21.431 0.002(S)
6 Monthly Transaction Frequency 19.595 0.003(S)
***Cohran’s criterion not satisfied
Source: Appendix C, Tables C62 to C66
Banks are generally expected to be technologically sound after merger
because of the improved access/ability of the merged bank to access the
financial markets, state of the art technologies and human talent. The
above table 4.40 indicates that there is a strong relationship between the
demographic/behavioral variables, except age and the opinion of the
respondents as to the technological soundness of the bank post-merger.
While 61% of the male respondents are of the opinion that there will be a
rapid technological advancement in the banks after merger, only 13.6%
of the female respondents strongly entertain this view. Out of the 57.1%
of the respondents who share this view, 26.4% of them account for
maximum frequency in transactions (more than five per month). It is also
significant to note that a sizeable percentage i.e. 70% of the professionals
and 50% of the post-graduates are optimistic in this regard.
194
Table: 4.41
Relationship between DBV and customer perception regarding the increased availability of bank’s ATM services after merger
S. NO.
DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)
CHI-
SQUARE
VALUE
P- VALUE
1 Gender 0.122 0.941
2 Age ( years) 3.138 0.719
3 Educational Qualification 7.768 0.256
4 Yearly Income( Rs. lakhs) 16.300 0.003(S)
5 Association with Bank (years) 24.400 0.000(S)
6 Monthly Transaction Frequency 35.500 0.000(S)
Source: Appendix C, Tables C67 to C72
The above table 4.41 shows significant association between the yearly
income, length of association of the customer with the bank, monthly
frequency of transaction and the perception of the customers in regard to
improved customer service following an increase in number of ATMs that
may be made available post-merger.
84.6% of the respondents have opined that a rise in ATM number post-
merger is a positive sign in improving customer service. 90% of the
respondents who have up to 5 years of association with the banks feel
that a greater number of ATMs after merger help in improving customer
service. 87.8% of people having 6-10 years of association have also
expressed their agreement with this view. Again, 91% of the respondents,
whose frequency of transaction is more than ten, are also of the same
view. It is also significant to note that 91.5% of the respondents, whose
income is less than Rs 2.5 lakh (low income group), also share this
195
opinion. Low income groups generally draw small amounts and quite
often because of the nature of their needs. Their opinion is expected to
carry more weight in view of their more intense use of ATMs as compared
to others.
Table: 4.42
Relationship between DBV and customer perception regarding the
quality of bank’s customer relationship management (CRM) after merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P-
VALUE
1 Gender 1.363 0.506
2 Age ( years) 1.670 0.796
3 Educational Qualification 27.300 0.000(S)
4 Yearly Income( Rs. lakhs) 32.375 0.000(S)
5 Association with Bank (years) 37.705 0.000(S)
6 Monthly Transaction Frequency 10.015 0.124
Source: Appendix C, Tables C73 to C78
A very strong association has been observed (Table 4.42) between the
variables, educational qualification, yearly income & the length of
association with the bank and the customer perception regarding the
improvement in Customer Relationship Management (CRM) after the
merger. However, the gender, age and the monthly transaction frequency
do not seem to significantly influence the opinions of the customers in
this regard.
196
58.5% of the respondents having an association of 6-10 years are of the
opinion that customer relationship management gets better after merger,
compared to only 9.1% of them who think otherwise. As regards the
impact of income differentials, it is found that about 25% of the total of
45% expressing themselves strongly in favor the improvement in CRM
post-merger are those with income levels below Rs.1.5 lacs & Rs.5 lacs
and above in equal proportion. While 55% of the professionals have
expressed strongly in favor of this view, only 48.7% of the post-graduates
and much less i.e. 13.6% of the graduates seem to veer around this view
indicating that the level of education is impacting the customer
perception significantly.
Table: 4.43
Relationship between DBV and customer perception regarding the impact of change of staff members of the bank after merger
S. NO. DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P- VALUE
1 Gender 2.609 0.271
2 Age ( years) 9.050 0.060
3 Educational Qualification 55.400 0.000(S)
4 Yearly Income( Rs. lakhs) 6.638 0.356
5 Association with Bank (years) 25.175 0.000(S)
6 Monthly Transaction Frequency 12.880 0.045(S)
Source: Appendix C, Tables C79 to C84
197
The general human tendency is the preference for continuous dealing
with the bank’s staff members whom they know well as polite and
knowledgeable over time. It is however revealed from the results of Chi-
square test (Table 4.43) that there is significant association between
respondents’ educational qualifications, their length of association with
the bank, monthly transaction frequency and their perception as to
whether mergers result in change of staff giving an impersonal feel.
