bank efficiency and lending propensity: evidence … · bank efficiency and lending propensity:...

I

BANK EFFICIENCY AND LENDING PROPENSITY:

EVIDENCE FROM COMMERCIAL BANKS IN INDONESIA

Thesis submitted for the degree of

Doctor of Philosophy

at the University of Leicester

by:

Mokhamad Anwar

School of Management

University of Leicester

December, 2014

II

BANK EFFICIENCY AND LENDING PROPENSITY:

EVIDENCE FROM COMMERCIAL BANKS IN INDONESIA

Abstract

Indonesia is one of the emerging economies1, which has been adopting a bank-based system

in the economy. It is very important to investigate the Indonesian commercial banks‟

performance given their substantial contribution to the development of the country. This

thesis aims to measure and analyse the performance of Indonesian banks in terms of their

efficiency and lending propensity over the period 2002-2010. The period testifies the

recovery phase after the turmoil caused by the Asian Financial Crisis of 1997-98 as well as

the revocation of the regulation of minimum threshold on commercial banks‟ small business

loans (finance) in 2001. This thesis employs frontier methods in estimating bank efficiency

where both parametric and non-parametric linear programming approaches are used, namely

Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA). The former is

used to estimate the technical efficiencies and the latter is used to estimate the cost

efficiencies of Indonesian banks over the period 2002-2010. This thesis also examines the

lending propensity of Indonesian banks reflected by the tendency of their total loans and

small business loans over the study period. The findings suggest that the technical efficiency

of Indonesian banks tends to decrease whilst the opposite tendency is associated with their

cost efficiency during the period. The downward trend of their technical efficiency stems

from the fact that the period was the post-crisis of 1997-98 where banks were still unsteady

to maintain high level of outputs over inputs. While the upward trend of their cost efficiency

reflects their intensity to operate more economically in employing their resources during the

period. The latter result testifies that Indonesian bank management took lessons learnt from

the failure of their previous operations during the crisis. In addition, their total lending

propensity is prone to increase over time during the period albeit they have not reached yet

the optimum proportions. In contrast, their lending propensity to small businesses witnessed

a diminishing pattern over the period. A regulatory change in 2001 seems to discourage

Indonesian banks to lend to small businesses. Loans to deposits or lending proportions

emerges to be the most important factor enhancing bank efficiency, whilst bank size and

bank deposits are the foremost factors influencing the lending propensity of Indonesian

banks over the study period.

Mokhamad Anwar

1World Economic Outlook (International Monetary Fund). April 2012 Edition.

III

This thesis is dedicated to my family

IV

Acknowledgment

First of all, I give thanks to Allah for all his special gifts with endless enjoyment and

happiness in life. I would like to thank Prof. Meryem Duygun and Dr. Mohamed Shaban

who have always given a lot of inspiration and guidance with kindness and sincerity

during my study at the PhD programme, School of Management, University of

Leicester.

I also thank the people of the University of Leicester, School of Management (ULSM)

especially Prof. Jo Brewis, Dr. Dimitris Papadopoulus, and Dr. Sarah Robinson (PhD

programmes Directors) and also Mrs. Teresa for all their help and kindness in guiding

me during my PhD studies at ULSM.

I would also like to thank all the members of my family; especially my mother

(H.Aminah), my father-in-law (H.E.Marwana), my wife (Aam Salamah), my children

(Fakhri & Fadli) and all my brothers and sisters as well as all my relatives who always

gave me their support and prayed for the success of my study with sincerity. Without

their support I would not have been able to finish my work.

My gratitude also goes to my friends: Saleem, Ting and Carol for their kind support

from the very beginning stages of my PhD journey until I finally finished this thesis.

Thanks also to the PhD Cohort at the ULSM. Thanks are also addressed to all my

friends from Masleics (Malaysian & Indonesian Societies in Leicester).

I would also like to thank Prof. Emmanuel Haven, Dr. Alexandra Dias, Dr Tomasz P.

Wisniewski, Dr. Christophe Schinkus, Dr Zsuzsanna Vargha as well as Prof. Meryem

Duygun and Dr. Mohamed Shaban over their trusts on me to work as an Associate

Tutor for the courses they foster at the School of Management.

My thanks are also addressed to Prof. Subal Kumbhakar and Prof. Ragnar Tveterås for

giving me the opportunity to participate in Summer School 2011 so that I could learn

SFA efficiency techniques at Stavenger Universitetet, Norway.

I am really thankful to the Directorate General of Higher Education (DIKTI),

Government of Indonesia, which has provided me a scholarship to study towards a PhD

at the ULSM. Many thanks also to all my colleagues at the Faculty of Economics and

Business, Universitas Padjadjaran (FEB UNPAD) Indonesia especially Dr. Nury Effendi

(Dean of FEB), Prof. Ernie T. Sule, Prof. Armida Alisjahbana, Dr. Sulaeman Rachman

and Dr. Poppy Rufaidah, who always encouraged me to study further and provided me

recommendations. Thanks also to my colleagues at the Center for Management Studies

(LMFE UNPAD) which is currently led by Dr. Aldrin Herwany) and Center for

Business Incubation, PT. Telkom-LMFE UNPAD.

Many thanks are also addressed to Prof. Jonathan Williams (Bangor Business School)

and Prof. Peter Jackson (ULSM) for their very valuable comments on this thesis.

Finally I am really grateful to all the academics of the University of Leicester and the

employees of the School of Management at the University of Leicester.

V

Table of Contents

Abstract.....................................................................................................................................II

Acknowledgment .................................................................................................................... IV

Abbreviations.........................................................................................................................XII

1. Chapter one: Introduction ................................................................................................... 1

1.1. Motivation............................................................................................................................2

1.2. Objectives and Contribution ............................................................................................ 5

1.3. The Choice of Methodology .............................................................................................. 6

1.4. Thesis Organisation ........................................................................................................... 8

2. Chapter Two: The Indonesian Banking Sector and Small Business Finance ............... 11

2.1. Introduction......................................................................................................................11

2.2. Industry Analysis of the Indonesian Banking Sector ................................................... 13

2.2.1. The Origin of Indonesian Banks ............................................................................ 13

2.2.2. Financial Deregulation Package (1983-1993) ........................................................ 18

2.2.3. The 1997-98 Asian Economic Crisis ...................................................................... 21

2.2.4. After the 1997-98 Crisis .......................................................................................... 23

2.2.5. The 2008 World Economic Crisis .......................................................................... 25

2.2.6. After the 2008 Crisis................................................................................................ 26

2.3 Small Businesses Finance and Total Finance ................................................................. 27

2.3.1 Definition of a Small Business (SB) and Small Business Finance (SBF) ............. 27

2.3.2. Definition of Small Businesses Finance in Indonesia ........................................... 28

2.3.3. The Regulation of minimum SBF Threshold in the Indonesian Commercial

Banks .................................................................................................................................. 30

2.3.4. The Development of Small Business Finance in Indonesia over the period 2002-

2009 ..................................................................................................................................... 31

2.3.5. The Development of Total Loans of Indonesian Banks during 2002-2010......... 34

2.4. Conclusions.......................................................................................................................38

3. Chapter three: Literature Review ..................................................................................... 40

3.1. Introduction......................................................................................................................40

3.2. Theoretical and Empirical Literature ........................................................................... 41

3.2.1. The Theory of Efficiency ........................................................................................ 41

3.2.1.1. The Brief Theory of Production Efficiency ........................................................ 41

3.2.1.2. Techniques of Measuring Efficiency ................................................................... 43

3.2.1.2.1. Non-parametric Techniques ............................................................................. 43

3.2.1.2.2. Parametric Techniques ..................................................................................... 44

3.2.2. The Empirical Literature on the Efficiency and Productivity Studies ............... 45

VI

3.2.2.1. Efficiency and Productivity Studies on Indonesian banks................................ 49

3.2.2.2. Efficiency and Productivity Studies in other Emerging Economies Banking . 52

3.2.2.2.1. South East Asia (SEA) ...................................................................................... 52

3.2.2.2. East Asia (EA) ....................................................................................................... 55

3.2.3. A Brief Theory of Loan Management in Commercial Banks ............................. 58

3.2.4. The Empirical Literature on the Bank Lending Propensities ............................. 61

3.3. Conclusion.........................................................................................................................66

4. Chapter Four: Methodology .............................................................................................. 69

4.1. Introduction.......................................................................................................................69

4.2. A Comparison of the DEA and SFA Approaches ......................................................... 70

4.3. Non-parametric Methods: Data Envelopment Analysis (DEA) .................................. 74

4.3.1. Technical Efficiency (DEA) .................................................................................... 75

4.3.2. The CCR and BCC Models .................................................................................... 75

4.3.3. Nonparametric Productivity Measurement: DEA-based Malmquist

Productivity Index ............................................................................................................. 78

4.3.3.1. The Malmquist Index of Productivity ................................................................ 79

4.4. Parametric Methods: Stochastic Frontier Analysis (SFA) .......................................... 81

4.4.1. The Stochastic Production Frontier....................................................................... 81

4.4.2. Estimating the parameters ..................................................................................... 82

4.4.2.1. Ordinary Least Squares (OLS) Estimation ....................................................... 82

4.4.2.2. Maximum Likelihood (ML) Estimation ............................................................. 83

4.4.2.3. Estimating Efficiency ........................................................................................... 84

4.4.2.4. The Stochastic Cost Frontier ............................................................................... 84

4.4.2.5. Stochastic Frontier Models for Panel Data. ....................................................... 86

4.4.2.5.1. Time-invariant Inefficiency Models ................................................................. 88

4.4.2.5.2. Time-varying Inefficiency Models ................................................................... 89

4.4.2.5.3. Environmental effects model ............................................................................ 90

4.4.2.5.4. Battese and Coelli 1995 Model ......................................................................... 92

4.5. Multiple Regression Estimation ..................................................................................... 93

4.5.1. Multiple Regressions for Panel Data ..................................................................... 93

4.5.2. Random Effect Model ............................................................................................. 95

4.5.3. Fixed Effect Model .................................................................................................. 96

4.5.4. TOBIT Regression ................................................................................................... 98

4.5.5. Two-Stage Least Squares (TSLS) Regression ..................................................... 100

4.6. Data and Variables ........................................................................................................ 100

4.6.1. The Variables Specification and Definition ........................................................ 101

VII

4.6.1.1. The Inputs and Outputs Specification .............................................................. 102

4.6.1.2. Inputs and Outputs Definition .......................................................................... 103

4.6.2. Environmental and Bank specific variables ........................................................ 104

4.6.2.1. Bank specific variables ....................................................................................... 104

4.6.2.2. Environmental variables .................................................................................... 104

4.7. Software:.........................................................................................................................104

5. Chapter Five: Small Business Finance and Indonesian Commercial Banks’ Technical

Efficiency: DEA Approach .................................................................................................. 105

5.1. Introduction.....................................................................................................................105

5.2. Descriptive Statistics ..................................................................................................... 106

5.2.1. Data of Inputs and Outputs .................................................................................. 106

5.2.2. Data of Bank-Specific Variables and Macroeconomic Variables ..................... 109

5.3. Efficiency and Productivity of Indonesian Commercial Banks ................................ 112

5.3.1. Technical efficiency of Indonesian commercial banks ....................................... 112

5.3.1.1. Overal performance of Indonesian banks ........................................................ 112

5.3.1.2. Ownership and Banks’ efficiency ..................................................................... 114

5.3.1.2. Size and Banks’ efficiency ................................................................................. 119

5.3.2. The Productivity of Indonesian commercial banks ............................................ 121

5.4. Determinants of Efficiency ........................................................................................... 123

5.5. Conclusions..................................................................................................................... 128

6. Chapter Six: Small Business Finance and Indonesian Commercial Banks’ Cost

Efficiency: SFA Approach ................................................................................................... 130

6.1. Introduction.................................................................................................................... 130

6.2. Descriptive Statistics ..................................................................................................... 131

6.2.1. Data for Estimating Cost Efficiency .................................................................... 131

6.2.2. Small Business Finance of Indonesian commercial banks ................................. 137

6.2.3. Data of Bank-Specific Variables and Macroeconomic Variables ..................... 139

6.3. Cost Efficiency of Indonesian Commercial Banks ..................................................... 141

6.3.1. Average Cost Efficiency of Indonesian Commercial Banks .............................. 141

6.3.1.1. Overall Performance of Indonesian Banks’ Cost Efficiency .......................... 142

6.3.1.2. Ownership and Banks’ Cost Efficiency ............................................................ 145

6.3.1.3. Size and Banks’ Cost Efficiency ........................................................................ 145

6.4. Determinants of Cost Efficiency ................................................................................... 152

6.5. Conclusions..................................................................................................................... 164

7. Chapter seven: The Lending Propensities of Indonesian Commercial Banks over the

Period 2002-2010 .................................................................................................................. 166

VIII

7.1. Introduction....................................................................................................................166

7.2. Previous studies ............................................................................................................. 167

7.3. The Importance of Banks’ Lending Propensities ....................................................... 172

7.4. Methodology ................................................................................................................... 173

7.4.1. Demand and Supply Model of Small Business Finance ..................................... 173

7.4.2. Two-Stage Least Square (TSLS) Technique ....................................................... 175

7.5. Data..................................................................................................................................177

7.6. Empirical results ............................................................................................................ 177

7.6.1. Descriptive Statistics ............................................................................................. 177

7.6.1.1. Descriptive Statistics of the key variables. ....................................................... 178

7.6.1.2. Distribution by Peer group ................................................................................ 179

7.6.2. Regression Results ................................................................................................. 186

7.7. Conclusions..................................................................................................................... 190

8. Chapter eight: Conclusion ............................................................................................... 195

8.1. Empirical Findings ........................................................................................................ 195

8.2. Policy implications ......................................................................................................... 201

8.3. Limitations...................................................................................................................... 204

8.4. Future Research ............................................................................................................. 205

Bibliography.......................................................................................................................... 206

APPENDICES: ..................................................................................................................... 218

Appendix 1. Figure 1.1. Thesis Objectives ......................................................................... 218

Appendix 2. Table 2.6. Indonesian Government Policy Responses in 1998 and 2008 .... 218

Appendix 3. Figure 5.4. Average technical efficiency of Indonesian banks (model 1) ... 219

Appendix 4. Figure 5.5. Average technical efficiency of Indonesian banks (model 2) ... 219

Appendix 5. Figure 5.6. Average technical efficiency of Indonesian banks (model 3) .. 219

Appendix 6. Figure 5.8. Average Technical Efficiency By Bank Peer Group (Model 2)

................................................................................................................... 220


................................................................................................................... 220

Appendix 8. Table 5.8. Malmquist Index Summary of Annual Means (Model 2) ......... 220

Appendix 9. Table 5.9. Malmquist Index Summary of Annual Means (Model 3) ......... 221

Appendix 10. Figure 6.7. Evolution of Indonesian Banks’ Cost Efficiency – Pooled -

Model 2 ................................................................................................................... 221

Appendix 11. Figure 6.8. The Evolution of Indonesian Banks’ Cost Efficiency – BC92 -

Model 2 ................................................................................................................... 221

IX

Appendix 12. Figure 6.9. Evolution of Indonesian Banks’ Cost Efficiency – Pooled -

Model 3 ................................................................................................................... 222

Appendix 13. Figure 6.10. The Evolution of Indonesian Banks’ Cost Efficiency – BC92 -

Model 3 ................................................................................................................... 222

Appendix 14. Figure 6.13. Cost Efficiency – Pooled - Model 2 (by Bank Peer Group) . 223

Appendix 15. Figure 6.14. Cost Efficiency – BC92 - Model 2 (by Bank Peer Group) ... 223

Appendix 16. Figure 6.15. Cost Efficiency – Pooled - Model 3 (by Bank Peer Group) . 224

Appendix 17. Figure 6.16. Cost Efficiency – BC92 - Model 3 (by Bank Peer Group) ... 224

Appendix 18. Table 6.17. Coefficient of Cost Efficiency Estimation Variables – Pooled

Model 1 ....................................................................................................................... 225

Appendix 19. Table 6.18. Coefficient of Cost Efficiency Estimation Variables – BC92

Model 1 ....................................................................................................................... 226


Model 2 ....................................................................................................................... 227


Model 2 ....................................................................................................................... 228


Model 3 ....................................................................................................................... 229


Model 3 ....................................................................................................................... 230

Appendix 24. First Regressions in completion to Table 7.3 (The Regression Results 1)

...................................................................................................................................... 230

Appendix 25. First Regressions in completion to Table 7.4 (The Regression Results 2)

...................................................................................................................................... 230

Appendix 26. Correlation Test........................................................................................... 230

List of Tables:

Table 2.1. Major Economic Reforms (1966-1982) ............................................................... 17

Table 2.2. Income and Inflation Indicators .......................................................................... 21

Table 2.3.Total Indonesian commercial banks, Pre- and Post-Crisis ................................ 22

Table 2.4. Indonesian Economic Indicator, 2006-2010 ....................................................... 25

Table 2.5. Indonesian Macroeconomic & Financial Indicator, 2005-2010 ........................ 26

Table 2.7. The Highlights of SBF and MSMEs Finance ..................................................... 33

Table 2.8. The Highlights of Total Loans & the Profitability of Indonesian Banks ......... 34

Table 2.9. Productive Loans & Total Loans of Indonesian Banks ..................................... 35

Table 3.1. A commercial bank’s balance sheet .................................................................... 58

Table 4.1.The Comparison between SFA and DEA methods ............................................. 73

X

Table 5.1. Inputs and Outputs ............................................................................................. 106

Table 5.2. The Proportion of Small Business Finance ....................................................... 107

Table 5.3. Bank-Specific and Macroeconomic Variables .................................................. 109

Table 5.4. Ranking of Indonesian Banks Efficiency (2002-2010) ..................................... 116

Table 5.5. Distribution of Technical Efficiency By Bank Peer Group .. Error! Bookmark not

defined.

