MEASURING BANK EFFICIENCY USING THE DISTRIBUTION-FREE
APPROACH AND TESTS OF THE EFFICIENT STRUCTURE THEORY
AND QUIET LIFE HYPOTHESIS
A Thesis
Presented to the
Faculty of the School of Economics
University of Asia and the Pacific
In Partial Fulfillment
of the Requirements for the Degree
Master of Science in Industrial Economics
By
Jose Vincent M. Lubat
May 2015
© Jose Vincent M. Lubat. 2015. All Rights Reserved
APPROVAL SHEET
This thesis entitled: MEASURING BANK EFFICIENCY USING THE
DISTRIBUTION-FREE APPROACH AND THE TESTS OF THE EFFICIENT
STRUCTURE THEORY AND QUIET LIFE HYPOTHESIS prepared and
submitted by JOSE VINCENT M. LUBAT in partial fulfillment of the requirements
for the degree of MASTER OF SCIENCE IN INDUSTRIAL ECONOMICS has been
examined and is recommended for acceptance and approval for ORAL
EXAMINATION.
THESIS COMMITTEE
Dr. George N. Manzano
CHAIRMAN
Dr. Victor A. Abola
Mr. Emilio S. Neri Jr.
Member
Member
______________________________________________________________________________________
PANEL OF EXAMINERS
Approved by the Committee on Oral Examination with a grade of Pass.
Dr. Victor A. Abola
CHAIRMAN
Mr. Emilio S. Neri Jr.
Dr. George N. Manzano
Member
Member
Accepted and approved in partial fulfillment of the requirements for the
Degree of MASTER OF SCIENCE IN INDUSTRIAL ECONOMICS.
Dr. Peter Lee U
Dean
Date: May 07, 2015
Acknowledgements
“When you set out on you journey to Ithaca, pray that the road is long,
full of adventures and full of knowledge.”
The short version of this acknowledgment is this: There was one time when
I wanted nothing in the world but to take the 5th year because I knew it’s the point
where my wants and my needs meet. Though I was aware of the struggles I had to
overcome, I pushed through. Now, I am writing this part of the thesis which means
I am almost at the end. And for those who know how I fell in love with Constantine
Cavafy’s poem describing Odysseus’ journey on his way back to his homeland after
the Trojan War, I am near Ithaca.
But the short version failed to capture the many things I experienced in the
process. The road to this has been nothing short of experiencing new adventures
and gaining new knowledge.
There have been a lot of struggles. The mental struggle of reading economic
theories and studies. The financial struggle of reprinting drafts and going to school
every day. The physical struggle of staying up late at night because there were
deadlines to meet. The emotional struggle to keep sane in the middle of
everything’s that’s happening. And despite the continuous struggle and giving your
best to finish everything, there were depressing consultations, wala-kong-nagawatoday days, kaya-ko-pa-ba moments, and hanggang-kelan-pa-ba-to rants.
But looking back, it was during those struggles that I gain some of the best
lessons I would live by forever. Yes, the road I have traveled has been a difficult
one, something not quite a pleasurable road to pass on. But more than being
difficult, I believe that passing this road is necessary. Difficult but necessary. Yes,
there are roads in life that are full of challenges but these are the roads that make
us contemplate on why we are in the journey in the first place, if the journey is still
worth continuing. And in the process I realized that wherever we are, what matters
is that we continue to look up and we continue to move forward. Because there is a
bigger dream. Because the struggles I faced everyday were probably what Cavafy
described as “the Lestrygonians and the Cyclops, the fierce Poseidon you will never
encounter if you do not carry them within your soul.”
And, more importantly, the short version of this failed to include all the
people I will forever be thankful for. The journey of writing this thesis has been a
lot less stressful and a lot more inspiring because I had you in my life.
To my family, thank you for the unending inspiration. There were times
when I did not want to continue this anymore but then I remember you. To my Ate
and Kuya up there in heaven, I know you have been guiding me ever since I started.
To Dr. George Manzano, my adviser, thank you for believing that this study
is possible. Thank you for believing that despite starting late, I can finish this on
time. Thank you for your wonderful insights. Thank you for asking the things that
I oftentimes overlook. Thank you Sir for the patience. I know that I could be
stubborn at times. I am sorry if I sometimes go to consultations unprepared. I am
really grateful that I had you as my mentor.
To Dr. Vic Abola, my internal reader, thank you for your valuable insights
that helped this study make so much sense. You are indeed the banking expert. This
thesis will not really be possible of I did not have those consultations with you. I
hope that you continue to help more students in the future.
To Mr. Emilio Neri Jr., my external reader, thank you for sharing your
valuable time to be part of this thesis even if I know that you have so much more
important things to do as BPI’s Chief Economist. Thank you for the practical
insights during my oral defense. The theories and the literature I have used in this
study wouldn’t have make sense if I didn’t have your real-life insights.
To Miss Tin Martin, thank you for always being available for my random
consultations. I am sorry for my sudden and frequent EViews questions. You have
been a blessing not just to me but for everyone whom you have always been there
for anytime.
To my SEC family, to Miss Jovi, Dr. U, Dr. T, Sir Perry, Miss Belandres,
Miss Viory, Miss Meg, Miss Arlene, Miss Glenda, Miss Ledy and everyone, my
last three years in college wouldn’t be as memorable as it has been if it weren’t for
you. Thank you for making me feel that SEC is more than just an academic place
but definitely an extension of home.
To Miss Jessy, thank you for being there during those times when I do not
have anyone to talk to. You have been my automatic to-run-to when I know I
couldn’t handle things on my own anymore.
To Bea Zapanta, thank you for the friendship. Thank you for tirelessly
listening to all my drama and rants in life even if you have your own thesis to finish.
Thank you for all our spontaneous and planned catch-ups that really helped me cope
with thesis stress.
And, of course, to my IEP batchmates: Joe, Rey, Ivy, Chela, Keng, Raf,
Apple, Sarmie, Mon, Keren, Lyndon, German, Mar, Rige, Rose, Althea, Rap, and
Francis, THANK YOU FOR EVERYTHING. You are every part of this journey.
No words will probably suffice in describing how thankful I am that I have you not
just as batchmates but really good friends. I know that the kind of friendship that
we have will last forever.
And until this moment, I find it hard to accept why a beautiful journey such
as this have to end. But I guess I have to go back to the reason of why I started this:
I said ‘YES!’ when God told me to go and set out to start this journey. Because
there is another journey to begin. Because there are more beautiful places to visit.
Because there are more people to meet. Because there are more lessons to learn.
Because this is not yet Ithaca. Yes, this is definitely not the end. Because after
everything I have been through, I realized Ithaca might not be a place but a state of
mind. And that state of mind would probably be to allow God to lead me wherever
He wants me to go. Because when God says “Go!”, we should go. Or in St. Marie
Eugenie’s words: “Life must be a constant ‘YES!’ to God.”
“Wise as you have become, with so much experience,
You must already have understood what Ithacas mean.”
Table of Contents
Section
Page
Acknowledgments
List of Tables
List of Figures
Executive Summary
ix
ix
x
INTRODUCTION
1
CHAPTER
I
A.
B.
C.
D.
E.
F.
II
III
IV
Background of the Study
Statement of the Problem
Objectives of the Study
Significance of the Study
Scope and Limitations
Definition of Terms
1
4
5
6
9
10
REVIEW OF RELATED LITERATURE
13
A. Theory of the Banking Firm
B. Banking Efficiency
C. Banking Structure and Competition
D. The Industrial Economics of Banking
E. Empirical Evidence
F. Banking Efficiency, Structure, and
Competition
13
16
23
29
37
40
THEORETICAL FRAMEWORK AND
METHODOLOGY
46
A. Theoretical Framework
B. Empirical Methodology
C. Data Source
46
53
64
RESULTS AND DISCUSSION
65
A. Descriptive Statistics
B. Objective 1: Efficiency Results
C. Objective 2: The Test of the Efficient Structure
Theory
D. Objective 3: The Test of the Quiet Life
Hypothesis
65
70
81
89
vii
V
CONCLUSIONS AND
RECOMMENDATIONS
93
A. Conclusions
B. Recommendations
94
95
98
ANNEX
APPENDIX
A. Appendix 1
B. Appendix 2
C. Appendix 3
BIBLIOGRAPHY
101
101
104
135
140
viii
List of Tables
Table
Page
4.1
4.2
4.3
4.4
4.5
4.6
4.7
4.8
4.9
4.10
4.11
65
71
75
76
78
80
81
84
85
87
89
Summary of Bank-specific Statistics in Relation to Total Assets
Summary of Translog Regressions
Average Cost and Profit Efficiency in Different Time Periods
Top 5 Most Cost Efficient Banks for Different Time Periods
Top 5 Most Profit Efficient Banks for Different Time Periods
Z-test Results
Summary of Regression Results for Cost Efficiency and Assets
Summary of Regression Results for Profit Efficiency and Assets
Summary of Regression Results for Cost Efficiency and Loans
Summary of Regression Results for Profit Efficiency and Loans
Summary of the Regression Results for the Quiet Life Hypothesis
using Cost Efficiency
4.12 Summary of the Regression Results for the Quiet Life Hypothesis
using Profit Efficiency
6.1 Cost Efficiency of BDO Unibank and Equitable-PCI Bank
6.2 Profit Efficiency of BDO Unibank and Equitable-PCI Bank
6.3 Cost Efficiency of BPI and Prudential Bank
6.4 Profit Efficiency of BPI and Prudential Bank
91
98
98
99
99
List of Figures
Figure
4.1
4.2
4.3
4.4
Revenue and Cost to Total Assets Ratio
Return on Asset
Average Costs to Total Assets Ratio
Average Cost and Profit Efficiency
Page
66
67
69
73
ix
EXECUTIVE SUMMARY
Banks are highly connected to households and businesses. Because of this,
a stress or failure such as inaccessibility of financial savings can have spillover
effect on the entire economic system. The extent of a bank’s failure can be linked
to its systemic importance which, on the other hand, is related to its size and its
linkages with other banks and other financial institutions. The 2008 Global
Financial Crisis, which started when some of the biggest banks in the United States
failed, can be the most recent example of this phenomenon.
In the Philippine context, the government has adapted significant reforms
in the financial system since the 1980s in order to improve efficiency and strengthen
the safety and soundness of the banking sector. Lamberte and Manlagnit (2004)
argued that the banking system must not only be efficient in carrying its tasks as
intermediaries in the financial market but also be ready to survive adverse shocks
such as major policy changes. Furthermore, the adoption of the RA 10641, “An Act
Allowing the Full Entry of Foreign Banks in the Philippines”, the 2015 ASEAN
Economic Community and the 2020 ASEAN Banking Integrated Framework are
expected to change the landscape of the banking industry.
This study attempts to measure bank efficiency, particularly cost and profit
efficiency, using frontier analysis, specifically the Distribution-Free Approach. A
‘best-performing’ bank, the bank at the frontier, was measured for each year and
the efficiency level of other banks was relative with the efficiency level of this
‘best-performing’ bank. Based on the data available in BankScope, the researcher
measured cost and profit efficiency of Philippine universal and commercial banks
x
from 1992 – 2013. The study has adapted the model used by Huang (2002) in
measuring the efficiency gains of banks that underwent mergers and acquisitions.
One important limitation of this study is its focus on core banking activities
when measuring cost and output levels. However, in terms of profit, the standard
Return on Asset (RoA) measure which measure total net income of banks was used
because of the difficulty of dissecting bank-specific data into core and non-core
banking activities.
Furthermore, this study also shed light on the empirical discussion on the
assumptions made by market power and efficient structure hypotheses. The
researcher used the cost and profit efficiency measures generated in the frontier
analysis to test the efficient structure theory; whether banks who are cost and profit
efficient grow in terms of assets and loans. Furthermore, the individual market
share of each bank for each year was used in order to test the quiet life hypothesis;
whether increase in concentration leads to bank inefficiency.
The results showed that banks are generally more cost efficient than profit
efficient. More importantly, the 2000 General Banking Law proved to have helped
banks become more cost efficient. In addition, the 1998 Asian Financial Crisis and
the 2008 Global Financial Crisis had negatively affected banks’ cost and profit
efficiency levels.
The analyses employed to test the efficiency structure hypothesis showed
inconclusive results both in terms of cost efficiency and profit efficiency. The
macroeconomic variables, particularly the Gross Domestic Product, were proven to
xi
affect banks’ growth in assets and loans. On the other hand, the analysis done to
test the quiet life hypothesis in terms of cost efficiency proved that the assumption
that banks with higher market shares are inefficient does not hold true in the case
of Philippine universal and commercial banks. However, the results were
inconclusive for profit efficiency.
The study focused on banking efficiency; the variables it affects and the
variables that affect it. Future researchers can focus on banking profitability,
fragility, and adaptability to different types of risks.
xii
CHAPTER I
INTRODUCTION
I.
Background of the Study
Financial institutions contribute to economic development and the
improvement in standards of living by providing services such as facilitating trade,
channeling financial resources between savers and borrowers, and offering various
products that entail risk and uncertainty. One main function of banking firms is to
assess the credit worthiness of borrowers and monitor them to ensure that they meet
their obligations. Another main function of banks is to transform savings into longterm assets such as housing loans and lending to businesses. Banking firms play an
important role in payment and settlement services of and between households,
businesses and other financial institutions both on a day-to-day and on a long-term
basis.
Given that banks are highly connected to households and businesses, a
stress or failure such as inaccessibility of financial savings can have spillover effect
on the entire economic system. Bollard et.al (2011) argued that the extent of this
disruption can be linked to the systemic importance of the concerned bank. A
bank’s importance in the economic system can be related to its size and its linkages
with other banks and other financial institutions. One accurate example of this
phenomenon is the 2008 Financial Crisis which started when some of the biggest
banks in the United States failed. Because of their size and their international
linkages, the financial crisis affected the entire world. This global financial crisis
1
has prompted the idea of putting an asset cap on banks to limit the possible
contagion that their failure can reach. However, putting an asset cap does not seem
to be favorable because of the existence of economies of scale.
Furthermore, Bollard et.al (2011) says that bank efficiency highly affects
the way the economy allocates the resources between varying needs. One aspect of
bank efficiency is the way depositing, lending, and other financial services are
provided in a cost effective manner both at the point of view of the consumers and
banking firms which should be able to both improve and innovate their financial
services over time. In addition, banking firms should be able to provide loans
available at the reasonable rates and receive deposits at competitive rates.
However, in highly concentrated banking systems where a handful of large
banks overpower the smaller banking firms, collusion may exist and impede
competition to prevail. In the end, household may earn lesser because of low deposit
rates while businesses pay higher due to above-normal lending rates.
In the Philippine context, the government has adapted significant reforms
in the financial system since the 1980s in order to improve efficiency and strengthen
the safety and soundness of the banking sector. Lamberte and Manlagnit (2004)
argued that the banking system must not only be efficient in carrying its tasks as
intermediaries in the financial market but also be ready to survive adverse shocks
such as major policy changes.
Currently, there are developments in the policy environment both in the
domestic and international level. These policy changes can affect the domestic
2
banking system and can have significant implications on the efficiency level of
domestic banks. On July 2014, President Benigno Aquino III has signed into law
RA 10641, “An Act Allowing the Full Entry of Foreign Banks in the Philippines”,
which now allows foreign banks to own 100% of domestic banks, from the 60%
provision in RA 7721 of 1994. This new law was geared towards giving the
Philippines an advantage in the 2015 Association of Southeast Asian Nations
(ASEAN) Economic Integration where a common banking framework, the ASEAN
Banking Integrated Framework (ABIF), will be adopted by 2020.
Bangko Sentral ng Pilipinas (2013) claimed that the deepening of banking
integration in ASEAN will generally contribute to the betterment of the Philippine
financial sector. This, in turn, could lead to job opportunities for Filipinos. Through
increased competition and technology transfer, this financial integration could lead
to improvement in efficiency such as cost reduction. Different financial services
such as micro financing and insurance can be more available to a larger consumer
base which can offer some of the poorest citizens to have alternative sources of
income, protection against risks, and investment opportunities.
Financial integration can be beneficial but can also be destructive. The
interdependence of financial systems that the ABIF will provide can increase the
risk of contagion. If one big financial institution in the region fails, the risk will
spread in the entire region. Other economies whose financial system has been
healthy can be threatened. Here lies the importance of ensuring that financial
institutions are healthy, or in a more economic sense; efficient. Efficiency lowers
the risk of failure. If financial institutions are efficient then the probability that a
3
severe contagion that can harm the region’s financial system could be prevented or
minimized.
Given that the banking sector plays an important role in the economic
system and that the policy environment is changing both domestically and
internationally, it is important to shed light on three important aspects of the
industry: efficiency, growth, and market concentration. It is interesting to know
whether the big players in the industry are also the efficient ones or are the efficient
banks growing more rapidly than the less efficient ones. Furthermore, with the
liberalization of the industry, more players are expected to participate, it is essential
to know whether the level of concentration affects the firms’ level of efficiency.
II.
Statement of the Problem
The role of efficiency in every industry, not just in banking, has always been
emphasized. In the case of the Philippines, several authors have attempted to
measure efficiency using different methodologies over time. However, a question
seems to be left unanswered: Does banking efficiency affect bank growth and does
market concentration affect banking efficiency? This question will be the focus of
this paper as tests on what affects and what is affected by efficiency will be
conducted. In order to fully answer the main question, the following sub-questions
should be considered and answered:
a. How does one scientifically measure bank efficiency, specifically cost
and alternative profit efficiency?
4
b. Does the level of efficiency affect bank growth? Do more efficient
banks grow faster, in terms of loan and deposit growth?
c. What is the relationship between bank efficiency and market
concentration? Does the level of market/industry concentration affect
the level of bank efficiency?
All these questions will be answered for each specific time period to account
for behavioral changes caused by key policy changes that are introduced from time
to time.
III.
Objectives of the Study
The main objective of the study is to assess the role, what affects and what
it affects, of efficiency in the Philippine banking industry, specifically in universal
and commercial banks. This study would like to shed light on the relationship that
bank efficiency has with other important variables such as market concentration
and firm growth. Specifically, the objectives of the study are:
a. To estimate bank efficiency, particularly cost and alternative profit
efficiency, using frontier analysis, specifically the Distribution-Free
Approach
b. To test the Efficient Structure Hypothesis; test and quantify, the effect
of bank efficiency on the growth of banks using the indicators of bank
efficiency developed in Objective 1
5
c. To test the Quiet Life Hypothesis; test and quantify, the effect of market
concentration on bank efficiency using the indicators of bank efficiency
developed in Objective 1
The first objective seeks to directly measure bank efficiency. The results of
the first objective will be used as an independent variable for the regression model
that will be employed in the second objective. On the other hand, the researcher
will make use of the results of the first objective in the regression model as the
dependent variable for the third objective. Thus, the results of the last two
objectives highly depends on the accuracy of the results of the first objective.
IV.
Significance of the Study
Banking firms serve as the main player in the financial system in the
Philippines, thus, banks have big roles in economic development such as in serving
as intermediaries between lenders and borrowers. For example, interest rates
imposed by banks can easily affect entry and development of businesses in the
country. In relation to the adoption of ABIF, BSP (2013) forewarned that ASEAN
members such as the Philippines should establish the necessary capacities; pursue
sound and consistent macroeconomic policies characterized by price stability and
fiscal discipline in order for the country to fully benefit from the integration of the
financial markets.
Banking crises such as the 2008 global financial crisis has focused
policymaker’s attention on making banking and financial systems safer and more
resilient to shocks. While the Philippines has not been much affected by the crisis
6
and did not experience the recession that other countries faced, the country should
continue to develop its prudential policies to improve the resilience of financial
institutions and the financial system. BSP should be able to make sure that banking
services continue to be provided even if some banks fail to work.
One of the most important concepts that needs to be understood is
efficiency. There is a need to understand the efficiency of the financial system when
assessing its performance. This study will be able to shed light on the efficiency of
Philippine commercial and universal banks through measuring cost and alternative
profit efficiency using the distribution-free approach. Moreover, aside from simply
measuring bank efficiency, two hypotheses tests will be employed that will further
the understanding of bank efficiency. The efficient structure hypothesis will shed
light on whether bank efficiency leads to bank growth while the quiet life
hypothesis will clarify whether higher market concentration will lead to bank
inefficiency. These two hypotheses have contrasting implications for regulation. If
the evidence favors the efficient structure hypothesis, then the banking sector
should be left on its own. On the other hand, if the evidence validates the quiet life
hypothesis, then it would imply that the government should face a tradeoff between
banking stability and efficiency.
Bangko Sentral ng Pilipinas’ guidelines ensure more competent bank
management, greater transparency, reduced moral hazard and more effecftive bank
regulation and supervision. These things could be summed up in the word:
efficiency. Since one of the primary objectives of this study is to measure bank
7
efficiency, then it can be analyzed whether bank efficiency, both on average and on
individual banks, has improved over the years.
In addition, the General Banking Law of 2000 has promoted greater
competition in the industry and one way of achieving this is the encouragement of
mergers and acquisitions. BSP believes that merged and/or acquired banks could
help each other and achieve greater efficiency through collective experience,
expertise, and technological knowledge. However, one downside of encouraging
mergers and acquisitions is its effects on competition. To address this issue, BSP
(2000) used Herfindahl-Hirschman Index (HHI), a statistical measure of market
concentration that indicates the level of monopoly power.
This study will attempt to contribute in the literature in two ways. First, the
researcher will measure cost and profit efficiency of universal and commercial
banks using the Distribution-Free Approach. This study can be considered as an
extension of the study of Huang (2002). However, the time period was extended
and more bank were included due to the availability of data. Other researchers have
measured cost and profit efficiency but used different approaches. Second, and
more importantly, aside from measuring banking efficiency, this study will attempt
to use the results to determine whether the efficient structure theory and quiet life
hypothesis apply in the Philippine universal and commercial banking industry.
There were already studies such as Lamberte and Manlagnit (2004) and Aquino
(2007) that have measured banking efficiency using different methodologies.
However, these studies have only focused on measuring banking efficiency but did
not attempt to establish its relationship with other variables.
8
V.
Scope and Limitations
In line with the objectives that this study would like to shed light on, there
are further things that shall be defined in order to set up the boundaries that this
paper will have.
The first point that needs to be emphasized is that this study only uses the
Philippine universal and commercial banks. Rural banks and thrift banks will not
be considered mainly due to unavailability of data. Moreover, the total assets of the
universal and commercial banks, as of end of 2014, comprises more than 90 percent
of the total resources of the Philippine banking industry and 73 percent of the entire
Philippine financial sector. This gives an idea that universal and commercial banks
can serve as a sufficient sample in studying the entire industry.
The next point is the selection of data sample and period of coverage used.
Only the universal and commercial banks that are present in the Philippines from
2005 – 2008 were considered based on the website of the Bangko Sentral ng
Pilipinas (BSP). There is no official list of all of the universal and commercial banks
that exist in the Philippines prior to 2005. More specifically, only the universal and
commercial banks which have data in BankScope, a global database of banks’
financial statements, ratings and intelligence, were considered in this study. There
were 28 banks used as sample for this study. The number of banks used per year
vary because of availability of data and other bank-specific changes. Specifically,
from 1992 to 2013, the minimum number of banks with complete data was 13,
which was on the year 1992, and the maximum is 25, recorded in 2006.
9
For the sake of consistency, all bank-specific data were gathered from
BankScope while all macroeconomic variable were sourced from the website of
BSP. Furthermore, the coverage of the study will be the period of 1992 – 2013 due
to the limited availability of data in BankScope.
More importantly, the study will only focus on core banking. Core banking
activities covers basic depositing and lending of money. Standard core banking
functions will include transaction accounts, loans, mortgages and payments.
Because of this, the output level and total cost that will be specified in the cost and
profit function in the Chapter 3 of this study will only capture the core banking
activities of banks used in this study. However, because of the difficulty of
dissecting the income of statement of the banks, the total net income was accounted
for the banks’ profit. The researcher used the standard Return on Asset (ROA)
measure of which is net income over total assets.
VI.
Definition of Terms
The following terms are frequently used in the entire paper. For reference,
their definitions are given below.
a. Alternative Profit Efficiency – similar with the standard profit efficiency,
however, efficiency is measured given output levels rather than output
prices. Instead of counting deviations from optimal output as inefficiency,
variable output is held constant while output prices are allowed to vary and
affect profits.