76.5% of the respondents having less than 2 years of association with
the banks are of the opinion that mergers do not result in change of staff
and hence the personal feel is not lost, when compared to the 17.6% of
respondents who think otherwise. It is of interest to note that 70% of the
respondents with minimum frequency of monthly transactions also share
the same opinion.
Table: 4.44 Relationship between DBV and customer perception regarding
change in competition scenario of banks post-merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI- SQUARE
VALUE
P- VALUE
1 Gender 0.056 0.972
2 Age ( years) 24.400 0.000(S)
3 Educational Qualification 3.92 0.417
4 Yearly Income(Rs. lakhs) 15.135 0.019(S)
5 Association with Bank (years) 23.026 0.001(S)
6 Monthly Transaction Frequency 10.264 0.114
Source: Appendix C, Tables C85 to C90
198
Mergers are generally expected to result in big players resulting in
monopolistic tendencies and reduced competition. The above table (Table
4.44) clearly hints at strong association between the customer
perceptions in this regard and their age, yearly income levels, the length
of association with the bank in years and their annual income levels. But
no significant association has been observed between the customer
perception in this regard and the gender, monthly transaction frequency
and educational qualifications.
57.3% of the respondents of the age group 18-29 and 52.4% of the
respondents of age group 30-49 opine that mergers result in big players
and significantly reduce competition. While a significant proportion, as
high as, 68.3% of respondents with 6-10 years of association with
banks concerned also share this opinion, 52.9% of the respondents
having less than 2 years of association with banks think otherwise.
Further, a sizeable proportion i.e 60.9% of the respondents having
income of Rs 2.6-5 lakh (middle income groups) are of the view that
mergers result in reduced competition and emergence of big players.
199
Table: 4.45
Relationship between DBV and customer perception regarding the
increase in opportunities for the merged bank to cross-sell banking
products post-merger
S. NO.
DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)
CHI-
SQUARE
VALUE
P- VALUE
1 Gender 2.385 0.304
2 Age ( years) 12.000 0.017(S)
3 Educational Qualification 7.39 0.116
4 Yearly Income(Rs. lakhs) 14.544 0.024(S)
5 Association with Bank (years) 23.834 0.001(S)
6 Monthly Transaction Frequency 6.47 0.373
Source: Appendix C, Table C91 to C96
Mergers facilitate cross-selling of products of the merging banks which
will result in exploitation of synergies arising out of complementary
strengths/resources. It would appear from the above table(Table 4.45)
that there is significant association between the respondents’ age,yearly
income, & their association with bank and their opinion in regard to the
merged entity acquiring added ability to cross sell products and thereby
enrich its product offerings.
While 76.5% of the respondents having an association of less than 2
years with the bank and 54.1% of respondents having income less than
Rs 1.5 lakh (low income groups) are of the view that merged bank will be
able to better cross-sell, the corresponding percentages are relatively less
ranging between 30% to 40% for other categories determined by annual
income levels and association with the bank (in terms of years).
200
Table: 4.46
Relationship between DBV and customer perception regarding the improvement in innovative ability of the bank after merger
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P-
VALUE
1 Gender 2.000 0.368
2 Age ( years) 2.930 0.569
3 Educational Qualification 30.900 0.000(S)
4 Yearly Income( Rs. lakhs) 5.215 0.517
5 Association with Bank (years) 36.901 0.000(S)
6 Monthly Transaction Frequency 8.700 0.191
Source: Appendix C, Tables C97 to C102
As depicted in the above table 4.46, there is very strong association
between the respondents’ educational qualification (in years) and their
customer perception of improvement in innovative ability of banks
following merger. 63.4% of the respondents having 6-10 years of
association with the banks do not opine that merged banks provide
innovative products and redefine the way banks interact with customers,
while 64.7% of the respondents having less than 2 years of association
have a neutral opinion. Also, it is significant to note that 60% of the
professionals also do not opine that merged banks redefine the way the
bank interacts with the customers. Of the total respondents, 53.60%
were of the view (medium to strong) that mergers could improve the
innovative ability of the banks and redefine the way they interact with
the customers. This makes the reasearcher conclude that the customer
opinion in this regard is evenly divided. This calls for further exploration
201
by the banks as to the reasons and how best they can convince their
customers of their ability to research and innovate in the post-merger
scenario.
Table: 4.47
Relationship between DBV and customer perception regarding the
importance of communication about merger to the bank customers
S.
NO.