Table 5.6. Malmquist Index Summary over the period 2002-2010 .................................. 121

Table 5.7. Malmquist Index Summary of Annual Means (Model 1) ............................... 122

Table 5.10. Correlation Matrix of Independent Variables-1 ............................................ 125


Tabel 5.12. Determinants of Indonesian banks’ technical efficiency (2002-2010) .......... 126

Table 6.1. Output Components ........................................................................................... 131

Table 6.2. Total Cost and Price of Inputs ........................................................................... 132

Table 6.3. Total Costs to Total Assets ................................................................................. 132

Table 6.4. Expenses of Indonesian banks (2002-2010) ...................................................... 134

Table 6.5. Price of Funds (2002-2010) ................................................................................. 135

Table 6.6. Price of Labour (2002-2010) .............................................................................. 136

Table 6.7. Price of Capital (2002-2010) ............................................................................... 136

Table 6.8. Banks’ Small Business Finance by Different Ownership & Operation (2002-

2010) ...................................................................................................................................... 137

Table 6.9. Bank-Specific and Macroeconomic Variables .................................................. 140

Table 6.10. Ranking of Indonesian Banks Cost Efficiency (2002-2010) .......................... 146

Table 6.11. Distribution of Average Cost Efficiency By Bank Peer Group .................... 146


Table 6.13. Correlation Matrix of Independent variables-2 ............................................. 154

Tabel 6.14. Determinants of banks cost efficiency – Model 1 ........................................... 154



Table 7.1. Descriptive statistics of the key variables ......................................................... 178

Table 7.2. Distribution by Peer group ................................................................................ 180

Table 7.3. TSLS Regression Results 1................................................................................. 187

Table 7.4. TSLS Regression Results 2................................................................................. 189

List of Figures:

Figure 2.1. The Indonesian Banking Architecture (IBA) ................................................... 24

Figure 2.2. The Trend of Loans of Indonesian Banks (2002-2010) .................................... 36

XI

Figure 2.3. The Trend of Loans Proportions of Indonesian Banks (2002-2010) ............... 36

Figure 2.4. The Trend of Loans Growth and GDP Growth (2002-2010) .......................... 37

Figure 3.1. Technical and Allocative Efficiency ................................................................... 42

Figure 5.1. Average technical efficiency of the Indonesian banks (Model 1,2 and 3) ..... 112

Figure 5.2. Numbers of sharia banks and Efficiencies Model 2 ....................................... 117

Figure 5.3. SBF percentage and Efficiencies Model 2 ....................................................... 118

Figure 5.7. Average Technical Efficiency By Bank Peer Group (Model 1) ..................... 120

Figure 6.1. Banks’ Small Business Finance Proportions (2002-2010) .............................. 138

Figure 6.2. Average Cost Efficiency of Indonesian Banks (Pooled-Model 1,2,3) ............ 142

Figure 6.3. Average Cost Efficiency of Indonesian Banks (BC92-Model 1,2,3) .............. 143

Figure 6.4. Cost Efficiency of the Indonesian Banks: By Different Ownership .............. 145

Figure 6.5. The Evolution of Indonesian Banks’ Cost Efficiency – Pooled - Model 1 .... 148

Figure 6.6. The Evolution of Indonesian Banks’ Cost Efficiency – BC92 - Model 1 ...... 149

Figure 6.11. Cost Efficiency – Pooled - Model 1 (by Bank Peer Group) ......................... 151

Figure 6.12. Cost Efficiency – BC92 - Model 1 (by Bank Peer Group) ......................... 1511

Figure 7.1. Average TLTA across different groups ........................................................... 182

Figure 7.2. Average TLTD across different groups ........................................................... 183

Figure 7.3. Average TL across different groups ................................................................ 184

Figure 7.4. Average SBLTL across different groups......................................................... 184

Figure 7.5. Average SBLTA across different groups ........................................................ 185

Figure 7.6. Average SBL across different groups .............................................................. 186

XII

Abbreviations

ADB Asian Development Bank

BC Battese and Coelli

BCC Banker, Charnes and Cooper

BI Bank Indonesia

BILC Bank Indonesia Liquidity Credit

BNI Bank Negara Indonesia

BRI Bank Rakyat Indonesia

BTN Bank Tabungan Negara

CAR Capital Adequacy Ratio

CCR Charnes, Cooper and Rhodes

CDO Collateral Debt Obligation

CE Cost Efficiency

CMAD Corrected Mean Absolute Deviation

CNY Chinese Yuan

COLS Corrected Ordinary Least Squares

CONV Conventional Banks

CPI Consumer Price Index

CRS Constant Returns to Scale

DEA Data Envelopment Analysis

DFA Distribution Free Approach

DMU Decision Making Unit

EA East Asia

EC Efficiency Change

EFFCH Efficiency Change

FA Fixed Assets

FB Foreign Banks

FDH Free Distribution Hull

FDIC Federal Deposit Insurance Corporation

FE Foreign Exchange

FEB Foreign Exchange Banks

FEM Fixed-Effects Model

GA General and Administrative expenses

GDP Gross Domestic Products

GDPGR Gross Domestic Products Growth

GOI Government of Indonesia

IBA Indonesian Bank Architecture

IBRA Indonesian Bank Restructuring Agency

ICT Information and Communication Technology

IDIA Indonesian Deposit Insurance Agency

IDR Indonesian Rupiahs

IF Instrumental Variable

IFC International Finance Corporation

IMF International Monetary Fund

INFL Inflation rate

ISE Indonesia Stock Exchange

JV Joint Venture Banks

KUR Kredit Usaha Rakyat (People‟s Business Credit)

LDR Loans to Deposits Ratio

XIII

LGOB Local Government Banks

LNTA Natural logarithm of total assets

LNTL Natural Logarithm of Total Loans

LOI Letter of Intent

LP Linear Programming

LR Likelihood Ratio

MKTINDEX Market Index

ML Maximum Likelihood

MPI Malmquist Productivity Index

MSME Micro, Small, and Medium Enterprises

MTFPI Malmquist Total Factor Productivity Index

NFEB Non-Foreign Exchange Banks

NICA Netherlands-Indies Civil Administration

NOR New Order Regime

NPL Non-Performing Loans

OF Other Finance

OI Other Income

OL Other Loans

OLS Ordinary Least Square

OOR Old Order Regime

PAKFEB the package of February 1991

PAKJAN the package of January 1990

PAKJUN the Package of June 1983

PAKMEI the Package of May 1993

PAKTO the package of October 1988

PB Private Banks

PC Price of Capital

PEFFCH Pure Technical Efficiency Change

PF Price of Fund

PL Price of Labour

REM Random-Effects Model

ROA Return on assets

SB Small Business

SBF Small Business Finance

SBL Small Business Lending

SBLTA Small Business Loans to Total Assets

SBLTD Small Business Loans to Total Deposits

SBLTL Small Business Loans to Total Loans

SEA South East Asia

SECH Scale Efficiency Change

SFA Stochastic Frontier Analysis

SHARIA Sharia (Islamic) Banks

SI Securities and Investment

SOB State-Owned Banks

TCI Technological Change Index

TCTA Total Costs per Total Assets

TD Total Deposits

TECHCH Technological Change

TF Total Finance

TFA Thick Frontier Approach

XIV

TFP Total Factor Productivity

TFPCH Total Factor Productivity Change

TL Total Loans

TLTA Total Loans to Total Assets

TLTD Total Loans to Total Deposits

TSLS Two-Stage Least Squares

UNEMP Unemployment rate

USD United States Dollars

VRS Variable Returns to Scale

WB World Bank

WTO World Trade Organization

1

1. Chapter one: Introduction

The role of commercial banks is very important in an economy. They serve customers‟

needs either by providing saving and deposit accounts as well as providing lending or

financing facilities. For some countries, the contribution of banks is very dominant as a

major source of funds for supporting the development of public sector as well as private

entities.

Considering their essential contributions, it is very crucial to assess their performance.

The performance of banks reflects their ability and quality of their management in

carrying out their operations efficiently. Mishkin (2010) and Saunders and Cornett

(2012) indicate that commercial banks mainly create profit by issuing loans or financing.

The contribution of loans in their revenues is regarded more than fifty percent of the

operations.

Efficiency performance is required to reveal the efficiency level of banks in terms of

managing their inputs to create outputs. In addition, the lending or financing profile of

banks in terms of their lending propensity is also useful to identify their loan

management. Macerinskiene and Ivaskeviciute (2008) suggest the importance of loan

management considering that improper loan management in turn has a negative impact

on the bank performance. Hence, the investigation of bank performance is very vital to

be carried out either in terms of bank efficiency as well as its lending propensity.

Indonesia is known as a developing country and a member of the Group of 20

economies (G20). The country continues to develop and support all of the sectors‟

economic activities by stimulating the role of commercial banks in the provision of

funds. Past experience of the 1997-98 financial crises has led Indonesian policy makers

and the management of commercial banks in Indonesia to create a more supportive

environment for Indonesian banks to exceed the last decade of the 2000s in which the

world economic crisis of 2008 occurred.

2

This thesis presents insight analysis of Indonesian bank performance in terms of bank

efficiency as well as bank lending propensity during the period 2002-2010. The period

is considered to be a recovery period post the 1997-98 crises and after the government

of Indonesia (GOI) undertook some significant policies during the period 1997-2000.

The period is also regarded as the implementation period after GOI issued the special

regulation to relax the commercial banks in providing small businesses finance (SBF) in

2001. The thesis examines the technical efficiency, cost efficiency as well as the lending

propensity of Indonesian banks during the period.

1.1. Motivation

During the late 1990‟s, Southeast Asian countries were engulfed by a financial turmoil

which later on was known as the Asian Financial Crisis 1997-98. Among other Asian

countries, Indonesia was regarded the most suffered country in the region reflected by

the sharp currency depreciation over the period [Enoch et al (2001), Fane and McLeod

(2002), Harada and Ito (2005), Sato (2005)].

This crisis has left the Indonesian banking sector in a fragile state leading to the possible

reasons that the International Monetary Fund (IMF) intervened the Government of

Indonesia (GOI) by proposing several adjusted programmes started in1997. The GOI

then carried out several actions as policy responses to the recommendations of the IMF

to overcome the problems of bank liquidity and solvency over the subsequent years. The

period of 1997-2001 testifies the era of banking collapse and restructuring in Indonesia

(Fane and McLeod, 2002).

The GOI commenced some restructuring programmes i.e. the liquidation of 16 banks on

November 1997; the establishment of the Indonesian Bank Restructuring Agency

(IBRA) on January 1998 to restructure and manage the assets of some particular banks;

the recapitalisation program2 to inject the commercial banks by government funds to

2 The recapitalisation program valued at IDR 658 trillion was equal to 52 per cent of GDP in 2000 (Sato,

2005).

3

empower banks‟ assets and capital; and the programme of mergers and acquisitions over

a number of banks as well as the liquidation of some particular banks3.

All of these programmes had some impact on the structure of Indonesian banks during

the period 1996-2001: 1) the total number of banks was reduced from 239 banks at the

end of 1996 to 145 banks at the end of 2001. 2) the total assets of banks to nominal

GDP declined from 72.8 per cent in 1996 to 70.9 per cent in 2001. 3) the loans to

deposits ratio dropped from 104.0 per cent in 1996 to only 38.0 per cent in 2001and 4)

gross non-performing loans increased from 9.3 per cent in 1996 to 58.7 percent in 1998

and 12.1 per cent in 2001 (Sato, 2005).

In terms of lending behaviour, before the crisis of 1997-98, banks were more focused on

corporate and commercial sectors in their loan portfolios. It is believed that these

segments of loans are more risky compared to loans to the small business sector. Small

business loans tend to be less risky because they are diversified over more borrowers.

During the Asian crisis period, in Indonesian commercial banks the non-performing

loans for the small business portfolio sector were much lower than those of other sectors

(Timberg, 1999). This indicates that loans to small businesses in Indonesia were more

resilient over the crisis period.

In fact, GOI through the Ministry of Finance and Central Bank of Indonesia (BI) always

give considerable contributions for the development of small businesses through the

issuances of some policies. One of the most influential policies is the implementation of

the obligations of commercial banks to provide small business loans with a certain

minimum percentage (minimum threshold) of their total loans. GOI through the policy

package of January 1990 (PAKJAN 90) imposed a compulsory clause for each

commercial bank operating in Indonesia to have a minimum of 20 per cent of their

portfolio had to be devoted to small business loans (finance). The package was then

revised by The Directors of BI Decree No. 30/4/KEP/DIR on April 1997 which stated

that the threshold had changed to 22.5 per cent of a bank‟ net loan expansion.

3 The liquidation was applied to the banks included in the Category C which had a capital adequacy ratio

(CAR) of less than minus 25 per cent (Fane and McLeod, 2002).

4

However, BI subsequently launched a controversial policy in 2001 when it abolished

the provision of minimum threshold of small business finance through the BI regulation

No. 3/2/PBI/2001. These regulations eradicate the compulsory clause of commercial

banks to fulfil a minimum threshold of their financing devoted to small businesses. The

portion of a bank‟s small business finance was then adjusted by the condition of each

bank. Banks were only required to include the projections of their small business

finance in their business plans and convey the achievement of their loans on their

published annual reports. The policy of 2001was considered unfavourable in terms of

discouraging commercial banks in Indonesia to provide financing for small businesses

whereas the loan quality of this scheme was much better than those of loans for other

sector.

The thesis focuses only on the period 2002 to 20104 considering that in the year 2012

the Government of Indonesia turned back to issue the regulation (BI Regulation

No.14/22/PBI/2012) to reapply the minimum threshold of 20% bank loan portfolios

should be devoted to micro, small and medium enterprises (MSMEs). The difference

from the latter regulation is that the obligation for commercial banks to fulfil the

minimum financing portfolio by 20% is not only intended to Small businesses (SBs) but

also to micro, small and medium enterprises (MSMEs). Indeed, SBs is a part of

MSMEs.

Some policies undertaken over the period 1997-2001 in terms of bank restructuring as

well as the abolishment of the minimum threshold for small business finance may have

had any impact on Indonesian banks performance in terms of bank efficiency as well as

lending propensity during 2002-2010. Hence, this has been one of the major motivations

of this thesis.

Another motivation of this thesis is that studies about the performance of Indonesian

banks particularly in terms of „bank efficiency and lending propensity‟ are quite rare to

date. There are few studies in the literature that discussed these issues on Indonesian

bank efficiency, but they mainly use one method [Margono et al (2010) and Besar

(2011) use the SFA method, whilst Harada and Ito (2005), Hadad et al (2011a, b),

4 The data of the year 2011 was not completely available during the process of writing the thesis.

5

Suzuki and Sastrosuwito (2011) employ the DEA method, in estimating Indonesian

bank efficiency, respectively]. This circumstance has motivated the thesis to include

both the DEA and SFA techniques in estimating Indonesian bank efficiency over the

period 2002-2010. In addition, to the best of my knowledge, there has not been any

research completed yet on the lending propensity of Indonesian banks to date. Hence,

this also motivates this thesis to reveal Indonesian bank lending propensity especially in

terms of total loans as well as small business loans over the period 2002-2010.

1.2. Objectives and Contribution

This thesis aims to measure and analyse the performance of Indonesian banks in terms

of efficiency and productivity. The study employs non-parametric Data Envelopment

Analysis (DEA) and parametric Stochastic Frontier Analysis (SFA) techniques in order

to estimate technical and cost efficiency respectively during the period 2002 to 2010.

This period can be described as a recovery period hence it is after the Government of

Indonesia (GOI) implemented some restructuring programmes in banking. The thesis

also investigates whether bank specific characteristics and the environmental variables

determine the efficiency level of Indonesian banks. Finally, the thesis also provides an

empirical analysis by examining the lending propensities of Indonesian banks as well as

their determinants over the study period. The study expands its analysis of Indonesian

bank performance by measuring bank productivity using the DEA Malmquist total

factor productivity index. The main objectives of this thesis are reflected in the figure

1.1 (see Appendix 1).

In summary, the research questions of this thesis are:

1. What was the performance of Indonesian banks in terms of DEA technical

efficiency and productivity during the recovery period (2002 to 2010)?

2. Did environmental and bank-specific variables affect the efficiency levels of

Indonesian banks in terms of DEA technical efficiency over the recovery

period?

3. What was the performance of Indonesian banks in terms of SFA cost efficiency

during the recovery period?

6

4. Did environmental and bank-specific variables affect the efficiency levels of

Indonesian banks in terms of SFA cost efficiency over the recovery period?

5. What were the lending propensities of Indonesian banks over the recovery

period and after GOI launched the regulatory change in 2001 on the abolishment

of minimum threshold of bank‟s small business finance?

6. What were the determinants of bank lending propensity of Indonesian banks

over the recovery period?

The major contribution of this thesis comes in three folds: i) it is the first empirical

study which measures the efficiency of Indonesian banks using both DEA and SFA over

the recovery period (2002-2010) with a sample that represents 93% of the banking

sector. ii) It is the first study on Indonesian banks that examines the impact of

environmental and bank-specific variables on the efficiency levels of Indonesian banks

particularly in terms of DEA technical efficiency and SFA cost efficiency. iii) Finally,

this is the first study to investigate the lending propensity of Indonesian banks and their

determinants over the recovery period (2002 to 2010).

1.3. The Choice of Methodology

In order to answer the objectives of this thesis in terms of estimating Indonesian bank

efficiency performance, this thesis employs a frontier approach. The approach measures

the performance of a bank by relatively comparing it as a distance from the best practice

frontier (the „best practice‟ bank(s)). This approach provides two prominent methods,

namely the DEA non-parametric method and the SFA parametric method. The DEA

non-parametric method estimates a firm‟s efficiency through mathematical linear

programming while the SFA parametric method estimates a firm‟s efficiency through

econometric principles.

This thesis adopts both methodologies to obtain efficiency estimates of the Indonesian

commercial banks during the period 2002-2010. In estimating the technical efficiency of

Indonesian banks, DEA technical efficiency is employed because of the possibility that

this measure decomposes the technical efficiency into two parts: “pure technical

efficiency” and “scale efficiency”. In addition, the first empirical chapter of this thesis

7

(chapter five) examines the efficiency levels of Indonesian banks by only accounting for

the conversion of physical inputs (general and administrative expenses, fixed assets, and

total deposits) into outputs (total finance, small business finance, other finance, and

securities and investments) relative to best practice. This efficiency measurement is

based on only the technical process without regarding price and cost.

While SFA cost efficiency is used in the second empirical chapter (chapter six),

considering that in the measurement of economic or cost efficiency of a bank, it

involved input prices as well as output quantities in the model. Cost efficiency is

composed of two components: allocative efficiency and technical efficiency. The former

computes the ability of a firm to employ inputs in optimal proportions given their prices

while the later measures the firm's ability to obtain the maximum output from given

inputs (Coelli et al, 2005). Unlike the DEA approach it is cumbersome to disentangle

allocative efficiency from scale efficiency given the cost efficiency measure obtained

from the SFA model.

Regarding the estimation of the SFA cost efficiency, the thesis uses Battese and Coelli

1992 (BC92). BC92 (the error correction model) is employed considering the focuses of

this study to use two-step method as it is also applied to the first empirical chapter and

this practice was also adopted by other authors [(Pitt and Lee, 1981), (Andries, 2011),

(Assaf and Josiassen, 2012), etc]. There are two alternatives using two step methods,

those are Kumbakar 1990 and Battese and Coelli 1992. The advantage of the Battese

and Coelli 1992 approach over that of Kumbakhar 1990 is that their model involves

only one unknown parameter whilst the latter involves two unknown parameters (Assaf

and Josiassen, 2012).

Indeed, the BC 1992 is less flexible compared to the BC 1995 particularly in terms of

concerning the yearly rank order over time. Since this study focuses on the average

basis of the efficiency estimation (achieved by commercial banks in Indonesia) over the

study period, not yearly basis, it is considered sufficient to estimate cost efficiency using

the BC1992. In addition, the study also employs the standard pooled method to enrich

the analysis. The findings of both methods have provided us in understanding the cost

efficiency attained by Indonesian commercial banks during the period.

8

Actually there are some alternative models could be employed instead of cost efficiency

estimation (i.e. technical efficiency, profit efficiency, etc) in terms of SFA technique.