10
b. Core banking - core banking activities covers basic depositing and lending
of money. Standard core banking functions will include transaction
accounts, loans, mortgages and payments.
c. Cost Efficiency - is a measure of how close a bank is to the ‘bestperforming’ bank in terms of producing the same set of products under the
same set of circumstances.
d. Cost Minimization - is the principle that firms follow in choosing the
combination of inputs in producing an output. Firms choose the
combination of inputs that will produce a certain level of output at minimum
cost.
e. Data Envelopment Analysis - is a non-parametric method which constructs
an envelope of outputs with respect to inputs using a linear programming
model.
f. Distribution-Free Approach - specifies a cost, profit, or production function
so as to determine the efficiency frontier. However, no assumption is made
about the distribution of errors, hence called the distribution-free approach.
g. Frontier Analysis - involves estimation of an efficient frontier and
measurement of the average deviations between observed banks and banks
on the frontier.
h. Profit Maximization – is the principle that firms follow where the revenue
that they will receive by producing an additional unit of output is equal to
the cost that it incurred by doing so
11
i. Standard Profit Efficiency – measures how close a bank is in generating the
maximum possible profit given the prices of inputs and outputs, as well as
other variables. This measure of profit efficiency specifies variable profits
instead of variable costs and takes variable output prices as given, instead
of holding output quantities fixed at their observed levels.
j. Stochastic Frontier Approach - specifies a cost, profit, or production
function so as to determine the efficiency frontier.
12
CHAPTER II
REVIEW OF RELATED LITERATURE
I.
The Theory of the Banking Firm
Matthews and Thompson (2008) discussed that banks are different from
other commercial and industrial enterprises because the monetary mechanism
enables them to attract deposits for onward investment. One major difference that
banks have over other firms is their leverage ratio. The debt – equity ratio of normal
commercial firm runs from 0.5 – 0.6. Banks, on the other hand, have liabilities as
high as nine times as their equity. Furthermore, each country has its own central
banks to monitor the entire banking system. These special characteristics of banks
deems that a different theory explains the banking firm distinct from the
conventional theory of the firm. In their book, The Economics of Banking,
Matthews and Thompson (2008) specified the different banking models for each
type of market structure:
A. Textbook Model
According to Matthews and Thompson (2008), the textbook model of the
banking firm assumes that the banking system supplies credit and takes deposits
according to a fixed coefficient relationship to the government financing condition.
The familiar criticism to this model is on the credit and deposit multipliers. The
ratio of currency to deposits (c) is a choice variable to the non-bank public, dictated
largely by the bank’s interest-rate-setting behavior. In the absence of a regulation,
k is a choice variable for the banks and as partial choice variable in the case when
13
a reserve ratio is imposed as a legal restraint. Moreover, the supply base of money
is endogenous and is decided upon by the central bank.
To address these issues, Baltensperger (1980) sets the objective function of
a bank as a profit function to develop a framework for the analysis of the banking
firm.
π = rLL - rDD – l – s – c
(2.1)
where rL is the rate of interest charged on loans, rD is the interest paid on deposits,
L is the stock of loans, D is the stock of deposits, l is the cost of illiquidity, s is the
cost due to default and c is the real resource cost. This framework provides
analysis to the determinants of bank’s profitability.
B. Perfect Competition
However, Matthews and Thompson (2008) admitted that the textbook
model is far from reality. To add a bit of realism, they added a market variable for
a risk-free, short-term, liquid asset such as government Treasury bills T, deposits in
the interbank market that incur interest rate rT, and a cost function that describes
the firm’s cost of servicing loans and deposits {C(D,L)}. In a perfectly competitive
environment, the individual firm is a price taker, so rL and rD are constant. The new
profit function of the bank is now
max π = rLL + rTT – rDD – C(D,L)
(2.2)
A competitive bank will adjust its volume of loans and deposits in a way
that the interest margin between the risk-free rate and the loan rate will equal the
14
marginal cost of servicing loans, and the margin between the reserve-adjusted riskfree rate and the deposit rate will equal the marginal cost of servicing deposits.
Moreover, in a competitive environment, the margin of intermediation is given by
the product of the reserve ration and the risk-free rate plus the sum of the marginal
costs of loan and deposit production by the bank.
C. Monopoly
Klein (1971) and Monti (1972) developed the monopoly bank model. The
model assumes that the bank faces fixed costs of operations. The monopoly bank
represents the entire banking industry and faces a downward-sloping demand for
loans with respect to loan rate and an upward-sloping demand for deposits with
respect to the deposit rate. The main assumptions of the model are: 1) the bank
faces a scale as well as an allocation decision, and scale is identified by the volume
of deposits; 2) the market for bills is perfectly competitive and the bank is a price
takes; 3) the loan and deposit markets are imperfectly competitive; 4) loans are
imperfect substitutes for bills; 5) reserves earn no profits; 6) the bank maximizes
profit; and 7) the bank faces a fixed cost schedule. The profit function of the bank
can be expressed as
π = rLL(rL) + rT [D(1 – k) – L] – rDD(rD) – C
(2.3)
D. Imperfect Competition
The main assumption of the imperfect competition model is that bank
spread will get narrow as competition increases. The slope of the demand for loans
and the slope of the demand for deposits gets flatter as competition intensifies.
15
Since more banking firms will compete against each other to attract both lenders
and borrowers, individual banks will offer higher deposit rates and lower borrowing
rates, thus, bank spreads will get narrower. The bank spread will continue to narrow
until the demand for loans and deposits get flat or horizontal.
In the oligopolistic version of this model, the number of firms in the market
measures the level of competition. However, in reality, competition can be
intensified even when the absolute number of firms decline especially in the case
of mergers and acquisitions. The primary motives behind mergers and acquisitions
are 1) market penetration and diversification; 2) increase in bank size; and 3)
diversification of products and attainment of economies of scope (Huang, 2002).
The firms who will stand after mergers and acquisitions can be expected to be more
efficient. This more efficient firms will then face tougher competition against more
efficient firms who also underwent mergers and acquisitions. Generally, the
imperfections or flaws associated with the banking firms are: 1) incomplete or
imperfect information; 2) uncertainty; and 3) transaction costs.
II.
Banking Efficiency
A. Measurement of Output
The primary concern when estimating bank efficiency is the measurement
of output to be used because of the lack of an unambiguous measure of the output
of banks [Matthews and Thompson (2008), Huang (2002) Lamberte and Manlagnit
(2004)]. In addition, bank output is not measured through physical quantities unlike
other firms. Measuring improvements in quality is also a difficulty. For example,
the existence of automated teller machines (ATMs) improves service quality as they
16
allow cash withdrawals anytime. Existence of ATMs also result to a reduction in
operating cost per transaction but can also lead to an increase in total costs. This is
in sharp contrast with the case of manufacturing firms where outputs are measured
in units.
Matthews and Thompson (2008) identified the ways by which bank output
can be measured: 1) the number of accounts; 2) the number of transactions; 3) the
average value of accounts; 4) assets per employee; 5) average employees per
branch; 6) assets per branch; 7) the total value of deposits and/or loans; and 8) the
value of income including interest and non-interest income. However, the literature
primarily uses loans and deposits when measuring bank output.
Another problem that lies ahead when estimating bank efficiency is the
choice of approach when measuring output. Matthews and Thompson (2008)
identified two approaches when measuring output: the intermediation method and
the production method. One difficulty that both approaches face is on how to weigh
the various bank services in the measurement of output as different banks have
major differences in their structure of assets and liabilities.
The intermediation approach considers the bank as an intermediary so that
its output is measured by the value of loans and investments together with offbalance-sheet income while its input is measured by the payments made to factors
of production, including interest payments. Using this method, deposits can be
either an input or an output. From the point of view of the bank, deposits are inputs
because they can be used to earn profits through the purchase of earning assets such
as loans and investments. On the other hand, from the point of view of the customer,
17
deposits are outputs which can create value for them in the form of payment, recordkeeping, and security facilities. This method can also make use of income in which
net interest income and non-interest income will be treated as output while net
interest expense and non-interest expense will be defined as inputs. In summary,
the intermediation approach claims that the main function of banking firms is to act
as financial intermediaries.
The production approach assumes that banks are firms that use factors of
production such as labor and capital to produce its products such as the different
categories of loans and deposit accounts. Hence, it recognizes that banking firms
are like ordinary firms in the product market. This method argues that all deposits
should be treated as outputs since they are associated with liquidity and generate
value added. However, one weakness of this method is that it ignores interest costs.
B. Measurement of Efficiency
One of the most fundamental ways of measuring bank performance is
through the use of accounting ratios such as return on assets, return on equity,
average earnings on asset, net interest margin, and cost-income ratio.1 Banks can
be compared using these accounting-based measures provided that they are under
1
Return on assets = profit after tax divided by total assets
Return on equity = profit after tax divided by total equity
Average earnings on asset = interest income plus non-interest income less provisions
divided by total assets
Net interest margin = interest earnings less interest costs divided by interest earning
assets
Cost-income ratio = operating expenses divided by operating income (net interest income
+ non-interest income)
18
common geographical and regulatory conditions. Hence, these measures are not
advisable to be used when comparing bank efficiency across countries.
The first alternative to accounting ratios is the neoclassical production
theory of the firm to the bank. This theory of production is straightforward and
clearly defines technical efficiency and cost efficiency. However, this method still
faces the problem on what measurement of output to be used Matthews and
Thompson (2008).
Parametric and Non-parametric Approach
A popular method in measuring bank efficiency is the Data Envelopment
Analysis (DEA) developed by Farrell (1957). DEA is a non-parametric method
which constructs an envelope of outputs with respect to inputs using a linear
programming model. DEA does not require a functional form for the production
function unlike the parametric methods. The DEA approach treats each bank as an
individual Decision Making Unit (DMU). Each DMU is assigned a single
efficiency score which allows DMUs in the sample to be ranked. DEA also
highlights the areas of improvement for each DMU. DEA is basically a
benchmarking method which treats the ‘most efficient’ banks in the frontier and
evaluates the other banks against the ‘most efficient’ bank.
However, Maudos (1999) found that one weakness of this approach is the
assumption that the data are perfectly measured and hence, there exists no random
disturbance term associated with the function unlike in an econometric
specification. More importantly, the main criticism against DEA and other non-
19
parametric approaches is that they ignore prices and focus on technological
optimization and not economic optimization.
To overcome the problem of the error term, other parametric approaches are
used. There are three types of parametric approaches: the stochastic frontier
approach, the distribution-free approach, and the thick-frontier approach.
The Stochastic Frontier Analysis (SFA) specifies a cost, profit, or
production function so as to determine the efficiency frontier. This approach treats
the residual as a composite error comprising: 1) random error with a symmetric
distribution – often normal and 2) inefficiency with an asymmetric distribution –
often a half-normal on the grounds that inefficiencies will never be a plus for
production or profit or a negative for cost.
The Distribution-Free Approach (DFA) is similar with SFA as a specific
functional form is also specified. However, no assumption is made about the
distribution of errors, hence called the distribution-free approach. Random errors
are assumed to be zero on average while the efficiency for each firm is stable over
time. Inefficiency is measured as the difference between the average residual of the
individual and the average residual for the firm on the frontier or the ‘most efficient’
bank.
The Thick-Frontier Approach (TFA) specifies a functional form to
determine the frontier based on the performance of the ‘most efficient’ firms.
However, unlike the previous two non-parametric methods, this approach does not
provide an efficiency rating for each individual firm but instead gave the efficiency
20
level of the industry as a whole. Still, firms are ranked based on performance based
on the assumptions that: 1) deviations from predicted performance values by firms
from the frontier within the highest and lowest quartiles represent random error;
and 2) deviations between highest and lowest quartiles represent inefficiencies.
Huang (2002) argued that these frontier analyses are more grounded on
economic theory. Frontier analysis involves estimation of an efficient frontier and
measurement of the average deviations between observed banks and banks on the
frontier. Furthermore, this method is reflected by the concepts of efficiency.
However, one major drawback of this method is its inability to identify the cause
for efficiency gains. Nevertheless, it is still the most applicable method to be used
in this study. Further explanation on the method used in this study can be found on
Chapter III.
Another parametric approach to estimate efficiency is the use of cost and
profit function specifications. The cost and profit efficiency assumptions are
grounded on two important economic objectives: cost minimization and profit
maximization. Cost efficiency is the ratio between the minimum cost at which it is
possible to achieve a certain volume of production and the actual cost at which the
volume of production was incurred. On the other hand, profit efficiency is a broader
concept which takes into account the effects of the choice of a certain vector of
production both on costs and on revenues.
Matthews and Thompson (2008) specified that one weakness associated
with the parametric approaches is that estimates of efficiency could be biased due
to misspecification of the functions used. To address this, Huang (2002), among
21
others, have used the translogarithmic specification. Using a translog specification
has three advantages: 1) it accommodates multiple outputs without necessarily
violating curvature conditions 2) it is flexible, in the sense that it provides a secondorder approximation to any well-behaved underlying cost frontier and 3) it has a
good record of empirical estimation for the decomposition of cost efficiency. The
functional form of a translog function is:
𝑙𝑛𝑞1 = 𝛽0 + 𝛽1 𝑙𝑛𝑥1𝑖 + 𝛽2 𝑙𝑛𝑥2𝑖 + 0.5 𝛽11 (𝑙𝑛𝑥1𝑖 )2 + 0.5 𝛽22 (𝑙𝑛𝑥2𝑖 )2 +
𝛽12𝑙𝑛𝑥1𝑖 𝑙𝑛𝑥2𝑖 + 𝑣𝑖 − 𝑢𝑖
(2.4)
where 𝑙𝑛𝑞1 is the dependent variable while 𝑙𝑛𝑥1𝑖 and 𝑙𝑛𝑥2𝑖 are two input independent
variables.
Furthermore, Huang (2002) noted that SFA and TFA tend to be problematic
because of the assumptions they make regarding the distribution of the error terms.
SFA claims that the errors are normally distributed and inefficiency terms are halfnormally distributed. However, there are little theoretical justifications that would
support such assumptions. On the other hand, DFA assumes that the random error
terms cancel each other out in the long run hence, the average of the bank’s
residuals from the cost and profit function regressions will be an estimate of the
inefficiency term.
Given the differences in the assumptions that each approach makes, variety
of results can be expected. The parametric methods have similar results so as the
non-parametric analyses. However, parametric and non-parametric approaches
have showed varying results when compared with one another.
22
Moreover, Claessens and Laeven (2003) noted that many other factors like
macroeconomic variations, tax policies, quality of information and judicial
systems, etc., as well as bank-specific characteristics like risk preferences or scale
of operations, affect bank profitability and margins, hence, efficiency.
A discussion of the different studies that have used the different methods
in measuring efficiency in order to test different economic theories and hypotheses
can be found at Part V of this section.
III.
Banking Structure and Competition
A. Mergers and Acquisitions
Matthews and Thompson (2008) listed several reasons on why mergers and
acquisitions occurred such as 1) increase in technical progress; 2) improvement in
financial condition; 3) excess capacity; 4) international consolidation of financial
markets; and 5) deregulation.
In context, the first reason is the technical progress which allows economies
of scale to exist such as the increase in the extent of use of Information Technology
(IT), the growth of financial innovation, spread of Automated Teller Machines
(ATMs) and online banking. The bigger banking firms have more capacity to adapt
to these developments and gain more benefits from them. The second motive is the
improvement in financial conditions such as return on assets and other accounting
measures. The third reason is the excess capacity brought by increasing competition
from other financial institutions such as investment trusts and other capital markets.
The excess capacity gives banks incentives to consolidate and hence, merge.
Fourth, globalization allows banks to have mergers and acquisitions across their
23
respective country territories. The final reason is the deregulation of the banking
system which removes restrictions such as asset caps and gives the banking system
more freedom to self-regulate.
The standard rationale which justifies mergers and acquisitions is the idea
that well-managed firms will take over poorly managed firms and improve the
performance of these firms. However, there are three motives that are used to justify
such take over activities (Ibid.).
The first one is the potential increase in value from the perceived increase
in efficiency and market power. Two types of efficiency can be considered: scale
efficiency and pure technical efficiency. Scale efficiency, which comes considers
the output side of the business, is achieved when the business operates at the
optimum size with appropriate degree of diversification. Excess diversification can
be detrimental to the banking firm. However, measuring potential for scope
economies can be difficult as it requires cost and revenue functions with and
without diversification. On the other hand, pure technical efficiency, which
considers the input side, requires the bank to use only the best practices when
producing its products. In the literature, this is also called as the X-efficiency or the
deviation from the optimum X-inefficiency. In addition, allocative efficiency refers
to the optimum mix of inputs given their relative prices or costs.
The second motive for mergers is the separation of ownership and
management. According to agency theory, managers can pursue self-interests or
those that do not necessarily coincide with those of the owners. For example,
24
managers may engage in acquisitions that do not necessarily increase the
shareholder’s wealth.
The third motive entails decisions of the management to acquire an
inefficient bank, improve its situation, and, in turn, make profits for their bank.
These managers assume that the market has got the valuation wrong and therefore,
they can identify bargains.
The empirical evidence on the pre and post-merger analysis uses five
different methods of analysis which can be divided into two broad categories, the
static studies and the dynamic studies. The static studies, which do not take consider
behavior before and after the merger are: 1) production functions; 2) cost functions;
and 3) the efficient frontier approach. The dynamic studies specifically take into
account bank behaviors before and after mergers. The two approaches under this
are: 1) accounting data; and 2) event studies.
The Production Function Approach is an expression that estimates output
as a function of a set of inputs. A more popular production function is the Cobb –
Douglas production function where capital and labor are considered to be the
inputs. This function can be easily altered to specify different types of labor and
capital. When transformed into logarithmic specification, this function becomes
linear. To make a comparison for pre and post-merger efficiency, dummy variables
are used.
The Cost Function Approach uses an estimate of a cost function which
normally takes the form of a translog cost function. The main goal of this method
25
is to observe whether economies of scale exists in the sample data, as banks grow
larger after experiencing mergers.
The Efficient Frontier Approach is a benchmarking method which treats the
‘most efficient’ banks in the frontier and evaluates the other banks against the ‘most
efficient’ bank. The most common method used is the Data Envelopment Analysis
(DEA) which was discussed earlier.
The Accounting Approach evaluates mergers through the use of accounting
ratios such as return on assets and/or equity. However, the use of accounting data
can be problematic because each type of banks have unique services and run under
different forms of regulation and these things cannot be captured by mere
accounting data.
An alternative approach, which is even more grounded on economic theory,
is the Frontier Analysis employed by Huang (2002) who studied the efficiency
gains from the mergers that occurred in the Philippine banking sector. Frontier
analysis involves estimation of an efficient frontier and measurement of the average
deviations between observed banks and banks on the frontier. Furthermore, this
method is reflected by the concepts of efficiency. However, one major drawback of
this method is its inability to identify the cause for efficiency gains. Nevertheless,
it is still the most applicable method to be used in this study.
B. Measure of Concentration in Banking Markets
One of the most common effects of increase in mergers and acquisitions is
the increase in market concentration. This consolidation have important
implications in banking competition. Most of the antitrust regulators measure
26
market power of firms in terms of level of concentration in the market. The
justification behind this is the Structure-Conduct-Performance paradigm developed
by Bain (1951). The SCP hypothesis assumes that fewer and larger firms will most
probably collude and engage themselves in anticompetitive behavior that can
disturb the industry’s efficiency.
The n-bank concentration (CRn) is used to measure market concentration.
This method measures the percentage share of the market held by top n banks in
the market. The three-bank and the five-bank concentration ratios are the widely
used measures. The level of concentration is calculated using the share of the top
three or five biggest banks in total deposits or assets in the market. The larger the
concentration is, the bigger the potential for anticompetitive behavior is.
However, to account for the information of each firm, most regulators use
the Herfindahl-Hirschman Index (HHI). HHI is the sum of the squared market
shares of firms in the market. This method is applicable for all industries and not
exclusive for the banking industry. Rhoades (1993) claimed that the method has
gained popularity since the US Department of Justice and Federal Reserve used it
in the analysis of the competitive effects of mergers in the US.
The HHI can be a value from 0 – 1. A value of 1 means the industry is a
monopoly or there is only one player. A value of 0 indicates perfect competition or
the existence of infinite players. In the US, merger that would increase the HHI by
0.1 would alarm the regulators. Aside from studying whether the merger is
anticompetitive or not, regulators also balance the perceived loss of consumer
27
welfare from the increase in market power caused by a merger against other issues
such as maintaining stability through the takeover of weak or failing banks.
However, one downside of using HHI as a measure of market concentration
is the need to have information for all the firms in the industry unlike the n-bank
concentration (CRn) which only accounts either the top three, five, or ten firms in
the industry (Rhoades, 1993). Nevertheless, the HHI still remains as the most
widely used method used by researchers and regulators including, as what was
mentioned above, the US Department of Justice and Federal Reserve, and even the
Bangko Sentral ng Pilipinas.
C. Competition Analysis in the Banking Market
The new empirical industrial organization measures the strength of market
power by estimating “deviations between observed and marginal cost pricing”,
without actually using any market structure indicator. One popular method is the
Panzar – Rosse method developed by Panzar and Rosse (1977). This method makes
use of a bank specific revenue function in terms of bank factor prices. This method
estimates an H-statistic, the sum of the elasticities of revenue in terms of factor
prices. The H-statistic estimates the effect of an increase in factor prices on revenue.
An H-statistic that is negative (H < 0) indicates that the market is either a
monopoly or a perfectly colluding oligopoly as increase in input prices will raise
marginal costs, decrease equilibrium output and, in turn, reduce total firm revenue.
On the other hand, an H-statistic that is equal to 1 (H = 1) indicates that the market
experiences perfect competition as increase in input prices will increase both
28
marginal and average costs without affecting the optimal output of any banking
firm. An H-statistic that is equal to 1 (H = 1) is also a sign of a natural monopoly
which operates in a perfectly contestable market and of a sales-maximizing firm
under breakeven constraints. If the H-statistic between 0 and 1 (0 < H < 1), the
market is under monopolistic competition.
The empirical studies that applied the Panzar – Rosse methodology
generally conclude that banking markets operate under monopolistic competition
with a few exceptions. Molyneux et al. (1996) found empirical evidence for
monopoly in Japan for 1986 – 1988. In the European banking market, De Bandt
and Davis (2000) found that big banks in France, Germany, and Italy are under
monopolistic competition while small banks are under a monopoly. On their study
of 23 banking markets, Biker and Haaf (2002) segmented their sample into either
small, medium, or large banks. They found that there is more competition among
big banks as compared to the small banks. Claessens and Laeven (2003) found that
the level of competitiveness was higher among where countries where there is
higher concentration of foreign banks. Most importantly, their results showed that
concentration was positively related to competitiveness, hence rejecting the SCP
hypothesis.
IV.
The Industrial Economics of Banking
VanHoose (2010) analyzes that the analysis of the economic policies that
concern the banking industry has been guided by two opposing paradigms: the
market power theory and the efficient structure theory. The market power theories
are supported by different hypotheses such as the Structure-Conduct-Performance
29
paradigm, the Relative Market Power hypothesis, and the Quiet Life hypothesis.
On the other hand, the efficient structure theory are divided into two measures of
efficiency: X – efficiency and scale efficiency. These two paradigms acknowledges
the presence of positive correlation between market concentration and profitability.
However, these two have different views on which comes first.
The market power theories explain that high level of concentration leads to
increase in market power by few firms. Given that the level of concentration and
market power affects firms’ conduct, the high level of concentration and market
power allow banking firms to increase lending rates and keep deposit rates at low
levels. This, in turn, reduces consumer welfare. On the other hand, the efficient
structure theory suggests that banking firms’ cost conditions highly affects their
performance. According to the theory, cost-efficiency is achieved through
economies of scale and/or scope and once cost-efficiency is experienced, banking
firms could lower lending rates and increase deposit rates. Therefore, mergers,
acquisitions, and other banking expansion activities face a perceived trade-off
between market power and cost-efficiency.
In his book, The Industrial Organization of Banking, VanHoose (2010)
explains the factors that influence the structure, conduct, and performance of the
banking industry. The following sections will explain each hypothesis and theory
that seek to explain the behavior of banking firms.
30
A. Structure-Conduct-Performance Paradigm
VanHoose (2010) explains that the Structure-Conduct-Performance (SCP)
hypothesis applies the basic oligopoly theories in the banking models. According
to the SCP hypothesis, the reduction of banking firms – that is, fewer number of
loan and deposit market competitors – will cause an imbalance in the market which,
in turn, will lead to higher market loan rates and lower market deposit rates. This
will lead to the decrease in the number of loans and deposits which, in turn, will
lead to the reduction in consumer and producer surpluses and increase in
deadweight losses. Hence, the SCP hypothesis predicts that increase in
concentration will ultimately result to worsened performance of the banking
industry.