DEMOGRAPHIC/BEHAVIORAL
VARIABLES(DBV)
CHI-
SQUARE VALUE
P- VALUE
1 Gender 10.470 0.005(S)
2 Age ( years) 22.842 0.001(S)
3 Educational Qualification 21.30 0.000(S)
4 Yearly Income( Rs. lakhs) 12.174 0.058
5 Association with Bank (years) 50.753 0.000(S)
6 Monthly Transaction Frequency 10.200 0.038(S)
Source: Appendix C, Tables C103 to C108
Communication to the bank customers about the merger is considered to
be important so that they are not exposed to undue stress and strain in
perceiving the implications of the merger to the safety and security of
their deposits and the lasting relationships which they have come to
develop with the bank’s employees and the bank itself over the years. It
may be inferred from the above table 4.47 that there is significant
association between the customer perceptions in this regard and the
variables listed above, except for yearly income.
69% of the male respondents and 62.5% of the female respondents opine
that communication about mergers to customers is not very important.
70.60% of the respondents having less than 2 years of association feel
202
that it is important to communicate to the customers about the merger.
However, 75.5% of respondents having 2-5 years association and 75.6%
of respondents with 6-10 years of association have responded otherwise.
Another important observation is that 72.3% of respondents with 3-4
transactions and 67.2% of respondents with more than 5 transactions
per month feel that the communication on bank merger to the customers
is not generally important. This view has been supported by 75% of the
professionals.
On balance, while it makes the researcher conclude that, a major chunk
of the respondents opine that communication to bank customers about
impending merger is not very important, it is also possible that a
majority of the customers could not have fully captured/appreciated the
intricacies or importance of this communication.
Table: 4.48
Relationship between DBV and customer perception regarding his/her switching preference after the bank’s merger
S. NO.
DEMOGRAPHIC/BEHAVIORAL VARIABLES(DBV)
CHI -
SQUARE
VALUE
P - VALUE
1 Gender 1.231 0.540
2 Age ( years) 5.210 0.267
3 Educational Qualification 3.740 0.443
4 Yearly Income(Rs. lakhs) 15.056 0.020(S)
5 Association with Bank (years) 31.775 0.000(S)
6 Monthly Transaction Frequency 7.626 0.267
Source: Appendix C, Tables C109 to C114
203
It is observed from the above table 4.48 that the opinions of the
customers as to whether they will continue with their present bank after
merger or not is influenced by their association with the bank in years
and their yearly income. The other demographic variables like gender
and age etc do not have significant association with the opinion of the
bank customers on this issue.
63.4% and 65.5% of respondents with 6-10 and more than 10 years of
association respectively prefer to switch to some other bank if their bank
gets merged. However, only 9.3% of the total respondents opined against
switching to another bank, while 35% of them are unsure about it.
67.6% of respondents with income less than Rs 1.5 lakh and 60.9% of
respondents with income in range Rs 2.6-5 lakh were in favor of
switching to another bank in case of merger. These switching tendencies
evidenced by bank customers should be examined and analyzed by the
banks concerned if they are not to lose sizeable chunks of customers in
the wake of bank mergers as profitability and customer loyalty are
strongly linked.
204
Table: 4.49
Difference in Customer Perception of Service Quality based on Whether Customer’s Bank Has Experienced Any Merger
S.
NO. VARIABLES
CHI-
SQUARE VALUE
P
VALUE
1
Mergers improve dependability of bank
service(say, improvement in after –sales
service)
1.4180 0.4920
2
It results in increase in number of services
provided 3.2850 0.1940
3
It increases the range of products available 2.1280 0.3450
4
It results in larger loan limits
0.5580 0.7560
5
After merger better accessibility to services
is possible 4.4860 0.1060
6
Online banking does not improves after
merger 2.4650 0.2910
7
Service time is not reduced after merger
1.8970 0.3870
8
Safety of deposits increases after merger 6.8930 0.3310
9
Bank mergers result in less competitive
interest rates 5.3110 0.0700
10
Mergers generally do not result in fee
reduction for different services 2.7070 0.2580
11
Goodwill of the bank is not enhanced after
merger 4.5220 0.1040
12
Post merger, banks get quickly
technologically advanced 0.3540 0.8380
205
13
More number of ATMs after merger help
in improving customer service. 8.2240 0.0160(
s)
14
Customer relationship management does
not get better after merger 4.4880 0.1060
15
Mergers result in change of staff which gives an impersonal feel 0.9460 0.6230
16
Mergers results in big players and reduces competition 5.5010 0.0640
17
I don’t like cross selling (ex: banks selling
insurance products) undertaken by the merged bank
0.4960 0.7800
18
Merged banks provide innovative products
and redefine the way banks interact with
customers.