This thesis uses the SFA cost efficiency estimation concerning the fact that during the

recovery period Indonesian banks embraced the consequences from the restructuring

programme (mergers, acquisitions, etc) undertaken by the Government of Indonesia i.e.

employee layoff, bank branches closures, etc. These could be regarded as the cost

efficiency programme initiated by Government of Indonesia during the period 1997-

2001. These circumstances are assumed to have any impacts on the cost efficiency

performance of Indonesian banks over 2002 to 2010. These become the reasons why the

cost efficiency model is employed in this thesis. Other authors [(Margono et al (2010)

and Besar (2011)] also used the SFA cost efficiency models in their research.

In revealing the factors affecting both efficiency levels of Indonesian banks, this thesis

uses TOBIT regression given that the efficiency scores (dependent variables) have the

features of censored value between 0 and 1. In addition, this thesis adopts two-stage

least square regression in estimating the determinants of Indonesian banks‟ lending

propensity over the study period. This regression accounts for the simultaneity problem

between the supply and the demand function for loan provisions. This method was also

adopted by McNulty et al (2013).

1.4. Thesis Organisation

This thesis is composed of eight chapters including the current introduction chapter.

Chapter two provides information about the circumstances of the Indonesian banking

sector and small business finance in details. Chapter three reviews previous studies and

chapter four presents the methodologies employed to obtain the empirical results.

Chapter five, six and seven provide analyses of the empirical findings covering the DEA

technical efficiency and the SFA cost efficiency of Indonesian commercial banks as well

as Indonesian bank lending propensity over the study period. Finally, chapter eight

provides conclusions for the empirical results of the thesis.

The contents of each chapter are summarised as follows:

9

Chapter two describes the history and the evolution of the Indonesian banking industry

since the first commercial bank was established in Indonesia up to date. The chapter

also explains the political and economic aspects through the policies imposed by the

Government of Indonesia (GOI) in managing the banking Industry for different eras i.e.

The post-Independence period, the deregulation era, until the post-Asian financial crisis

1997-98 that hit the Indonesian banking industry. The last two sections of this chapter

reveal the efforts of GOI (through Bank Indonesia) to reconstitute the new architecture

of Indonesian banking. The last section explains the conditions of small business

finance covering the definition and the legislation that prevails in Indonesia particularly

within the study period.

The third chapter reviews the literature examining the study of bank efficiency as well

as bank lending propensity. The chapter reviews the theory of production efficiency

revealing the Farrel‟s (1957) propositions on technical efficiency, allocative efficiency,

and economic efficiency as well as describing the techniques used to estimate

efficiency. The chapter then exposes some empirical evidence on efficiency and

productivity studies in Indonesia as well as in some emerging economies in South East

Asia and the East Asia region. The chapter also describe the brief theory of loan

management in commercial banks and then provides some empirical evidence on the

lending propensity of commercial banks.

Chapter four initiates with a brief summary on the comparison between the non-

parametric DEA approach and the parametric SFA approach. The chapter then

elucidates in detail the DEA approach in estimating efficiency as well as the DEA-based

Malmquist productivity index computation especially for the production function on

technical efficiency. The following section describes the parametric SFA method with

some underlying estimation models specifically for the cost frontier. This chapter also

presents the multiple regression techniques including TOBIT regression for examining

the determinants of efficiency and the two-stage least square (TSLS) regression for

determining the lending propensities of commercial banks.

Chapter five analyses the tendency of the technical efficiency of Indonesian commercial

banks as well as their determinants over the study period. The chapter comprises two

major sections. The first section discusses the empirical findings of the technical

10

efficiencies of Indonesian banks in terms of the non-parametric DEA approach as well

as the DEA-Malmquist productivity index. It then compares the performance of bank

efficiency across different groups based on their ownership and operation. The second

section analyses the determinants of Indonesian bank technical efficiency over the study

period. The section reveals some significant factors either from bank specific factors

and macroeconomic variables that significantly impact on the Indonesian bank

efficiency over the study period.

Chapter six provides an empirical analysis of Indonesian bank efficiency particularly in

terms of cost efficiency resulting from the estimation of the SFA parametric method

over the study period. The chapter also presents a comparison of cost efficiency

performance across different groups of banks by ownership and operation. The last

section of this thesis then analyses the determinants of Indonesian bank cost efficiency

during the period.

Chapter seven discusses the empirical evidence of Indonesian commercial banks

regarding their lending propensity over the period 2002 to 2010. This chapter follows

McNulty et al (2013) in revealing the lending propensity of Indonesian banks across

different groups of banks and examines the determinants of the lending propensity of

Indonesian banks.

The thesis provides concluding remarks in the last chapter (chapter eight). The chapter

begins with a summary of the empirical findings followed by policy implications on

Indonesian bank efficiency as well as lending propensity. Finally, the chapter concludes

by summarizing the limitations of the thesis and suggesting new opportunities for future

research.

11

2. Chapter Two: The Indonesian Banking Sector and Small Business Finance

2.1. Introduction

The banking sector in Indonesia has been present since the pre-independence period.

The Dutch colony authority holder5 in Indonesia established some commercial banks to

operate in several cities of Indonesia in the 18th century6. The Indonesian banking has

experienced various periods since then (i.e. the pre- and post-independent period, the

deregulation period, the 1997-1998 Asian financial crises period and the post-Asian

financial crises period).

The period between 1980s-1990s can be described as the deregulation7 era of the

Indonesian banking sector. The aim of the deregulation policies is to improve the

effectiveness and efficiency of the Indonesian banking sector. This in turn aims to have

a positive impact on the real sector in terms of stimulating the development of the

Indonesian economy. The deregulation process consists of some packages launched by

the Government of Indonesia (GOI) during the period 1983-1993. These packages8 [i.e.

the package of June 1983 (PAKJUN 83), the package of October 1988 (PAKTO 88), the

package of January 1990 (PAKJAN 1990), the package of February1991 (PAKFEB 91),

and the package of May 1993 (PAKMEI 93)] have brought the Indonesian banking

sector to grow very rapidly.

The rapid development of Indonesian banks, especially in terms of increasing the

number of banks has not been accompanied by the sound quality of bank management.

Thus, when the financial crisis occurred in neighbouring countries (e.g. Thailand,

Malaysia), the effects easily spread to Indonesia, starting in mid-1997.

During the crises period (1997-1998), the Indonesian banking sector was at the worst

condition. Many commercial banks were forced into liquidation9or merged with other

5 As a Dutch colony, Indonesia was under the authority of a Governor General.

6 See more details in the section 2.2.1.

7 Deregulation means the process of eliminating or reducing state regulations. In this case, the

deregulation of the banking sector in Indonesia. 8 See in detail at the Section 2.2.2 in this chapter.

9 It began with the liquidation of 16 banks in November 1997, followed by the closure of other 38 banks

in 1999 as the impact of the financial and banking crisis in 1997-1998 in Indonesia.

12

banks (arranged by the Central Bank of Indonesia (BI)) due to their poor performance.

The government of Indonesia (GOI) then established the Indonesian Bank Restructuring

Agency (IBRA)10

on January 26, 1998, with the authorities to manage and restructure

non-performing banks as well as their non-performing assets.

The following period (2000-2010) was a period of recovery and re-establishment of the

Indonesian banking sector after suffering from the crisis. In this period, the GOI through

BI launched a program known as the "Indonesian Banking Architecture (IBA)" in 2004.

IBA was designed with the aim of achieving a sound, strong, and efficient banking

sector to promote the stability of the Indonesian financial system as well as enhance the

Indonesian economic growth (Indonesian Banking Report, 2004).

In terms of bank efficiency and bank lending propensity, the performance of Indonesian

banks over the decade of 2000s is very important to examine particularly after the

regulation11

launched in 2001. The regulation states that all commercial banks are not

obliged to maintain the minimum threshold of small business finance (SBF). The SBF

portion of each bank is then adjusted to its own ability and willingness. These

circumstances along with the previous programme undertaken by the GOI during the

period 1997-2001 (liquidation, recapitalisation, mergers and acquisitions) seem to have

impacted on the Indonesian bank efficiency as well as bank lending propensity over the

period 2002-2010.

This chapter is organised as follows. The first section discusses the historical

background of the Indonesian banking sector (e.g. the pre-independence period, the

post-independence period, the financial deregulations period, the 1997-98 Asian

economic crises period, the post-1998 Asian crises period, 2007-2008, the world

economic crisis period, and the post-2008 crises period). The second section explains

the evolution of small business finance in Indonesia in terms of the regulatory

framework and its highlights over the period 2002-2010. Finally, the last section

concludes.

10

IBRA was established as one item in a series of Letter of Intent (LOI) between the GOI and IMF with

the first LOI was signed on October 31, 1997. 11

BI Regulation No. 3/2/PBI/2001 about Small Business Finance on January 4, 2001. It states that the

provision of small business finance is readjusted based on the ability of each bank respectively.

13

2.2. Industry Analysis of the Indonesian Banking Sector

2.2.1. The Origin of Indonesian Banks

The evolution of Indonesian banking started in 1746 when Bank Van Leening was

firstly established as a commercial bank operated in Indonesia. It was the first bank to

serve banking transactions of people in Indonesia during the time. Following Bank Van

Leening other commercial banks were established to meet the needs of the economy

(Nederlandsche Handel Maatschapij in 1824, De Javasche Bank in 1828,

Escomptobank in 1857, and Nederlandsche Indische Handelsbank in 1864). All these

banks were affiliated with or under the authority of the Dutch Governor General12

in

Indonesia. In order to expand the scope of banking transactions in Indonesia, several

banks affiliated with other countries were also established [the Chartered Bank of India,

Australia and China were founded in 1859, Hongkong and Shanghai Banking

Corporation (HSBC) in 1884, Bank of China in 1915, Yokohama Specie Bank in 1919

and Mitsui Bank in 1925].

As the development of economic activities increased rapidly in different provinces

during the early 1900s, some local commercial banks were then established (Bank

Vereeniging Oey Tiong Ham was established in Semarang (1906), Chung Hwa

Shangieh Maatschapij in Medan (1913), Batavia Bank in Batavia/Jakarta (1918) and

Bank Nasional Indonesia in Surabaya (1928)). The aim of the local bank

establishments was to serve banking transactions of commercial activities in several

major cities in Indonesia during the early 1900s. These cities are four big cities in

Indonesia located in Java and the Sumatra islands.

Indonesia proclaimed the independence on August 17, 1945. Since then, Indonesia's

banking sector has entered a new era characterized by the formation of state-owned

banks. Bank Negara Indonesia (BNI) and Bank Rakyat Indonesia (BRI) were established

in 1946 as the first two state-owned banks operated in Indonesia. The aims of

establishing BNI were to facilitate new business entities and entrepreneurs through

several services including foreign exchange transactions, commercial loans and

12

During that time Indonesia was a Dutch colony, governed or managed by the Dutch Governor General.

14

government subsidized loans. While BRI was established to support the development of

rural banks spread over thousands of villages across Indonesia. Both banks have been

continuously contributing to serve their customers through an extensive network of

branches in Indonesia and several branches overseas up to the present.

Bank Tabungan Pos was then reactivated in 1950 as a legacy of the Dutch authorities

(NICA13

). Bank Tabungan Pos was operated to focus on providing loans to local

governments in terms of developing public infrastructures: market development,

electricity distribution facilities, and bus terminals. It is important to note that public

infrastructures were very crucial for many provinces and districts to stimulate economic

activities in their regions after five years of declaring the Independence.

In addition, Bank Industri Negara and Bank Tani Nelayan were also established in 1955

and 1957 respectively. Bank Industri Negara was assigned to enhance the development

of the plantation, mines and industry sectors. These sectors required the assistance of

commercial banking mainly to finance their activities. Bank Tani Nelayan was

appointed to assist farmers and fishermen in enhancing their production and marketing

activities (Emergency Law Republic of Indonesia No. 18/1957).

The period (1959-1960), witnessed the nationalisation of several Dutch banks

[Nationale Handels Bank NV was nationalised to be Bank Umum Negara, whilst

Escomptobank and Nederlandsche Handels Maatschappij were nationalised to be Bank

Dagang Negara and Bank Expor-Impor Indonesia respectively]. These last two banks14

then became very popular as state-owned banks which support commercial and trade

activities in Indonesia. Bank Dagang Negara was assigned to provide banking

transactions for customers concerning commercial and trade affairs while Bank Expor-

Impor Indonesia was in charge of supporting the export and import transactions

between its customers and their counterparts.

13

Netherlands-Indies Civil Administration (NICA) was in charge of controlling the Allied Forces Netherlands East

Indies region after Japan surrendered unconditionally to the Allies in World War II in mid-August 14th 1945. Area

located in the Dutch East Indies now Indonesia. NICA rode ally when it came to Indonesia after the end of World

War II. 14

These banks, along with the other two banks (Bank Pembangunan Indonesia and Bank Bumi Daya) are

then merged in 1998 to be PT. Bank Mandiri (Persero), a state-owned bank with the biggest assets in

Indonesia today.

15

The history of Indonesian banking arrived at a new era when the GOI established Bank

Indonesia (BI) as a central bank on July 1, 1953. BI had a duty to oversee the

operations of the entire system of commercial banks in Indonesia. This duty is

reinforced by Law no. 11/1953 on the Central Bank. Since then, the operations of all

commercial banks operating in Indonesia were under the supervision of BI.

In 1965 the GOI decided to merge BI with other state-owned banks to mark the launch

of the “single bank15

” concept. With the concept of the single bank in place, four

commercial banks, namely Bank Negara Indonesia (BNI), Bank Koperasi Tani dan

Nelayan, Bank Umum Negara and Bank Tabungan Negara (BTN) merged and were

named as Bank Negara Indonesia (BNI). The problem was laid in the context of

unification between the function of a banking supervision as well as a commercial bank

into one bank. This seemed to be unusual practice since there must be a conflict of

interests as a dual function bank, although the bank operation was divided into several

units (from unit 1 until unit 5). BI turned into BNI Unit 1 and kept its position as the

central bank. The remaining units were established based on the different focus of

financing as of a commercial bank16

. The concept finally ended and was no longer valid

after the tragedy of Black September in 196517

. The change of national leadership

regime from the old order to the new order in 1967 also sparked a desire to convert this

entity into its original state.

The period (1967-1983) witnessed the rise of the New Order Regime (NOR) under the

command of President Suharto. Under NOR the functions of Bank Indonesia was

formally restored under the provision of the Decree No. 600 of 1968 by the Minister of

Finance. In the early period of the new regime, BI began to play a strategic role in

economic development. BI has three main roles as the holder of the monetary policy

authority, the regulator and supervisor of commercial banks, as well as the regulator for

the payment system. BI was very important in this period because Indonesia was

15

The single bank concept was conducted during the Sukarno‟s regime (Old Order Regime) with the aim

of operating the monetary and banking affairs efficiently in one bank. 16

Unit 2 to unit 5 has its own function respectively as a saving bank, a trade and commercial bank, a

cooperative bank and a farmers‟ bank. 17

The tragedy was named G30S (the 30th

of September movement, 1965). It was a self-proclaimed

organization of Indonesian national armed forces members who, in the early hours of 1 October 1965,

assassinated six Indonesian army generals in an abortive coup (coup d'état). Finally, by the end of the

day, the coup attempt had failed in Jakarta and the city can be controlled under the command of Major

General Suharto.

http://en.wikipedia.org/wiki/Coup_d%27%C3%A9tat

16

situated in the economic recovery after a downturn in the economy at the end of the Old

Order regime (OOR). Economic performance during the NOR was quite well marked

by the achievement of economic growth of around 6-7 per cent a year, and the inflation

rate was reduced to under 10 per cent a year.

In addition, during the NOR period BI also played a significant role in ensuring that the

financial system including commercial banks operated efficiently. Moreover, in this

regime, State-owned banks (SOB) carried out the greater duty in the process of bank

lending channel to finance various development projects proposed by the GOI. The

program of financing was mainly focused on the development of infrastructure as well

as the agricultural and industrial sectors.

Based on Law no. 14/1967 on banking and Law no. 13/1968 on Central bank, banks are

classified based on their functions. There are 5 types of banks: bank central,

commercial banks, savings banks, development (construction) banks, and rural banks.

The central bank is the monetary authority as well as the bank regulatory and

supervisory body. Commercial banks engage in providing short-term loans, while

development-banks provide medium and long term financing, especially in the

construction purposes. A savings-bank is a bank that concentrates on customer savings

accounts while rural-banks focus on financing the agricultural sector in rural areas.

The NOR has implemented several economic reforms within the period of 1966-1982.

These reforms consist of some economic and financial reforms dealing with the

economic activities and financial transactions during the time. Those transactions are

mostly held either by the Central Bank of Indonesia and commercial banks (Ariff and

Khalid, 2005).

The completed major economic reforms 1966-1982 are shown in the following table:

17

Table 2.1. Major Economic Reforms (1966-1982)

First reform (1966-69): Economic stabilization

1967 Legalized private trade in foreign exchange through Foreign Exchange Bourse.

New Bank Act permits foreign exchange by foreign banks and joint venture banks.

1969 Approved local banks permitted to trade in foreign exchange.

Second reform (1970-78): Non-oil exporting capacity

1970 Foreign investment laws to ease entry of foreign capital, technology and skill, especially

for primary sector, capital intensive activities.

1971 Peg rupiah to USD at 415 rupiah after 10% devaluation; exchange rates unified to two,

one for all transactions and 10% lower for essential items.

Central bank takes over foreign exchange transactions; Foreign exchange bourse almost

abolished;

Major policy of directed credits to preferred firms to create industrial capacity.

1970-76 PT.Danareksa18 established to distribute national investment certificate in unit trusts.

1977 First local firm listed on the Jakarta Exchange.

1978 When the aim of making Indonesia competitive for non-oil exporting, the rupiah devalued

by 50% in November 1978.

Third reform (1978-82): Towards non-oil export capacity and foreign investments

1978 Exchange liberalization; basket peg to trading partners‟ currencies.

Special exchange rate for foreign capital and central bank takes a swap to cover the risk.

Directed credit over 1970-82 failure; reduced directed.

Source: Ariff and Khalid (2005)

The first economic reform occurred in 1966-1969 which was recognized as the

Economic stabilization. The reform was intended to manage the economy after the

worst civil unrest of 1965 with the goal of creating many new jobs to reduce

unemployment. Of those reforms, there was also a new policy that allowed foreign

banks and joint venture banks to participate in trading currencies in the foreign

exchange market. Furthermore, as can be seen from Table 2.1, local banks were also

allowed to conduct foreign exchange transactions. It seemed that the GOI induced the

participation of various groups of banks to stimulate Indonesian economic activity

during this period.