Banks with different costs and size compete and operate within an industry.
A dominant bank model considers the potential for larger and more efficient banks
to compete in market rivalry with smaller and less efficient banks. Dominant banks
may engage in strategic entry deterrence such as predatory pricing to hinder the
entry of potential rivals. In this way, banks to can limit the number of players in the
market and keep their market.
The literature on the industrial organization of banking has offered both
time-series and cross-section evidence supporting the SCP hypothesis regarding the
reduction in loan and deposit quantities and the increase in bank spreads, difference
between lending rate and deposit rate.
31
B. Relative Market Power Hypothesis
A similar hypothesis to the SCP is the Relative Market Power (RMP)
hypothesis. RMP predicts that only the firms with significant market shares and
well-differentiated products will be able to exercise market power in pricing. The
difference between the two hypotheses revolves around whether market power
proves to be generic to a market (SCP) or specific (RMP) to individual banks within
a market.
Al-Jarrah and Gharaibeh (2009) argued that the RMP hypothesis claims that
the variation in the performance of individual banks is attributed to efficiency as
well as by the residual influence of the market share, given that market share
captures the other factors unrelated to efficiency such as market power and product
differentiation. This hypothesis uses individual market share as the proxy variable
for assessing market power.
C. Quiet Life Hypothesis
Similar to the SCP hypothesis, the quiet life hypothesis assumes the
negative effect of market power on efficiency. This hypothesis postulates that the
higher the market power is, the lower the effort of the managers will be to maximize
efficiency; hence, a negative correlation exists between market power and
managerial efficiency.
Punt and van Rooji (2000) noted that the quiet life hypothesis can be
considered as special case of the market power hypothesis. The hypothesis claims
that there is a positive relationship between market power and inefficiency and this
32
relationship is called the “quiet life”. This is a state where the management become
less focused on efficiency, since setting prices at more favorable levels can increase
revenues. Firms do increase revenues as a result of increase market power but, as a
result of higher inefficiencies, do not show a superior profitability.
The quiet life hypothesis was initiated by Hicks (1935). According to Hicks
(1935), firms use their market power to allow for inefficient allocation of resources
rather than maximizing their profits instead of using monopolistic power to gain
rents because the cost of reaching optimal profit will outweigh the marginal gains.
Berger and Hannan (1998) further specified the four ways in which market
structure influence cost efficiency in the banking industry. First, if high levels of
market concentration allow firms to charge prices in excess of competitive levels,
then managers may take part of the benefits of the higher prices not as higher profits
but in the form of a “quiet life”, a condition where they do not work hard to control
costs. Second, market power may allow managers to pursue objectives other than
firm profits such as the growth of the firm, of the staff, or the reduction of labor
conflict by means of higher wages, at the expense of efficiency. Third, managers
may expend resources to obtain and maintain market power on expenditure that
would reduce cost efficiency. Fourth, the higher price provided by market power
may allow inefficient managers to persist without any willful shirking of work
effect, pursuit of other goals, or efforts to defend or obtain market power.
The above-mentioned ways in which market structure influence cost
efficiency somehow supports the SCP hypothesis. They claim that the firms in
highly concentrated market uses more market power in determining prices.
33
Punt and van Rooji (2000) discussed that these market power hypotheses
claim that there is a direct causal relation from market structure to profitability,
even after controlling for other variables. If the market structure variables have an
impact on profitability, these other variables (including efficiency) may also
influence profitability. Furthermore, the strictest versions of market-power
hypotheses claim that there is no causal relationship from efficiency to market
structure. The reverse, no causal relationship form market structure to efficiency
might also be true. These reversed relationships can be used to test the quiet life
hypothesis, being a special case of the market-power hypotheses. This theory
claims that because of more market power, management becomes less focused on
efficiency, resulting in a negative relationship between market power and
efficiency.
D. The Efficient Structure Theory and Its Challenge to the Market Power
Theory
The assumptions made by the market power hypotheses were challenged by
Demsetz (1973) using the efficient structure theory. The main argument of this
hypothesis is that the relationship between market structure and performance is
controlled by the level of efficiency of the firm. Al-Muharrami and Matthews
(2009) argues that following this hypothesis, firms with superior management have
lower costs, earn higher profits, and grow. Thus, the mechanism behind more
profitable and bigger firms is not based on market power, as suggested by the SCP
hypothesis, but by the efficiency level.
34
The efficient structure (ES) theory suggests that economies of scale and
scope are the reasons behind the existence of relatively large banking organizations.
The presence of economies of scale and scope within the industry have implications
on their structure, conduct, and their performance.
The ES theory inverts the key assumptions made by the SCP dominant-bank
hypothesis. In contrast to the SCP hypothesis, the ES theory assumes that the cost
advantages from economies of scale and/or scope would result to lower, rather than
higher, loan rates and higher, instead of lower, deposit rates. Larger banks have
lower per-unit costs that results to such rates and not because of market power that
they got due to high concentration in the market nor from predatory conduct aimed
at hindering entry of new players. Thus, the ES theory does not claim any clear
relationship between loan and deposit rates and market concentration.
Moreover, the ES theory also claims that the level of market competition
and market concentration are not necessarily negatively related which means that
there can be few players but high competition. Indeed, Classens and Laevens
(2004), in a study of 50 nations and 4,000 banks, concluded that competition is
related to market concentration.
More importantly, Jeon and Miller (2005) noted that the ES theory includes
two hypotheses. The X-efficiency hypothesis suggests that banks with better
management and practices control costs and raise profit. The scale-efficiency
claims that banks achieve better scale of operation and, thus, lower costs. Lower
costs gives higher profits and faster growth.
35
E. Policy Implications
Davila (2005) argued that the market power and efficient structure
hypotheses have contrasting implications for regulation, particularly in relation to
mergers and antitrust policies. If the evidence favors the efficient structure
hypothesis, then mergers are motivated by efficiency considerations, which should
increase consumer and producer’s surplus. If on the other hand the evidence
validates the market power hypotheses it would imply that the motivation behind
mergers is monopolistic price setting.
Matthews and Thompson (2010) claims that if SCP hypothesis holds true,
dominant banks in the market should be broken up by competition legislation. On
the other hand, if the ESH prevails, the banking sector should be left alone since
the high concentration is a mere product of the efficiency of some of the large
banking firms.
In the Philippine context, if barriers to entry supports the existence of
market power in the banking sector, the removal of such barriers such as the recent
liberalization of the foreign bank entry assumes that market concentration is not an
impediment to competition. Hence, the banking sector is a ‘contestable market’.
Baumol (1982), as cited in Matthews and Thompson (2010), defined a ‘contestable
market’ as “one where an entrant has access to all production techniques available
to the incumbents, is not prohibited from wooing the incumbent’s customers and
entry decisions can be reversed without cost”. Other conditions include inexistence
of legal restrictions to enter the market, ability to use the best available technology,
and the absence of sunk costs.
36
V.
Empirical Evidence
A good amount of studies have been done to shed light on which theory or
hypothesis explains the behavior of a specific banking sector. Each study uses
different time frames and sample countries. Furthermore, most of the studies
employ different methodologies in order to test the relationship between market
structure and efficiency. More importantly, most studies have taken off from the
existing studies, altered the models, and made the necessary changes in order to
gain more accurate results.
Weiss (1974) and Smirlock (1985) were among the first ones to study the
ESH and SCP paradigm. They used firm profit as the dependent variable while
market share and market concentration as the independent variables. Given the set
of variables, it can be inferred that market share was used as the proxy for
efficiency. ESH holds true if the market share variable has significant positive
effect on profit. On the other hand, SCP holds true if market concentration has
significant positive effect on profit. Problem arise when the two independent
variables prove to be both statistically significant and have positive effect on profit.
In addition, when we look closely to the two hypotheses, both ESH and SCP
paradigm assumes a positive relationship between concentration and profits but
with contrasting mechanisms. Hence, these early studies are quite problematic in
terms of which of the two hypotheses really holds true.
Berger and Hannan (1989) investigated the price-concentration relationship
and offered an alternative specification. This is a more rational approach as ESH
predicts that prices are lower in highly concentrated markets while the SCP
37
paradigm claims that prices are higher in more concentrated markets. They found
that the SCP paradigm holds true in the US deposit market.
Berger (1995) further tested the ESH using an augmented specification. He
proposed the use of cost efficiency as the main independent variable in model of a
firm’s profitability. In addition, he also used market concentration and market share
as independent variables. ESH hold true if all the three independent variables
proved to be statistically significant and have positive impacts on cost efficiency.
However, one major drawback of this approach is that ESH does not imply any
relationship of efficiency on profitability.
Recent studies that dwelled on the debate between ESH and SCP paradigm
and used the model pioneered by Berger (1995) includes Jeon and Miller (2005)
for US and Samuel and Polius (n.d) for the Eastern Caribbean Currency Union
(ECCU). The significance of the time period covered was highlighted when a study
on the same issue was done for Latin American countries. Chortareas, GarzaGarcia, and Girardone (2010) used the Data Envelopment Approach and found
evidence supporting the efficient structure hypothesis in the period of 1997 – 2005
especially in Brazil, Argentina, and Chile. On the other hand, Davila (2005)
conducted a similar study, and used the same method, for Latin America but for the
period 1995 – 2000. The results suggested that the SCP hypothesis did not hold true
but the study did not also provide any clear explanation for the relationship between
structure and competition.
To address the problems that these previous studies committed, Homma
et.al (2012) goes back to the fundamental idea of ESH: efficient firms are the ones
38
who grow but adopted Berger’s (1995) use of cost efficiency in testing ESH in
Japan’s banking sector. However, in order to further refine the test of the ESH, this
study, aside from using cost efficiency only, uses alternative profit efficiency as
another dependent variable.
On the other hand, Berger and Hannan (1998) also tested the QLH for the
US banking system. They predicted that market structure will negatively affect
efficiency because firms in a concentrated market do not minimize costs, hence are
inefficient. They treated bank efficiency as a dependent variable while market
concentration as an independent variable. The results showed that QLH holds true.
Maudos and Fernandez de Guevara (2007) rejected the quite life hypothesis
for the European loan and deposit markets. However, unlike Berger and Hannan
who used concentration as a measure of competition, the study employed Lerner
indices, a measure of percentage profitability of banks as the difference between
average revenues and marginal costs. Lerner indices are known to be time-variant
and bank-specific.
Homma et.al (2012) adapted the test that Berger and Hannan (1998) used
but included several bank-specific variables in order to test ESH and QLH in
Japan’s banking industry. To test the efficient-structure hypothesis, they directly
regress firm growth, measured by loans and/or assets, by the firm’s level of
efficiency and other control variables. In addition, they also tested the quiet life
hypothesis by regressing the firm’s level of efficiency by market concentration,
measured by HHI, and other control variables. The results showed that more
efficient banks tend to grow, which is consistent with the efficient-structure
39
hypothesis. However, the results also proved the quiet life hypothesis; that the level
of market concentration reduces the bank’s cost efficiency. This implies there lies
a growth-efficiency dynamic among Japanese banks.
The regression models employed by Homma et.al (2012) to test the efficient
structure and quiet life hypotheses were adapted by the author to test the same
hypotheses in the Philippine banking sector. However, some alterations were made
in order to fit the model with the Philippine case.
VI.
Banking Efficiency, Structure, and Competition in the Philippines
The General Banking Act of 1948 had strictly protected the banking system
from foreign competition through restricting further entry and limiting the scope of
operations of foreign banks. Because of this, no other foreign bank had been
allowed in the country except for the four who had already existed prior to 1948.
However, the experience of other ASEAN members who have tried to liberalize
their banking systems has ignited the debate on whether the Philippines should
adapt the same policy or not.
During the 1990s, one of the issues that surrounded the Philippine banking
sector is to whether to allow the liberalization of the entry of foreign banks and the
expansion of their scope and operations. Using a theoretical model on foreign bank
growth and entry based on the model of foreign bank organizations in the U.S by
Goldberg and Saunders, Garcia (1992) made an empirical study on the feasibility
of liberalizing the Philippine banking system. Based on the theoretical model,
banking markets that are less regulated and more liberal in the treatment of new
40
and potential competitors are better off than regulated banking markets in terms of
a more efficient financial intermediation process between users and sources of
funds. He used a single equation model and employed a cross-section time-series
analysis using generalized least squares. In 1994, the local banking market was
more regulated and less liberal in terms of foreign bank entry. Garcia (1992)
concluded that there was a need to lift the barriers to entry which discourages the
flow of foreign capital, more innovative financial instruments, and modern banking
technology. The country’s banking sector then had higher opportunity costs than
those countries who had a more competitive environment.
In 1994, Republic Act No. 7721 or An Act Liberalizing the Entry and Scope
of Operations of Foreign Banks in the Philippines was passed. This allowed foreign
banks to operate in the Philippines through (only) one of the following modes of
entry: 1) acquire, purchase or own up to 60 percent of an existing domestic bank;
2) invest in up to 60 percent of the voting stock of a new banking subsidiary
incorporated in the Philippines; or 3) establish a branch with full banking authority.
The last mode had a sunset provision of 5 years and was limited to ten foreign banks
that were allowed to open at most six branches each. As a result, twenty two foreign
banks applied, and the ten chosen banks were announced in February 1995, raising
the number of branches of foreign banks to 14.
Whether the perceived efficiency gains brought by the liberalization of the
banking sector in 1994 were actualized was analyzed by Bondoc (2003) using the
Structure-Conduct-Performance
framework.
She
adapted
Saunders
and
Schumacher’s “dealership” model to measure changes in bank spreads before the
41
enactment of RA 7721 (1987 – 1994) and after (1995 – 2001). Using data aggregate
analysis, cross-sectional analysis and difference of means analysis, she found that
the increase in competition caused by the entry of foreign banks resulted to a
reduction in bank spreads both in the aggregate and cross-sectional level. However,
even if the entry and presence of foreign banks reduce banks spreads, the reductions
were not statistically significant.
Lamberte and Manlagnit (2004) used the Stochastic Frontier Approach to
estimate the cost and profit efficiency of Philippine commercial banks and evaluate
the impacts of competition policy reforms on the efficiency of the commercial
banking system. The results showed that, on average, commercial banks only used
85 percent of their resources efficiently while 39 percent of their total costs are
wasted, both relative to the best-practice commercial banks. Cost and profit
efficiency improved after the liberalization of the banking industry but these
improvements were halted due to the 1997 Asian Financial Crisis. However, cost
and profit efficiency once gain improved after the passage of the General Banking
Law of 2000 which further liberalized the entry of foreign banks and implemented
key policies such as the encouragement of mergers and acquisitions. Moreover,
they found that small banks are more cost and profit efficient than big banks. In
addition, foreign banks tend to be more efficient than domestic bank in general and
the gap widens during crisis period and narrows during stable economic conditions.
Furthermore, Milo and Pasadilla (2005) studied the impact of the 1994
liberalization on bank competitiveness. Using the Panzar-Rosse H-statistic, they
found that the Philippine banking system remained under monopolistic competition
42
and characterized by the presence of few large expanded commercial and universal
banks and a lot of very small rural banks. The HHI also indicated the there was no
undue banking concentration. Generally, the results showed that the entry of foreign
and domestic banks have increased banking competition. Specifically, the
competition was toughest in the loans business.
As cited by Lamberte and Manlagnit (2004), some of the other researches
that study the impact of the 1994 liberalization on the Philippine banking industry
include Unite and Sullivan (2001), Montinola and Moreno (2001), Milo (2001),
and Hapitan (2001). Unite and Sullivan (2001) investigated how the relaxation of
foreign entry regulation affects competition among domestic banks using
qualitative analysis and random effects model and concluded that foreign bank
entry motivates local banks to be more efficient. Milo (2001) conducted a similar
study and employed the same methodology but also took into account deregulation
in branching. She concluded that the deregulation of bank entry and branching
positively affected financial intermediation and positively contributed to the
efficiency of local commercial banks. In addition, Hapitan (2001) used survey to
examine the reactions of ten local commercial banks to the entry of foreign banks.
His survey results concluded that there was an increase in competition but there
was no clear evidence to the perceived increase in the variety of financial services
brought by new technologies and processes of foreign banks that have entered the
market.
On the other hand, Montinola and Moreno (2001) employed a more
sophisticated methodology; the Data Envelopment Analysis (DEA), to study how
43
the changes in political and economic factors influence the timing and scope of
financial liberalization by affecting a political equilibrium of competing factors.
They concluded that the declines in banking efficiency reduced resistance to foreign
bank entry but the effects if liberalization on efficiency were modest.
In terms of mergers and/or acquisitions, Huang (2002) analyzed the
efficiency gains, if there were any, of mergers and acquisitions in the banking
industry. He analyzed three concepts of efficiency – cost, standard profit, and
alternative profit – using the Distribution-Free Approach (DFA). He compare the
pre- and post-merger efficiency ranking of financial institutions to see whether
efficiency gains were realized or not. He concluded that the perceived gains from
mergers and acquisitions were realized in the Philippines. Profit efficiency, both
standard and alternative, was positively affected by mergers. However, there was
an unclear effect on cost efficiency as his findings showed that mergers do not
necessarily increase cost efficiency.
Saliently, the studies on the Philippine banking industry regarding structure
and competition have focused on the effects of the RA 7721 of 1994, the law that
liberalized the sector to foreign entry. In addition, there were also some who have
attempted to measure banking efficiency using different methodologies. This study
will contribute to the literature in three ways: a) measure banking efficiency using
a different methodology, the Distribution-Free Approach, b) estimate the effect, if
any, of banking efficiency on the growth of loans and deposits, and c) estimate the
effect, if any, of market concentration, measured by HHI, on banking efficiency.
44
This study will contribute both on the evaluation of the competition policies
of BSP and on the methodological aspect of measuring efficiency. This study will
employ a scientific and economic method of measuring banking efficiency; the
distribution-free approach. Furthermore, instead of just looking on whether either
the market structure or efficiency structure hypotheses prevails in the Philippine
case, this study will look on both to see whether the two co-exists in the Philippines,
either of the two or neither can explain the behavior of the Philippine universal and
commercial banking industry.
45
CHAPTER III
THEORETICAL FRAMEWORK and EMPIRICAL METHODOLOGY
I.
Theoretical Framework
Frontier Analysis
The frontier analysis approach in estimating efficiency involves the
estimation of an efficient frontier and measurement of the differences between the
sample banks and the ‘best-performing’ bank. In order to test the quiet life and
efficient structure hypothesis, the researcher will employ the frontier analysis to
measure bank efficiency, a variable that the two hypotheses attempts to explain.
Berger and Mester (1997) noted that this type of analysis entails making a lot
considerable choices such as choice of which efficiency concepts to use, the
estimation technique, the specification of the functional form, and the selection of
the variables. They used this approach to determine the differences in the
efficiencies of financial institutions.
Frontier analysis entails the construction of a cost and/or profit efficiency
frontier. The cost and profit function will then be applied to all the bank samples.
The bank which will turn out to be the closest in the frontier will then be used as a
benchmark for all the other banks. The efficiency of all the other banks will be
relative to this ‘best-performing’ bank.
One of the first questions that need to be answered in using a frontier
analysis is on which efficiency concept to use. Since this is an economic study, cost
46
and profit efficiency will be utilized. This is grounded on economic theory that
firms are naturally cost minimizing and profit maximizing.
Cost efficiency, in a frontier analysis, is a measure of how close a bank is
to the ‘best-performing’ bank in terms of producing the same set of products under
the same set of circumstances. A cost function should be specified wherein variable
costs are dependent on a set of vector variables such as the prices if variable inputs,
the quantities if variable outputs, some fixed inputs or outputs, macroeconomics
variables, random error and inefficiency. It can be represented as:
𝐶 = 𝐶(𝑤, 𝑦, 𝑧, 𝑣, 𝑢𝑐 , 𝜀𝑐 ),
(3.1)
where C represents variable costs, w represents the vector of prices of variable
inputs, y represents the vector of quantities of variable outputs, z represents the
quantities of the fixed netputs, v represents the vector of common variables (such
as macroeconomic indicators), uc is the inefficiency term and εc is the random error.
uc and εc are assumed to be multiplicatively separable from the rest of the cost
function so that when expressed in natural logarithm, the cost function will be:
ln 𝐶 = 𝑓(𝑤, 𝑦, 𝑧, 𝑣) + 𝑙𝑛 𝑢𝑐 + 𝑙𝑛 𝜀𝑐
(3.2)
where f signifies functional form and ln means natural logarithm. Based on this
estimation, a ‘best-performing’ bank with the lowest cost will be identified and
efficiency is calculated in relation to the ‘best-performing’ bank. The cost
efficiency of bank b is defined as the estimated cost needed to produce to bank b’s
output as efficient as the ‘best-performing’ bank. The cost efficiency is estimated
as:
47
𝐶𝑜𝑠𝑡 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
[exp 𝑓̂ (𝑤 𝑏 ,𝑦 𝑏 ,𝑧 𝑏 ,𝑣 𝑏 )][exp ln 𝑢
̂𝑚𝑖𝑛 ]
[exp 𝑓̂ (𝑤 𝑏 ,𝑦 𝑏 ,𝑧 𝑏 ,𝑣 𝑏 )][exp ln 𝑢
̂𝑏 ]
=
̂𝑐𝑚𝑖𝑛
𝑢
̂𝑐𝑏
𝑢
, (3.3)
where 𝑢𝑐𝑚𝑖𝑛 is the minimum 𝑢𝑐𝑏 of all the banks in the sample and b denotes the
bank for which the efficiency is calculated. The cost efficiency ratio can be thought
of as the proportion of costs that are used efficiently. For example, a ratio of 0.8
implies that 80% of the banks costs are used efficiently and 20% are put to waste
relative to the ‘best-performing’ bank.
To measure profit efficiency, the alternative profit efficiency function is
adopted. The alternative profit function relates profit to input prices indicating that
the output is held constant. Alternative profit efficiency is used because some of
the implicit assumptions of the standard profit efficiency measure do not hold.
Berger and Mester (1997) advised that the alternative profit efficiency should be
used when: 1) there are substantial unmeasured differences in the quality of banking
services; 2) output are not completely variable so that a bank cannot achieve every
output scale and mix; 3) output markets are not perfectly competitive so that banks
have some market power over the prices they charge; and 4) output prices are not
accurately measured so that they do not provide accurate guides to opportunities to
earn revenues and profits in the standard profit function. Lamberte and Manlagnit
(2004) believe that these conditions exist in the Philippines. Therefore, the
alternative profit function is used in this study.
Efficiency in terms of alternative profit is measured given output levels
rather than output prices. Instead of estimating deviations from optimal output as
inefficiency, variable output is held constant while output prices are free to vary
48
and affect profits. The dependent variable of the alternative profit function is the
same as that of the standard profit function while the independent variables are the
same as those present in the cost function. It can be represented as:
𝜋 + 𝜃 = 𝐶(𝑤, 𝑦, 𝑧, 𝑣, 𝑢𝑐 , 𝜀𝑐 ),
(3.4)
In natural logarithm, the alternative profit function will be:
ln (𝜋 + 𝜃) = 𝑓(𝑤, 𝑦, 𝑧, 𝑣) + 𝑙𝑛 𝑢𝜋 + 𝑙𝑛 𝜀𝜋
(3.5)
where 𝜋 denotes variable profit of the bank, 𝜃 is the constant added to every firm’s
profit to make the natural log is always a positive number, 𝑦 is the vector of prices
of variable outputs, 𝑙𝑛 𝑢𝜋 is the inefficiency and 𝑙𝑛 𝜀𝜋 is the random error.
The alternative profit function is the ratio of the predicted actual profits to
the predicted maximum profits of the ‘best-performing’ bank. It can be represented
as:
[exp 𝑓̂ (𝑤 𝑏 ,𝑦 𝑏 ,𝑧 𝑏 ,𝑣 𝑏 )][exp ln 𝑢
̂𝑏 ]− 𝜃
Alt π 𝐸𝐹𝐹 𝑏 = [exp 𝑓̂ (𝑤𝑏,𝑦 𝑏,𝑧 𝑏,𝑣 𝑏)][exp ln 𝑢̂𝑚𝑎𝑥 ]− 𝜃 =
𝑎𝜋 𝑏
𝑎𝜋 𝑚𝑎𝑥
,
(3.6)
Similar to the cost efficiency measure, the value of Alt π 𝐸𝐹𝐹 𝑏 is the
proportion of maximum profit that is actually earned. A value of 0.8 means that the
bank is 80 percent profit efficient, relative to the ‘best-performing’ bank.