0.6580 0.7190
19
Communication about merger to the
customers in general is not very important 2.1570 0.3400
20
I prefer to switch to some other bank if my bank gets merged 0.6590 0.7190
Source: Processed Data
It is clear from the above table 4.49 that except for a larger number of
ATMs helping in improved customer service after merger, there is no
significant association between the customer perception about the
marketing implications of commercial bank mergers and whether the
respondent is a customer of the bank which has undergone any merger
or not. This conclusion has significant implications to our study as quite
a few respondents in our sample belong to commercial banks which have
not gone through any merger.
206
Table: 4.50
Difference in Customer Perception Of Service Quality Based On The Nature Of Bank’s Ownership (Public Sector /Private Sector)
S.
NO. VARIABLES
CHI -
SQUARE VALUE
P -
VALUE
1
Mergers improve dependability of bank
service(say, improvement in after –sales
service)
3.2770 1.9400
2
It results in increase in number of
services provided 1.9730 0.3730
3
It increases the range of products
available 7.1010 0.0290(s)
4
It results in larger loan limits 16.0750 0.0000(s)
5
After merger better accessibility to
services is possible 1.4210 0.4910
6
Online banking does not improves after
merger 21.4490 0.0000(s)
7
Service time is not reduced after merger 2.6900 0.2610
8
Safety of deposits increases after merger 2.4770 0.2900
9
Bank mergers result in less competitive interest rates 7.7890 0.0200(s)
10
Mergers generally do not result in fee
reduction for different services 4.1080 0.1280
11
Goodwill of the bank is not enhanced
after merger 10.4640 0.0050(s)
12
Post merger, banks get quickly technologically advanced 0.1220 0.9410
13
More number of ATMs after merger
help in improving customer service. 18.9160 0.0000(s)
207
14
Customer relationship management
does not get better after merger 5.3770 0.0680
15
Mergers result in change of staff which gives an impersonal feel 1.2090 0.5460
16
Mergers results in big players and
reduces competition 6.8630 0.0320(s)
17
I don’t like cross selling (ex: banks
selling insurance products) undertaken by the merged bank
4.6920 0.0960
18
Merged banks provide innovative
products and redefine the way banks
interact with customers.
2.7430 0.2540
19
Communication about merger to the customers in general is not very
important
0.1900 0.9090
20
I prefer to switch to some other bank if my bank gets merged 8.2920 0.0160(s)
Source: Processed Data
In respect of eight out of twenty items listed in the questionnaire (Table
4.50), it is found that there is significant association between the
customer perception of bank service quality post-merger and the nature
of ownership of the bank of the customer, i.e. whether he/she is a
customer of a public or private sector bank. It is possible that the
customer perception is broadly influenced by the diversified range of
products and services, tech-savvy nature and the promptness in services
of the private sector banks as compared to those in the public sector
where the process of technological upgradation started relatively late.
208
Graph 4.28
Influence of merger on the commercial bank services: Customer response wise break-up
Source: Processed Data
While 62% of the respondents opined that the bank service would get
better after the merger, 21% were of the view that it would remain about
the same. Only 15% of the respondents were pessimistic in this regard
stating that it would worsen after merger (Graph 4.28). On balance, it
can be seen that the customers of the banks expect an improvement in
service quality post-merger.
62%
21%
2% 15%
Get Better Remain about the same Get worse Don’t know
209
Graph: 4.29
Customers classified based on their opinion on the strategy to be followed in commercial bank mergers in India
Source: Processed Data
It is interesting to note that only 4% of the respondents feel that mergers
of banks should not take place. While 44% of the respondents expressed
the view that the merged bank (Combined entity) should be a private
sector bank, 38% maintained that public sector banks should be
preferably merge with public sector banks only (Graph 4.29).
44%
38%
14% 4%
PSU Banks should merge with private sector banks
PSU Banks should merge with PSU banks
Private sector Banks should merge with private sector banks
Merger should not take place at all
210
4.3.1 Factor analysis
Factor analysis has been performed with the 20 statements (items) in the
questionnaire using Statistical Package for Social Sciences (SPSS)
version 16. [Cronbach Alpha coefficient, which demonstrates internal
consistency and reliability of the established scale turned out to be
0.6697 which is sufficiently larger than the acceptable standard of
0.50(Kline, 1998)]
Table 4.51 shows KMO and Bartlett’s test
Table: 4.51
Source: Processed Data
The Kaiser-Meyer-Olkin (KMO) measures the sampling adequacy which
should be greater than 0.50 for a satisfactory factor analysis. Looking at
the above table, the KMO measure is 0.653. From the same table, we can
see that the Bartlett’s Test of Sphericity is significant. The approximate
chi-square statistic is 1348.388 with 190 degrees of freedom which is
significant at the 0.01 level.This implies that the population correlation
matrix is not an identity matrix. The determinant’s value of R-matrix
211
(correlation matrix) is 0.007 which is much greater than the necessary
value of 0.00001 and hence the matrix does not suffer from the problem
of multicollinearity. Also that no correlation coefficient in the R-matrix is
greater than 0.9 implying that the data set does not suffer from
singularity problem either.