The second economic reform wave of 1971-1978 was intended to expand the non-oil

exporting capacity. These reforms were undertaken to enhance indigenous

manufacturing capacity away from the heavy dependence on manufacturing imports that

had drained foreign exchange (Ariff and Khalid, 2005). The reform also extended to the

banking sector by means of allocating a directed credit (with funds originated from GOI

channelled to State-owned banks) mostly towards companies that could create and

develop the capacity of labour intensive industries. In addition, the GOI began to further

develop the capital market by providing an opportunity for the public to invest their

18

PT Danareksa is an Indonesian State-Owned Enterprise engaged in the field of financial services. The

company was established in 1976 with the main activity in the field of capital markets and money markets

as a finance company, securities broker, underwriter, as well as investment management and mutual

funds.

18

funds in the form of unit trusts in PT. Danareksa (Mutual funds Company) and allowing

local companies to list their shares on the Jakarta Stock Exchange.

The third reform was still aimed at increasing non-oil production capacity as well as

foreign investment. In support of these objectives, the central bank seems to be more

involved with providing special rates for foreign capital and take foreign currency

swaps to cover the risk.

2.2.2. Financial Deregulation Package (1983-1993)

One of the most important stages in the evolution of Indonesian banking sector is the

period of financial deregulation during 1983-1995. In this period, the GOI launched

some deregulation packages that had brought substantial implications for the banking

industry in Indonesia then. The objectives of these packages are to develop a sound

banking system and to support a wider involvement of privately-owned banks in the

provision of funds for the development of Indonesia as a whole. One of the visible

impacts of the deregulation is the establishment of large number of private banks and

the support of government institutions given to them by utilising their functions in terms

of current accounts, saving accounts, and credit facilities.

The first deregulation package was launched on June 1983, known as PAKJUN 83 (the

policy package of June 1983). This package has several objectives: First; abolishing the

credit ceiling for commercial banks. Second; commercial banks were given the authority

to freely set their interest rates for both loans and deposits. Third; Bank Indonesia

Liquidity Credit (BILC) through commercial banks was terminated except for the

certain loans related to cooperative development and exports. Fourth; the reserve

requirement was reduced from 15% to 2% in order for banks to expand their loan

facilities to customers. All these policies succeeded in increasing the involvement of the

private sector and private banks in supporting the Indonesian economy in the era of

competition and the on-going globalization.

The second package was named PAKTO 88 (the policy package of October 1988). The

package was a trigger for the establishment of a large number of new private

19

commercial banks since the minimum paid-in capital required for establishing a bank

was very low at that time (IDR 10 billion or USD 5 million). The package also consists

of the ease of the requirements for licensing to be a foreign-exchange bank19

as well as

joint-venture banks. BI also set up a secondary banking market represented in

traditional-market banks and rural banks in all regions of Indonesia. The aim of this

program is to expand the reach of financial aid, especially in rural areas, in order to

increase their involvement in the development of the Indonesian economy.

Through these two comprehensive reform packages, the structure of banking deposits

has dramatically changed in 1989. As Ariff and Khalid (2005) elaborate there was a

movement of deposits towards private banks at large-scale. The market share of

deposits had a severe shift from 80% of the deposits at state-owned banks in 1983 to

80% of the deposits moved to private banks by the end of 1989. This shift in market

power was supported by an increase in the quality of private banking services and the

enormous branch network across the country (Ariff and Khalid, 2005).

The third package is PAKJAN 90 (the policy package of January 1990). Through this

policy, banks and financial institutions are encouraged to be more independent and are

able to carry out funding as well as lending activities effectively while gradually

reducing dependence on Bank Indonesia Liquidity Credit (BILC). The package aimed

for the following: First, Lending rates for some activities or sectors which previously

provided subsidies were transferred to the market mechanism. Second, the BILC was

granted only in limited quantities to support self-sufficiency, cooperatives development

and increased investment. In addition, to support the development of small businesses

(SBs) and in an effort to equitable development, banks are obliged to provide at least 20

per cent of the loan portfolios to finance the small business sector (small business loans

or small business finance).

Another package is PAKFEB 91 (the policy package of February 1991). This package

contains the prudential banking principles that commercial banks should comply to.

Through this package, the capital adequacy requirement of commercial banks as well as

their liquidity and asset quality were strictly monitored by the central bank. Similarly,

19

A foreign exchange bank is a commercial bank which also provides transactions in other currencies

besides Indonesian Rupiahs.

20

loans to individual borrowers or groups’ affiliated with the bank20

as well as net

foreign-exchange positions were also strictly monitored. This package has restrained the

excessive growth of the bank at time created a concern of inducing inflationary state for

the economy.

In the period 1992-1993, Indonesian banks began to experience a crucial issue because

of the growing number of bad loans (non-performing loans) that have the potential to

reduce the profitability of banks. Due to the fact that non-performing loans (NPL)

increased, some commercial banks were reluctant to expand their financing. This has

further augmented the restrictive effect of PAKFEB 1991 on bank loan expansion. To

overcome the problems encountered by the commercial banks, the GOI then launched

PAKMEI 93 (the policy package of May 1993). This package aimed to relax some of

the strict rules put in place by the previous policy so that commercial banks can operate

more freely in expanding their lending activities to customers.

The Indonesian economy started to grow rapidly starting from the year 1994 with the

real-estate sector being the top choice for investors. PAKMEI 93 has been successful in

encouraging the credit growth, but it might have passed through the level that could put

a heavy pressure on the monetary control. In other words, the amount of credit given to

a variety of business sectors, especially the real-estate sector, has led Indonesia to face

an overheated economy characterized by a high inflation rate during that period. The

fallout from the overheated economy coupled with the weak Indonesian economy paved

the way to the financial and economic crisis in 1997-1998 to occur. The crisis in turn

triggered a succession of national leadership with the fall of the New Order regime and

replaced by the Reformation Order in 1998.

In general, the condition of the Indonesian economy was very manageable before the

crisis occurred. A comparison of fundamental macroeconomic indicators across the

several Association of South East Asian Nations (ASEAN) countries during the period

1985-1997 is as follows:

20

Provisions of legal lending limit (LLL) for an individual or a group affiliated with a bank is 10 per cent,

while for those who are not affiliated with the bank is 20 per cent.

21

Table 2.2. Income and Inflation Indicators

Indonesia Philippines Malaysia Thailand

Real GDP Growth 1985-97 6.6% 7.6% 3.8% 8.5%

GDP per head, 1986 $480 $530 $1700 $820

GDP per head, 1996 $1100 $1200 $4600 $3100

Annual Increase in GDP per

head

12.92% 12.64% 17.06% 27.80%

Inflation, 1996 6.5% 8.4% 3.5% 5.8%

Source: Heffernan (2009)

The above table shows that the average real GDP growth of Indonesia during the period

1985-1997 was 6.6 per cent a year which was above the average real GDP growth of

Malaysia (3.8 per cent) in the same period. The data also show that Indonesia's GDP per

capita has increased more sharply compared to the Philippines (12.92 per cent compared

to 12.64 per cent). Indonesia's inflation rate in 1996 was also well below that of the

Philippines (6.5 per cent compared to 8.4 per cent, respectively). Although the

performance of the Indonesian economy was still below that of Malaysia and Thailand,

Indonesia had emerged as one of the emerging economies in the region during the

period.

2.2.3. The 1997-98 Asian Economic Crisis

The crises of 1997-98 were the most vulnerable era in the history of Indonesian banks.

Having enjoyed a boom in the period of liberalisation, the crisis occurred without any

official forecast of a sudden down turn (Heffernan, 2009). The crisis originated in

Thailand starting by a stock market crash in February 1997 that had led to a sharp

devaluation of the Thai currency (The Bath decreased by up to 30 per cent from its

value) by the end of the year. This incident then quickly turned into a currency crisis

that spread over the financial sector in Thailand. That currency and financial crisis in

Thailand had a contagion effect and transmitted quickly to other Asian economies i.e.

Indonesia, South Korea, Malaysia and the Philippines. The wave of the crisis has forced

all these countries to take rescuing actions through various monetary policies such a

devaluation policy, currency pegging, or the implementing of a currency board system.

22

The currency crisis has led the Indonesian Rupiah (IDR) to float in August 1997. On

July 1997, one USD was equal to 2,400 IDR, but after that date it started to devaluate

sharply afterward, and by February 1998, one USD had exceeded 10,000 IDR. The GOI

then took action to address the situation through several policies: restructuring the

banking sector, cancelling some government projects, and providing liquidity credit for

some insolvent banks. These policies were implemented to rescue the banking system

during the period and to save the Indonesian economy as a whole.

The GOI was under an agreement with the international monetary fund (IMF) to start

restructuring the banking system through implementing a series of actions that both

parties agreed upon. The restructuring package included 50 banks, of which 16 banks

were closed, and the remaining 34 banks underwent different treatment depending on

their financial condition: the six largest private banks were under more intensive

supervision; the ten insolvent banks were instituted to a rehabilitation program; and at

least one merger programme was implemented (Heffernan, 2009). In addition to the

restructuring programme other policies were also implemented for example: providing

liquidity assistance (BILC) for some commercial banks; introducing a blanket guarantee

scheme for all the liabilities of commercial banks in terms of rebuilding confidence in

the Indonesian banking system; and the establishment of the Indonesian Bank

Restructuring Agency (IBRA) which had the authority to take over, manage and

restructure the assets of non-performing (insolvent) banks.

The dramatic changes in the Indonesian banking sector as a result of the restructuring

programme within the two-year period can be observed in the table 2.3 as follows:

Table 2.3.Total Indonesian commercial banks, Pre- and Post-Crisis

Source: Heffernan (2009)

Bank by ownership Pre-crisis, July 1997 Post-crisis, August 1999

No. Of banks Market share No. Of

banks

Market

share

Private domestic banks 160 50% 82 17%

State domestic banks 34 42% 31 73%

Joint ventures/foreign banks 44 8% 40 10%

23

From the table above, it can be seen that the number of private domestic banks has

declined sharply to almost half, due to the closure, merger and acquisition programmes.

The market share of state domestic banks has increased from 42 per cent in 1997 to 73

per cent in 1999. The sharp rise of the state-owned banks‟ market share was caused by

the lack of confidence of private domestic bank customers that moved their funds into

state-owned banks as a safer resort.

2.2.4. After the 1997-98 Crisis

The period after the crisis is considered an important phase in the history of the

Indonesian banking sector particularly within the framework of the improvement of the

banking quality in the country. There were two milestones in this period: the

restructuring and the reconstruction programs. Both programs were very beneficial for

the survival of the Indonesian banking sector in the present and the future.

The restructuring program is characterized by the formation of IBRA which was formed

on January 26, 1998. IBRA had a duty to restructure some particular banks requiring

special assistance and take over some of the many non-performing loans of commercial

banks through an asset transfer scheme. IBRA also managed and took over some poor

performing banks by merging several banks and arranging the acquisition of some weak

banks by the healthy banks.

According to the Indonesia‟s Economic Report (2004), IBRA had successfully managed

several non-performing assets transferred by commercial banks and in return, they were

given government bonds as compensation. Regarding the debt restructuring program,

until September 2004, the total volume of bank restructuring funds in the 15 largest

banks was about IDR 38.4 trillion.

The Indonesian Banking Architecture (IBA) was launched by Bank Indonesia on 9th

January, 2004 (Indonesia‟s Economic Report, 2004). The vision of IBA is to realise the

sound, strong, and efficient banking system in order to create the stability of financial

system as well as stimulate national economic growth (see the figure 2.1).

24

The IBA vision is based on six pillars that are considered essential to support the

achievement of IBA‟s objectives:

1. Creating a healthy domestic banking structure which is able to meet the needs of

the society and encourage sustainable national economic development.

2. Creating an effective bank regulation and supervision system which conducts to

the international standards.

3. Constructing a strong Indonesian banking industry which is very high

competitive as well as having resilient at risk.

4. Creating good corporate governance in order to strengthen the internal condition

of the national banking system.

5. Realising a complete infrastructure to support the creation of a healthy banking

industry.

6. Realising an empowerment of consumer protection for banking services.

Figure 2.1. The Indonesian Banking Architecture (IBA)

Source: Bank Indonesia (www.bi.go.id)

Through these programs, it is expected that by 2019, the structure of the Indonesian

banking system will be improved. The structure would be envisaged as follows (Besar,

2011:22-23):

2 or 3 banks are likely to emerge as international banks. These banks possess the

capacity and ability to operate on an international scale and having total capital

exceeding IDR 50 trillion (USD 5 billion).

Up to 5 national banks. These banks have a broad scope of business and

operating nationwide with total capital between IDR 10 trillion (USD 1 billion)

and IDR 50 trillion (USD 5 billion).

25

30 to 50 specialized banks with operations focused on particular business

segments according to the capability and competence of each bank. These banks

will have capital of IDR 100 billion (USD 10 million) up to IDR 10 trillion

(USD 1 billion).

Rural Banks are the banks operating in rural area, and banks with limited scope

of business, having capital of less than IDR100 billion (USD 10 million).

In addition, to improve the quality of the banking infrastructure and maintain the

Indonesian banking consolidation process, GOI has also established the Indonesian

Deposit Insurance Agency (IDIA) on December 22, 2005 (Indonesian Economic

Report, 2005). The short program of this agency was the reduction of the guaranteed

bank deposit accounts. For example, until March 2006, the maximum deposit insurance

of IDR 5 billion was reduced to IDR 1 billion in September 2006, and became only IDR

100 million in March 2007. The purpose behind the policy was to direct commercial

banks in Indonesia to undertake their businesses more prudently (Indonesian Economic

Report, 2005).

2.2.5. The 2008 World Economic Crisis

The world economic crisis in 2008 had some impact on the Indonesian economy by the

weakening in the macroeconomic indicators (See Table 2.4). The BI was forced to

increase interest to maintain liquidity in the banks as a defective policy to face the

negative consequences of the crisis on the Indonesian banking sector.

Table 2.4. Indonesian Economic Indicator, 2006-2010

Indicators 2006 2007 2008 2009 2010

GDP growth (%) 5.5 6.3 6.0 4.6 6.1

Time deposits rate, 12months (%) 10.7 7.7 10.3 7.9 7.2

Time deposits rate, 6months (%) 11.6 8.2 10.4 9.6 7.9

IDR/USD (end of period) 9020 9419 10950 9400 8991

IDR/USD (Average of period) 9159 9141 9699 10390 9090

Source: www.adb.org/statistics

The effect of the crisis on the Indonesian economy seemed to continue until 2009

although there have been improvements in some economic indicators. Nonetheless, the

26

GDP growth dropped slightly to 4.6 per cent from 6 per cent in the previous year and an

Indonesian Rupiah decreased from IDR 9,699 to IDR 10,390 per one USD. These

incidents had its toll on slowing down the Indonesian economic activities throughout the

year. The Central Bank's discretionary policy taken has obtained good results. The

decision to reduce the BI rate (from 9.25 per cent in 2008 to 6.5 per cent in 2009),

managed to suppress the inflation rate as well as the bank deposit rate and stimulated

the Indonesian stock exchange index.

In addition, the crisis reduced trust among the commercial banks. The inter-bank

lending and borrowing declined by 59.3 per cent from IDR 206.0 trillion on December

2007 to IDR 83.8 trillion on December 2008. The decline in confidence among

commercial banks was almost similar to the 1997-98 crises at which they experienced a

shortage of liquidity so that they were reluctant to lend each other in the interbank

market (Basri and Siregar, 2009)

2.2.6. After the 2008 Crisis

The period after the crisis of 2008 is considered as a recovery phase for the Indonesian

economy after a slight decline occurred in 2008. In general, table 2.5 shows the progress

of Indonesian economic performance in terms of lower inflation, lower interest rates,

lower unemployment rates and rising stock market indices in 2009-2010.

Table 2.5. Indonesian Macroeconomic & Financial Indicator, 2005-2010

Indicator 2005 2006 2007 2008 2009 2010

GDP Growth (%) 5.7 5.5 6.3 6 4.6 6.1

Inflation rate (%) 17.1 6.6 6.6 11.1 2.8 7.0

Un-employment rate

(%) 11.2 10.3 9.1 8.4 7.9 7.1

ISE composite index 1163 1806 2746 1355 2534 3704

Interest rate/BI rate (%) 12.75 9.75 8 9.25 6.5 6.5

Source: BAPPENAS RI (Ministry of National Planning Republic of Indonesia)

Table 2.5 shows that the economic condition in Indonesia has significantly improved in

2010. The inflation rate, the unemployment rate, the Indonesia Stock Exchange (ISE)

composite index, and the interest rate all have improved in 2009 compared to 2008

27

levels. The adopted policy by BI to lower interest rates in 2009 and 2010 had attained

satisfactory results: stimulated economic growth; reduced unemployment and increased

the ISE composite index in 2010. Given the above statistics one can argue that

Indonesia has successfully managed the crisis of 2008 compared to the 1997-1998 one.

Basri (2011) provides a comparison table (See Table 2.6., Appendix 2) that elaborates

how the GOI dealt with the 2008 crisis differently compared to the 1998 crisis from

different aspects: monetary policy, fiscal policy, banking health, response towards

banking sector, and structural reform policies.

2.3 Small Businesses Finance and Total Finance

This section presents overview knowledge on commercial banks‟ overall lending

(financing) activities and to small business lending (financing) in Indonesia. The content

of this chapter is essential for understanding the effects of small business finance as a

proportion of total finance on the Indonesian bank performance.

2.3.1 Definition of a Small Business (SB) and Small Business Finance (SBF)

In general, the definition of a small business and small business finance across the globe

is different depending on the provisions prevailing in the country. Every country

decides on their criteria for a small business and small businesses finance.

International Finance Corporation (IFC)21

has compiled the definition of micro small

medium enterprises (MSMEs) across 120 countries. Several criteria for SBs in these

countries are as follows:

In the UK, a small business is defined as follows: a company that has turnover of

not more than £5.6 million, balance sheet total, not more than £2.8 million of

total assets, and not more than 50 employees.

In the US, a small business is a company that has no more than 500 employees

21 International Finance Corporation (IFC) is an organization and a member of the World Bank Group

that has two primary goals i.e. to end poverty of the world by 2030 and to boost shared prosperity in

every developing country in the world. See in details in www.ifc.org.

http://www.ifc.org/

28

(for most manufacturing and mining industries) and has average annual receipts

of $7 million (for most non-manufacturing industries).

In Australia, a small business is a business employing less than 20 people. The

small business also covers non-employing businesses i.e. sole proprietorships

and partnerships without employees, micro businesses which employ less than 5

people, and other small businesses which employ 5 or more people but less than

20 people..

In China, a small business is a business which has total assets of less than ¥40

million and average business revenue of less than ¥30 million. It is also

classified as a small business a company that has number of employees less than

300 (for the Industry sector), 600 (for construction sector), 100 (for wholesale

and retail industries), 500 (for the transportation industry) and 400 (for hotel and

restaurant industries).

The criteria for small business finance also vary across countries in the world. Some

countries defined small business finance (SBF) as follows:

In the US, small business finance is all commercial and industrial (C&I) loans

with original amounts under to $1 million [Strahan and Weston (1998), Devaney

and Weber (2002)].

In Argentina, the Central bank of Argentina has defined small business loans as

the total debts in between $50,000-$2.5 million (Clarke et al, 2005).

The Chilean Banking Superintendence has defined small business loans as the

total debts of less than $1.5 million (Clarke et al, 2005).