As mentioned in the literature, the translog specification is used in order to:
1) accommodate multiple outputs without necessarily violating curvature
conditions 2) be flexible, in the sense that it provides a second-order approximation
to any well-behaved underlying cost frontier and 3) have a good record of empirical
49
estimation for the decomposition of cost efficiency. The functional form of a
translog function is:
𝑙𝑛𝑞1 = 𝛽0 + 𝛽1 𝑙𝑛𝑥1𝑖 + 𝛽2 𝑙𝑛𝑥2𝑖 + 0.5 𝛽11 (𝑙𝑛𝑥1𝑖 )2 + 0.5 𝛽22 (𝑙𝑛𝑥2𝑖 )2 +
𝛽12𝑙𝑛𝑥1𝑖 𝑙𝑛𝑥2𝑖 + 𝑣𝑖 − 𝑢𝑖
(3.7)
where 𝑙𝑛𝑞1 is the dependent variable while 𝑙𝑛𝑥1𝑖 and 𝑙𝑛𝑥2𝑖 are two input independent
variables.
The Market Structure-Efficiency Hypotheses
As stated in the literature, the Structure-Conduct-Performance hypothesis
assumes that higher concentration increases the possibility that banks will collude.
Thus, this will result to higher-than-normal profits for all the banks in the market.
The traditional model of SCP specifies a bank’s profit function as:
𝜋𝑖 = 𝛼0 + 𝛼1 𝐶𝑅𝑛 + ∑𝑘𝑗=2 𝛼𝑗 𝑍𝑖,𝑗 + 𝜀𝑖
(3.8)
where Z is a set of {k} control variables for the {i} banks, CRn is the n-bank
concentration ratio (which, based on the literature, can be measured using the HHI)
and πi is the profit measured by Return on Asset (ROA) or Return on Equity (ROE).
The SCP hypothesis prevails when αi is positive and is statistically significant.
A special case of the SCP paradigm is the quiet life hypothesis which was
introduced by Hicks (1935). According to Hicks (1935), firms use their market
power to allow for inefficient allocation of resources rather than maximizing their
profits instead of using monopolistic power to gain rents because the cost of
reaching optimal profit will outweigh the marginal gains.
50
Similar to the SCP hypothesis, the quiet life hypothesis assumes the inverse
relationship between market concentration and efficiency. This hypothesis
postulates that the higher the market power is, the lower the effort of the managers
will be to maximize efficiency; hence, a negative correlation exists between market
power and managerial efficiency.
Berger and Hannan (1998) argued that there are four ways in which market
structure influence cost efficiency in the banking industry. First, if high levels of
market concentration allow firms to charge prices in excess of competitive levels,
then managers may take part of the benefits of the higher prices not as higher profits
but in the form of a “quiet life”, a condition where they do not work hard to control
costs. Second, market power may allow managers to pursue objectives other than
firm profits such as the growth of the firm, of the staff, or the reduction of labor
conflict by means of higher wages, at the expense of efficiency. Third, managers
may expend resources to obtain and maintain market power on expenditure that
would reduce cost efficiency. Fourth, the higher price provided by market power
may allow inefficient managers to persist without any wilful shirking of work
effect, pursuit of other goals, or efforts to defend or obtain market power. They
were the first to test QLH with the model:
𝐸𝐹𝐹𝑖 = 𝑓(𝐶𝑂𝑁𝐶, 𝑋𝑖 ) + 𝜀𝐼
(3.9)
where 𝐸𝐹𝐹𝑖 is a measure of a firm’s efficiency, CONC is a measure of
concentration, Xi represents a vector of other characteristics such as degree of
financial leverage, the non-deposit borrowings over total assets, the bank size and
a time trend which influence bank efficiency, and 𝜀𝐼 is a random error term. A
51
significant negative relationship between efficiency and concentration will be
consistent with QLH. The same model has been adopted by several researchers
particularly Al-Jarrah and Gharaibeh (2009) for Jordan’s banking industry.
The assumptions made by the market power hypotheses, that market
concentration lead to inefficiency, has been challenged by Demsetz (1973). The
main argument has been on the assumption that market concentration is not a
random event but an endogenous variable. Demsetz argued that concentration,
instead of causing bank inefficiency, is actually the result of bank efficiency. This
assumption makes any relationship between concentration and profitability, which
the SCP paradigm assumes to have positive relationship, spurious. In other words,
Demsetz argued that efficient firms grow and the increase in market concentration
is a product of the growth of the most efficient firms. This has been referred to as
the efficient structure hypothesis.
Berger (1995) attempted to shed light on the debate by empirical analysis
using the standard model:
𝑅𝑂𝐴𝑖𝑡 = 𝛼𝑖 + 𝛽1 𝐻𝐻𝐼𝑡 + 𝛽2 𝑀𝑆𝑖𝑡 + 𝛽3 𝐸𝑆𝑋𝑖𝑡 + 𝛽4 𝐸𝑆𝑆𝑖𝑡 + ∑3𝑗=1 𝜂𝑗 𝑋𝑗,𝑖𝑡 +
∑4𝑛=1 𝛿𝑛 𝑍𝑛,𝑡 + 𝜀𝑖𝑡
(3.10)
HHI is used as a measure of market concentration while MS is the market share of
bank i of assets at time t. ESX is a measure of managerial cost efficiency which
means that firms with superior management incur lower costs. ESS is a measure of
scale efficiency which means that firms with superior management produce at
larger scales. Based on the SCP argument, a positive impact of concentration on
profitability would mean that firms collude. A positive and significant market share
52
supports the Relative Market Power hypothesis which means that banks with
relatively high market share set prices without facing the usual market constraints.
If either or both the ESX and ESS is/are positive and significant, then the efficient
structure theory prevails.
However, the studies that tests the ESH using this model have not directly
used efficiency as a variable that affect bank growth. The problem with this test is
that there is no clear delineation on whether the test really proved the hypotheses
as both hypotheses actually predicts positive relationship between profit and market
concentration but with different mechanisms. Most importantly, Demsetz (1973)
did not assume any relationship between profitability and concentration.
II.
Empirical Methodology
The methodology of this paper is divided into three parts; one for each
objective. The first part deals with measuring the level of efficiency for universal
and commercial banks in the Philippine banking sector. The second part is the test
of the Efficient Structure Hypothesis. The last part is the test of the Quiet Life
Hypothesis.
Objective 1: To estimate bank efficiency, particularly cost and alternative profit
efficiency, using frontier analysis, specifically the Distribution-Free Approach
As what was mentioned earlier, there are a variety of methods that are used
in measuring efficiency: the parametric and non-parametric approaches. Huang
(2002) found non-parametric approaches problematic because they ignore the
53
prices and focus on technological optimization, hence, not economic in nature.
Parametric approaches are the ones considered for this paper: 1) the stochastic
frontier analysis (SFA); 2) the Thick Frontier Analysis (TFA) and 3) the
Distribution-Free Approach (DFA).
Huang (2012) noted SFA and TFA tend to be problematic because of the
assumptions they make regarding the distribution of the error terms. SFA claims
that the errors are normally distributed and inefficiency terms are half-normally
distributed. However, there are little theoretical justifications that would support
such assumptions. On the other hand, DFA assumes that the random error terms
cancel each other out in the long run, hence, the average of the bank’s residuals
from the cost and profit function regressions will be an estimate of the inefficiency
term. For this study, the Distribution – Free Approach will be used as it is more
economic in nature as compared to other available methodologies.
However, one weakness associated with the parametric approaches is that
estimates of efficiency could be biased due to misspecification of the functions
used. To address this, Matthews and Thompson (2008) argued that researchers have
used the translogarithmic specification. Using a translog specification has three
advantages: 1) it accommodates multiple outputs without necessarily violating
curvature conditions 2) it is flexible, in the sense that it provides a second-order
approximation to any well-behaved underlying cost frontier and 3) it has a good
record of empirical estimation for the decomposition of cost efficiency.
The next task is to select the variables to be used for the actual estimation.
This paper follows the same variables used by Huang (2002) in studying the
54
efficiency of banking firms which underwent mergers. Revenues (REVS) is defined
as total interest and fee receipts divided by total assets. Total variable costs (COST)
is defined as the sum of interest costs and fees over total assets. Profits (PROF) is
measured by return on assets (ROA). All the three variables were divided by total
assets to normalize differences in size and to trend nominal growth.
As what was mentioned in the review of literature, unlike other firms, it is
difficult to measure bank output. For this study, total earning assets; which include
loans and advances, holdings of securities and bills purchased and discounted, cash
balances with other banks, as well as other investments; will be used to measure
bank output (OUTP). This variable is also divided by total assets to achieve the
same objective. The price of funds (INTC) is defined as total interest expenses and
fees divided by payable liabilities such as deposits and amounts due to other banks,
which comprise the inputs of banks. Other non-interest operating costs (OTHC) is
defined as wage costs, rent, and depreciation, all divided by earning liabilities.
However, due to changes in the structure of the banking system, the author has
made minor revisions with the bank-specific data in order to more accurately
estimate the variables for the cost and profit function.
In addition, it was stated in the scope and limitations of this study that the
focus of this paper is core banking activities. However, there were some variables
where it will be difficult to separate the core banking activities from the other
activities of the banks. For example, in terms of profit, the total net income as stated
in the banks’ income statement was used. It will be difficult to dissect the net
55
income into different component. Therefore, this might be overstated because it
includes the income from all banking activities.
Table 3.1 Variables in Measuring Efficiency
Symbol
Variable
COST
Costs; Total Interest Expense divided by Total Assets
REVS
Revenue; Net Interest Income, Gross Interest and Dividend
Income, and Total Non-interest Operating Income, all divided
by Total Assets
PROF
Profit; Net Income divided by Total Assets
OUTP
Bank Output; Total Earning Assets divided by Total Assets
INTC
Price of Funds; Total Interest Expense divided by Payable
Liabilities
OTHC
Other Non-interest Operating Costs; Total Non-interest
Expense divided by Interest-bearing Liabilities
Source: Huang, 2002
Given the variables stated, the translog model for the cost and alternative
profit functions are:
COST = 𝛽0 + 𝛽1 𝑙𝑛(𝑂𝑈𝑇𝑃) +
1
1
2
𝛽2 𝑙𝑛(𝑂𝑈𝑇𝑃)2 + 𝛽3 𝑙𝑛(𝐼𝑁𝑇𝐶) +
1
+ 𝛽5 𝑙𝑛(𝑂𝑇𝐻𝐶) + 2 𝛽6 𝑙𝑛(𝑂𝑇𝐻𝐶)2 +
𝛽7 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛 (𝐼𝑁𝑇𝐶) + 𝛽8 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛(𝑂𝑇𝐻𝐶) +
𝛽9 𝑙𝑛(𝐼𝑁𝑇𝐶) 𝑙𝑛(𝑂𝑇𝐻𝐶)
𝛽 𝑙𝑛(𝐼𝑁𝑇𝐶)
2 4
2
Alt PROF = 𝛽0 + 𝛽1 𝑙𝑛(𝑂𝑈𝑇𝑃) +
1
2
𝛽2 𝑙𝑛(𝑂𝑈𝑇𝑃)2 + 𝛽3 𝑙𝑛(𝐼𝑁𝑇𝐶) +
1
+ 𝛽5 𝑙𝑛(𝑂𝑇𝐻𝐶) + 2 𝛽6 𝑙𝑛(𝑂𝑇𝐻𝐶)2 +
𝛽7 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛 (𝐼𝑁𝑇𝐶) + 𝛽8 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛(𝑂𝑇𝐻𝐶) +
𝛽9 𝑙𝑛(𝐼𝑁𝑇𝐶) 𝑙𝑛(𝑂𝑇𝐻𝐶)
𝛽 𝑙𝑛(𝐼𝑁𝑇𝐶)
2 4
2
1
(3.11)
(3.12)
To reiterate, the measure of output and interest costs will only focus on core
banking activities. However, the measure of profit will cover all banking activities
56
as the variable used to measure profit is the standard Return on Asset (ROA)
measure which is net income over total assets.
More specifically, only the universal and commercial banks which have
data in BankScope, a global database of banks’ financial statements, ratings and
intelligence, were considered in this study. Specifically, from 1992 to 2013, the
minimum number of banks with complete data was 13, which was on the year 1992,
and the maximum was 25, recorded in 2006.
A cross-section linear regression will be employed to generate the
efficiency level of each bank for each year. The error terms or the residuals that
will be derived from the individual regressions for each time period shall be used.
The values shall be transformed and make it an exponent of e, the value that will
be obtained is the core inefficiency of the firm for the year specified. To generate
the actual efficiency level, the following equations will be followed:
𝐶𝑜𝑠𝑡 𝐸𝐹𝐹 =
̂𝑐𝑚𝑖𝑛
𝑢
̂𝑐𝑏
𝑢
𝑎𝜋 𝑏
Alt π 𝐸𝐹𝐹 𝑏 = 𝑎𝜋𝑚𝑎𝑥
(3.13)
(3.14)
For cost efficiency, the ratio of the minimum value for the specific year to the value
of bank b will be computed. On the other hand, for alternative profit efficiency, the
ratio of the value of bank b to the maximum value for the specific year will be
generated. Since a frontier analysis was adopted, the ratios that will be generated
will be the cost efficiency and profit efficiency measures.
57
From these translog equations, the cost and alternative efficiency measures
will be generated. The generated cost and profit efficiencies will then be used for
the next part of this study: the empirical test of the efficient structure theory and
quiet life hypothesis. Therefore, the results of the second and the third objectives
are conditional with the results of the first objective.
For the second and third objectives, to test the efficient structure and quiet
life hypotheses, the researcher draws the empirical models from Homma et.al
(2012) who studied firm efficiency, growth, and market concentration of Japan’s
banking industry.
The next two objectives have used the same set of data. For the sake of
consistency, all bank-specific data were also gathered from BankScope while all
macroeconomic variables were sourced from the website of BSP. Furthermore, the
coverage of the study will be the period of 1992 – 2013 due to the limited
availability of data in BankScope.
Objective 2: To test the Efficient Structure Hypothesis; test and quantify, the effect
of bank efficiency on the growth of banks
The test of the ESH directly investigates the effect of firm efficiency on firm
growth:
𝐺𝑅𝑂𝑊𝑇𝐻𝑖,𝑡 = 𝛾𝑖 + 𝛾1 𝐸𝐹𝑖,𝑡−1 + 𝛾2 𝑋𝑖,𝑡 + 𝜀𝑖,𝑡 ,
(3.15)
58
where i and t are indices for firm and time. The dependent variable is firm growth.
Growth of loans and/or assets is/are to be used as proxy for firm growth. The term
𝐸𝐹𝑖,𝑡−1 is the measure of firm efficiency which is one year lagged due to the
assumption that the effect of efficiency is realized after a year. The variable 𝑋𝑖,𝑡
represents a vector of control variables such as macroeconomic conditions and/or
firm heterogeneity. Specifically, two alternative specifications are used:
𝐿
ln 𝐿𝑖,𝑡 = 𝛾0 + 𝛾1 𝐸𝐹𝑖,𝑡−1 + 𝛾2 𝑙𝑛𝐺𝐷𝑃1 + 𝛾3 𝐼𝑁𝐹𝐿𝑡 + 𝜛𝑖,𝑡
,
(3.16.1)
Or
𝐿
Δln 𝐿𝑖,𝑡 = 𝛾0 + 𝛾1 𝐸𝐹𝑖,𝑡−1 + 𝛾2 Δ𝑙𝑛𝐺𝐷𝑃1 + 𝛾3 Δ𝐼𝑁𝐹𝐿𝑡 + 𝜛𝑖,𝑡
(3.16.2)
The first equation focuses on the effect of 𝐸𝐹𝑖,𝑡−1 on the level 𝐿𝑖,𝑡 while the second
equation is a difference equation that focuses on the change in 𝐿𝑖,𝑡 from the previous
period. The variable 𝐿𝑖,𝑡 is the amount of loans (another specification is run but with
assets as the dependent variable). Loans is one of the key products of a bank. As
discussed earlier, the focus of this regression is 𝐸𝐹𝑖,𝑡−1 which is measured in the
first part of the methodology. If 𝛾1 𝐸𝐹𝑖,𝑡−1will be positive, then the ESH holds true.
Noticeably, there are quite a number of explanatory variables in the model which
serves as control variables for asset or loan supply and demand. 𝐺𝐷𝑃1 , real GDP,
serves as a measure of demand. Inflation,𝐼𝑁𝐹𝐿𝑡 , can be both a demand and a supply
variable. Table 3.2 summarizes the variables used to test the efficient structure
theory.
59
Table 3.2 Variables for the Test of the Efficient Structure Hypothesis
Symbol
L
Loans
Expected Sign
NA; dependent variable
A
Assets
NA; dependent variable
EF
Bank Efficiency
(measured in the first objective)
Gross Domestic Product
(real GDP)
Inflation rate
GDP
INFL
Variable
+
+
-
Source: Homma et.al, 2012
To account for the cross-section and time-series data, panel regression
analysis will be employed to test the efficient structure theory. However, because
of the existence of mergers and/or acquisitions and data unavailability, the data will
be unbalanced. Thus, an unbalanced panel regression analysis shall be used instead.
In addition, two specifications will be employed: the fixed effects model
and the random effects model. The fixed effects model assumes that the intercept
of the model does not vary over time, that is, time-invariant. On the other hand, the
random effects model assumes that the intercept is a random variable. The results
of these two models will generate substantial differences between the two. The
decision on whether which of the two will be used shall depend on the assumption
made about the correlation between the intercept and the regressors. If the intercept
and regressors are uncorrelated, the random effects model is appropriate but if they
are correlated, the fixed effects model shall be used. To answer the question, the
Hausman test shall be employed.2
2
Gujarati (2011) explains that the null hypothesis underlying the Hausman test is that the
fixed effects model and the random effects model do not differ substantially. The Hausman
60
Objective 3: To test the Quiet Life Hypothesis; test and quantify, if any, the effect
of market concentration on bank efficiency
For the purposes of this study, the Herfindahl – Hirschman Index (HHI) will
be used as the measure of market concentration. HHI is the sum of the squared
market shares of firms in the market. This method is applicable for all industries
and is not exclusive for the banking industry. The HHI can be a value from 0 – 1.
A value of 1 means the industry is a monopoly or there is only one player. A value
of 0 indicates perfect competition or the existence of infinite players. In the US,
merger that would increase the HHI by 0.1 would alarm the regulators.
The formula for HHI is given by:
𝐻𝐻𝐼 = ∑𝑛𝑖=1 𝑠𝑖2
(3.17)
where s is the market share of the ith bank. The value of the HHI for each year will
then be used as the market concentration proxy to test the QLH. Since this study
only considers the universal and commercial banking industry, only universal and
commercial banks will be used to find the HHI.
On the other hand, to test the QLH, we estimate efficiency as:
𝐸𝐹𝑖.𝑡 = 𝛽𝑖 + 𝛽1 𝐶𝑂𝑁𝐶𝑡−1 + 𝛽2 𝑍𝑖,𝑡 + 𝜛𝑖,𝑡 ,
(3.18)
test statistic has an asymptotic X2 distribution with df equal to number of regressors in the
model. If the computed chi-square value exceeds the critical chi-square value for given df
and the level of significance, the random effects model cannot be used because it means
that the random error terms are correlated with one or more of the regressors. If this is the
case, then the fixed effects model should be used.
61
The dependent variable is the measure of efficiency, 𝐸𝐹𝑖.𝑡 . The main independent
variable is the measure of market concentration,𝐶𝑂𝑁𝐶𝑡−1, which, in this study will
be measured using the Herfindahl-Hirschman Index (HHI). According to the QLH,
higher concentration will cause inefficiency, hence, a negative relationship between
𝐸𝐹𝑖.𝑡 and 𝛽1 𝐶𝑂𝑁𝐶𝑡−1 will prove the QLH. Specifically, the following two
specifications will be used:
𝐸𝐹𝑖,𝑡 = 𝛽1 𝐻𝐻𝐼𝑡−1 + 𝛽2 𝐷𝑠𝑚𝑙𝑏𝑎𝑛𝑘 + 𝛽3 𝐷𝑚𝑒𝑑𝑏𝑎𝑛𝑘 + 𝛽4 𝐷𝑙𝑎𝑟𝑏𝑎𝑛𝑘 + 𝛽5 𝐷𝑚𝑒𝑟𝑔𝑒𝑟 +
𝑈
𝛽6 𝐷𝑓𝑜𝑟𝑒𝑖𝑔𝑛 + 𝛽7 𝐴𝐺𝐸𝑖 + 𝛽8 𝐿𝐴𝑖,𝑡 + 𝛽9 𝐷𝐴𝑖,𝑡 + 𝜛𝑖,𝑡
(3.19)
or
∆𝐸𝐹𝑖,𝑡 = 𝛽1 𝐻𝐻𝐼𝑡−1 + 𝛽2 𝐷𝑠𝑚𝑙𝑏𝑎𝑛𝑘 + 𝛽3 𝐷𝑚𝑒𝑑𝑏𝑎𝑛𝑘 + 𝛽4 𝐷𝑙𝑎𝑟𝑏𝑎𝑛𝑘 + 𝛽5 𝐷𝑚𝑒𝑟𝑔𝑒𝑟 +
𝑈
𝛽6 𝐷𝑓𝑜𝑟𝑒𝑖𝑔𝑛 + 𝛽7 𝐴𝐺𝐸𝑖 + ∆𝛽8 𝐿𝐴𝑖,𝑡 + ∆𝛽9 𝐷𝐴𝑖,𝑡 + 𝜛𝑖,𝑡
(3.20)
The first equation focuses on the effect of 𝐻𝐻𝐼𝑡−1 on the level of efficiency
while the second equation is a difference equation that focuses on the change in the
efficiency level. The focus of this regression is the market concentration variable,
𝐻𝐻𝐼𝑡−1. A negative relationship between 𝛽1 𝐻𝐻𝐼𝑡−1 and 𝐸𝐹𝑖,𝑡 will be consistent with
the QLH. Meanwhile, several bank-specific control variables are used. Dummy
variables for bank size, 𝐷𝑠𝑚𝑙𝑏𝑎𝑛𝑘 , 𝐷𝑚𝑒𝑑𝑏𝑎𝑛𝑘 , and 𝐷𝑙𝑎𝑟𝑏𝑎𝑛𝑘 , are used in order to
account for the differences in bank sizes among the sample. A dummy variable for
a merger, 𝐷𝑚𝑒𝑟𝑔𝑒𝑟 , is also accounted if the bank experienced a merger before the
relevant year. A dummy variable is also noted if a bank is a foreign bank, 𝐷𝑓𝑜𝑟𝑒𝑖𝑔𝑛 .
The bank’s age is also considered, 𝐴𝐺𝐸𝑖 .
In addition, accounting ratios are also used in the analysis in order to take
into account the difference in efficiency levels based on the dependence on the
62
traditional deposit-to-loan models. The first one is the total loans to total assets
ratio, 𝐿𝐴𝑖,𝑡 , and the second one is the total deposits to total assets ratio, 𝐷𝐴𝑖,𝑡 . To
control for bank risk, the standard deviation of ROA over the sample period,
𝑆𝐷𝑅𝑂𝐴𝑖 , is accounted. Table 3.3 summarizes the variables used to test the quiet
life hypothesis.
Table 3.3 Variables for the test of the Quiet Life Hypothesis
Symbol
EF
AGE
Variable
Bank Efficiency
(measured in the first objective)
Herfindahl – Hirschman Index
(measure of market concentration)
Dummy for bank size
(either small, medium or large
bank)
Dummy for merger
(1 if the bank has experienced a
merger)
Dummy for foreign bank
(1 if the bank is a foreign bank)
Age of the bank
LA
Loan-Asset Ratio
+
DA
Deposit-Asset Ratio
+
HHI
𝐷𝑏𝑎𝑛𝑘𝑠𝑖𝑧𝑒
𝐷𝑚𝑒𝑟𝑔𝑒𝑟
𝐷𝑓𝑜𝑟𝑒𝑖𝑔𝑛
Expected Sign
NA; dependent
variable
+
+
+
-
Source: Homma et.al, 2012
Similar with the test if the ESH, to account for the cross-section and timeseries data, panel regression analysis will be employed to test the QLH. However,
because of the existence of mergers and/or acquisitions, the data will be
unbalanced. Thus, an unbalanced panel regression analysis shall also be used
instead. In addition, the Hausman test will also be employed in order to identify
whether the fixed effects model or the random effects model shall be used.