The communalities furnished below measure the percent of variance in a
given variable explained by all the factors. Communality for a variable is
the sum of squared factor loadings for that variable (row), and thus is the
percent of variance in a given variable explained by all of the factors. For
full orthogonal PCA, the communality will be 1.0 and all of the variance
in the variables will be explained by all of the factors, which will be as
many as there are variables. In the communalities chart, SPSS labels
this column the “initial” communalities. The extracted “communalities” is
the percent of variance in a given variable explained by the factors which
are extracted, which will usually be fewer than all the possible factors,
resulting in coefficients less than 1.0.
212
The “Total Variance Explained” table shows the eigen values. A factor’s
eigen value may be computed as the sum of its squared factor loadings
for all the variables. The ratio of Eigen values is the ratio of explanatory
importance of the factors with respect to the variables. If a factor’s eigen
value is low, then its contribution little to the explanation of variances in
the variables is small and hence may be ignored vis-à-vis more important
factors. Though the table shows 20 factors, one for each variable, only
the first six are extracted for analysis because, under the Extraction
options, SPSS was directed to extract only those factors with Eigen
values of 1.0 or higher.
The Initial Eigen values and Extraction Sums of Squared Loadings are
the same except that the latter only lists factors which have actually
been extracted in the solution. The Rotation Sums of Squared Loadings
(Varimax rotation has been used) gives the eigen values which improve
the interpretability of the factors. This means that after rotation each
extracted factor counts for a different percentage of variance explained,
even though the total variance explained is the same.
213
Table: 4.52
Total Variance Explained
Source: Processed Data
214
The Cattell Scree test, below, plots (Graph 4.31) the components on the
X-axis and the corresponding eigen values on the Y-axis. As one moves to
the right, towards the later components, the eigen values drop. When the
drop ceases and the curve makes an elbow towards less steep decline,
Cattell’s scree test says “Drop all further components after the one
starting the elbow”. Such a change in the slope in the graph is known as
scree and the point is known as scree point. The factors which are
marked up to the scree point from the origin are to be retained for the
study and all the factors to the right of the scree point are to be dropped
from the study. Based on this criterion and the eigen value criterion
stated earlier, six factors have been retained.
Graph: 4.30
Scree Plot
Source: Processed Data
0
0.5
1
1.5
2
2.5
3
3.5
4
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
Eige
n V
alu
e
Component Number
215
Table: 4.53
Source: Processed Data
Component Matrix: The above Component Matrix (Table 4.53) gives the
factor loadings. This is the central output for factor analysis. The factor
loadings which are also called the component loadings in Principal
Components Analysis(PCA) methodology are the coefficients of correlation
between the variables (rows) and factors (columns).Factor loadings are
the basis for imputing a label to the different factors. The above table
216
gives the unrotated solution and the one below (Table 4.54) gives the
rotated solution.
Table: 4.54
Source: Processed Data
Interpretation: A look at the rotated component matrix indicates that
the first factor has fairly high loadings from six primary banking service
quality variables X2, X3, X13, X1, X5, X6. Because these six service
quality items sort on the same factor, these items may be combined in a
scale which might be called “Primary Banking Service Quality
Determinants”. It may however be noted that naming a factor is a matter
of subjectivity and at times, even disputes arise on this issue. X4, X8,
217
X12, and X16 are associated strongly with the second factor which
might be called Size/Scale benefits. X7, X10, X14 and X9* are strongly
associated with the third factor which might be called “Customer
Relationship Management”(CRM). The fourth factor is strongly
associated with X15, X11 and X20 all of which have a bearing on Brand
image and might be called Brand image scale. As one goes on, the factors
become harder to interpret. The fifth factor is strongly associated with
the variable X19 (Importance of communication about merger to the
customers) which is a very critical aspect in integration (of the merging
entities) implementation. The sixth factor is strongly associated with
variables X17 and X18 which have a bearing on the ability of merged
banks to provide innovative products. This factor might therefore be
labeled as “Opportunities for innovation”.
* Though the loading for the variable X9 (movement in bank interest
rates post-merger) is -0.417(less than 0.50), it has been clubbed, having
regard to the fact that the difference is small, with the remaining three
variables falling under the construct/factor Customer Relationship
Management(CRM). Alternatively, this variable may be excluded as well.