The Banking Superintendence of Peru has defined small business finance as the

loans amounted in the range between $20,000 and $500,000 (Clarke et al, 2005).

2.3.2. Definition of Small Businesses Finance in Indonesia

Small business finance (SBF) is one of the commercial banks‟ financing schemes in

addition to corporate and consumer financing. Commercial banks provide various

financing with the aim of diversifying their financing portfolios as well as reaching

numerous customer segmentations. Through diversified portfolios, bank managers

29

expect that the risk associated with financing will be reduced.

The Indonesian government defines a small business as well as small business finance

through the Indonesian government laws notified by parliament and the regulations

issued by Bank Indonesia. Accordingly, commercial banks then adjust their financing

schemes to small businesses in accordance with prevailing regulations.

The Government of Indonesia (GOI) has defined a small business as follows:

“A small business (SB) is an economic and productive entity, carried out by an

individual or a business entity that is not a subsidiary or a branch of a company and is

not owned and controlled or a part, directly or indirectly, of a medium or large

enterprise that meets the following criteria: 1) It has a net worth of more than IDR 50

million up to IDR 500 million, not including its land and buildings. 2) It has the annual

sales of more than IDR 300 million up to IDR 2,500 million ” (The Law No. 20/2008

about micro, small, and medium enterprises, Ch. 1 article 1 page 2 and Ch. 4 article 6

page 5-6) .

The definition of small business finance in Indonesia is as follows:

“Small business finance (SBF) is loans or financing facility provided by commercial

banks for investment or working capital purposes to small businesses with a maximum

amount of IDR 500 million” (Bank Indonesia Regulation No. 3/2/PBI/200122

).

Based on those criteria and the definition of a small business above, commercial banks

provide financing to small businesses along with other financings i.e. corporate

financing and consumer financing. Then, commercial banks regularly report the

exposures of their finance portfolios to the Central Bank of Indonesia on a monthly and

yearly basis. Accordingly, the Central Bank of Indonesia announces the level of total

small business finance maintained by all commercial banks in Indonesia in its reports.

22

BI Regulation No. 3/2/PBI/2001 on Small business loans (finance) on January 4, 2001.

30

2.3.3. The Regulation of minimum SBF Threshold in the Indonesian Commercial

Banks

Basic rules concerning the obligation of commercial banks to allocate 20 per cent of

their loan portfolios was first stipulated in one of the banking deregulation packages

which was published in 1990 (PAKJAN 90)23

. The package policy states that banks are

obliged to provide at least 20 per cent of their loan portfolio towards small business

finance. The policy was later revised by the Decree of BI Directors No. 30/4/KEP/DIR

on April 1997 which changed the minimum threshold to 22.5 percent of the net loan

(finance) expansion. The latter regulation was applied onwards until 2001.

The regulations about the minimum SBF requirements for Indonesian commercial banks

were then revoked by BI Regulation Number 3/2/PBI/2001 about Small Business

Finance on January 4, 2001. The regulation states that the provision of small business

finance is readjusted based on the ability of each bank. In other words, commercial

banks were no longer compelled to provide a minimum percentage of their finance

portfolios to small businesses.

Although the government provides flexibility for commercial banks to manage their

small business finance without giving the minimum limit, the central bank keeps

requiring all commercial banks to stipulate the distribution plan of SBF on their annual

business plans. In addition, banks are required to report the disbursement of SBF to BI

at the end of each month. For transparency purposes, the commercial banks are required

to inform the public about their achievements of their SBF through the publication of

financial statements/annual report.

In 2012 the GOI turned back to issue new regulations24

regarding the obligation of

commercial banks to distribute at least 20 per cent loan portfolios in this sector. The

difference is, the obligation to fulfil the minimum financing portfolio by 20 per cent is

23

On January 29, 1990 the GOI issued a policy package known as PAKJAN (the package of January

1990). The package is a continuation of the previous packages in the banking sector with the aim of

mobilizing funds, improving efficiency and reducing subsidies from Bank Indonesia. The most important

issue of this package is the improvement of the provision of Small businesses (SBs) finance with the

provision of at least 20 percent of the bank financing shall be distributed to SBs. 24

BI Regulation No.14/22/PBI/2012 on 21 December, 2012 regarding the commercial bank loans or

finance for MSMEs and the technical assistance for the development of MSMEs.

31

currently not only intended to Small businesses (SBs) but it is aimed at financing Micro

Small and Medium Enterprises (MSMEs). Indeed, SBs is a part of MSMEs.

In addition, banks are allowed to achieve the ratio up to the following 6 years with the

stages as follows:

Until the end of 2013 and 2014, the ratio of MSMEs finance is tailored to the

capabilities of commercial banks, which must be included in their business plan.

Until the end of 2015, the ratio of the total MSMEs finance should achieve at

least five percent.

Until the end of 2016, the ratio of the total MSMEs finance should achieve a

minimum of ten percent.

Until the end of 2017, the ratio of the total MSMEs finance should achieve a

minimum of fifteen percent.

Until the end of 2018 onwards, the ratio of the total MSMEs finance should

achieve a minimum of twenty percent.

Sanctions against banks that do not comply with the above are as follows:

The banks are required to organize training for Micro, Small and Medium

Enterprises which are not or have never received loans or financing of MSMEs.

The amount of training funds is calculated based on the percentage difference

between the ratio of MSMEs financing that must be met and the realization of

the ratio achievement at the end of each year, with a maximum of IDR 10

billion.

The launch of this new regulation could be interpreted that the GOI has considered the

importance of MSMEs as an important driver for the Indonesian economy. This thesis

aims to investigate the performance of Indonesian banks in terms of bank efficiency and

bank lending propensity over the period 2002 to 2010. The study period witnessed the

period of time when there were no government regulations that forced Indonesian banks

to provide a minimum particular percentage (minimum threshold) of their financings to

small businesses. Therefore, the findings of this thesis are very useful as an evidence of

whether the decision of the GOI to re-enact the regulation of small business finance (in

this case, MSMEs finance) in 2012 was right or not.

32

2.3.4. The Development of Small Business Finance in Indonesia over the period

2002-2009

Indonesian commercial banks maintained small business finance as one of their loan

portfolios during the decade of the 2000s. During the period, there was only one scheme

launched by the government of Indonesia which was classified as SBs finance. The

scheme was named Kredit Usaha Rakyat (KUR) in 2008. The purpose of the scheme is

to assist commercial banks in accelerating the SBF as well as the development of small

businesses in Indonesia.

KUR is one of the loan schemes provided by the commercial banks to micro, small and

medium enterprises and cooperatives with the guarantee pattern, cooperating with the

insurance agency established by the Government. KUR can be used to finance all

productive enterprises including those in the agriculture sector which are viable

(feasible) but not yet bankable25

of additional collateral aspects (Technical Guidelines

People's Business Credit, Ministry of Agriculture, GOI, 2012).

For commercial banks, this scheme should have increased the amount of SBF exposure

owned by the banks. With this scheme, commercial banks transfer the credit risk to the

insurance company. The role of commercial banks is only to assess feasibility and

decide on granting loans or financing. The guarantee fees on the loans are paid by the

government to the credit insurance company. Hence, the role of the GOI in this scheme

is laid on the payment of the guarantee fees only and not providing the interest subsidy

as the previous policies in the decade of 1990s.

The table below presents the comparison between Small Business (SBs) Finance, Micro

Small and Medium Enterprises (MSMEs) Finance, and the Total Finance of Indonesian

commercial banks for the period of 2002-2009:

25

Bankable means that the project or the company has met all the requirements set by commercial banks

in terms of the project feasibility and collateral availability.

33

Table 2.7. The Highlights of SBF and MSMEs Finance IDR Billion

No Type of finance 2002 2003 2004 2005 2006 2007 2008 2009

1 SBs Finance 62,266 72,647 85,191 96,580 102,028 112,575 132,115 153,553

2 MSMEs Finance 160,977 207,088 271,092 354,908 410,442 502,798 633,944 737,386

3 Total Finance 371,058 440,505 559,470 695,648 792,297 1,002,012 1,307,688 1,437,930

No %Type of finance 2002 2003 2004 2005 2006 2007 2008 2009

1 SBs Finance 16.78% 16.49% 15.23% 13.88% 12.88% 11.23% 10.10% 10.68%

2 MSMEs Finance 43.38% 47.01% 48.46% 51.02% 51.80% 50.18% 48.48% 51.28%

3 Total Finance 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Source: Indonesian Bank Statistics, 2002-2009

Table 2.7 reveals that SBF has increased in the Indonesian Rupiahs (IDR) absolute

value from IDR 62.27 trillion in 2002 to IDR 153.55 trillion in 2009. The average

increased rate of SBF over the period is 13.84 per cent a year. However in terms of the

portfolio percentage, SBF of Indonesian commercial banks tended to decrease from

16.78 per cent in 2002 to the only 10.68 per cent in 2009. The regulation to revoke the

obligation for commercial banks to maintain 20 per cent of their finance portfolios to

small businesses26

has impacted to the major decline in the SBF portions over time

during the period 2002-2009.

On the other hand, MSMEs finance is likely to increase either in nominal value or in

percentage terms. MSMEs finance of Indonesian commercial banks has increased from

IDR 160.98 trillion in 2002 to IDR 737.39 trillion in 2009. The average increase rate of

MSMs finance over the period is 24.43 per cent which is much higher than that of the

average increase rate of SBF which achieved 13.84 per cent. In terms of the percentage

of finance portfolio, MSMs finance also experienced a considerable increase from 43.38

percent in 2002 to 51.28 percent in 2009. Because MSMEs actually consists of micro,

small and medium enterprises, then it can be concluded that the high increase rate of

MSMEs finance is caused by a high increase in the financing portfolios of micro and

medium enterprises.

The upward tendency of Indonesian commercial banks to provide MSMEs finance also

indicates that there is a change in the orientation of commercial bank financing in

Indonesia. They are now more likely to focus on financing micro, small and medium

26

BI Regulation Number 3/2/PBI/2001 about Small Business Finance on January 4, 2001.

34

enterprises (MSMEs) rather than financing on small businesses (SBs) only. This

tendency is in line with the new regulation issued by the GOI, namely the Law Number

20/200827

regarding micro, small and medium enterprises. Under the new rules,

commercial banks should have adjusted their progress reports on the micro, small and

medium enterprises (MSMEs) finance rather than on SBs finance only. These reports

are submitted regularly at the end of the month. These policies are in line with the new

regulations as stipulated in the BI Regulation No.14/22/PBI/2012 on 21 December,

2012 regarding commercial bank loans or finance for MSMEs and technical assistance

for the development of MSMEs.

2.3.5. The Development of Total Loans of Indonesian Banks during 2002-2010

In terms of total lending facilities (total loans) of Indonesian banks over the period 2002

to 2010, some following tables and figures are presented as follows:

Table 2.8. The Highlights of Total Loans & the Profitability of Indonesian Banks

Main Indicators 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total Loans (IDR Trillion) 371.1 440.5 559.5 695.6 792.3 1,002 1,307.7 1,437.9 1,765.8

Loans to Deposits ratio (%) 44.4 49.6 58.1 61.7 61.6 66.3 74.6 72.9 75.5

Gross Non-Performing Loans (%) 7.5 6.8 4.5 7.6 6.1 4.1 3.2 3.3 2.6

Return on Assets (%) 2 2.6 3.5 2.6 2.6 2.8 2.3 2.6 2.9

Net Interest Margin (%) 4.1 4.6 6.4 5.6 5.8 5.7 5.7 5.6 5.7

Source: Indonesian Economic Report 2011, Bank Indonesia

Table 2.8 testifies the increasing tendency of the total loans of Indonesian commercial

banks over the period 2002 to 2010 in terms of total absolute value of Rupiahs. It shows

the upward tendency from IDR 371.1 trillion in 2002 to IDR 1.765.8 in 2010. In terms

of loans to deposits ratio (LDR), it is also likely to increase over time from 44.4 per cent

in 2002 to 75.5 per cent in 2010. These two indicators imply the upward propensity of

all Indonesian commercial banks over the period 2002 to 2010. These also show that the

function of banks as financial intermediary institutions has increased over the period

particularly in the role of the provision of loans to lending customers. The increased

27

As mandated by the Law No. 20/2008 regarding the micro, small and medium enterprises (MSMEs), the

GOI has changed the focus of attention not only for small businesses but also for micro and medium

enterprises.

35

trend of the banks‟ loans also reflects the revival phase of the Indonesian banking

industry after suffering from the Asian crisis of 1997-98.

In terms of the loan quality, the table witnesses the increasing loan quality of Indonesian

banks evidenced by the downward trend of the gross non-performing loans from 7.4 per

cent in 2002 to the only 2.6 in 2010. In addition, in terms of bank profitability, the table

shows good performance of banks in terms of return on assets and net interest margin

which obtained an average performance of 2.7 per cent and 5.5 per cent respectively.

In the assessment of the contribution of the commercial loans on the economy, it can be

observed by their productive loans (working capital loans and investment loans)

compared to the total loans (see table 2.9).

Table 2.9. Productive Loans & Total Loans of Indonesian Banks

IDR Billion

Year Working Capital Loans Investment Loans Total Productive Loans TOTAL Loans

2002 282,486 82,924 365,410 371,058

2003 343,626 94,316 437,942 440,505

2004 436,684 116,864 553,548 559,470

2005 557,207 132,463 689,670 695,648

2006 405,551 148,770 554,321 792,297

2007 518,339 183,694 702,033 1,002,012

2008 668,007 254,373 922,380 1,307,688

2009 686,983 295,914 982,897 1,437,930

2010 868,356 345,700 1,214,056 1,765,845

Average 529,693 183,891 713,584 930,273

Source: Indonesian Banking Statistics, 2002-2010, Bank Indonesia

On average, of the total loans provided yearly over the period 2002 to 2010 by

commercial banks which are accounted for IDR 930.27 trillion, an average of IDR

713.58 was bestowed to productive loans, and the rests are for consumptive purposes

loans. Of all productive loans, commercial banks in Indonesia tend to finance

predominantly to working capital purposes which accounted for an average of IDR

529.69 trillion a year over the period. While an average loan of IDR 183.89 was

attributed to investment purposes.

36

Figure 2.2. The Trend of Loans of Indonesian Banks (2002-2010)


Meanwhile, the trend of productive loans and total loans which are presented by the

above figure demonstrate that all productive loans (working capital and investment

loans) have an upward trend over the period 2002-2010. These achievements have

supported the trend of the total loans in similar pattern over time across the period.

Figure 2.3. The Trend of Loans Proportions of Indonesian Banks (2002-2010)


In terms of loan proportion to the total loans, the figure 2.3 shows that the percentage of

productive loans has tended to decrease since 2006. This means that since that year, the

contribution of consumptive loans increased over time until 2010. The decreasing trend

of productive loans seems to be affected by the decreasing pattern of working capital

loans during 2006 to 2010. While investment loans appear to be constant at an average

of about 20 per cent. The total loans provided by commercial banks are still very

important in the bank-based system in the vein of Indonesia. Banks‟ loans fuel the

-

500,000

1,000,000

1,500,000

2,000,000

2002 2003 2004 2005 2006 2007 2008 2009 2010

IDR

Bill

ion

Year

The Trends of Loans (Indonesian banks, 2002-2010)

Working Capital

Investment

Total Productive Loans

TOTAL Loans

0.0%

20.0%

40.0%

60.0%

80.0%

100.0%

120.0%

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

The trend of Loans Proportions (Indonesian banks, 2002-2010)

Working Capital

Investment

Total Productive Loans

TOTAL Loans

37

engine of the economy to produce goods and services in the country. The figure below

can be used to briefly observe the association between the growth of loans and the

growth of gross domestic products (GDP).

Figure 2.4. The Trend of Loans Growth and GDP Growth (2002-2010)


Figure 2.4 represents loan growth in terms of working capital loans, investment loans

and the total loans in supporting the growth of the GDP. The growth of working capital

loans accounted for 17.30 per cent on average. It seems that the negative growth in 2006

and the small growth in 2009 affected the average growth of the loans over the period.

The decline in working-capital loans growth in 2006 and small growth achievement in

2009 seem to be affected by the mini-crisis of 2005 and the world crisis of 2008.

Meanwhile, the growth of the investment loans accounted for 19.03 per cent on average

yearly over the period. The mini-crisis of 2005 and the world crisis of 2008 only slightly

affected the achievement of investment loan growth in 2006 and 2009 but the growth

was still positive in two digits.

Overall, the growth of the total loans which accounted for the average of 21.23 percent

has contributed to the positive growth of the GDP of Indonesia which accounted for

5.39 per cent growth on the average. The figure reflects the contributions of the total

loans as well as the productive loans of commercial banks in enhancing economic

activities in Indonesia over the period 2002 to 2010.

2002 2003 2004 2005 2006 2007 2008 2009 2010

Working Capital Loans Growth 20.65% 21.64% 27.08% 27.60% -27.22% 27.81% 28.87% 2.84% 26.40%

Investment Loans Growth 12.87% 13.74% 23.91% 13.35% 12.31% 23.48% 38.48% 16.33% 16.82%

TOTAL Loans Growth 17.40% 18.72% 27.01% 24.34% 13.89% 26.47% 30.51% 9.96% 22.80%

GDP Growth 4.50% 4.80% 5.00% 5.70% 5.50% 6.30% 6.00% 4.60% 6.10%

-40.00%-30.00%-20.00%-10.00%

0.00%10.00%20.00%30.00%40.00%50.00%

Loans Growth & GDP Growth

38

2.4. Conclusions

Indonesia's banking industry has a long history since the colonial era when Indonesia

was a Dutch colony. Some commercial banks were established in the period, and they

were owned either by colonial powers or by local communities in several cities in

Indonesia.

After Indonesia proclaimed its independence in 1945, some significant progress was

achieved by the Indonesian government through the establishment of several new

commercial banks that concentrated on particular sectors in accordance with the

requirements in the economy. Several periods took place with tight regulations set by

Bank Indonesia as a central bank as well as commercial banks regulatory and oversight

agency. Regulations were applied to commercial banks to serve the public in accordance

with the objectives of the government.

The 1980s-1996 periods is considered to be a very dynamic one since some

deregulation packages were launched for the purpose of accelerating the evolution of

Indonesian banking effectively. However, success in augmenting the number of banks

was actually not accompanied good quality bank management. Thus, when the crisis

occurred in the neighbouring countries in 1997, it easily spread to Indonesia.

The crisis of 1997-98 provided some very important lessons for the Indonesian

government and banking authorities to launch their new architectural Indonesian bank

in 2004. The new architecture was in keeping with the policy of Indonesian banking

prudence and sound supervision. This programme successfully led Indonesian banking

to pass the global crisis of 2008 where Indonesian banks are less affected compared to

other Asian countries.

The performance of Indonesian banks witnessed improvement during the decade of

2000s. Many banks rebounded to make profits after suffering from losses during the

crisis of 1997-98. The portfolio composition of earning assets particularly in lending or

finance facilities is very interesting to explore. In this regard, the thesis provides

efficiency estimates of Indonesian commercial banks, deriving the involvement of small

business finance or small business loans in the estimation of the bank efficiency for both

DEA and SFA techniques.