63
Data Source and Software Used
For the sake of consistency, all bank-specific data were gathered from
BankScope while all macroeconomic variable were sourced from the website of the
Bangko Sentral ng Pilipinas. BankScope is a global database of banks’ financial
statements, ratings and intelligence. All the bank-specific variables are measured
in billion pesos. Moreover, only the universal and commercial banks that are
present in the Philippines from 2005 – 2008 are considered based on the website of
the Bangko Sentral ng Pilipinas (BSP). There is no official list of all of the universal
and commercial banks that exist in the Philippines prior to 2005. Furthermore, the
coverage of the study will be the period of 1992 – 2013 due to the limited
availability of data in BankScope.
A total of 28 universal and commercial banks were used in this study.
However, as discussed earlier, from 1992 to 2013, the minimum number of banks
with complete data was 13, which was on the year 1992, and the maximum was 25,
recorded in 2006. In total, there should be 616 observations in the study. However,
due to data constraints and inevitable circumstance such as mergers and
acquisitions, only 448 observations were used.
The statistical software used to generate the regression results for this study
is EViews7.
64
CHAPTER IV
EMPIRICAL RESULTS and DISCUSSION
The discussion of the results of this study will be divided into four parts: 1)
the descriptive statistics of the bank-specific variables used in measuring cost and
profit efficiency; 2) the efficiency results based on the methodology employed; 3)
the effect of bank efficiency on the growth of loans and assets; and 4) the effect of
market concentration on the level of bank efficiency. The first and second part of
the discussion answers the first objective. The third part deals with the second
objective while the fourth part seeks to shed light on the third objective.
Descriptive Statistics
Table 4.1 summarizes the standard deviation, average, minimum, and
maximum values of the mean of the bank-specific variables for each year that were
computed as variables for the cost and profit translogarithmic functions.
Table 4.1 Summary of Bank-specific Statistics in Relation to Total Assets
STDEV
MIN
MAX
AVERAGE
2.17
8.59
16.60
12.18
REVENUE
1.47
1.05
6.32
3.71
COST
2.46
(8.59)
5.65
8.46
PROFIT
3.51
80.77
92.38
85.91
OUTPUT
4.52
1.30
23.67
6.05
INTEREST COSTS
OTHER NON7.82
7.83
41.52
6.32
INTEREST
OPERATING COSTS
Source: Author’s computations; Data Source: BankScope
65
The values of the bank-specific variables above are percentages in relation
to total assets. Based on the results of the standard deviation, the cost measure had
the least variation among all the variables used for Objective 1 for the period of
1992 – 2013. On the other hand, other non-interest operating costs has the highest
variation. In terms of profit, the minimum value was at -8.59 percent of total assets
which was recorded in 2008. This can be explained by the 2008 Global Financial
Crisis which started when some of the biggest banks in the U.S failed. In terms of
output, the maximum value was achieved in 2013 which means that the, in general,
banks total earning assets peaked in the most recent year used in this study. The
following sections will further explain each variable.
Figures 4.1 and 4.2 show the average of the ratio of average revenue, costs,
and profits to total assets from 1992 – 2013.
Figure 4.1 Revenue and Cost to Total Assets Ratio
18.00
16.00
14.00
12.00
10.00
8.00
6.00
4.00
2.00
0.00
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
REVENUE
TOTAL VARIABLE COST
Source: Author’s computations; Data Source: BankScope
66
By looking at the graph, it is apparent that the revenues and costs of
Philippine universal and commercial banks have been following a decreasing trend.
However, one should take note that the revenues and costs considered in this study
were only the short-term undertakings of the banks and were only the core banking
activities. Long-term investments and other activities such as trading of
government securities were not considered in this study. Both revenues and costs
rose in 1996, peaked in 1998, and tapered off after. The decline in both revenues
and costs after 1998 can be explained by the macroeconomic conditions at that time.
It was after 1998 when the bulk of the effect of the Asian Financial Crisis exacted
its toll on all sectors of the economy including the universal and commercial banks.
Because of the situation, it is obvious why total costs of banks increased. On the
revenue side, it can be concluded that, in general, the behavior of the universal and
commercial banks was geared towards maintaining their profit margins because
they were able to experience increase revenues when their total costs increased.
Figure 4.2 Return on Asset
PROFIT
8.00
6.00
4.00
2.00
0.00
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
-2.00
-4.00
-6.00
-8.00
-10.00
Source: Author’s computations; Data Source: BankScope
67
For return on asset (ROA), there has been a stable net income for banks
from 1992 – 2013 if the time period of 2005 – 2008 will not be considered. The
banks’ ROA peaked in 2005 while it had its record-low in 2008. This sudden drop
in the banks’ net income can be attributed to the 2008 Global Financial Crisis. Local
banks could have investments outside the country that were affected by the crisis.
However, after 2008, the banks’ net income immediately bounced back a year after
in 2009 and has consistently been stable. On the other hand, the reason behind the
skyrocketing of average profits in 2005 can be attributed on the abnormal income
statement and balance sheet statistics of the United Overseas Bank Philippines for
the said year. The bank has managed to maintain an increase in net income despite
the drastic decrease in total assets. Looking at the history of United Overseas Bank
Philippines, it was in 2006, and not in 2005, that the bank converted from a
commercial bank into a thrift bank and sold its 66 branches to BDO Unibank. It
could be possible that the conversion and the selling of the banks started in 2005
and were completed in 2006 that’s why total assets dropped. Mathematically
speaking, the drop in total asset will result to a much higher ROA ratio.
68
Figure 4.3 Average Costs to Total Assets Ratio
45.00
40.00
35.00
30.00
25.00
20.00
15.00
10.00
5.00
0.00
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
COSTS OF FUNDS (INTC)
OTHER NON-INTEREST OPERATING COSTS (OTHC)
Source: Author’s computations
The banks’ average interest costs, which is total interest expense divided by
total deposits and other short-term funding, have been stable but following a
decreasing trend from 1992 – 2013. Saliently, there was a sudden peak in 2005. The
reason behind the skyrocketing of interest costs in 2005 can be attributed on the
abnormal income statement and balance sheet statistics of the United Overseas
Bank of the Philippines. However, removing the specific bank from the sample
would show the same pattern in banks’ ROA over time. As what was mentioned
earlier, looking at the history of United Overseas Bank Philippines, it was in 2006,
and not in 2005, that the bank converted from a commercial bank into a thrift bank
and sold its 66 branches to BDO Unibank. It could be possible that the conversion
and the selling of the banks started in 2005 and were completed in 2006 that’s why
deposits and other short-term funding and total assets dropped which affected the
ratios above.
69
One notable observation was that other non-interest operating costs, which
is total non-interest expense over interest-bearing liabilities, has been consistently
higher than interest costs. Based on standard economic theory, this suggests that
banks have higher fixed costs than variable costs. OTHC followed an increasing
trend from 1992 – 1997, though there was a dip in 1996. Similarly, there was a
sudden peak in 2005 and this can also be attributed on the abnormal income
statement and balance sheet statistics of the United Overseas Bank Philippines.
Efficiency Results
The previous section dealt with the discussion of the bank-specific data and
their concurrence with macroeconomic conditions that have affected the industry
during the time period covered. This section will deal with the results of the
regressions ran following the method that was discussed in the previous chapter.
The specification of the two translog functions ran using cross-section data annually
are:
COST = 𝛽0 + 𝛽1 𝑙𝑛(𝑂𝑈𝑇𝑃) +
1
1
2
𝛽2 𝑙𝑛(𝑂𝑈𝑇𝑃)2 + 𝛽3 𝑙𝑛(𝐼𝑁𝑇𝐶) +
1
+ 𝛽5 𝑙𝑛(𝑂𝑇𝐻𝐶) + 2 𝛽6 𝑙𝑛(𝑂𝑇𝐻𝐶)2 +
𝛽7 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛 (𝐼𝑁𝑇𝐶) + 𝛽8 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛(𝑂𝑇𝐻𝐶) +
𝛽9 𝑙𝑛(𝐼𝑁𝑇𝐶) 𝑙𝑛(𝑂𝑇𝐻𝐶)
𝛽 𝑙𝑛(𝐼𝑁𝑇𝐶)
2 4
2
Alt PROF = 𝛽0 + 𝛽1 𝑙𝑛(𝑂𝑈𝑇𝑃) +
1
2
𝛽2 𝑙𝑛(𝑂𝑈𝑇𝑃)2 + 𝛽3 𝑙𝑛(𝐼𝑁𝑇𝐶) +
1
+ 𝛽5 𝑙𝑛(𝑂𝑇𝐻𝐶) + 2 𝛽6 𝑙𝑛(𝑂𝑇𝐻𝐶)2 +
𝛽7 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛 (𝐼𝑁𝑇𝐶) + 𝛽8 𝑙𝑛(𝑂𝑈𝑇𝑃) 𝑙𝑛(𝑂𝑇𝐻𝐶) +
𝛽9 𝑙𝑛(𝐼𝑁𝑇𝐶) 𝑙𝑛(𝑂𝑇𝐻𝐶)
𝛽 𝑙𝑛(𝐼𝑁𝑇𝐶)
2 4
2
1
(4.1)
(4.2)
To determine the appropriateness of a statistical model, one of the most
commonly-used statistics is the R2 and adjusted R2. They serve as an indication of
70
goodness of fit. R2 measures the variation in the dependent variable explained by
the independent variables. On the other hand, adjusted R2 is used to compare two
or more regression models that have the same regressand but have different number
of regressors. Adjusted R2 is usually lower than R2 because it takes into
consideration the addition of independent variables to the model. Usually, adding
variables, whether proven statistically significant or not, increases a model’s R2.
Table 4.2 summarizes the average R2, adjusted R2, and standard deviation
of the regressions ran using the above-specified translog functions.
Table 4.2 Summary of Translog Regressions
Cost Efficiency
R^2
0.959
Profit Efficiency
Adjusted R^2
St.
Dev.
R^2
Adjusted R^2
St.
Dev.
0.925
0.306
0.772
0.583
1.416
Source: Author’s computations; Data Source: BankScope
The primary observation from Table 4.2 is that the regressions ran using the
translog functions seem to fit the actual data for cost efficiency than the profit
efficiency. It should be noted again that alternative profit efficiency was the
measure used in this study. Alternative profit efficiency measures efficiency given
output levels rather than output prices which is the case of the standard profit
efficiency. The lower goodness of the profit efficiency regressions could mean that
in the Philippines, profit is more related to the price of bank output rather than the
quantity of such output. In addition, the regression model for cost efficiency had
lower standard deviation as compared to the model for profit efficiency. Standard
deviation is the square root of variance, a measure of variability of the random
71
variables in the model. A lower standard deviation means that the value of the
variables are closer to the mean or the expected value.
R2 and adjusted R2 are not the only variables that can be considered in
determining the correctness of the fitted model. Another widely used variable is the
t-statistics. It is used to measure the individual significance of the explanatory
variables. However, Berger and Mester (1997) argued that t-statistics of the translog
function is difficult to interpret because of the model’s inherent collinearity. In
addition, they also pointed out that individually interpreting the individual
coefficients is problematic because the functional form of the cost and profit
function is not related but is imposed on the firm, is assumed to be in natural
logarithmic terms, and the independent variables were used more than once in the
equation, hence, multicollinear.
After running the annual cross-sectional data, the error terns or the residuals
derived from the individual regressions were used to compute the actual cost and
profit efficiency statistics. For reference, the construction of the variables are as
follows:
𝐶𝑜𝑠𝑡 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 =
[exp 𝑓̂ (𝑤 𝑏 ,𝑦 𝑏 ,𝑧 𝑏 ,𝑣 𝑏 )][exp ln 𝑢
̂𝑚𝑖𝑛 ]
𝑏
𝑏
𝑏
𝑏
̂
[exp 𝑓 (𝑤 ,𝑦 ,𝑧 ,𝑣 )][exp ln 𝑢
̂𝑏 ]
=
[exp 𝑓̂ (𝑤 𝑏 ,𝑦 𝑏 ,𝑧 𝑏 ,𝑣 𝑏 )][exp ln 𝑢
̂𝑏 ]− 𝜃
̂𝑐𝑚𝑖𝑛
𝑢
(4.3)
̂𝑐𝑏
𝑢
𝐴𝑙𝑡 𝜋 𝑒𝑓𝑓𝑖𝑐𝑖𝑒𝑛𝑐𝑦 = [exp 𝑓̂ (𝑤𝑏,𝑦 𝑏,𝑧 𝑏,𝑣𝑏)][exp ln 𝑢̂𝑚𝑎𝑥 ]− 𝜃 =
𝑎𝜋 𝑏
𝑎𝜋 𝑚𝑎𝑥
(4.4)
Figure 4.4 presents the average cost and profit efficiency statistics based on
the entire sample of Philippine universal and commercial banks.
72
Figure 4.4 Average Cost and Profit Efficiency
0.900
0.800
0.700
0.600
0.500
0.400
0.300
0.200
0.100
0.000
1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013
Cost Efficiency
Profit Efficiency
Source: Author’s computations
The first thing that is salient with the graph is the high volatility of the
efficiency results. This can be attributed to the nature of the data used being
unbalanced. The number of banks for each year was inconsistent because of data
unavailability and bank-specific changes such as mergers and acquisitions. Some
of the banks even started operations after 1992, the starting year of the scope of this
study.
Another thing that is apparent with the result is that universal and
commercial banks, starting in 1998, are more cost efficient than profit efficient even
if both efficiency measures showed high fluctuations throughout the years. The
mean cost efficiency from 1992 – 2013 was 0.652. This means that around 34.8
percent of costs were wasted on average relative to the best-performing bank. On
the other hand, the mean profit efficiency from 1992 – 2013 was 0.282 which
73
suggests that on average only 28.2 percent of the highest possible profit was earned
by the firms.
Another important analysis is the average efficiency in different time
periods. The period of 1992 – 2013 has seen a lot of changes in banking policies
and regulations. For the purposes of this study, seven time periods were considered.
The first one is the whole 1992 – 2013 period, the entire coverage of this study. In
terms of general banking laws, two periods were considered: 1992 – 2000 and 2001
– 2013. The years 1992 – 2000 are still covered by the 1948 General Banking Act.
As stated in the literature, within this period, a key policy was adopted: RA 7721
which allowed, with details discussed earlier in the literature, the entry of foreign
banks in the country. The foreign banks which entered the country after the
enactment of the law got their licenses in 1995. This is the reason why 1992 – 1994
and 1995 – 2000 were two additional time periods.
In May 23, 2000, the General Banking Law of 2000 has amended the then
52-year-old General Banking Act of 1948, thus the period 2001 – 2013. However,
one very important event in banking history occurred within the period: the 2008
Global Financial Crisis. Though the Philippines was not generally affected by the
crisis as compared to our neighbor countries, the researcher still included the 2001
– 2007 and 2008 – 2013 time periods in the discussion. Table 4.3 summarizes the
average cost and profit efficiency during different time periods discussed above.
74
Table 4.3 Average Cost and Profit Efficiency in Different Time Periods
Cost
Profit
Time Period
Efficiency
Efficiency
1992 - 2013
0.652
0.282
1992 - 2000
0.565
0.358
2001 - 2013
0.696
0.235
1992 - 1994
0.667
0.504
1995 - 2000
0.532
0.315
2001 - 2007
0.643
0.216
0.753
0.270
2008 - 2013
Source: Author’s computations
When taken into consideration the different time periods mentioned above,
the results showed interesting variations. The changes were different for cost
efficiency and profit efficiency. For cost efficiency, banks generally became more
cost efficient after the adoption of the General Banking Law of 2000. However, this
was not the case with profit efficiency. Banks, in general, were more profit efficient
before 2000. However, this lower general profit efficiency after 2000 can be caused
by the sudden drop of banks’ profitability in 2008 which was discussed earlier.
In 1992 – 2000, the liberalization of the banking industry seem to negatively
affect banking efficiency. Generally, banks had higher cost and profit efficiency
from 1992 – 1994 compared to 1995 – 2000. However, it should be noted that the
Asian Financial Crisis occurred in the latter period of 1990s. This could explain
why banks had lower cost and profit efficiency in 1995 – 2000.
In 2001 – 2013, the 2008 Global Financial Crisis, all things equal, had
positively affected the banks’ cost efficiency. In general, banks had much higher
cost efficiency after the financial crisis. Similarly, the crisis seem to positively
75
affect the level of profit efficiency of banks as they generally had higher average
profit efficiency after the crisis. The crisis could have prompted local banks to be
more efficient to avoid experiencing the same situation that US banks faced.
To be more specific, Table 4.4 lists the five most cost efficient banks for
different time periods in order to see whether the relative efficiency of banks vary
or there are banks that proved to be consistently cost efficient. Furthermore,
because the data employed is naturally unbalanced, it is important to consider
different time periods and not just the average in order to account for the possibility
that some banks may not exist or lacks data for some specific periods. Again, the
author used the same rationale for the different time periods used for the following
analysis.
Table 4.4 Top 5 Most Cost Efficient Banks for Different Time Periods
Cost
Efficiency
1
1992 2013
ChinaTrust
1992 2000
EastWest
2001 2013
DBP
1992 1994
PNB
1995 2000
EastWest
2001 2007
Union
2
DBP
MBT
Union
ChinaBank
MBT
Prudential
3
Union
BDO
Private
Prudential
DBP
BDO
Private
DBP
RCBC
Commerce
ChinaTrust
BPI
Commerce
Equitable
Overseas
Union
AUB
UCPB
UCPB
AUB
ChinaTrust
4
5
BDO
Private
AlAmanah
2008 2013
DBP
BDO
Private
Source: Author’s analysis
The general average shows that ChinaTrust Banking Corporation, a foreign
bank and is one of the largest privately owned bank in Taiwan, was the most cost
efficient bank in the country. However, the data of this bank only started in 2006
which means that it had fewer observations. This could have affected the result
considering that based on the previous discussion, banks generally had lower cost
efficiency measures in 1992 – 2000. On the other hand, the Development Bank of
76
the Philippines was the second most cost efficient bank in 1992 – 2013 but the most
cost efficient in 2001 – 2013. Interestingly, except for 2009, DBP was the most
coefficient bank in 2007 – 2013. Because of this, it recorded the most number of
times to be the bank at the frontier or the ‘most cost efficient’ bank. However, the
relatively lower cost efficiency estimates in 1997 – 2000 made DBP only the second
most coefficient bank in the sample. UnionBank of the Philippines was, in general,
the third most cost efficient bank and was consistently part of the five most
coefficient banks in 1992 – 2000 and 2001 – 2013. BDO Private Bank was only not
part of the five most cost efficient banks in 2001 – 2007. This explains why, in
general, it was the fourth most cost efficient bank. Surprisingly, Al-Amanah Islamic
Investment Bank was the fifth most coefficient bank. However, it should be noted
that data of the bank was only available starting 2007. Similar with ChinaTrust
Banking Corporation, the lack of data could have overly state the bank’s average
efficiency considering that banks, in general, had lower cost efficiency levels in
1992 – 2000.
On the other hand, United Coconut Planters Bank was part of the most cost
efficient banks in 1992 – 2000 but disappeared from the list in 2001 – 2013. East
West Banking Corporation also had the same case.
The cost efficiency results showed that there are banks like DBP,
UnionBank, and BDO Private Bank that consistently showed high cost efficiency
measures regardless of time period considered. However, there are some banks such
as CityTrust and Al-Amanah who have high average cost efficiency due to
77
unavailability of data during the periods where banks generally had lower cost
efficiency measures.
The same analysis was made for the most profit efficient banks. Table 4.5
lists the five most profit efficient banks for different time periods.
Table 4.5 Top 5 Most Profit Efficient Banks for Different Time Periods
Profit
Efficiency
1
1992 2013
Union
1992 2000
ChinaBank
2001 2013
Security
1992 1994
ChinaBank
1995 2000
Veterans
Philtrust
BDO
Private
ChinaTrust
Union
RCBC
Philtrust
3
4
BDO
Private
ChinaBank
Philtrust
MBT
PBCom
5
ChinaTrust
MBT
AUB
Equitable
UCPB
BDO
Private
Union
2
Veterans
UCPB
2001 2007
BDO
Private
ChinaTrust
2008 2013
Security
UCPB
ChinaBank
Philtrust
ChinaTrust
EastWest
AUB
Union
Source: Author’s analysis
Unionbank of the Philippines proved to be the most profit efficient bank in
the Philippines from 1992 – 2013. However, unlike ChinaTrust Banking
Corporation who was the most cost efficient in the same period, Unionbank had
consistent data in 1992 – 2013 which means that the assumption earlier that the
unavailability of data prior to 2006 overly stated the general cost efficiency of
ChinaTrust bank did not hold true for Unionbank. Interestingly, aside from being
one of the most coefficient banks, BDO Private Bank was also, on average, the
second most profit efficient bank in the country. China Banking Corporation, the
third most profit efficient bank, was only not part of the most profit efficient banks
in 2008 – 2013. Philtrust Banking Corporation, on the other hand, was only not part
of the most profit efficient banks in 2001 – 2007. ChinaTrust Banking Corporation,
aside from being the most cost efficient bank on average in 1992 – 2013, was also
part of the most profit efficient banks. However, in contrary with the discussion
78
above, banks were generally more profit efficient in 1992 – 2000 as compared to
2001 – 2013. However, this can be attributed with the 2008 Global Financial Crisis
which, as discussed above, saw a sudden decline in the banks’ general profitability.
Another profit efficient bank that should be noted is the United Coconut
Planters Bank. It was the third most profit efficient bank in 1995 – 2007. In
addition, it should be taken into consideration that Security Banking Corporation
was the most profit efficient bank in 2001 – 2013 but it, in general, it was only the
seventh most profit efficient bank.
It can be said that banks tend to be more consistent in profit efficiency than
cost efficiency. The most profit efficient banks were consistent regardless of time
period considered as compared to the cost efficient banks.
In general, UnionBank of the Philippines and BDO Private Bank proved to
be consistent in being both cost and profit efficient. Interestingly, these two banks
are not the biggest banks in the Philippines in terms of asset size as of 2013.
Unionbank was part of the ten biggest banks but had a total assets that is only
equivalent of a quarter of the biggest bank. BDO Private Bank, on the other hand,
is a subsidiary of BDO Unibank and has one of the smallest total assets among all
universal and commercial banks. Even if it was part both of the most cost and profit
efficient banks, it would be difficult to make a generalization for CityTrust Banking
Corporation because of its limited data availability.
79
To measure the relationship between cost efficiency and profit efficiency,
Z-test for two sample for means was employed. Table 4.6 summarizes the result of
the z-test.
Table 4.6 Z-test Results
Cost
Profit
0.651953
0.305452
Mean
0.014837
0.029585
Known Variance
7.591652386
z
1.959963985
z Critical two-tail
3.15303E-14
P-value
Source: Author’s computations
Since the calculated z-statistic is much higher than the critical z-value for
two-tail, then we can reject the null hypothesis that the cost and profit efficiency
are significantly related. Furthermore, because the p-value is much lower than the
alpha of 0.05, we can also reject the null hypothesis. We do not have enough
evidence that cost and profit efficiency are related. On the contrary, the results show
that there is significant difference between cost and profit efficiency.
Now that the efficiency measures are already computed using the
Distribution-Free Approach, the next discussion would focus on identifying the
relationship of these efficiency measures to bank growth and market concentration,
hence, the tests of the efficient structure theory and quite life hypothesis. The
succeeding parts can be considered the author’s contribution to the literature of
study as other researchers have focused mainly on measuring bank efficiency.
However, an important note for the following discussion is that they rely on the
accuracy and preciseness of the results of Objective 1. Therefore, any findings
80
below are conditional with the results of the frontier analysis made above. Results
could vary depending on the measure of efficiency used and the methodology
employed.
Objective 2: The Test of Efficient Structure Theory Results
In order to test whether the efficient structure theory (ESH) holds true in the
Philippine universal and commercial banking industry, a panel regression was
employed where 28 banks for the period of 22 years were considered. Growth in
loans and assets were used as proxy for bank growth, the dependent variable. On
the other hand, cost and profit efficiency, which were the results of the first
objective, serve as independent variables together with macroeconomic variables
such as Gross Domestic Product (GDP) and inflation.
ESH predicts that efficient banks grow. Taken into consideration this
assumption, efficiency variables should have a positive coefficient and should be
statistically significant. Table 4.7 summarizes the regression results for cost
efficiency and bank assets. The Hausman test employed revealed that the fixed
effects model is the one to be used in this study.
Table 4.7 Summary of Regression Results for Cost Efficiency and Assets
Prob.
0.0000
Generalized Least
Squares
Coefficient Prob.
-7.875872 0.0027
-0.282017 0.0274
2.471044 0.0000
-0.030057 0.0113
0.905137
0.318495
0.338236 0.3567
1.427055 0.0000
-0.002729 0.9389
NA
NA
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-16.05453
First-Difference
Coefficient
0.094232
Prob.
0.0011
0.066373 0.1995
0.808377 0.1657
-0.007834 0.0360
0.160406
1.623614
Source: Author’s computations; Source of basic data: BankScope and BSP
81
Using the panel least squares regression, all the variables were statistically
significant based on the t-Statistic and p-values. However, the slope coefficient of
the cost efficiency variable was opposite of its expected sign. On the other hand,
the slope coefficient of the macroeconomic variables had their expected signs.
Furthermore, the R2 of the model also indicated high goodness of fit. The result of
the panel least squares regression indicated that ESH, using cost efficiency, does
not hold true for the Philippine universal and commercial banks.
However, the Durbin – Watson statistic showed that the model suffers from
the problem of autocorrelation. One of the assumptions of the classical linear
regression models is that the error term, ut, are uncorrelated – the error term at time
t is not correlated with the error term at time (t-1) or any other error term in the
past3. If the error terms are correlated, then autocorrelation exists. To avoid drawing
misleading conclusions from the least squares regression above, autocorrelation is
taken care of. There are several models to address autocorrelation and this study
used two: the generalized least squares transformation and the first-difference
transformation.
GLS transformation automatically corrects for serial correlation. After the
GLS transformation, the slope coefficient of all the variables had their expected
3
Gujarati (2011) explains that the consequences of autocorrelation are as follows: (1) The
OLS estimators are still unbiased and consistent; (2) They are still normally distributed in
large samples; (3) But they are no longer BLUE (best linear unbiased estimator). In most
cares OLS standard errors are underestimated, which means the estimated t values are
inflated, giving the appearance that a coefficient is more significant than it actually may
be; and (4) As result, as in the case of heteroscedasticity, the hypothesis-testing
procedure becomes suspect, since the estimated standard errors may not be reliable, even
asymptotically (i.e. in large samples). In consequence, the usual t and F tests may not be
valid.
82
signs. Based on the new results, cost efficiency positively contributed to bank
growth in terms of assets. In addition, the results also showed that banks’ assets
goes hand in hand with the economy which was represented by the natural
logarithm of GDP. Inflation, on the other hand hampered bank growth. However,
only the GDP variable proved to be statistically significant. The cost efficiency and
the inflation variables might had both their expected signs but were statistically
insignificant.
Interestingly, the first-difference transformation generated similar results in
terms of the slope coefficients of the variables. Based on the results, cost efficiency
and GDP positively contributed to the growth of bank assets while inflation
hampered its growth. If it was highly statistically insignificant in the generalized
least squares model, inflation was statistically significant in this model. However,
even if they had their expected coefficients, cost efficiency and GDP were both
statistically insignificant. In addition, the R2 values in the level form and in the firstdifference form were not directly comparable because the dependent variables in
the two models are different.
Overall, the regression results, accounting for autocorrelation, suggested
that ESH applies in the Philippine universal and commercial banking industry in
terms of cost efficiency and bank assets. However, this assumption was statistically
insignificant. Moreover, the macroeconomic variables proved to be significant in
the growth of bank assets depending on the test of autocorrelation that will be used.
The same analysis was made for the relationship between profit efficiency
and growth of assets. The Hausman test employed revealed that the fixed effects
83
model is the one to be used in this case. Table 4.8 summarizes the regression results
for profit efficiency and bank assets.
Table 4.8 Summary of Regression Results for Profit Efficiency and Assets
Prob.
0.0000
Generalized Least
Squares
Coefficient Prob.
-7.693677 0.0037
-0.179944 0.0466
2.421884 0.0000
-0.020612 0.0828
0.904931
0.292811
-0.099874 0.6998
1.436845 0.0000
-0.005724 0.8728
NA
NA
Least Squares
Variable
C
PROF_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-15.82546
First-Difference
Coefficient
0.087589
Prob.
0.0025
-0.062831 0.0676
0.943277 0.1069
-0.007229 0.0538
0.164118
1.606342
Source: Author’s computations; Source of basic data: BankScope and BSP
Same with the case of cost efficiency, all the variables proved to be
statistically significant based on t-Statistics and p-value using the panel least
squares regression. However, the slope coefficient of the profit efficiency variable
was opposite of its expected sign. On the other hand, the slope coefficient of the
macroeconomic variables had their expected signs. Furthermore, the R2 of the
model also indicated high goodness of fit. The result of the panel least squares
regression indicated that ESH, using profit efficiency, does not hold true for the
Philippine universal and commercial banks.
However, similar with the results using cost efficiency, the Durbin – Watson
statistic showed that the model also suffered from the problem of autocorrelation.
Therefore, the generalized least squares transformation and the first-difference
transformation were also employed.
After the GLS transformation, the slope coefficient of profit efficiency was
still opposite of its expected sign. On the other hand, the macroeconomic variables
84
had their expected signs already. The results in indicated that profit efficiency
negatively contributes to bank assets which is contrast with the ESH.
The first-difference transformation generated similar results as with GLS
transformation. The profit efficiency variable also had a negative coefficient which
was in contrast with the ESH. The macroeconomic variables had their expected
signs. Again, the R2 values in the level form and in the first-difference form were
not directly comparable because the dependent variables were different.
Overall, the regression results, accounting for autocorrelation, suggested
that ESH did not apply in the Philippine universal and commercial banking industry
in terms of profit efficiency and bank assets but this assumption was statistically
insignificant. However, the macroeconomic variables proved to be significant in
the growth of bank assets.
On the other hand, similar results were generated when bank loans were
used as a measure of bank growth. The Hausman test showed that the fixed effects
model is the one to be used in this study. Table 4.9 summarizes the results for the
regression analysis using loans as the dependent variable and cost efficiency.
Table 4.9 Summary of Regression Results for Cost Efficiency and Loans
Prob.
0.0000
Generalized Least
Squares
Coefficient Prob.
-1.958304 0.5403
-0.384464 0.0717
1.734802 0.0000
-0.045428 0.0220
0.810989
0.311578
0.205966 0.6455
0.628851 0.0876
-0.021739 0.6169
NA
NA
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-10.69494
First-Difference
Coefficient
0.038408
Prob.
0.4325
0.093995 0.2833
1.455049 0.1427
0.000593 0.9253
0.114057
1.440718
Source: Author’s computations; Source of basic data: BankScope and BSP
85
As mentioned previously, the results were similar when bank assets were
used. The variables were statistically significant using the panel least squares
regression. The cost efficiency had a negative coefficient which is contrast with
ESH. The Durbin – Watson test also indicated that autocorrelation exists thus, the
generalized least squares and first-difference transformation were also employed.
Similar again with the previous results, all the variables had their expected slope
coefficients after the generalized least squares transformation. Similarly, only the
GDP variable remained statistically significant.
However, there was a difference with the results of the first-difference
transformation. The cost efficiency and GDP variables both had their expected
slope coefficients but proved to be statistically insignificant which are similar when
bank assets was used as the measure of bank growth. Unlike with the previous
results, the inflation variable had a positive coefficient, opposite of the expected.
Moreover, it became statistically insignificant as well.
Again, the R2 values in the level form and in the first-difference form were
not directly comparable because the dependent variables in the two models were
different.
Overall, the regression results, accounting for autocorrelation, suggests that
ESH does not apply in the Philippine universal and commercial banking industry
in terms of cost efficiency and bank loans. This conclusion was made even if the
slope coefficient of cost efficiency is parallel to its expected sign. Moreover, the
GDP variable proved to positively affect bank loans but this assumption was only
significant when the generalized least squares transformation is used.
86
The same analysis was made for the relationship between profit efficiency
and growth of assets. The Hausman test employed revealed that the fixed effects
model is the one to be used in this case. Table 4.10 summarizes the regression
results for profit efficiency and bank loans.
Table 4.10 Summary of Regression Results for Profit Efficiency and Loans
Prob.
0.0000
Generalized Least
Squares
Coefficient Prob.
-1.836082 0.5698
-0.118427 0.4329
1.690494 0.0000
-0.035823 0.0719
0.809792
0.289246
-0.068612 0.8281
0.633618 0.0868
-0.023378 0.5919
NA
NA
Least Squares
Variable
C
PROF_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-10.59133
First-Difference
Coefficient
0.029548
Prob.
0.5471
-0.082111 0.1582
1.632485 0.1015
0.001278 0.8406
0.116007
1.429195
Source: Author’s computations; Source of basic data: BankScope and BSP
The panel least squares regression showed that only the macroeconomic
variables were statistically significant in affecting bank loans. These
macroeconomic variables also had their expected slope coefficients. Profit
efficiency had the opposite expected slope coefficient and was statistically
insignificant. However, the model suffers from autocorrelation.
After employing generalized least squares regression, the macroeconomic
variables remained to have their expected slope coefficients but inflation became
statistically insignificant. Profit efficiency still had the opposite expected slope
coefficient and still statistically insignificant.
On the other hand, the first-difference transformation resulted with the same
dynamics for the GDP variable. The inflation variable had a positive coefficient but
87
still highly insignificant. Profit efficiency remained to negatively affect the growth
of loans and to be statistically insignificant.
Overall, because of the consistent results among the three model, it can be
inferred that ESH does not hold true for the Philippine universal and commercial
banks in terms of profit efficiency and loans. Inflation is statistically insignificant
in the growth of loans. However, it can be assumed that GDP positively contribute
to the growth of loans.
As mentioned in the Chapter 3 of this study, the same set of analyses was
done with the change form of the variables as specified earlier in this study.
However, the empirical results showed very low goodness of fit for the analyses.
Thus, only the level form results were discussed. The results of the change form
analyses are found in Appendix 2.
Furthermore, another alternative specification was used as advised by Neri
(2015). In addition to the natural logarithm of GDP, the ratio of Gross International
Reserves over External Debt was also added as a macroeconomic variable to
measure the adequacy of the country’s reserve relative to its debt. Instead of
inflation, which has been fairly stable since inflation-targeting was adapted by BSP,
the 91-day T-bill rate was also added as an independent variable in order to account
for interest rates. However, the empirical results were proven to be statistically
insignificant and hence, inconclusive. The results of this additional alternative
specification were attached in Appendix 2.
88
Objective 3: The Test of Quiet Life Hypothesis Results
In order to test whether the quiet life hypothesis (QLH) holds true in the
Philippine universal and commercial banking industry, a panel regression was
employed where 28 banks for the period of 22 years were considered. The
efficiency results computed in the first objective were used as the dependent
variables. On the other hand, HHI was used as the measure of market concentration
as the independent variable together with other bank-specific variables.
QLH predicts that higher concentration leads to higher inefficiency. As the
market becomes more concentrated, firms become more inefficient. Big banks, who
hold significant market shares of the market, live a ‘quiet life’ or an inefficient life
because they do not engage themselves in competitive measures. Taken into
consideration this assumption, market concentration should have a negative slope
coefficient. The Hausman test employed revealed that the fixed effects model is the
one to be used in this case. Table 4.11 summarizes the results of the regression
analyses using cost efficiency.
Table 4.11 Summary of the Results for the Quiet Life Hypothesis using Cost
Efficiency
Least Squares
Variable
C
HHI(-1)
AGE
GBL
Rsquared
D-W
stat.
Coefficient
0.576959
3.361660
-0.000505
0.130448
Prob.
0.0000
0.0209
0.0495
0.0000
Generalized Least
Squares
Coefficient
Prob.
0.576959 0.0000
3.361660 0.0204
-0.000505 0.0489
0.130448 0.0000
0.123146
NA
1.481127
NA
First-Difference
Coefficient
Prob.
-0.028343
0.8405
-5.612304
0.3696
-0.000166
0.9621
0.057348
0.1619
0.019864
2.711685
Source: Author’s computations; Source of basic data: BankScope and BSP
89
It is apparent that a lot of variables were omitted from the original
specifications defined in the previous chapter. After some attempts, most of the
variables have been proven to be highly statistically insignificant. Due to this, only
the key independent variable, which is HHI as the measure of market concentration,
and AGE variable, which proved to be consistently significant in all the regressions
that were ran, were retained. In addition, the dummy variable used for the General
Banking Law of 2000 was also statistically significant. A value of 1 was given to
years 2001 – 2013 in order to account for the change in the overall policy that the
industry operates in.
Using the panel least squares regression, the results showed that quiet life
hypothesis does not apply to the Philippine universal and commercial banks.
However, the Durbin-Watson statistic showed that the error terms of the variables
suffer from serial correlation. Therefore, the results of the panel least squares
regression are inconclusive.
Similar with the process done in Objective 2, generalized least squares and
first-difference transformation were employed in order to address the problem of
serial correlation. The generalized least squares regression results showed the
positive relationship between cost efficiency and market concentration. This means
that as the market becomes concentrated, more firms are able to gain significant
market share, the general efficiency of banks increases. This implication is contrary
with the assumption made by the quiet life hypothesis. On the other hand, the slope
coefficient of the age variable results showed that as banks gets old, they tend to
become less cost efficient. However, the coefficient is of very low value.
90
The dummy variable for the 2000 General Banking Law proved to be highly
significant in the model. The positive slope coefficient of the variable means that
the adoption of the new general policy by the central bank made banks more cost
efficient.
On the other hand, the results after the first-difference transformation
showed insignificant and inconclusive results.
In summary, the results showed that the quiet life hypothesis does not apply
to the Philippine universal and commercial banking system. In contrary, as banks
gain increase market share, they become more cost efficient. This inference was
proven statistically.
On the other hand, Table 4.12 summarizes the results of the regression
analyses using profit efficiency as the dependent variable. The Hausman test
employed revealed that the fixed effects model is the one to be used in this case.
Table 4.12 Summary of the Results for the Quiet Life Hypothesis using Profit
Efficiency
Least Squares
Variable
C
HHI(-1)
AGE
GBL
Rsquared
D-W
stat.
Coefficient
0.366864
-0.890850
0.000165
-0.125832
Prob.
0.0000
0.6831
0.6684
0.0000
Generalized Least
Squares
Coefficient
Prob.
0.366864 0.0000
-0.890850 0.6829
0.000165 0.6682
-0.125832 0.0000
0.049839
NA
1.658419
NA
First-Difference
Coefficient
Prob.
-0.558263
0.0100
-2.588077
0.7870
0.013057
0.0148
-0.079564
0.2052
0.025700
3.079318
Source: Author’s computations; Source of basic data: BankScope and BSP
Similar with the first test of the quiet life hypothesis, the results of the panel
least squares regression are inconclusive because of the Durbin-Watson statistic
91
which shows that the error term of the variables suffer from serial correlation.
Hence, the generalized least squares regression and first-difference transformation
were also employed.
Based on the slope coefficient of the regression results after the generalized
least transformations, it can be noted that increase in market concentration
decreases banks’ profit efficiency which is the assumption made by the quiet life
hypothesis. Opposite with the results in the previous discussion, the slope
coefficient of the age variable was positive which means that as banks age, they
become more profit efficient. The slope coefficient of the 2000 General Banking
Law was negative which means that the adoption of the new policies starting in
2000 had negatively affected, in general, the banks’ profit efficiency. However, the
results of the generalized least model were inconclusive because of very low
statistical
significance.
Similarly,
the
results
after
the
first-difference
transformation showed insignificant and inconclusive results as well.
Therefore, no definitive conclusions can be made with the relationship
between profit efficiency and market concentration.
Similarly, as mentioned in the Chapter 3 of this study, the same set of
analyses was done with the change form of the variables as specified earlier in this
study. However, the empirical results showed insignificant and inconclusive
results. Thus, only the level form results were discussed. The results of the change
form analyses are found in the Appendix.
92
CHAPTER V
CONCLUSIONS and RECOMMENDATIONS
The previous chapter dealt with the empirical findings that this study shed
light on. This part will attempt to conclude and generalize the empirical findings
and make recommendations on how the results can be further improved by future
researchers.
However, it should be taken into consideration that the following
conclusions will be made on the assumption that the translog specification used in
the frontier analysis is correct. Other studies which use different methodologies of
measuring banking efficiency could result to different conclusions. In addition, this
study uses cost and profit as measures of efficiency. There are other measures of
efficiency such as scale and technical efficiency that could be used in order to test
the two hypotheses/theories. Therefore, the conclusions made from the regression
results of the tests of the efficient structure theory and quiet life hypothesis will be
conditional on the result of the frontier analysis.
Furthermore, as stated in the scope and limitations of this study, the study
only focuses on core banking. Core banking activities covers basic depositing and
lending of money. Core banking functions will include transaction accounts, loans,
mortgages and payments. However, because of the difficulty in dissecting the data
into core banking activities and other activities, the measure of profit used in this
study is the standard Return on Asset (ROA) measure.
93
Conclusions
1. Cost efficiency and profit efficiency are statistically unrelated
Based on the z-test of the two efficiency measures, banks who were cost
efficient were not necessarily profit efficient. Though there were a few firms who
were consistently part of the most cost and profit efficient banks, there was no
statistical significance linking the two. The most cost efficient banks were not
necessarily the most profit efficient banks even if different time periods were
considered. Furthermore, the two efficiency measures also had very low
correlation. In addition, based on the average efficiency measures for each year,
profit efficiency was more volatile than cost efficiency.
2. In terms of cost efficiency, the efficient structure hypothesis does not hold true.
The regression results testing the efficient structure theory showed that the
theory does apply in the Philippine universal and commercial banks when cost
efficiency was used as the measure of efficiency. However, the case was opposite
when profit efficiency is used. Nevertheless, these assumptions lacks statistically
significance. It should be taken into consideration that the alternative profit function
was used to measure profit efficiency. A different result that would be aligned with
the efficient structure theory can be obtained if the standard profit function was
used.
3. The macroeconomic environment highly affects bank growth.
Macroeconomic variables such as Gross Domestic Product (GDP) and
inflation rate affected the growth of banks both in terms of loans and assets. The
94
GDP variable proved to be consistently significant in the growth of banks
regardless of the methodology used. Other macroeconomic variables such as
exchange rate and unemployment rate can be used in order to see whether the
macroeconomic variables indeed bank growth.
4. In terms of cost efficiency, the quiet life hypothesis does not apply to the
Philippine universal and commercial banks.
The positive and statistically significant slope coefficient of the market
concentration variable when cost efficiency was the dependent variable proved that
banks who had significant market share had higher cost efficiency, a conclusion
which is opposite of the assumption of the quiet life hypothesis. Moreover, the
results also show that the change in the general banking policy in 2000 through the
enactment of the 2000 General Banking Law had helped banks attain higher cost
efficiency. On the other hand, in terms of profit efficiency, the results were
statistically insignificant and inconclusive.
Recommendations
1. Based on the changes in the general average of cost and profit efficiency
measures when different time periods are considered, the effect of outside
forces such as changes in general policies of the Central banks and significant
events such as financial crisis should be further analysed.
The average cost and profit efficiency levels of universal and commercial
banks varied when a new general banking law was adopted in 2000. Moreover, the
95
liberalization of the banking industry in 1994 and the 2008 Financial Crisis had also
affected the general cost and profit efficiency of banks. This implies that the
policies governing universal and commercial banks have significant effects on the
general efficiency levels of universal and commercial banks. Furthermore, even if
the 2008 Financial Crisis did not happen in the country, the profitability of local
banks were highly affected. In addition, banks generally had lower cost and profit
efficiency in 1995 – 2000, the period when the Asian Financial Crisis occurred. The
effects of these specific events can be further analysed by future researchers.
2. In terms of increasing efficiency, both in terms of cost and profit, the important
role of other factors such as age should be further investigated.
In connection to the previous recommendation, it should be noted that there
are several other bank-specific factors that can affect a bank’s level of efficiency.
The regression results showed bank age as one of them. Since banking entails risk
and uncertainty, people and businesses would only trust those who are in the
business for a long time already. This leads to more customers and, consequently,
the possibility of economies of scale. If economies of scale indeed exist in the
banking industry, then it can be an area of further studies in order to find out
whether banks become more cost efficient as they serve more savers and borrowers.
Based on the literature, other variables that could be incorporated in the model are
foreign ownership, bank age, and accounting ratios. These measures were tested in
this study’s model but were proven statistically insignificant. Increasing the sample
size or measuring efficiency using a different approach may affect the results of
96
further studies and may statistically prove that the quiet life hypothesis may or may
not hold true in the case of Philippine banking industry.
This study have contributed in the literature in two ways. First, the
researcher have extensively studied universal and commercial banks using the
Distribution-Free Approach. This study can be considered as an extension of the
study of Huang (2002) because the time period was extended and more banks were
included due to the availability of data. Second, and more importantly, as far as the
researcher is aware, this is the first study that has measured banking efficiency and
attempted to use the results to determine whether the efficient structure theory and
quiet life hypothesis apply in the Philippine universal and commercial banking
industry. There were already studies such as Lamberte and Manlagnit (2004) and
Aquino (2007) that have measured banking efficiency using different
methodologies. However, these studies have only focused on measuring banking
efficiency but did not attempt to establish its relationship with other variables.
97
ANNEX
The perceived efficiency gains from bank mergers were also analyzed.
However, in order to maintain consistency in the study, the analysis on the
efficiency gains of mergers was only included as a supplement. One of the biggest
mergers in the Philippine banking industry is the merger of BDO Unibank and
Equitable-PCI bank in 2006 which made BDO Unibank, the surviving bank, the
biggest Philippine bank in terms of assets. Table 6.1 and 6.2 summarizes the cost
and profit efficiencies of the two banks before and after the merger.
Table. 6.1 Cost Efficiency of BDO Unibank and Equitable-PCI Bank
Cost Efficiency
Before
2006
After
BDO Unibank
0.6759
0.5127
0.7111
Equitable-PCI
0.6733
0.4742
NA
Source: Author’s computations
Table. 6.2 Profit Efficiency of BDO Unibank and Equitable-PCI Bank
Profit Efficiency
BDO Unibank
Equitable-PCI
Before
0.245
0.313
2006
0.057
0.325
With 2008
0.213
NA
Without 2008
0.248
NA
Source: Author’s computations
In terms of cost, it can be noted that BDO Unibank and Equitable-PCI bank
have increased their cost efficiency after the merger in 2006. The lower cost
efficiency in 2006 can be explained by the assumption that the bank was in
transition that year and it could have affected the overall operations and
performance of the bank. In terms of profit, two computations were made to account
for the very low profit efficiency, in general, during the 2008 Financial Crisis. If
2008 will be accounted, it can be inferred that the average profit efficiency of BDO
Unibank actually decreased. If 2008 will not be accounted, the bank had only
maintained its average profit efficiency. However, if it will be compared to the
average profit efficiency of Equitable-PCI bank, the average profit efficiency of
BDO Unibank had decreased.
98
Therefore, we can conclude that the merger of BDO Unibank and EquitablePCI bank has achieved its perceived efficiency gains in cost efficiency but not
necessarily in profit efficiency.
Another significant merger in the history of the Philippine banking industry
is the union of the Bank of the Philippine Islands and Prudential Bank in 2005.
Table 6.3 and 6.4 summarizes the cost and profit efficiencies of the two banks
before and after the merger.
Table 6.3 Cost Efficiency of BPI and Prudential Bank
Cost Efficiency
BPI
Prudential
Before
0.6249
0.6352
2005
0.9309
NA
After
0.6854
NA
Source: Author’s computations
Table 6.4 Profit Efficiency of BPI and Prudential Bank
Profit Efficiency
BPI
Prudential
Before
0.316
0.335
2005
0.003
NA
With 2008
0.208
NA
Without 2008
0.238
NA
Source: Author’s computations
In terms of cost, it can be noted that BPI and Prudential bank have increased
their cost efficiency after the merger in 2006. This average cost efficiency will even
be higher if we include the cost efficiency estimate for 2005. In terms of profit,
similar with the analysis above, two computations were made to account for the
very low profit efficiency, in general, during the 2008 Financial Crisis. However,
regardless if the profit efficiency estimate for 2008 will be included or not, the
resulting average profit efficiency is lower than the average profit efficiency of the
two banks prior to the merger in 2005.