39

In addition, the lending propensity of Indonesian banks is also interesting to explore.

The propensity of Indonesian banks in terms of small business loans and total loans over

the period 2002-2010 is very important to be examined since the period was considered

to be a revival period after the Asian financial crisis of 1997-98 as well as a period of

implementing the government policy28

to relax the minimum threshold of SBF exposure

that should be maintained by Indonesian commercial banks.

28


40

3. Chapter three: Literature Review

3.1. Introduction

Measuring bank performance has been a crucial issue in banking research for decades.

There are two mainstream approaches in assessing the performance of banks, namely

the financial ratios approach and the frontier approach. The financial ratios approach

explores the financial condition of each bank in terms of liquidity, profitability,

leverage, and activity ratios and compares the ratios with the average industry ratios as

the benchmarks. Meanwhile, the frontier approach measures bank performance by

comparing the performance of each bank relative to a best practice frontier. In the

frontier approach, there are two prominent methods employed, namely the non-

parametric method and the parametric method. This thesis investigates the efficiency of

Indonesian banks by both the DEA non parametric method and SFA parametric method,

presented in the two empirical chapters (chapters five and six). These chapters also

expand their analysis by revealing the determinants of DEA technical efficiency as well

as SFA cost efficiency of Indonesian banks during the period 2002-2010.

On the other hand, studies on banks‟ lending propensity have been growing in the

academic literature [see for example Strahan and Wetson (1998), Peek and Rosengren

(1998), Goldberg and White (1998), Berger et al. (2001, 2007), Akhavein et al. (2004),

Laderman (2008), Shen et al. (2009), McNulty (2013)]. These studies mostly discuss the

propensity of commercial banks to provide small business loans as the main point of

view. This is applied due to the fact that not all commercial banks have the willingness

to provide loans for small businesses. Thus, it has attracted many authors to examine the

lending propensities of banks including the propensity of banks to provide lending to

SBs. This thesis also investigates the propensities of Indonesian commercial banks in

providing their lending facilities in terms of total loans and small business loans

(chapter 7). The chapter also provides an extensive analysis by examining the

determinants of Indonesian bank lending propensity over the period 2002 to 2010.

41

This chapter provides a brief theoretical background and empirical literature on two

main issues in this thesis: bank efficiency and bank lending propensity. The chapter is

organised as follows. The first section reviews a brief theory of production efficiency as

well as the empirical evidence on bank efficiency in some countries. The focus of the

empirical evidence presented is the efficiency and productivity studies on the

Indonesian commercial banks as well the efficiency and productivity studies on the

emerging economies particularly in areas adjacent to Indonesia i.e. the South East Asia

and East Asia countries. The second section also provides a brief theory of loan

management and the previous literature on bank lending propensity. The final section

provides conclusions.

3.2. Theoretical and Empirical Literature

3.2.1. The Theory of Efficiency

3.2.1.1. The Brief Theory of Production Efficiency

Efficiency measurement was firstly discussed by Farrel (1957) based on the previous

work of Koopmans (1951) and Debreu (1951). He accounts for multiple inputs for the

measurement of efficiency. Farrel (1957) identified that firm efficiency covers two

components: 1) technical efficiency, which denotes the ability of a firm to obtain

maximal output from a given sets of inputs and 2) allocative efficiency, which indicates

the ability of a firm to employ the inputs in the optimal allocations given the price and

the technology of production. The combination of the two measures (technical

efficiency and allocative efficiency), are then recognized as an economic efficiency

measurement.

Figure 3.1 illustrates the original ideas of Farrel. He simplifies his explanation through

the usage of the two factor of productions, where, in this case ( and as the two

inputs to obtain an output (q) under the constant-return to scale assumption. denotes

the unit isoquant of fully efficient firms, which reflects the various combination of the

two inputs that can be employed by a perfectly efficient firm to produce unit output.

42

is the isocost-line (slope) which reflects the ratio of the price of the two inputs.

Figure 3.1. Technical and Allocative Efficiency

S

X2/q

P

Q

A

R

Q’

0

X1/q

Source: Coelli et.al (2005), Greene (2013)

If a firm produces an output by using a quantity of inputs at point P, the distance of QP

is the technical inefficiency of the firm. That distance reflects the amount of all inputs

that could be reduced without influencing the output. The technical inefficiency of the

firm would value QP/0P.

The point Q is technically efficient since it lies on the efficient isoquant. Thus, the

technical efficiency (TE) of the firm would be as follows (Coelli et al., 2005):

(3.1)

In general, the technical efficiency of a firm would always be between 0 and 1 (0 ≤ TE

≤1) and the score reflects the technical efficiency level of the firm. 0 means least

efficient while 1 denotes the most efficient one.

However, a firm can reallocate their inputs with the new combination along the isoquant

curve to obtain the technical efficient as well as the allocative efficient. At the

intersection of the isocost line and isoquant curve (at the point ) the firm can achieve

the overall efficiency or economic efficiency since it can experience the lowest cost for

the optimal output. If a firm produces at , the distance RQ would be the amount of

43

reduction in the cost of production that could be gained by the firm. Hence, the firm that

operates at would be technical efficient since it is on the isoquant curve (producing

maximal output) and also allocative efficient because it undergoes the optimal

allocation of production inputs. In other words, the firm operates in economic (overall)

efficient.

In the case of cost efficiency, we can assume w as the vector of input prices, and x as

the observed vector of inputs employed (related with P), while is the input vector

employed in the technically efficient point Q and denotes the input vector in the cost

minimizing condition at . Then, we can state that cost efficiency (CE) of the firm is

measured as follows (Coelli et al, 2005):

(3.2)

3.2.1.2. Techniques of Measuring Efficiency

There are two mainstreams in estimating the efficiency of a firm, namely parametric

techniques and non-parametric techniques. Each technique has its own advantages and

weaknesses. This section describes the two mainstreams in estimating efficiency:

3.2.1.2.1. Non-parametric Techniques

There are some techniques applied using the non-parametric method, i.e. Data

Envelopment Analysis (DEA) and the Free Disposal Hull (FDH). The DEA approach is

the most widely used in this kind of research to date. An explanation about DEA in

more details is presented in the Chapter 4 in the methodology section.

FDH analysis was firstly introduced in a study of the relative efficiency of post office

operations in Deprins et al. (1984). Tulkens and Eeckaut (1995) then discussed more

advanced about issues, particularly about the technological change and shift in the

production possibility frontier. The central principle of the FDH is that, a producer is

relatively inefficient if another producer uses less or an equal amount of inputs to

generate more output (Gupta & Verhoeven, 2001). The Free Disposal Hull (FDH)

model is a special case of DEA, which has a different assumption than the CCR and

44

BCC models29

. Unlike those two models, the FDH does not operate with a convexity

assumption. In practice, the efficient reference point for an inefficient DMU is among

the existing DMUs and not chosen as a point of a continuous efficiency frontier. Thus,

this model has a discrete nature. Moreover, as stated by Tulkens (1993), the evolution of

the FDH has adopted the replications of the existing DMU in the extension of the

hypothetical DMUs.

3.2.1.2.2. Parametric Techniques

In general, a parametric approach accounts for the assumption of the error term in the

efficiency estimation. Various methods have been developed in this parametric

mainstream and the choice of methods is mainly dependent on the assumptions of the

distribution of the error components.

There have been some methods can be applied i.e. stochastic frontier approach (SFA),

distribution free approach (DFA) and thick frontier approach (TFA). Detail explanation

of the SFA is presented in the methodology section in Chapter 4.

The distribution free approach (DFA) which was first mentioned by Berger (1993) is a

method which does not make specific distributional assumptions on the error

components. DFA requires panel data and is based on a translog system of cost and

input cost share equations that generate estimates of cost inefficiency for each producers

in each time period (Kumbhakar and Lovell, 2000). The main advantage of this method

is that the estimation results do not depend on the distributional assumption on

(inefficiency term). The other advantage is as being based on a sequence of time

separate cross sectional regressions, it allows the structure of production technology to

vary flexibly through time (Kumbhakar and Lovell, 2000). While a disadvantage of

DFA is the prerequisite that cost efficiency is time invariant, and the assumption would

become less tenable as time increases.

29

Charnes, Cooper, and Rhodes (CCR in 1978) and Banker, Charnes and Cooper (BCC in 1984) models.

45

Some of the techniques that could be classified in this distributional free approach are

Corrected OLS (COLS)30

, and Corrected Mean Absolute Deviation (CMAD)31

. Both

techniques do not make distributional assumptions on the error components.

The thick frontier approach (TFA) was proposed by Berger and Humprey (1991). This

approach is mostly applied within the cost frontier model, although it may also be

employed for a production frontier model. This approach clusters samples into N

quartiles (e.g. 4 quartiles) based on the indicator of an observed efficiency i.e. the

average cost or the average output. In general, the advantage of the TFA approach is it

could obtain the inference of the cost inefficiency although it does not rely on particular

distributional assumptions. While some drawbacks of the approach are as follows: First,

it needs a large dataset; otherwise it would apply too small data because of the

stratifications. Second, it is not easy to stratify the data for multiple outputs.

3.2.2. The Empirical Literature on the Efficiency and Productivity Studies

There is abundant research on the efficiency and productivity of commercial banks.

However, the number of studies on emerging markets is fewer compared to studies on

developed ones (Fethi and Pasiouras, 2010).

Bonin et al. (2005) study the impact of ownership, particularly a strategic foreign owner,

on bank efficiency in eleven transition countries in Europe within the period 1996-2000.

They carry out the research by the reason that the asset majority of commercial banks in

those countries were dominated by foreign ownership during that period. They used data

of 225 banks from eleven countries (the Czech Republic, Hungary, Poland, Slovakia,

Bulgaria, Croatia, Romania, Slovenia, Estonia, Latvia, and Lithuania) and employ SFA

technique for finding the efficiency. Their results suggest that state-owned banks are not

less efficient than domestic private banks, meaning that privatization itself does not

guarantee that the commercial banks are more efficient. The other results indicate that

foreign-owned banks are more cost-efficient than their peer banks. Foreign banks also

30

COLS was proposed by Winsten (1957). The model is a frontier that is deterministic (non-stochastic),

since it excludes the statistical error . Hence, the deterministic frontier model is

31

CMAD is also known as the median regression. We can estimate efficiency with the same procedure as

the OLS, but the difference is that CMAD regression passes through the median, while the OLS

regression passes through the mean of the data.

46

provide better services in terms of their ability to gain more deposits and make more

loans than domestic private banks and government banks.

Mamatzakis et al. (2008) employ SFA to estimate commercial bank cost and profit

inefficiencies in the 10 new European members during the period 1998-2003. The 10

countries included in their research are Cyprus, the Czech Republic, Estonia, Hungary,

Latvia, Lithuania, Malta, Poland, the Slovak Republic, and Slovenia. Based on the

inefficiency scores for both cost and profit functions, the results indicate that in general

there has been la ow level of cost efficiency and profit efficiency and there are no

significant differences in inefficiency scores across the countries. The other findings

suggest that foreign banks are more profit efficient than their peers, while in terms of

cost efficiency, government-owned banks are the most efficient banks amongst the

others.

Hamiltona et al. (2010) undertook the research with the aim to examine cost and profit

efficiency in Jordanian banks over the period 1993-2006. They employed SFA to

estimate the banks efficiency using data from all 22 banks, which covers foreign and

domestic banks in Jordan. The observed banks are classified into three types of banks

namely Islamic, investment, and commercial banks. The findings of the study show that

the average levels of cost-efficiency in these types of banks are much higher than their

profit-efficiency. The levels of standard profit-efficiency of the commercial banks are

higher than those of the Islamic and investment banks. The last finding in terms of

alternative profit-efficiency is that the Islamic banks are more efficient than investment

banks and commercial banks.

The association between macro-economic factors and bank efficiency has been studied

by Chan and Karim (2010). Their study covers some developing countries in Asia, the

Middle East/North Africa, and Africa. They employ SFA to estimate the bank efficiency

of 43 developing countries during the period 2000-2005. The findings of the research

indicate that the impact of those macroeconomic factors on bank efficiency across the

regions is not uniform. Commercial banks in the Middle Eastern/North African region

are the most efficient in terms of cost efficiency with an average cost efficiency of 94.9

percent, whilst commercial banks in Asia are the most profit efficient with an average

efficiency score of 97.6 percent over the period. Lastly, commercial banks in the

47

African region are the least profit and cost efficient with an average level of 42.4

percent and 82.8 percent. In the Asia region, the GDP per capita, credit to private

sector32

, and market concentration33

have negative impacts, but trade openness34

has a

positive impact, on bank cost inefficiency respectively. In the Middle East/North Africa,

market concentration has a positive impact but trade openness has a negative impact,

both on the banks cost inefficiency. However, both market concentration and trade

openness have positive influences on the banks cost inefficiencies in the African region.

The role of ownership, market power, and institutional development in regards to

enhance bank efficiency has been examined by Fang et al. (2011). They examined the

profit and cost efficiency of commercial banks in South-Eastern Europe during the

period 1998-2008 using the SFA technique. They employed data of 171 banks in six

South-Eastern Europe countries (i.e. Albania, Bulgaria, Croatia, Macedonia, Romania,

and Serbia) and 209 banks in Central and Eastern Europe (i.e. the Czech Republic,

Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, and Slovenia) for the

comparison. The results suggest that the average bank cost efficiency in South-Eastern

Europe countries is 68.59 percent which is lower than those of Central and Eastern

Europe countries which accounts for 70.77 percent, but the average bank profit

efficiency of SEE countries denotes 53.87 percent which is higher than those of Central

and Eastern Europe countries as they mark only 52.95 percent. The other results show

that domestic private banks experience higher profit efficiency than state-owned banks.

Foreign-owned banks obtain lower cost-efficiency but higher profit-efficiency than their

peers. Nevertheless, the gap of efficiency scores over the foreign banks, domestic

private banks, and government-owned banks are not quite wide over the study period.

The regression results suggest that market power35

and institutional development36

have

positive impacts on bank efficiency. These findings reflect that banks with high market

power tend to gain more cost and profit efficient and the institutional developments

32

They employed the ratio of private credit to GDP to reflect the financial market depth in the countries. 33

They used the three firms‟ concentration ratio for representing a market structure where the commercial

banks operate. The three firms‟ concentration ratio is the proportion of the three largest banks‟ total assets

over the banks‟ total assets in the sample. 34

Trade openness is measured by the sum of export and import as a percentage of GDP (Fang et al.,

2011). 35

They use a learner index for a proxy for a market power. Learner index is calculated by the mark-up of

the price over marginal cost and divided by the price (Fang et al., 2011) 36

For representing the institutional development, they employ the development in banking regulatory

reforms, privatization, and enterprise corporate governance restructuring (Fang et al., 2011).

48

through banking reform, enterprise restructuring and privatization are important factors

to promote both banks profit and cost efficiency.

Besides SFA, many authors estimate bank efficiency using the DEA technique [See for

example, Sufian (2010), Fethi, et al. (2011), Gardener, et al. (2011), Andries (2011)].

Fethi, et al. (2011) undertook the research to assess the Egyptian performance banks

within a period characterised by changes in economic policies and investigated the

influence of the privatisation process at the end of 1995 on the efficiency and

productivity of the Egyptian banks. They also evaluated whether the liberalisation

affected the various kinds of bank ownership and size with consistent magnitude. The

DEA-based Malmquist technique was employed to measure the efficiency and

productivity of 25 commercial Egyptian banks over the period 1984-2002. The results

reveal that the productivity growth of Egyptian banking sector tends to be volatile with

a Malmquist Total Factor Productivity Index (MTFPI) of 0.9930 within the period.

Total Factor Productivity (TFP) growth of the Egyptian banking sector was -0.7 per cent

during the period with the high volatility in the pattern of Technological Change Index

(TCI).

Other findings suggest that the liberalisation policies directed to enhance bank

performance, evidenced by a growth of productivity achievement of 7.6 per cent

compared to the pre-liberalisation productivity regress. The Liberalisation had an effect

on dissimilar ownership and size with varied magnitudes. In the post liberalisation, the

most productive banks are small banks. In the post privatisation, all the privatised banks

went through productivity regress compared to the previous period, and the joint

venture banks which altered to foreign ownership inclined to gain better productivity

growth than any other ownership forms.

Andries (2011) undertook the research about bank efficiency in Central and Eastern

European countries with both approaches, SFA and DEA. The study examined factors

impacting on the banking system‟s efficiency and productivity in seven Central and

Eastern European countries over the period 2004-2008. He used a dataset which was

derived from the Bankers almanac database covering 112 banks in 7 countries:

Romania, Poland, Bulgaria, Hungary, Slovenia, the Czech Republic, and Slovakia. The

results of the research suggest that for the period 2004-2008 the average efficiency of

commercial banks in Central and Eastern European countries has increased. The highest

49

average technical efficiency based on the SFA technique is Romania with a score of

0.8644, whilst according to the DEA technique is the Czech Republic with an average

efficiency score of 0.9135. Meanwhile, the lowest average SFA efficiency is Slovakia

with a score of 0.6275 and for the DEA is Slovenia with a score of 0.6338.

The other findings reveal that based on the Malmquist index, the productivity of the

banks have increased over the study period by 24.27 per cent due to an increase in

technological modification. State-owned banks are less efficient than private banks, but

in terms of productivity, state-owned banks experience a larger increase than private

banks. Small banks (with assets of less than $1 billion) are the most efficient banks

compared to the medium banks (with assets greater than $1 billion but less than $10

billion) and large banks (with assets greater than $10 billion). But in terms of

productivity, the highest growing banks are the medium banks. The findings over the

OLS regression reveal that in order to enhance the efficiency, banks should improve

their asset quality by enhancing the quality their loan processing and implementing their

risk management to reduce their non-performing loans. The finding also suggests that

an increase in assets results in an increase in efficiency. From a macroeconomic

perspective, the finding suggests that government authorities should support the banking

liberalisation and reforms and exert some efforts to maintain the inflation rate in low

level.

3.2.2.1. Efficiency and Productivity Studies on Indonesian banks

There have been few studies about Indonesian bank efficiency (Harada and Ito (2005),

Margono et al. (2010), Hadad et al. (2011a,b), Suzuki and Sastrosuwito (2011), Besar

(2011)). These studies investigated the performance of Indonesian commercial banks by

employing the parametric and the non-parametric techniques.

Harada and Ito (2005) are among the first to estimate commercial banks efficiency in

Indonesia. They focused only on large commercial banks over the period 1999-2003

using the DEA method. Personnel expenses, general and administrative expenses, and

interest expenses are employed as inputs, while interest income and commission income

are chosen as outputs. The results show that the efficiency of those banks were on a

recovery trend as reflected by the average efficiency scores across the years. The

50

average efficiency score bottomed out at an average of 0.80 in 1999 and it then trended

to increase at the remaining years at an average of more than 0.90. It is also revealed

that the efficiency of private banks that had not accepted public funds after the 1997-

crisis performed well during the study period.