Therefore, we can also conclude that the perceived efficiency gains from
mergers were actualized in terms of cost efficiency. However, profit efficiency, on
average, decreased after the merger. These conclusions are made given the
measures of efficiency used.
99
A conclusion that can be made from these results is that efficiency gains
from bank mergers were realized in profit efficiency and not in cost efficiency. Two
of the biggest mergers in the history of the Philippine banking industry were
analysed in terms of change in profit and cost efficiency: the BDO Unibank and
Equitable-PCI bank in 2006 and the BPI and Prudential bank merger in 2005. Based
on the average efficiency before and after the merger, it can be concluded that in
the case of the two mergers, the hypothesis that bank mergers increase cost
efficiency applies. This is proven by the higher average cost efficiency of the
surviving bank compared to the average cost efficiency of the two banks prior to
the merger. On the other hand, the hypothesis that bank mergers increase profit
efficiency does not necessarily hold in the case of the two mergers mentioned
previously. The two mergers included in the study failed to realize the hypothesized
profit efficiency gains form bank mergers. The resulting average profit efficiency
of the surviving bank was lower to the average profit efficiency of the two banks
prior to the merger.
This conclusion is different with the findings of Huang (2002) where he
inferred that bank merger prior to 2002 experienced efficiency gains in terms of
profit efficiency and not necessarily with cost efficiency. This can be considered as
an additional finding in the literature of efficiency gains from mergers.
100
APPENDIX 1
OBJECTIVE ONE: MEASURING BANK EFFICIENCY USING THE
DISTRIBUTION-FREE APPROACH
Table 1. Summary of Bank-specific Variables
REV
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
MEAN
COST
PROF
OUTPUT
(OUTP)
COSTS
OF
FUNDS
(INTC)
OTHER NONINTEREST
OPERATING
COSTS
(OTHC)
16.60
5.91
2.34
87.76
7.91
14.31
4.66
1.85
87.90
6.57
14.06
4.59
1.75
87.06
7.32
13.16
4.35
1.76
88.41
6.72
13.95
4.65
1.90
89.65
6.67
14.33
5.52
1.41
88.31
7.97
16.49
6.32
1.09
87.08
9.35
12.96
4.58
-0.11
83.28
6.80
12.12
4.68
0.15
82.47
6.67
13.12
4.61
0.63
81.94
6.74
11.07
2.95
0.67
81.54
4.23
10.53
2.96
1.03
80.77
4.10
10.67
3.20
0.83
81.44
4.37
12.53
4.57
5.65
86.02
23.67
12.09
3.26
0.65
83.51
5.17
10.72
3.62
-1.77
81.78
4.47
10.06
2.99
-8.59
84.87
3.77
10.34
2.24
0.76
87.16
2.88
10.42
1.83
1.26
84.65
2.34
9.80
1.62
1.10
89.89
2.08
9.99
1.52
1.28
92.09
2.04
8.59
1.05
0.93
92.38
1.30
12.18
3.71
0.75
85.91
6.05
18.69
11.92
15.45
23.17
22.42
17.34
27.54
26.64
24.10
22.72
19.95
16.33
15.13
16.50
41.52
25.82
14.38
20.45
11.43
9.78
9.84
11.01
7.83
101
Table 2. Summary of Regression Results
Cost Efficiency
R^2
Adjusted
Profit Efficiency
St.
Dev.
R^2
Adjusted
St.
Dev.
1992
0.966
0.865
0.495
0.868
0.471
0.431
1993
0.987
0.965
0.205
0.789
0.410
0.397
1994
0.964
0.924
0.277
0.780
0.532
0.953
1995
0.965
0.933
0.397
0.864
0.741
0.474
1996
0.977
0.956
0.270
0.785
0.592
0.555
1997
0.932
0.877
0.563
0.698
0.452
0.627
1998
0.931
0.879
0.563
0.571
0.250
1.154
1999
0.893
0.819
0.469
0.794
0.652
1.971
2000
0.931
0.887
0.352
0.765
0.614
1.018
2001
0.974
0.955
0.224
0.621
0.336
0.701
2002
0.970
0.949
0.188
0.695
0.484
1.330
2003
0.972
0.954
0.186
0.215
-0.289
1.526
2004
0.932
0.885
0.301
0.706
0.502
1.083
2005
0.994
0.991
0.515
0.994
0.990
2.338
2006
0.861
0.777
0.399
0.796
0.674
1.295
2007
0.995
0.991
0.292
0.995
0.991
1.189
2008
0.986
0.977
0.318
0.971
0.950
9.848
2009
0.989
0.982
0.125
0.906
0.841
0.717
2010
0.976
0.961
0.132
0.644
0.416
0.993
2011
0.964
0.941
0.169
0.887
0.815
0.814
2012
0.958
0.931
0.171
0.761
0.608
0.866
2013
0.977
0.961
0.114
0.875
0.789
0.876
Average
0.959
0.925
0.306
0.772
0.583
1.416
102
Table 3. Average Cost and Profit Efficiency in 1992 - 2013
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
Cost
Profit
Efficiency Efficiency
0.690
0.554
0.771
0.704
0.605
0.400
0.462
0.527
0.761
0.376
0.512
0.508
0.420
0.184
0.562
0.102
0.502
0.290
0.760
0.424
0.806
0.109
0.666
0.093
0.646
0.370
0.576
0.133
0.535
0.211
0.543
0.228
0.682
0.043
0.833
0.318
0.817
0.123
0.735
0.381
0.660
0.339
0.798
0.428
103
APPENDIX 2
OBJECTIVE TWO: EFFICIENT STRUCTURE THEORY
Original Specification - Summary
Table 1. Cost Efficiency and Assets
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-16.05453
Prob.
0.0000
Generalized Least
Squares
Coefficient
Prob.
-7.875872
0.0027
-0.282017
0.0274
2.471044
0.0000
-0.030057
0.0113
0.905137
0.318495
0.338236
1.427055
-0.002729
NA
NA
0.3567
0.0000
0.9389
First-Difference
Coefficient
0.094232
Prob.
0.0011
0.066373
0.1995
0.808377
0.1657
-0.007834
0.0360
0.160406
1.623614
Table 2. Cost Efficiency and Loans
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-10.69494
Prob.
0.0000
Generalized Least
Squares
Coefficient
Prob.
-1.958304
0.5403
-0.384464
0.0717
1.734802
0.0000
-0.045428
0.0220
0.810989
0.311578
0.205966
0.628851
-0.021739
NA
NA
0.6455
0.0876
0.6169
First-Difference
Coefficient
0.038408
Prob.
0.4325
0.093995
0.2833
1.455049
0.1427
0.000593
0.9253
0.114057
1.440718
Table 3. Profit Efficiency and Assets
Least Squares
Variable
C
PROF_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
Coefficient
-15.82546
Prob.
0.0000
-0.179944
0.0466
2.421884
0.0000
-0.020612
0.0828
0.904931
0.292811
Generalized Least
Squares
Coefficient
Prob.
-7.693677
0.0037
-0.099874
1.436845
-0.005724
NA
NA
0.6998
0.0000
0.8728
First-Difference
Coefficient
0.087589
Prob.
0.0025
-0.062831
0.0676
0.943277
0.1069
-0.007229
0.0538
0.164118
1.606342
Table 4. Profit Efficiency and Loans
Least Squares
Variable
C
Coefficient
-10.59133
Prob.
0.0000
Generalized Least
Squares
Coefficient
Prob.
-1.836082
0.5698
First-Difference
Coefficient
0.029548
Prob.
0.5471
104
PROF_EFF(1)
LN_GDP
INFLATION
R-squared
D-W stat.
-0.118427
0.4329
1.690494
0.0000
-0.035823
0.0719
0.809792
0.289246
-0.068612
0.633618
-0.023378
NA
NA
0.8281
0.0868
0.5919
-0.082111
0.1582
1.632485
0.1015
0.001278
0.8406
0.116007
1.429195
Alternative Specification - Summary
The alternative specification in this study used the ratio of Gross
International Reserve and External Debt and the 91-day T-bill rate as additional
macroeconomic variables. Inflation was removed in the model.
Table 5. Cost Efficiency and Assets
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
GIR_ED
INT
R-squared
D-W stat.
Coefficient
-20.34612
Prob.
0.0000
-0.189678
0.1406
2.990309
0.0000
-0.482861
0.0079
-0.006629
0.6529
0.905419
0.299852
Generalized Least
Squares
Coefficient
Prob.
-14.28710
0.0797
0.408352
2.224269
-0.717416
0.003294
NA
NA
0.2700
0.0205
0.1844
0.9408
First-Difference
Coefficient
0.105952
0.069695
0.870245
-0.382697
-0.007758
0.161055
1.598026
Prob.
0.0004
0.1758
0.1371
0.0459
0.2019
Table 6. Cost Efficiency and Loans
Least Squares
Variable
C
COST_EFF(1)
LN_GDP
GIR_ED
INT
R-squared
D-W stat.
Coefficient
-13.27184
Prob.
0.0038
-0.286586
0.1855
1.982812
0.0003
0.172603
0.5705
0.015158
0.5409
0.808956
0.278896
Generalized Least
Squares
Coefficient
Prob.
-7.810945
0.4333
0.295045
1.287436
-0.102825
0.030378
NA
NA
0.5144
0.2727
0.8762
0.5756
First-Difference
Coefficient
0.034404
0.112966
1.362730
0.328233
0.010706
0.118023
1.461425
Prob.
0.4978
0.1946
0.1705
0.3105
0.2969
105
Table 7. Profit Efficiency and Assets
Generalized Least
Squares
Coefficient
Prob.
-12.50354
0.1230
Least Squares
Variable
C
PROF_EFF(1)
LN_GDP
GIR_ED
INT
R-squared
D-W stat.
Coefficient
-20.44126
Prob.
0.0000
-0.153528
0.0959
2.984293
0.0000
-0.444005
0.0167
0.000236
0.9871
0.905556
0.285120
-0.027470
2.044983
-0.617024
-0.005092
NA
NA
0.9174
0.0332
0.2645
0.9078
First-Difference
Coefficient
0.099659
-0.068287
1.012431
-0.384910
-0.006535
0.165819
1.581217
Prob.
0.0009
0.0453
0.0841
0.0428
0.2853
Table 8. Profit Efficiency and Loans
Least Squares
Variable
C
PROF_EFF(1)
LN_GDP
GIR_ED
INT
R-squared
D-W stat.
Coefficient
-13.46079
Prob.
0.0031
-0.221807
0.1527
1.979208
0.0003
0.226570
0.4663
0.025356
0.3012
0.809090
0.268025
Generalized Least
Squares
Coefficient
Prob.
-6.069215
0.5399
-0.123714
1.102667
0.023477
0.026217
NA
NA
0.7023
0.3471
0.9722
0.6254
First-Difference
Coefficient
0.025678
-0.094259
1.564239
0.314085
0.012098
0.120314
1.452843
Prob.
0.6138
0.1021
0.1165
0.3278
0.2422
ORIGINAL SPECIFICATION
Cost Efficiency and Assets
Fixed Effects
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:36
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-16.05453
-0.282017
2.471044
-0.030057
0.914726
0.127441
0.105586
0.011812
-17.55118
-2.212932
23.40306
-2.544518
0.0000
0.0274
0.0000
0.0113
Effects Specification
Cross-section fixed (dummy variables)
106
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905137
0.898312
0.460512
88.43380
-272.2357
132.6270
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.353731
1.637768
1.465701
0.318495
Random Effects
Dependent Variable: LN_ASSETS
Method: Panel EGLS (Cross-section random effects)
Date: 05/08/15 Time: 23:39
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-16.18355
-0.275935
2.442933
-0.029895
0.935418
0.127357
0.105438
0.011811
-17.30087
-2.166626
23.16942
-2.531043
0.0000
0.0308
0.0000
0.0117
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
1.025132
0.460512
Rho
0.8321
0.1679
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.620450
0.617885
0.480308
241.9353
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.452501
0.770489
102.4289
0.274041
Unweighted Statistics
R-squared
Sum squared resid
-0.030573
960.7265
Mean dependent var
Durbin-Watson stat
4.260746
0.029217
107
Hausman Test
Correlated Random Effects - Hausman Test
Equation: EST2
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
32.881678
3
0.0000
Random
Var(Diff.)
Prob.
-0.275935
2.442933
-0.029895
0.000021
0.000031
0.000000
0.1873
0.0000
0.2985
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
COST_EFF(-1)
LN_GDP
INFLATION
Fixed
-0.282017
2.471044
-0.030057
Cross-section random effects test equation:
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:40
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-16.05453
-0.282017
2.471044
-0.030057
0.914726
0.127441
0.105586
0.011812
-17.55118
-2.212932
23.40306
-2.544518
0.0000
0.0274
0.0000
0.0113
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905137
0.898312
0.460512
88.43380
-272.2357
132.6270
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.353731
1.637768
1.465701
0.318495
Generalized Least Squares
Dependent Variable: LN_ASSETS
108
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 23:41
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-7.875872
0.338236
1.427055
-0.002729
2.625552
0.366959
0.302344
0.035599
-2.999702
0.921727
4.719979
-0.076672
0.0027
0.3567
0.0000
0.9389
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
4.260746
864.4280
3.513049
3.527497
1.946910
34.82332
864.4280
1.946910
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.444132
-782.9230
3.549699
864.4280
932.2259
0.000000
1.946910
First-Difference
Dependent Variable: D(LN_ASSETS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:44
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(COST_EFF(-1))
D(LN_GDP)
D(INFLATION)
0.094232
0.066373
0.808377
-0.007834
0.028708
0.051644
0.582107
0.003723
3.282418
1.285210
1.388710
-2.103891
0.0011
0.1995
0.1657
0.0360
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.160406
0.094642
0.222735
19.00098
50.40411
2.439102
0.000059
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.132857
0.234088
-0.093740
0.207714
0.025477
1.623614
109
Cost Efficiency and Loans
Fixed Effects
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:43
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-10.69494
-0.384464
1.734802
-0.045428
1.527933
0.212906
0.176296
0.019756
-6.999614
-1.805796
9.840251
-2.299475
0.0000
0.0717
0.0000
0.0220
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.810989
0.797391
0.769296
246.7872
-502.1207
59.64083
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.380003
2.664040
2.491973
0.311578
Random Effects
Dependent Variable: LN_LOANS
Method: Panel EGLS (Cross-section random effects)
Date: 05/08/15 Time: 23:44
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-10.64164
-0.376233
1.679674
-0.045715
1.546985
0.212668
0.175853
0.019752
-6.878955
-1.769106
9.551574
-2.314403
0.0000
0.0776
0.0000
0.0211
Effects Specification
S.D.
Rho
110
Cross-section random
Idiosyncratic random
1.307969
0.769296
0.7430
0.2570
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.244934
0.239832
0.795755
48.00941
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.450912
0.901834
281.1524
0.273457
Unweighted Statistics
R-squared
Sum squared resid
-0.071911
1399.571
Mean dependent var
Durbin-Watson stat
3.314079
0.054933
Hausman Test
Correlated Random Effects - Hausman Test
Equation: EST5
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
22.304221
3
0.0001
Random
Var(Diff.)
Prob.
-0.376233
1.679674
-0.045715
0.000101
0.000156
0.000000
0.4128
0.0000
0.4601
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
COST_EFF(-1)
LN_GDP
INFLATION
Fixed
-0.384464
1.734802
-0.045428
Cross-section random effects test equation:
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:44
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-10.69494
-0.384464
1.734802
-0.045428
1.527933
0.212906
0.176296
0.019756
-6.999614
-1.805796
9.840251
-2.299475
0.0000
0.0717
0.0000
0.0220
111
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.810989
0.797391
0.769296
246.7872
-502.1207
59.64083
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.380003
2.664040
2.491973
0.311578
Generalized Least Squares
Dependent Variable: LN_LOANS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 23:45
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
INFLATION
-1.958304
0.205966
0.628851
-0.021739
3.197824
0.447751
0.368142
0.043459
-0.612386
0.460002
1.708177
-0.500221
0.5403
0.6455
0.0876
0.6169
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
3.314079
1286.886
3.910961
3.925409
2.898391
6.484027
1286.886
2.898391
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.709088
-872.0554
3.947611
1286.886
1305.679
0.090295
2.898391
First-Difference
Dependent Variable: D(LN_LOANS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:46
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 413
Variable
Coefficient
Std. Error
t-Statistic
Prob.
112
C
D(COST_EFF(-1))
D(LN_GDP)
D(INFLATION)
0.038408
0.093995
1.455049
0.000593
0.048876
0.087479
0.990706
0.006322
0.785830
1.074489
1.468699
0.093807
0.4325
0.2833
0.1427
0.9253
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.114057
0.044480
0.376804
54.23696
-166.8091
1.639299
0.020155
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.104488
0.385475
0.957913
1.259915
1.077358
1.440718
Profit Efficiency and Assets
Fixed Effects
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:47
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-15.82546
-0.179944
2.421884
-0.020612
0.926756
0.090145
0.106571
0.011853
-17.07619
-1.996161
22.72560
-1.738989
0.0000
0.0466
0.0000
0.0828
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.904931
0.898092
0.461011
88.62546
-272.7207
132.3101
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.355896
1.639933
1.467866
0.292811
113
Random Effects
Dependent Variable: LN_ASSETS
Method: Panel EGLS (Cross-section random effects)
Date: 05/08/15 Time: 23:48
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-15.98105
-0.177309
2.397252
-0.020629
0.949388
0.090077
0.106428
0.011852
-16.83301
-1.968419
22.52463
-1.740484
0.0000
0.0496
0.0000
0.0825
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
Rho
1.080923
0.461011
0.8461
0.1539
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.623334
0.620789
0.477545
244.9211
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.429869
0.769588
101.2537
0.256023
Unweighted Statistics
R-squared
Sum squared resid
-0.027113
957.5013
Mean dependent var
Durbin-Watson stat
4.260746
0.027074
Hausman Test
Correlated Random Effects - Hausman Test
Equation: EST8
Test cross-section random effects
Test Summary
Cross-section random
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
27.245724
3
0.0000
Var(Diff.)
Prob.
Cross-section random effects test comparisons:
Variable
Fixed
Random
114
PROF_EFF(-1)
LN_GDP
INFLATION
-0.179944
2.421884
-0.020612
-0.177309
2.397252
-0.020629
0.000012
0.000030
0.000000
0.4523
0.0000
0.8993
Cross-section random effects test equation:
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:48
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-15.82546
-0.179944
2.421884
-0.020612
0.926756
0.090145
0.106571
0.011853
-17.07619
-1.996161
22.72560
-1.738989
0.0000
0.0466
0.0000
0.0828
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.904931
0.898092
0.461011
88.62546
-272.7207
132.3101
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.355896
1.639933
1.467866
0.292811
Generalized Least Squares
Dependent Variable: LN_ASSETS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 23:49
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-7.693677
-0.099874
1.436845
-0.005724
2.654225
0.258980
0.304036
0.035751
-2.898653
-0.385644
4.725897
-0.160102
0.0037
0.6998
0.0000
0.8728
115
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
4.260746
865.7921
3.514626
3.529074
1.949982
34.06893
865.7921
1.949982
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.444132
-783.2762
3.551276
865.7921
932.2259
0.000000
1.949982
First-Difference
Dependent Variable: D(LN_ASSETS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:47
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(PROF_EFF(-1))
D(LN_GDP)
D(INFLATION)
0.087589
-0.062831
0.943277
-0.007229
0.028769
0.034278
0.583664
0.003737
3.044595
-1.833006
1.616129
-1.934322
0.0025
0.0676
0.1069
0.0538
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.164118
0.098645
0.222242
18.91698
51.32131
2.506628
0.000034
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.132857
0.234088
-0.098171
0.203283
0.021046
1.606342
Profit Efficiency and Loans
Fixed Effects
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:50
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
116
C
PROF_EFF(-1)
LN_GDP
INFLATION
-10.59133
-0.118427
1.690494
-0.035823
1.551080
0.150870
0.178313
0.019856
-6.828358
-0.784956
9.480491
-1.804112
0.0000
0.4329
0.0000
0.0719
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.809792
0.796108
0.771728
248.3501
-503.5348
59.17803
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.386316
2.670353
2.498286
0.289246
Random Effects
Dependent Variable: LN_LOANS
Method: Panel EGLS (Cross-section random effects)
Date: 05/08/15 Time: 23:51
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-10.56844
-0.113496
1.639480
-0.036359
1.571066
0.150673
0.177866
0.019854
-6.726921
-0.753260
9.217509
-1.831370
0.0000
0.4517
0.0000
0.0677
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
1.350596
0.771728
Rho
0.7539
0.2461
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.242548
0.237430
0.795287
47.39186
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.438300
0.900415
280.8216
0.256306
Unweighted Statistics
R-squared
-0.069153
Mean dependent var
3.314079
117
Sum squared resid
1395.971
Durbin-Watson stat
0.051560
Hausman Test
Correlated Random Effects - Hausman Test
Equation: EST11
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
19.716217
3
0.0002
Random
Var(Diff.)
Prob.
-0.113496
1.639480
-0.036359
0.000060
0.000159
0.000000
0.5227
0.0001
0.1104
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
PROF_EFF(-1)
LN_GDP
INFLATION
Fixed
-0.118427
1.690494
-0.035823
Cross-section random effects test equation:
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/08/15 Time: 23:52
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-10.59133
-0.118427
1.690494
-0.035823
1.551080
0.150870
0.178313
0.019856
-6.828358
-0.784956
9.480491
-1.804112
0.0000
0.4329
0.0000
0.0719
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.809792
0.796108
0.771728
248.3501
-503.5348
59.17803
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.386316
2.670353
2.498286
0.289246
118
Generalized Least Squares
Dependent Variable: LN_LOANS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 23:52
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
INFLATION
-1.836082
-0.068612
0.633618
-0.023378
3.230384
0.315984
0.369939
0.043606
-0.568379
-0.217137
1.712765
-0.536110
0.5698
0.8281
0.0868
0.5919
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
3.314079
1287.362
3.911332
3.925779
2.899465
6.317252
1287.362
2.899465
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.709088
-872.1383
3.947982
1287.362
1305.679
0.097155
2.899465
First-Difference
Dependent Variable: D(LN_LOANS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:49
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 413
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(PROF_EFF(-1))
D(LN_GDP)
D(INFLATION)
0.029548
-0.082111
1.632485
0.001278
0.049032
0.058067
0.994419
0.006353
0.602623
-1.414081
1.641647
0.201223
0.5471
0.1582
0.1015
0.8406
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
0.116007
0.046583
0.376390
54.11760
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
0.104488
0.385475
0.955710
1.257712
119
Log likelihood
F-statistic
Prob(F-statistic)
-166.3541
1.670999
0.016472
Hannan-Quinn criter.
Durbin-Watson stat
1.075155
1.429195
ALTERNATIVE SPECIFICATIONS
Cost Efficiency and Assets
Fixed Effects
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:09
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-20.34612
-0.189678
2.990309
-0.482861
-0.006629
2.713665
0.128480
0.319513
0.180959
0.014728
-7.497654
-1.476320
9.358944
-2.668347
-0.450074
0.0000
0.1406
0.0000
0.0079
0.6529
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905419
0.898371
0.460380
88.17097
-271.5690
128.4626
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.355219
1.648418
1.470801
0.299852
Random Effects
Dependent Variable: LN_ASSETS
Method: Panel EGLS (Cross-section random effects)
Date: 05/09/15 Time: 00:09
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
120
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-20.59086
-0.184145
2.976703
-0.493191
-0.006662
2.722205
0.128415
0.319479
0.180916
0.014727
-7.564039
-1.433984
9.317356
-2.726079
-0.452354
0.0000
0.1523
0.0000
0.0067
0.6512
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
Rho
1.132059
0.460380
0.8581
0.1419
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.628245
0.624888
0.474181
187.1609
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.410094
0.768837
99.60733
0.264853
Unweighted Statistics
R-squared
Sum squared resid
-0.026433
956.8678
Mean dependent var
Durbin-Watson stat
4.260746
0.027570
Hausman Test
Correlated Random Effects - Hausman Test
Equation: ESH2
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
26.264277
4
0.0000
Random
Var(Diff.)
Prob.
-0.184145
2.976703
-0.493191
-0.006662
0.000017
0.000022
0.000016
0.000000
0.1757
0.0036
0.0088
0.8419
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
COST_EFF(-1)
LN_GDP
GIR_ED
INT
Fixed
-0.189678
2.990309
-0.482861
-0.006629
Cross-section random effects test equation:
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:10
121
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-20.34612
-0.189678
2.990309
-0.482861
-0.006629
2.713665
0.128480
0.319513
0.180959
0.014728
-7.497654
-1.476320
9.358944
-2.668347
-0.450074
0.0000
0.1406
0.0000
0.0079
0.6529
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905419
0.898371
0.460380
88.17097
-271.5690
128.4626
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.355219
1.648418
1.470801
0.299852
Generalized Least Squares
Dependent Variable: LN_ASSETS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/09/15 Time: 00:11
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-14.28710
0.408352
2.224269
-0.717416
0.003294
8.152361
0.370236
0.959977
0.540469
0.044387
-1.752511
1.102952
2.317003
-1.327395
0.074219
0.0797
0.2700
0.0205
0.1844
0.9408
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
4.260746
860.9982
3.513560
3.531620
1.943563
36.64802
860.9982
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.444132
-782.0375
3.559373
860.9982
932.2259
0.000000
1.943563
122
Dispersion
1.943563
First-Difference
Dependent Variable: D(LN_ASSETS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:52
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(COST_EFF(-1))
D(LN_GDP)
D(GIR_ED)
D(INT)
0.105952
0.069695
0.870245
-0.382697
-0.007758
0.029837
0.051380
0.584151
0.191056
0.006069
3.551047
1.356467
1.489760
-2.003067
-1.278364
0.0004
0.1758
0.1371
0.0459
0.2019
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.161055
0.092973
0.222940
18.98631
50.56406
2.365604
0.000087
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.132857
0.234088
-0.089681
0.221497
0.033381
1.598026
Cost Efficiency and Loans
Fixed Effects
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:14
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-13.27184
-0.286586
1.982812
0.172603
0.015158
4.564502
0.216074
0.537440
0.303986
0.024771
-2.907621
-1.326338
3.689364
0.567799
0.611904
0.0038
0.1855
0.0003
0.5705
0.5409
123
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.808956
0.794720
0.774351
249.4420
-504.5175
56.82291
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.395167
2.688367
2.510749
0.278896
Random Effects
Dependent Variable: LN_LOANS
Method: Panel EGLS (Cross-section random effects)
Date: 05/09/15 Time: 00:15
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-13.47895
-0.276698
1.959863
0.145874
0.015083
4.571773
0.215870
0.537337
0.303844
0.024768
-2.948298
-1.281782
3.647365
0.480096
0.608944
0.0034
0.2006
0.0003
0.6314
0.5429
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
1.387044
0.774351
Rho
0.7624
0.2376
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.240500
0.233642
0.795932
35.06966
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.428411
0.899327
280.6443
0.247355
Unweighted Statistics
R-squared
Sum squared resid
-0.072320
1400.106
Mean dependent var
Durbin-Watson stat
3.314079
0.049581
124
Hausman Test
Correlated Random Effects - Hausman Test
Equation: ESH5
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
21.049438
4
0.0003
Random
Var(Diff.)
Prob.
-0.276698
1.959863
0.145874
0.015083
0.000088
0.000111
0.000086
0.000000
0.2917
0.0294
0.0040
0.8426
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
COST_EFF(-1)
LN_GDP
GIR_ED
INT
Fixed
-0.286586
1.982812
0.172603
0.015158
Cross-section random effects test equation:
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:15
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-13.27184
-0.286586
1.982812
0.172603
0.015158
4.564502
0.216074
0.537440
0.303986
0.024771
-2.907621
-1.326338
3.689364
0.567799
0.611904
0.0038
0.1855
0.0003
0.5705
0.5409
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.808956
0.794720
0.774351
249.4420
-504.5175
56.82291
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.395167
2.688367
2.510749
0.278896
125
Generalized Least Squares
Dependent Variable: LN_LOANS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/09/15 Time: 00:16
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
COST_EFF(-1)
LN_GDP
GIR_ED
INT
-7.810945
0.295045
1.287436
-0.102825
0.030378
9.967688
0.452558
1.173751
0.660072
0.054263
-0.783627
0.651949
1.096856
-0.155779
0.559825
0.4333
0.5144
0.2727
0.8762
0.5756
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
3.314079
1286.679
3.915288
3.933347
2.904467
6.541602
1286.679
2.904467
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.709088
-872.0245
3.961100
1286.679
1305.679
0.162188
2.904467
First-Difference
Dependent Variable: D(LN_LOANS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:53
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 413
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(COST_EFF(-1))
D(LN_GDP)
D(GIR_ED)
D(INT)
0.034404
0.112966
1.362730
0.328233
0.010706
0.050693
0.086940
0.992272
0.323191
0.010250
0.678659
1.299359
1.373343
1.015603
1.044437
0.4978
0.1946
0.1705
0.3105
0.2969
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
0.118023
0.046261
Mean dependent var
S.D. dependent var
0.104488
0.385475
126
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.376453
53.99415
-165.8825
1.644650
0.018206
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.958269
1.270013
1.081567
1.461425
Profit Efficiency and Assets
Fixed Effects
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:18
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-20.44126
-0.153528
2.984293
-0.444005
0.000236
2.691923
0.091991
0.318929
0.184855
0.014557
-7.593553
-1.668933
9.357238
-2.401909
0.016239
0.0000
0.0959
0.0000
0.0167
0.9871
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905556
0.898518
0.460047
88.04343
-271.2447
128.6681
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.353771
1.646971
1.469353
0.285120
Random Effects
Dependent Variable: LN_ASSETS
Method: Panel EGLS (Cross-section random effects)
Date: 05/09/15 Time: 00:18
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
127
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-20.69418
-0.149880
2.971924
-0.454186
3.35E-05
2.701390
0.091935
0.318896
0.184814
0.014556
-7.660570
-1.630278
9.319402
-2.457533
0.002304
0.0000
0.1038
0.0000
0.0144
0.9982
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
Rho
1.190089
0.460047
0.8700
0.1300
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.631585
0.628258
0.471296
189.8619
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.390004
0.768109
98.39898
0.254943
Unweighted Statistics
R-squared
Sum squared resid
-0.024689
955.2416
Mean dependent var
Durbin-Watson stat
4.260746
0.026262
Hausman Test
Correlated Random Effects - Hausman Test
Equation: ESH8
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
22.809130
4
0.0001
Random
Var(Diff.)
Prob.
-0.149880
2.971924
-0.454186
0.000034
0.000010
0.000021
0.000015
0.000000
0.2567
0.0065
0.0092
0.1956
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
Fixed
-0.153528
2.984293
-0.444005
0.000236
Cross-section random effects test equation:
Dependent Variable: LN_ASSETS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:19
128
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-20.44126
-0.153528
2.984293
-0.444005
0.000236
2.691923
0.091991
0.318929
0.184855
0.014557
-7.593553
-1.668933
9.357238
-2.401909
0.016239
0.0000
0.0959
0.0000
0.0167
0.9871
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.905556
0.898518
0.460047
88.04343
-271.2447
128.6681
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
4.260746
1.444132
1.353771
1.646971
1.469353
0.285120
Generalized Least Squares
Dependent Variable: LN_ASSETS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/09/15 Time: 00:19
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-12.50354
-0.027470
2.044983
-0.617024
-0.005092
8.106854
0.264800
0.960171
0.553004
0.043973
-1.542342
-0.103738
2.129810
-1.115768
-0.115787
0.1230
0.9174
0.0332
0.2645
0.9078
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
4.260746
863.3416
3.516278
3.534338
1.948852
35.34610
863.3416
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.444132
-782.6464
3.562091
863.3416
932.2259
0.000000
1.948852
129
Dispersion
1.948852
First-Difference
Dependent Variable: D(LN_ASSETS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:55
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(PROF_EFF(-1))
D(LN_GDP)
D(GIR_ED)
D(INT)
0.099659
-0.068287
1.012431
-0.384910
-0.006535
0.029888
0.034007
0.584578
0.189344
0.006108
3.334407
-2.008028
1.731903
-2.032856
-1.069904
0.0009
0.0453
0.0841
0.0428
0.2853
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.165819
0.098124
0.222306
18.87849
51.74291
2.449491
0.000044
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.132857
0.234088
-0.095376
0.215802
0.027686
1.581217
Profit Efficiency and Loans
Fixed Effects
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:23
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-13.46079
-0.221807
1.979208
0.226570
0.025356
4.529609
0.154828
0.536654
0.310708
0.024493
-2.971732
-1.432602
3.688055
0.729205
1.035220
0.0031
0.1527
0.0003
0.4663
0.3012
130
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.809090
0.794864
0.774080
249.2670
-504.3603
56.87220
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.394466
2.687665
2.510048
0.268025
Random Effects
Dependent Variable: LN_LOANS
Method: Panel EGLS (Cross-section random effects)
Date: 05/09/15 Time: 00:23
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Swamy and Arora estimator of component variances
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-13.67924
-0.214133
1.958164
0.199508
0.024949
4.537433
0.154650
0.536551
0.310567
0.024491
-3.014753
-1.384627
3.649542
0.642399
1.018729
0.0027
0.1669
0.0003
0.5209
0.3089
Effects Specification
S.D.
Cross-section random
Idiosyncratic random
1.435387
0.774080
Rho
0.7747
0.2253
Weighted Statistics
R-squared
Adjusted R-squared
S.E. of regression
F-statistic
Prob(F-statistic)
0.242795
0.235958
0.792855
35.51159
0.000000
Mean dependent var
S.D. dependent var
Sum squared resid
Durbin-Watson stat
0.414079
0.897790
278.4780
0.239725
Unweighted Statistics
R-squared
Sum squared resid
-0.070266
1397.424
Mean dependent var
Durbin-Watson stat
3.314079
0.047772
131
Hausman Test
Correlated Random Effects - Hausman Test
Equation: ESH11
Test cross-section random effects
Chi-Sq.
Statistic
Chi-Sq. d.f.
Prob.
19.400837
4
0.0007
Random
Var(Diff.)
Prob.
-0.214133
1.958164
0.199508
0.024949
0.000055
0.000111
0.000088
0.000000
0.3005
0.0454
0.0038
0.2570
Test Summary
Cross-section random
Cross-section random effects test comparisons:
Variable
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
Fixed
-0.221807
1.979208
0.226570
0.025356
Cross-section random effects test equation:
Dependent Variable: LN_LOANS
Method: Panel Least Squares
Date: 05/09/15 Time: 00:24
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-13.46079
-0.221807
1.979208
0.226570
0.025356
4.529609
0.154828
0.536654
0.310708
0.024493
-2.971732
-1.432602
3.688055
0.729205
1.035220
0.0031
0.1527
0.0003
0.4663
0.3012
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.809090
0.794864
0.774080
249.2670
-504.3603
56.87220
0.000000
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
3.314079
1.709088
2.394466
2.687665
2.510048
0.268025
132
Generalized Least Squares
Dependent Variable: LN_LOANS
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/09/15 Time: 00:24
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
PROF_EFF(-1)
LN_GDP
GIR_ED
INT
-6.069215
-0.123714
1.102667
0.023477
0.026217
9.900884
0.323684
1.172657
0.674762
0.053705
-0.612997
-0.382207
0.940315
0.034793
0.488157
0.5399
0.7023
0.3471
0.9722
0.6254
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
3.314079
1287.489
3.915917
3.933977
2.906296
6.258799
1287.489
2.906296
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
1.709088
-872.1654
3.961730
1287.489
1305.679
0.180637
2.906296
First-Difference
Dependent Variable: D(LN_LOANS)
Method: Panel Least Squares
Date: 05/09/15 Time: 00:57
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 413
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(PROF_EFF(-1))
D(LN_GDP)
D(GIR_ED)
D(INT)
0.025678
-0.094259
1.564239
0.314085
0.012098
0.050844
0.057526
0.994273
0.320581
0.010329
0.505024
-1.638556
1.573249
0.979736
1.171278
0.6138
0.1021
0.1165
0.3278
0.2422
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
0.120314
0.048738
Mean dependent var
S.D. dependent var
0.104488
0.385475
133
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.375964
53.85391
-165.3455
1.680937
0.014356
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.955668
1.267412
1.078967
1.452843
134
APPENDIX 3
OBJECTIVE THREE: QUIET LIFE HYPOTHESIS
Summary
Table 1. Cost Efficiency and Market Concentration
Least Squares
Variable
C
HHI(-1)
AGE
GBL
Rsquared
D-W stat.
Coefficient
0.576959
3.361660
-0.000505
0.130448
Prob.
0.0000
0.0209
0.0495
0.0000
Generalized Least
Squares
Coefficient
Prob.
0.576959
0.0000
3.361660
0.0204
-0.000505
0.0489
0.130448
0.0000
0.123146
NA
1.481127
NA
First-Difference
Coefficient
Prob.
-0.028343
0.8405
-5.612304
0.3696
-0.000166
0.9621
0.057348
0.1619
0.019864
2.711685
Table 2. Profit Efficiency and Market Concentration
Least Squares
Variable
C
HHI(-1)
AGE
GBL
Rsquared
D-W stat.
Coefficient
0.366864
-0.890850
0.000165
-0.125832
Prob.
0.0000
0.6831
0.6684
0.0000
Generalized Least
Squares
Coefficient
Prob.
0.366864
0.0000
-0.890850
0.6829
0.000165
0.6682
-0.125832
0.0000
0.049839
NA
1.658419
NA
First-Difference
Coefficient
Prob.
-0.558263
0.0100
-2.588077
0.7870
0.013057
0.0148
-0.079564
0.2052
0.025700
3.079318
Cost Efficiency and Market Concentration
Least Squares
Dependent Variable: COST_EFF
Method: Panel Least Squares
Date: 05/08/15 Time: 15:10
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
HHI(-1)
AGE
GBL
0.576959
3.361660
-0.000505
0.130448
0.017596
1.449932
0.000257
0.017401
32.78952
2.318495
-1.969390
7.496767
0.0000
0.0209
0.0495
0.0000
R-squared
Adjusted R-squared
0.123146
0.117221
Mean dependent var
S.D. dependent var
0.651501
0.185197
135
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.174005
13.44326
149.7302
20.78523
0.000000
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.650581
-0.613931
-0.636133
1.481127
Generalized Least Squares
Dependent Variable: COST_EFF
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 15:08
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
HHI(-1)
AGE
GBL
0.576959
3.361660
-0.000505
0.130448
0.017596
1.449932
0.000257
0.017401
32.78952
2.318495
-1.969390
7.496767
0.0000
0.0204
0.0489
0.0000
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
0.651501
13.44326
-0.650541
-0.636093
0.030278
62.35569
13.44326
0.030278
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
0.185197
149.7212
-0.613891
13.44326
15.33124
0.000000
0.030278
First-Difference
Dependent Variable: D(COST_EFF)
Method: Panel Least Squares
Date: 05/09/15 Time: 01:29
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(HHI(-1))
AGE
GBL
-0.028343
-5.612304
-0.000166
0.057348
0.140724
6.247266
0.003480
0.040918
-0.201412
-0.898362
-0.047603
1.401556
0.8405
0.3696
0.9621
0.1619
Effects Specification
136
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.019864
-0.056909
0.222809
19.01367
50.26592
0.258733
0.999981
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.003434
0.216728
-0.093072
0.208382
0.026145
2.711685
Profit Efficiency and Market Concentration
Least Squares
Dependent Variable: PROF_EFF
Method: Panel Least Squares
Date: 05/08/15 Time: 15:10
Sample (adjusted): 1993 2013
Periods included: 21
Cross-sections included: 28
Total panel (unbalanced) observations: 448
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
HHI(-1)
AGE
GBL
0.366864
-0.890850
0.000165
-0.125832
0.026468
2.180979
0.000386
0.026174
13.86091
-0.408463
0.428642
-4.807550
0.0000
0.6831
0.6684
0.0000
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.049839
0.043419
0.261737
30.41670
-33.16893
7.763112
0.000046
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
0.288046
0.267611
0.165933
0.202583
0.180380
1.658419
Generalized Least Squares
Dependent Variable: PROF_EFF
Method: Generalized Linear Model (Quadratic Hill Climbing)
Date: 05/08/15 Time: 15:09
Sample (adjusted): 1993 2013
Included observations: 448 after adjustments
Family: Normal
Link: Identity
Dispersion computed using Pearson Chi-Square
Coefficient covariance computed using observed Hessian
Convergence achieved after 1 iteration
137
Variable
Coefficient
Std. Error
z-Statistic
Prob.
C
HHI(-1)
AGE
GBL
0.366864
-0.890850
0.000165
-0.125832
0.026468
2.180979
0.000386
0.026174
13.86091
-0.408463
0.428642
-4.807550
0.0000
0.6829
0.6682
0.0000
Mean dependent var
Sum squared resid
Akaike info criterion
Hannan-Quinn criter.
Deviance statistic
LR statistic
Pearson SSR
Dispersion
0.288046
30.41670
0.165973
0.180421
0.068506
23.28933
30.41670
0.068506
S.D. dependent var
Log likelihood
Schwarz criterion
Deviance
Restr. deviance
Prob(LR statistic)
Pearson statistic
0.267611
-33.17792
0.202623
30.41670
32.01216
0.000035
0.068506
First-Difference
Dependent Variable: D(PROF_EFF)
Method: Panel Least Squares
Date: 05/09/15 Time: 01:36
Sample (adjusted): 1994 2013
Periods included: 20
Cross-sections included: 28
Total panel (unbalanced) observations: 414
Variable
Coefficient
Std. Error
t-Statistic
Prob.
C
D(HHI(-1))
AGE
GBL
-0.558263
-2.588077
0.013057
-0.079564
0.215616
9.572027
0.005332
0.062694
-2.589155
-0.270379
2.449023
-1.269092
0.0100
0.7870
0.0148
0.2052
Effects Specification
Cross-section fixed (dummy variables)
R-squared
Adjusted R-squared
S.E. of regression
Sum squared resid
Log likelihood
F-statistic
Prob(F-statistic)
0.025700
-0.050616
0.341387
44.63689
-126.3883
0.336762
0.999682
Mean dependent var
S.D. dependent var
Akaike info criterion
Schwarz criterion
Hannan-Quinn criter.
Durbin-Watson stat
-0.006409
0.333062
0.760330
1.061784
0.879547
3.079318
138
139
BIBLIOGRAPHY
Al-Muharrami, Saeed and Matthews, Kent. Market Power versus Efficient
Structure in Arab GCC Banking, (United States: Cardiff Business School,
2009).
Allen, Albert J., Edwards, Seanicaa, and Shaik, Saleem, Market Structure
Conduct Performance (SCP) Hypothesis Revisited using Stochastic
Frontier Efficiency Analysis, (United States: MSU Department of
Economics, 2005)
Al-Jarrah, Idries and Gharaibeh, Hisham, The efficiency cost of market power in
banking: a test of the “quiet life” and related hypotheses in the Jordan’s
banking industry, (Jordan: Investment Management and Financial
Innovations, 2009)
Bangko Sentral ng Pilipinas. ASEAN Economic Community (AEC) 2015:
Financial Integration in ASEAN.
Bangko Sentral ng Pilipinas, General Banking Law of 2000.
http://www.bsp.gov.ph/downloads/Regulations/gba.pdf
Bangko Sentral ng Pilipinas. Income Statement and Key Ratios.
http://www.bsp.gov.ph/banking/pbs_new/4.html
Behname, Mehdi, The Compare of Concentration and Efficiency in Banking
Industry: Evidence from the OPEC Countries, (Eurasian Journal of
Business and Economics, 2012)
Berger, A. N., The Profit-Structure in Banking--Tests of Market Power and
Efficient Structure Hypotheses, (Journal of Money, Credit, and Banking,
1995) 27 (2), 404-431.
Berger, A. N. and T. H. Hannan, The Price–Concentration Relationship in
Banking, (Review of Economics and Statistics, 1989) 71, 291–299.
Berger, Allen and Hannan, Timothy, The Efficiency Cost of Market Power in the
Banking Industry: A Test of the “Quiet Life” and Related Hypotheses,
(United States: President and Fellows of Harvard College and the
Massachusetts Institute of Technology, 1998)
Bollard, Alan., Hodgetts, Bernard., and Hunt, Chris. The role of banks in the
economy – improving the performance of the New Zealand banking system
after the Global Financial Crisis. (New Zealand Shareholders Association,
2011).
140
Bondoc, Kristine, Bank Spread Analysis of Foreign Bank Entry and Presence in
the Philippines Commercial Banking System, (Pasig City: University of
Asia and the Pacific, 2003)
Byeongyong, Paul Choi and Weiss, Mary A., An Empirical Investigation of
market structure, efficiency, and performance in property-liability
insurance, (Journal of Risk and Insurance, 2005)
Chortareas, Georgios E., Garza-Garcia, Jesus Gustavo, and Girardone, Claudia,
Banking Sector Performance in Some Latin AmericanCountries: Market
Power versus Efficiency, (Mexico, Banco de Mexico, 2010)
Claessens, S. and Laeven, L., What drives bank competition? Some international
evidence, (Journal of Money, Credit and Banking, 2004)
Dávila, José Carlos Wong, Market structure, competition and intermediation in
the banking industry, (Germany: University of Frankfurt, 2005)
Fajardo, Feliciano and Manansala, Manuel, Central Banking, (Navotas: Navotas
Press, 1994), 215.
Garcia, Paul Joseph, Do We Need to Liberalize the Entry of Foreign Banks?,
(Pasig City: Center for Research and Communication, 1992)
Gujarati, D. Econometrics by Example. (England: Palgrave Macmillan, 2011).
Hicks J., Annual survey of economic theory: The theory of monopoly,
(Econometrica, 1935)
Homma, T., Tsutsui, Y., and Uchida, H., Firm Growth and Efficiency in the
Banking Industry: A new test of the efficient structure hypothesis,
(Japan: The Research Institute of Economy, Trade and Industry, 2012)
Huang, Stephen, Efficiency Gains from Bank Mergers in the Philippine Banking
Industry: An Analysis of Recent Mergers, (Pasig City: University of Asia
and the Pacific; 2002)
Jeon, Yongil and Miller, Stephen M., Bank Performance: Market Power or Effient
Structure?, (United States: University of Connecticut, 2005)
Koetter, Michael, Kolari, James W., and Spierdijk, Laura, Efficient Competition?
Testing the 'quiet life' of U.S. banks with adjusted Lerner indices, (USA
and The Netherlands: University of Groningen and Mays Business School,
2008)
141
Lamberte, Mario and Manlagnit, Ma. Chelo, Evaluating the Impacts of
Competition Policy Reforms on the Efficiency of Philippine Commercial
Banks, (Philippine Institute of Development Studies: Makati City, 2004)
Leuterio, Mercedes, Banking Theory and Practice, (Manila: St. Anthony
Bookservice, 1967), 71.
Madura, Jeff. Financial Institutions and Markets 7th Edition. (Ohio, USA:
Thomson, 2006), 509 512
Matthews, Kent and Thompson, John, The Economics of Banking: Second
Edition, (USA: John Wiley & Sons, Ltd., 2010)
Maudos, J. and de Guevara, J. Fernández, The cost of market power in banking:
Social welfare loss vs. cost inefficiency, (Journal of Banking and Finance,
2007)
Mensi, Sami and Zouari, Abderrazak, Efficient Structure versus Market Power:
Theories and Empirical Evidence, (International Journal of Economics
and Finance, 2010)
Polius, Tracy and Samuel, Wendell, Banking Efficiency in the Eastern Caribbean
Currency Union: An Examination if the Structure-Conduct-Performance
Paradigm and the Efficiency Hypothesis, (Eastern Caribbean, n.d)
Punt, L.W and van Rooij, M.C.J, The Profit-Structure Relationship and Mergers
in the European Banking Industry: An Empirical Assessment,
(Econometric Research and Special Studies Department: De
Nederlandsche Bank, 2000)
Reyes, Alberto, The Philippine banking industry: competition, consolidation and
systemic stability, (Philippines: Bangko Sentral ng Pilipinas, 2000)
Rhoades, S.A, Bank Mergers and Banking Structure in the United States, 1980
98. (United States: Board of Governors of the Federal Reserve System,
Staff Study No. 174, 2000).
Smirlock, M. Evidence of the (Non) Relationship Between Concentration and
Profitability in Banking, (Journal of Money, Credit, and Banking, 1985)
17, 69–83.
VanHoose, David, The Industrial Organization of Banking, (United States:
Springer, 2010)
Weiss, L. The Concentration-profits Relationship and Antitrust,” in H.
Goldschmid et al., eds., Industrial Concentration: The New Learning,
(Boston: Brown, Little & Co., 1974)
142