Margono et.al (2010) used the stochastic frontier approach (SFA) to estimate cost

efficiency, scale economies, technological progress, and the productivity growth of

Indonesian banks, over the period 1993 to 2000. They employ a flexible Fourier form to

attain the estimation of Indonesian bank cost efficiency with the intermediation

approach applied. They utilize two outputs namely total aggregate loans and total

aggregate securities respectively, together with the three prices of inputs, namely price

of labour, price of funds, and price of capital over the consistent data of 134 commercial

banks in Indonesia. In order to be able to identify the effect of the 1997 Asian crisis on

the efficiency, they also divide the two different periods, 1993-1997 (pre-crisis) and

1998-2000 (post-crisis).

The results show that the average cost efficiency of all Indonesian banks was 70 percent

during the research period, with 79.7 percent and 53.4 percent for the period of the pre-

Asian crisis and post-Asian crisis respectively. These findings indicate that the Asian

crisis of 1997-1998 extremely affected Indonesian bank performance as evidenced by

their reduced average cost efficiency in the period of after the crisis compared with the

period of before the crisis. In addition, there was an increase at an average annual rate of

banks‟ efficiencies for both periods, with an annual increase of 6.3 per cent for the

period 1993-1997 and 1.4 per cent for the period 1998-2000. Their findings also show

that joint venture/foreign banks were more efficient than private banks and state-owned

banks. State-owned banks were the lowest efficient banks for all the consecutive years.

Moreover, the relationship between cost efficiency and total assets suggests an optimum

bank asset size reached at between 500 to 750 billion rupiahs of annual asset size, since

the most efficient ones occurred at that interval size of assets.

Hadad et.al (2011a,b) used non-parametric slack-based DEA to examine the profit-

based technical efficiency and productivity of listed Indonesian banks and their market

performance. They found that listed banks‟ average efficiency varied widely over the

sample period between 34 percent to 97 percent and that banks‟ efficiency scores were

positively correlated with share prices and return on equity in all models. In addition,

51

they also analyze productivity changes for Indonesian banks over the period Q1 2003 to

Q2 2007. The research results show that the average productivity changes in Indonesian

banking industry tended to be driven by technological progress rather than by frontier

shift and that most of the balance sheet variables have had the expected impact on risk

management efficiency.

Suzuki and Sastrosuwito (2011) also used DEA to investigate the efficiency of

Indonesian commercial banks and examine productivity change during the period of

1994-2008. They employed 70 banks in Indonesia classified into four groups based on

their ownership, namely government-owned banks, local-government owned banks,

private owned banks, joint venture and foreign-owned banks. The results show that the

average efficiency of Indonesian banks over the study period was 0.866. The most

efficient banks were government-owned banks with an average efficiency of 0.953 and

then followed by joint venture and foreign owned banks with an average of 0.943. In

addition, the productivity growth of Indonesian banks increased by 0.5 percent annually

during the period. The achievement was supported by the technological change that had

grown by 1.7 percent whilst the efficiency change had declined by 1.1 percent annually

over the period.

Besar (2011) examined the cost efficiency of commercial banks in Indonesia employing

the SFA technique. He specifies his analysis on the influence of foreign ownership on

bank performance covering data of 119 commercial banks over the period September

2000 (2000Q3) to September 2009 (2009Q3). The intermediation approach was chosen

with two output quantities (total loans and total securities) and three input prices (the

price of labour, the price of physical capital and the price of deposits). The dependent

variable is total costs and the control variables are NPL and capital ratio. He classified

the observed banks into 4 groups: state owned banks, private domestic owned banks,

old foreign owned banks, and new foreign owned banks37

.

The results suggest that the total cost of Indonesian banking has decreased over time

with an average yearly decreasing rate of 2 percent. The highest cost efficient bank is

one of the old foreign banks that obtained an efficiency score of 92 percent, whilst the

37

Old foreign owned banks refer to banks that are majority owned by foreign owners that were

established before the crisis, whilst new foreign banks are the banks majority owned by foreign owners

that were established after crisis and as a result of foreign acquisitions.

52

lowest cost efficient was a domestic private owned bank with an efficiency score of

27.5 percent. In general, the highest average cost efficiency score was obtained by the

state owned banks, followed by the new foreign banks, domestic private owned banks

and lastly, the old foreign banks.

3.2.2.2. Efficiency and Productivity Studies in other Emerging Economies Banking

3.2.2.2.1. South East Asia (SEA)

The last decade witnessed a growing literature on bank performance in the South East

particularly discussing the effect of the financial crisis in 1997-1998 on bank

performance. However, there is still a paucity of bank efficiency and productivity

research compared to those of developed countries. The studies are fragmented mostly

in Malaysia, Thailand and Indonesia as many commercial banks are operating there [For

example, see Williams and Nguyen (2005), Sufian (2010), Chunhachinda and Li (2010),

Suhaemi et al. (2010), Gardener, et al. (2011), Abdul-Majid et al. (2011)].

Williams and Nguyen (2005) examined the comparison of bank performance and bank

governance in the South East Asia (SEA) nations during the period 1990-2003. They

measured bank governance in terms of its ownership and rank order alternative

efficiency, technical change and productivity as the measurement of bank performance.

The research highlights the important years in which the financial liberalisation was

done before the crisis and the financial restructuring was done afterwards. By using

dataset covering 231 commercial banks between 1990-2003 in South East Asia

(Indonesia, Korea, Malaysia, the Philippines, and Thailand), the results of the research

suggest that the privatisation has been successful in that region since the privatised

banks showed better performance for the post-privatisation period than before the

privatisation. In terms of ownership, private ownership banks outperformed the state-

owned banks. Furthermore, foreign acquisition banks were the best performing banks in

terms of profit efficiency. The closure and absorption banks had on average

underperformance and low profit efficiency.

53

Sufian (2010) examined the effect of 1997-financial crisis in Asian countries on bank

efficiency in Malaysia and Thailand. He chose these countries considering that the

countries are the two of four South East Asian (SEA) countries having severely

experienced crises besides Indonesia and Philippines. He adopted three different

approaches to find out the efficiency scores, namely the intermediation approach, the

value-added approach and the operating approach with the DEA technique.

In general, the results suggest that their technical efficiency scores are consistently

higher under the operating approach than those of the intermediation and value-added

approaches. The empirical findings also advise that in the post-crisis, Malaysian

banking experienced higher technical efficiency scores under the intermediation and

value-added approaches than that of under the operating approach, while the Thailand

banking sector exhibited a lower technical efficiency level under all approaches during

the post-crisis than those of the pre-crisis period. In the multivariate regression section,

it is revealed that some explanatory variables (i.e. bank size, expense behaviour and

liquidity) have negative relationships with banks efficiency in Malaysian banks. On the

other hand, bank size, loans intensity, and capitalization have positive relationships but

expense behaviour and liquidity have the positive signs with banks efficiency in

Thailand.

Chunhachinda and Li (2010) compared the profit and cost the efficiency of Thai

commercial banks during the 1997 crisis period (they sub-divided the period into the

pre-crisis, the crisis, and the post-crisis periods). The benefit of this research compared

to others is the usage of both parametric and non-parametric techniques in estimating

cost and profit efficiency scores through the SFA and DEA methods. The research

findings indicate that the average cost inefficiency for the pre-crisis, crisis, and post

crisis are 8 percent, 9 percent, and 15 percent respectively, and the average profit

inefficiency for the periods are 15 percent, 28 percent, and 17 percent respectively.

The results of the second stage empirical analysis suggest that annual real GDP has a

positive impact on expected profit efficiency while the age of bank has also a positive

impact on cost efficiency. Small banks experience lower cost efficiency levels compared

to medium and large banks. The average cost efficiencies of private banks are much

54

higher than those of state-owned banks. The ratio of loan provision38

has a negative

impact on profit efficiency, while loan deposit ratios39

have also a negative impact on

cost inefficiency. The other results suggest that equity to total assets40

has not any

impact on bank efficiency but the ratio of non-interest income to interest income41

has a

negative impact on cost efficiency.

Suhaemi et al. (2010) investigated the determinants of commercial banks profit

efficiency in Malaysia over the period 1995-2007. They employed SFA to find out the

efficiency scores for 23 commercial banks operating in Malaysia. They chose profit

efficiency instead of cost efficiency as their arguments stated that profit efficiency could

take into account the two effects, namely cost effect and revenue effect, although the

previous studies on bank efficiency have almost always employed cost efficiency. Cost

effects are related to the bank that do not apply the minimum input and revenue effects

are regarding the banks that do not reach the maximum output.

The findings reveal that the average profit efficiency for both local banks and foreign

banks tend to have a downtrend during the period with an overall average of 0.6916 in

1995 and 0.3557 in 2007. A downtrend of profit efficiency is identified due to the fact

that the interest income has faced the same downtrend within the period. The second

empirical evidence demonstrates that information and communication technology (ICT)

infrastructure has a positive impact on the profit efficiency although the effect is not

significant. The significant factors for bank profit efficiency are non ICT infrastructure,

market share, and ownership with each having a positive direction, whilst size has a

negative association with the profit efficiency.

Gardener, et al. (2011) study covers five South East Asian countries. They employed the

non-parametric DEA and TOBIT regression to answer the research questions. The study

aims to investigate bank efficiency in the South East Asian economies after the 1997

financial crisis and to assess the effect of post-crisis restructuring. The findings of this

38

Loan provision represents the credit risk of a bank.

39

Loans deposits ratio is a proxy for a bank liquidity risk.

40

Equity to total assets denotes a bank capital risk.

41

Non-interest income to interest income represents market risk for a bank.

55

research reveal that the structure of ownership has significantly affected bank efficiency

in South East Asia over the period 1998-2004. Foreign banks operate more efficiently

than domestic banks. Within each country, government-owned banks are recorded more

efficient than private banks. The other results suggest that the restructuring programme

implemented in that region (post the 1997-crisis) does not have a positive effect on bank

efficiency, reflected by the downtrend of the efficiency scores over the period 1998-

2004.

One of the most recent studies on bank efficiency in Malaysia was completed by Abdul-

Majid et al. (2011). They estimated the efficiency and the TFP (total factor productivity)

change of commercial banks in Malaysia during the period 1996-2002. The findings

indicate that the average net inefficiency of commercial banks in Malaysia was around

4-6% annually from 1996 to 2002. Foreign-owned banks are on average more efficient

than domestic ones. Mergers, Islamic full-pledge banks and conventional banks which

also operate the Islamic banking windows experienced less efficient. The results also

suggest that the banks‟ average productivity change of 2.37 percent was assigned to

technical change and it has declined during the period.

3.2.2.2. East Asia (EA)

Many studies about the efficiency of commercial banks have been completed in East

Asia region covering new industrial countries (i.e. China, Korea, and Taiwan) as well as

Japan which is recognized as one of the developed economies in the world [Among

others, see Altunbas et al. (2000), Drake and Hall (2003), Yao et al. (2008), Yeh (2011),

Shin and Kim (2011)].

Altunbas et al. (2000) examined risk and quality factors and their influence on bank

performance, especially bank cost efficiency and technical change using the SFA

method. They utilize data from commercial banks in Japan during the period 1993-1996

which covered 139 banks for 1993-1995 and 136 banks in 1996. By comparing the cost-

function specifications between addressing and not addressing the risk and quality

factors, the results show that when the function is not taking into account the risk and

quality factors, all Japanese banking except for the largest Japanese banks experience

56

scale economies. When the function is controlled by the risk and quality factors, the

majority of commercial banks experience diseconomies of scale excluding the very

smallest banks. The findings indicate that the scale of economies of bank has become

smaller for controlling the risk. This result also suggests that bank cost can be reduced

by technical change. However, it happens at a declining rate, during the period 1993-

1996.

Drake and Hall (2003) evaluated Japanese bank performance with a non-parametric

frontier approach, the DEA method. The aims of their research were to estimate the

technical and scale efficiency in Japanese banking employing a cross-section sample of

149 banks on March 1997. The results do not conform to the wave of mergers occurring

during the time since in terms of economies of scale the evidence indicates that

Japanese‟s large banks are operating well above the minimum efficient scale, thus the

mergers among them tended to intensify their diseconomies of scale. Moreover, the

findings also suggest that those large banks, especially the Long-Term Credit Banks

(LTCBs), incline to have the lowest points of pure technical inefficiency among all

Japanese banks. On the other hand, a contradictory result occurred for smaller banks

since the evidence of significant economies of scale for the smallest Japanese banks

during the period.

Yao et al. (2008) studied the technical efficiency of Chinese banks using DEA. They

concentrated on the efficiency issue regarding the new era of the pre and post-World

Trade Organization (WTO), by investigating the positive or negative reaction of

Chinese state commercial banks through the new challenges. The research employs data

from all commercial banks in China during the period 1998-2005. Besides using the

DEA technique in gaining efficiency scores, they also carried out a Malmquist index

analysis to find out bank productivity changes. The research findings show that the gap

of average efficiency scores from high and low levels of efficiency is at only 15 percent

indicating that Chinese national banks do not have significant differences in their

technical efficiency. Another finding is that the Chinese national banks react positively

and aggressively to the new reforms of ownership and challenges by foreign

competition since the total factor productivity (TFP) has increased by 5.6 percent

annually during the period 1998-2005.

57

The association between capital structure and cost efficiency in the banking industry

was accomplished by Yeh (2011). The study aimed to examine whether banks are able

to diminish their agency costs through the optimization of capital structure in enhancing

their efficiency. They adopted the stochastic frontier approach to estimate the bank cost

efficiency of 44 commercial banks in Taiwan from 1999 to 2004. They also employed

two-stages least squares to investigate the effect of capital structure on banks efficiency.

The findings of this research suggest that optimal capital structure can be used by

managers to reduce the agency problem and increase bank efficiency as a proxy of bank

performance. These results are evidenced by the positive association between debt ratio

and bank efficiency. Another finding is that insider share ownership has a negative

effect on bank efficiency. This means that by reducing managerial share-holding, banks

can decrease agency costs and finally enhance their performance. In addition, bank size

has a positive impact on bank efficiency reflecting that larger banks are more efficient.

Another study was done by Sufian (2011). His research aimed to examine the efficiency

of the Korean banking industry over the period 1992-2003. The advantage of this

research is the usage of three various approaches to obtain different efficiency scores as

the changes in input and output components. The three approaches are intermediation,

value added, and the operating approach. The study employed the DEA technique to

estimate bank efficiency on around data of 31 banks in Korea during the period 1992-

2003. The result shows that the technical efficiency estimates are constantly higher

under the operating approach than those of derived from the intermediation and value-

added approaches. The lowest level of technical efficiency scores are shown by the

intermediation approach.

Shin and Kim (2011) also carried out research about bank efficiency in Korea. The

objective of this research was to examine the relationship between the efficiency and

profitability of the Korean banking industry. The paper also investigated whether the

Korean banking efficiency increased when the bank restructuring program was started

in 1997. By using the DEA technique for estimating efficiency, the results of this

research suggest that the higher efficiency banks exhibit higher profitability and bank

efficiency influences positively on the market share and market concentration.

Nevertheless, in the period of the post-1997 crisis, bank profitability was not improved

by technological efficiency rather than by the increased scale efficiency.

58

3.2.3. A Brief Theory of Loan Management in Commercial Banks

Finance or lending is one of the most important activities in commercial banking.

Commercial banks play a unique role by channelling funds from depositors in the form

of loans to borrowers (Heffernan, 2009). The role reflects a commercial bank as a

financial intermediary institution between depositors and borrowers. Howells and Bain

(2008) formulate the main activities of a commercial bank in a balance sheet as follows:

Table 3.1. A commercial bank’s balance sheet

Assets Liabilities

Notes and coin Capital and shareholder's funds

Deposits at the central bank Customer deposits

Loans to the money markets

Investments

Loans to public sector

Loans to the general public

Source: Howells and Bain (2008)

A Commercial bank engages with the maturity transformation42

activities, thus it

manages the deposits collected on the liabilities side and provides some accounts on the

assets side through as bank reserves, as loans to money market or other

financial institutions, as investments in the form of securities and other investments.

denote loans to public sector and to non-bank private sector, respectively.

Loans to non-bank private sector are an important part in the asset side since it

commonly dominates bank‟ assets.

Banks make profit mainly by issuing loans or financing. Loans have mostly generated

more than half of bank revenues [Mishkin (2010), Saunders and Cornett (2012)].

However, loans are also recognized as the major sources of the credit43

and liquidity

risks44

in commercial banks. Considering the circumstances, it is considerably vital by

bank managers to manage loans correctly in order to create revenue with less risk

incurred.

42

It relates to arrangement of the short-terms deposits into longer-term loans and investments. 43

Credit risk or default risk is the event when borrowers failed to repay loans. 44

Liquidity risk, since loans cannot be turned into cash until the loans matures.

59

Loan portfolio management has been one of the most crucial aspects in bank

management in the sense that inappropriate management of the bank loan portfolios

could lead to a negative impact on bank performance, and generally on the banking

system of a country (Macerinskiene and Ivaskeviciute, 2008).

Banks can classify their financings into the many categories45

. Each loan category

entails a wide variety of characteristics that must be evaluated to determine the risk

involved and to decide whether they should grant loans to potential customers. For

instance, in the US, it is common practice to stipulate the total loans along with their

components, i.e. commercial and industrial loans, real estate loans, individual loans, and

other loans46

. Such classifications are very beneficial for bank managers to assess the

performance of each loan as well as the total loans of each category respectively.

Bank managers should carefully organise all loan processes starting from the search of

potential borrowers to the maintenance and monitoring of their loans. Banks will incur

intermediary costs in the process of searching, verifying, and monitoring potential

borrowers‟ creditworthiness (Heffernan, 2009). The intermediation process and its costs

also encompass administration cost and other costs of transactions related to savings and

financings products provided by banks. In this regard, the aim of a bank is to create

qualified loans or financings in order to cover all those expenses together along with the

expected return.

Bank managers apply different methods to assess the credit worthiness of potential

customers (loan applicants) based on the type of loans using the principle of 5 C‟s47

of

credit analysis. The emphasis of the assessment in the credit scoring can be relatively

different accordingly. For instance, in the process of assessing a mortgage loan,

commercial banks‟ decisions are mainly dominated by two considerations48

: the ability

and willingness of the applicant in making timely repayments of the principal and

interest, and the applicant‟s collateral value. Thus, collateral value is very important in

taking out mortgage loans, whereas for nonmortgage consumer loans, the focus is on the

45

According to the intended use, loans can be classified as investment loans, working capital loans and

consumer loans. Based on the size of the borrowers, the loans can be divided as small business loans,

medium-business loans, and large business loans. 46

See Saunders and Cornett (2012: 352) 47

5 C‟s of credit: character, capacity, capital, collateral, and conditions. 48

For more details, see Saunders and Cornett (2012).

60

individual‟s ability evidenced by the personal characteristics i.e. annual gross income

and the total debt service49

.

Regarding the evaluation of small business loans, Saunders and Cornett (2012)

recommend that credit scoring for the small business loans model should be more

sophisticated compared to those that are applied to mortgages and consumer loans. They

argue that the credit risk of small business loans is higher due to incomplete accounting

and financial information (information asymmetry). To mitigate the risk emerging from

information asymmetry a rather deeper analysis should be applied with regard to small

businesses. Moreover, the credit scoring should complement the computer-based

analysis of borrower‟s financial statements with the behavioural analysis of small

business owner.

Credit analysis for the larger segment of customers, namely mid-market corporate50

, is

quite different compared to small business loans credit analysis. The focus is on the

business itself besides the character of the company‟s management. Thus, it covers

several dimensions, including the five C‟s of credit, financial ratio analysis, analysis of

cash flow, and the comparison of financial statements.

Mishkin (2010) sheds light on the importance of banks in understanding the concept of

adverse selection51

and moral hazard52

problems that could lead loans more likely to

default. Adverse selection occurs when a bank mistakenly chooses potential borrowers.

Banks may choose those who are particularly at the very high risk because of

information asymmetry however they are likely to produce adverse outcome. Moral

hazard happens if borrowers have inducement to involve in undesirable activities from

the viewpoint of the lender. Hence, in this circumstance, bank may face the hazard of

default.

49

Total debt service ratio is calculated by total accommodation expenses plus all other debt service

payments divided by gross income (Saunders and Cornett, 2012). 50

In the US, mid-market corporates typically have sales revenues from $5 million to $100 million a year

(Saunders and Cornett, 2012). 51

Borrowers with very risky investment projects but they are chosen by a bank is an example of adverse

selection, since they are most likely to be unable to payback their loans (Mishkin, 2010). 52

For instance, when borrowers obtain loans, they tend to invest in high risky projects (that pay high

returns if successful). Nevertheless, the high risk project creates them less likely to be able to pay the loan

back (Mishkin, 2010).

61

To overcome the problem of adverse selection, Mishkin (2010) advises the lenders to

collect more reliable information about the potential borrowers and screen out the bad

credit risk from the good ones. By these actions, it is expected that bank would only

grant the loans or financings to the prospective borrowers. The other way to mitigate the

risk of adverse selection is becoming more specialized bank in lending53

. While in

reducing the risk of moral hazard, Mishkin suggested bank to apply restrictive

covenants54

into loan contract.

Regarding the association between bank size and lending activities, Saunders and

Cornett (2012) elaborate that small banks mostly concentrate on the retail side of the

business (i.e. making loans and issuing deposits to customers and small businesses),

whilst larger banks engage in both retail and wholesale banking55

. These circumstances

are underpinned by the relatively easy access of larger banks to purchased funds and

capital markets compared to small banks.

In reaching the goal of maximizing profits, banks would also deal with macroeconomic

risks that surrounding their circumstances. These environment factors could be directly

and indirectly influencing bank performance. For instance, inflation and recession in the

economy are among the risks that should be accounted for by bank managers

(Heffernan, 2009).

3.2.4. The Empirical Literature on the Bank Lending Propensities

The literature that discusses the lending propensities of commercial banks mostly

focuses on the tendency of banks to provide lending to small businesses. One of the

main objectives of this thesis is to discuss the trend of Indonesian commercial banks in

providing their lending facilities (total loans and small business loans) to their

customers (borrowers) as well as to examine the effect of the asset size of Indonesian

banks on their propensity to lend to their borrowers.

53

This means that a bank should concentrate its lending only to companies in specific industries until it

becomes more knowledgeable about the industries. 54

Covenants restrict borrowers from engaging in risky activities. 55

Wholesale banking is commercial oriented banking, providing commercial & industrial loans funded

with purchased funds (Saunders and Cornett, 2012).

62

Most of the literature in this area use surveys data for their studies [Jayaratne and

Wolken, (1999), Scott and Dunkelberg (2003), Berry and Grant (2004), Clarke et al.

(2005)]. Other studies discuss the determinants of small business lending using

secondary data from small businesses [Strahan and Wetson (1998), Peek and Rosengren

(1998), Berger et al. (2001), Shen, et al., (2009), McNulty, et al. (2013)]. This thesis

focuses on the lending propensity of commercial banks and their determinants.

Jayaratne and Wolken (1999) investigated the importance of small banks in providing

small business finance in the US. They employed data of the National Surveys on Small

Business Finance (NSSBF) in 1993, to test whether the wave of bank mergers which

occurred in 1990s in the US significantly affected on the probability of a small business

to have a credit line from a commercial bank. They also examined whether small

businesses experienced difficulties in paying their trade-credit. Their findings suggest

that the probability of a small firm to obtain credit facilities from banks did not decrease

in the long-run although in areas where there were only a few numbers of small banks

available. They also found that small businesses do not have difficulties in paying their

trade-credit reflected in their on-time payment records.

Scott and Dunkelberg (2003) scrutinized the impact of bank mergers on the

opportunities for small firms in obtaining financing in the US. The study employed data

resulted from small business survey which was undertaken by the National Federation

of Independent Business in 1995. The results of the study exhibit that there is no

influence of bank mergers on the ability of small businesses to obtain loans. Mergers

have also no significant effects on new loan interest rates except for some other non-

price terms such as collateral requirement, compensating balance, and other fees.

Berry and Grant (2004) conducted a survey on small business lending in the United

Kingdom. They used survey data to examine the extent of the involvement of European

Banks operating in the UK in providing financing for Small Medium Enterprises

(SMEs) in the UK. The study analysed the approach (i.e. the going concern56

approach

or the gone concern approach57

) employed by these banks as a lending practice.

56

Going concern approach is the assessment of the credit worthiness by looking at the future prospects of

potential customers which are reflected from their ability of future cash generation.

57

Gone concern approach, also called liquidation approach, is the evaluation method of creditworthiness,

with emphasis on what will happen in the worst case scenario, through the sale of the assets and other

security taken in the form of fixed and floating charges, guarantees, and restrictive covenants.

63

Additional information was gathered by interviewing the lending managers of all those

European banks that are presumably active in lending to SMEs in UK. Their results

reveal that only 10 European banks out of 97 banks were active in providing loans to

UK SMEs. All these banks are branch offices with headquarters in their home countries.

Most of them adopted a mixture both approaches in assessing loan feasibility.

Clarke et al. (2005) investigated the relation between bank origins and share and growth

of small business financing. The study employed commercial bank data of four Latin

American countries (Argentina, Chile, Colombia, and Peru) during the period of the late

1990s. The results show that on average, foreign banks in Chile and Peru experienced

less financing for small businesses compared to domestic banks. In Argentina and

Colombia, foreign and domestic banks lent to small businesses with a nearly equal

proportion. In addition, in the case of Chile and Argentina in general, the findings

suggest that large foreign banks exceed large domestic banks in terms of share and

growth rate for small business loans.

Strahan and Wetson (1998) investigated the relationship between small business

lending, bank size and bank consolidation in the US banking industry within the period

1993-1994. They aimed to examine whether the size and complexity of a bank 58

has an

impact on its ability to provide loans to small businesses. In addition, they also

investigated the influence of consolidation, which will result in a larger and more

complicated bank, on the availability to provide lending to small businesses. The results

of the study state that within the period, small business lending has increased in terms of

the percentage to total assets for the small banking companies until their assets reached

around $300 million, and it downturns after that point. Whereas for the larger banking

companies, small business lending has risen slowly as the size of banks increased with a

decline in portfolio percentage. The complexity of the bank has no significant

relationship with lending to small businesses. Consolidation through mergers and

acquisitions (M&A) among small banks have a positive impact on the increase in the

small business lending, but the M&A among large banking companies and small

banking ones have also a positive effect although the increase is not significant.

58

Strahan and Weston (1998) explained complexity of a bank as a more complex organizational structure

of a bank. They assume that the complexity of large banks may cause diseconomies in the organization

which provide relationship loans more expensive for them. The total numbers of subsidiaries held by a

banking company and the number of states in which the bank operates are the proxies for the complexity.

64

Peek and Rosengren (1998) investigated the influence of mergers on bank willingness59

to finance small businesses. The study corresponds to the concerns over public policy

stemming from the wave of mergers that caused the shrinkage of the number of banks

by nearly 30 per cent in the US over the period 1985-1995. They aim to explore whether

bank consolidation has an impact on the availability of loans to small businesses. In

order to answer the research questions, they employed a survey conducted annually

based on the June Bank Call Report over the period 1993-1996. The results demonstrate

that in around half of the mergers, the acquirers had more small business loans than

those of their target banks before mergers and they tended to enhance their small

business loans after the mergers. The results also reveal that small acquiring banks

tended to be more active in small business lending than the large acquirers.

Berger et al. (2001) examined the impact of bank size, foreign ownership60

and

distress61

on lending to informational opaque small businesses in Argentina. By

employing the data set of 61,295 non-financial firms and total loans from 115 different

commercial banks as of the end of 1998, the results suggest that large and foreign-

owned banks experienced some difficulties in extending relationship loans to opaque

small businesses. These conditions were caused by the higher expenses experienced by

those banks in providing relationship lending services to small businesses together with

other services to large companies. This was also magnified for small businesses with

delinquencies in the repayment of their debts. However, distressed banks experienced

no significant matters in providing loans for small firms during the period.

Shen, et al. (2009) examined the determinants of small business finance through

investigating the association between bank size and small business lending in some

commercial banks in China. They employed panel data of county level banks operating

in China assessing how bank size and other determinants namely discretion over credit,

incentive schemes, competition, and the institutional environment62

affected SMEs

59

Peek and Rosengren (1998) consider the „willingness of banks to finance the SBs‟ as reflected in the

percentage of small business loans to total assets. 60

Foreign ownership in this case means a branch or subsidiary in which its headquarters abroad or in

which at least 50% of the capital is foreign-owned (Berger et al., 2001). 61

Berger et al. (2001) define a distressed bank as an unhealthy bank or a bank experiencing financial

difficulties due to some reasons i.e. the credit crunch or the financial crisis. 62

Loans approval share is a proxy for the discretion over credit. Bank‟s loan market share is a proxy for

the competition. For the incentive scheme, the study uses the dummy of „linkage of wage with the loan

quality‟ with the hypothesis link wage and loan quality tend to increase SMEs loans. For the institutional

environments, they use dummies for government influence on the loans approval, degree of law

65

lending in China. The data covers 79 counties within 12 provinces detailing the

information the banks‟ structure of governance, loans and deposit policy, the system of

incentive, and banks‟ balance sheet over the period 2001-2004. The study provided the

treatment of instrumental variables for the endogeneity problem resulting from the

association between some explanatory variables and error term in the study. The results

show that bank size had no impact on the SMEs lending. Some variables (the linkage of

wage and loan quality, the competition, and institutional arrangement) significantly

affected the SMEs lending. In other words, if loan quality is connected with the

manager‟s wage, if there is greater competition, and if an institution has more self-loan

approval right, then financing to SMEs will be higher. In addition, law enforcement also

influenced the SMEs lending. The weak law enforcement leads to low SMEs lending.

The most recent study that elaborates the determinants of small business finance has

been undertaken by McNulty, et al. (2013). They investigated the relationship between

small business lending and bank size by using 2SLS cross sectional regressions with

fixed effects approach over 5,537 banks for 1993-2006. Their findings suggest that there

was a negative association between bank size and small business lending proportion,

meaning that the propensity to lend to small businesses declined when bank size

increased. Small business lending growth did not compensate for the growth in the size

of the bank. When a bank asset size increased from $ 1 billion to $ 100 billion, the

proportion of small business loans was reduced by 28 percentage points. The results

also suggest that small banks have recorded most of their loans to small businesses.

Small banks have made 28.4% of total banking loans for small businesses even though

they only accounted for 14.1% of total banking deposits and 9.7% of total banking

assets during the period 1993-2006. They also provide recommendations for continuing

research on lending propensity because it contains very valuable information as a proxy

of the supply of loans.

One of the chapters of this thesis examines the determinants of Indonesian bank lending

propensity (in terms of total loans and small business loans) on the commercial banks in

Indonesia over the period 2002-2010. It particularly explores the influence of size of the

banks on their total loans and small business loans. The study gives the contributions to

the literature since it would be the first study to exhibit the factors explaining total

enforcement when enterprises defaulted. All those data are particularly collected by the surveys on the

banks managers.

66

finance as well as small business finance in Indonesia whilst it has been done by authors

in several countries to date.

3.3. Conclusion

This chapter aim to provide brief background on theoretical and empirical literature on

banks efficiency, productivity and propensity to lending to small businesses. The

chapter covered studies from emerging economies, with particular focus on studies from

South East Asia and East Asia.

The first section of this chapter reviews the brief theory of production efficiency as well

as empirical evidence on bank efficiency in some emerging countries. The focus of the

empirical evidence presented is the efficiency and productivity studies on Indonesian

commercial banks as well efficiency and productivity studies on emerging economies

particularly in areas adjacent to Indonesia i.e. the South East Asia (SEA) and East Asia

(EA) countries. These studies collectively cover bank efficiency and productivity

studies in Indonesia and some emerging economies amounting to twenty six articles in

totals are reviewed.

The second section also provides a brief theory of loan management and the previous

literature on the determinants of bank lending propensity. It covers the importance of

loan portfolio management in managing commercial banks and the determinants of

bank lending propensity mainly in small business lending as one of the important banks

portfolio financing in some countries covering the US, the UK, Latin America Countries

and China.

Regarding the studies of bank efficiency in Indonesia and the adjacent regions,

concerning the seventeen studies reviewed, nine studies employed the non-parametric

technique DEA, seven studies employed the parametric technique SFA and only one

study used the SFA and DEA simultaneously.

The literature on Indonesian bank efficiency and productivity is limited. To the author

knowledge there are only five published studies on Indonesian bank efficiency, of which

three have employed the DEA ([Harada and Ito (2005), Hadad et al. (2011a,b), Suzuki

67

and Sastrosuwito (2011)], while the rest [Margono et al. (2010) and Besar (2011)] use

the SFA. Harada and Ito (2005) are the first authors examine the efficiency of 10

Indonesian banks over the period 1999-2003 with the DEA method. The results indicate

that the average efficiency score bottomed out in 1999 and it then trended to increase

during the remaining years. A distinctive feature of these studies is that they have

covered the performance of Indonesian banks over a limited number of years or a

limited number of banks.

It is worth noting that most of studies about small business lending are concerned with

the two issues: Firstly, investigating the involvement of commercial banks in providing

financing for small businesses. They mainly reveal survey or interview findings.

Secondly, examining the determinants of small business finance in commercial banks,

particularly exploring the association between bank size and willingness to provide

financing for small businesses.

Regarding the first issue, the studies‟ findings reveal that small banks experienced less

difficulties in providing small business finance and the probability of Small businesses

to have access to bank finance from small banks did not change (Jayaratne and Wolken,

1999), there was no influence of bank mergers on bank financing for small businesses

(Scott and Dunkelberg, 2003), there was a small amounts of European banks operating

in the UK which provided financing for small businesses (Berry and Grant, 2004).

Foreign banks in some Latin American Countries practiced less financing for Small

businesses (Clarke et al., 2005).

For the second issue, the results reveal that small banks tended to finance small

businesses and the consolidation between small banks increased small business finance

(Strahan and Wetson, 1998). Small acquiring banks were more likely to finance small

businesses compared to large acquiring banks (Peek and Rosengren, 1998). Large and

foreign-owned banks experienced some difficulties in extending relationship loans to

opaque small businesses (Berger et al., 2001). Bank size did not impact on SMEs

lending (Shen, et al., 2009). There was a negative association between bank size and

small business lending proportion (McNulty, et al., 2013). The last results mean that

that the propensity to lend to small businesses declined when bank size increased.

68

The present thesis contributes to the literature in six fold; first, it investigates the

performance of Indonesian banks by estimating both technical and cost efficiency.

Second, it utilises both the DEA and SFA techniques to provide more insight on the

performance of Indonesian banks using different methodological techniques. Third, it

covers a sample of Indonesian banks that represent 93% of the total banks operating in

Indonesia. Fourth, it is the first to investigate lending propensity to small businesses in

Indonesia. Fifth, the sample covers the years of change in banks‟ regulations as well as

the financial crisis in 2007, hence the study investigates the impact of the crisis and

regulatory change on the Indonesian banking sector. Sixth, it is the first to investigate

the effect of small business lending on bank technical and cost efficiency.

69

4. Chapter Four: Methodology

4.1. Introduction

This thesis employs the frontier approach in measuring bank performance. The frontier

approach is supposed to be robust compared to the traditional approach such as financial

ratio analysis (Berger and Humphrey, 1997). The advantage of frontier approach

compared to the traditional approach is that the frontier approach compares the

performance of banks to best practice frontier which is more beneficial in terms of

regulatory purposes. Therefore, it provides an analytical tool that is useful for policy

makers to investigate the impact of government policies (i.e. deregulation,

liberalization, privatization, mergers and acquisitions) on the performance of banking

sector. It is also a useful comparison tool in case of the occurrence of certain events e.g.

financial crisis or disruption [see Bauer et al. (1998), Shaban (2008)]. Frontier approach

uses either linear programming or econometric techniques to eliminate the effects of

exogenous market factors on inputs and inputs prices. Therefore, the method can

provide more reliable estimates of firms‟ performance (Shaban, 2008).

Technically, the frontier approach identifies an unobservable best practice

(benchmark(s)) bank(s) in terms of efficiency and measure the performance of other

banks in the sample relative to the best practice bank(s). This helps to provide

recommendation to the non-efficient banks (non-frontier banks) to improve their

performance to catch-up with the efficient one (frontier-banks).

There are two prominent methods in estimating the efficiency of commercial banks,

namely the non-parametric approach Data Envelopment Analysis (DEA) and the

parametric approach Stochastic Frontier Analysis (SFA). Each of these approaches has

its own advantages and disadvantage. So far there is no consensus in the literature on

which approach is the most preferred in determining the best practice frontier and

estimating the efficiency scores (Berger and Humphrey, 1997).

This thesis adopts both methodologies to obtain efficiency estimate for Indonesian

commercial banks during the period 2002-2010. The analysis of Indonesian banks‟

70

performance is extended by estimating non-parametric productivity using the DEA-

based Malmquist Total Factor Productivity Index/MTFPI (Färe et.al, 1994).

The thesis also employs TOBIT-regression to investigate the determinants of Indonesian

bank efficiency scores obtained from DEA. Preceding studies adopted TOBIT

regression on their efficiencies [For example, see (Chang et.al (1998), Nikiel and Opiela

(2002), Casu and Molyneux (2003), Havrylchyk (2005), Grigorian and Manole (2006),

Ariff and Can (2008), Sufian (2010), Gardener et.al (2011)].

The chapter is organised as follows. The first section discusses the comparison between

the non-parametric DEA approach and the parametric SFA approach, and the review on

the concept of DEA approach and DEA-based Malmquist productivity index

particularly for the production function on the technical efficiency. The second section

presents a review on the parametric SFA approach and some underlying estimation

models more specifically for the cost frontier panel data model. The third section

explains the estimation of the regression equations employed in this thesis covering the

TOBIT regression for the efficiency scores, multiple regressions with fixed-

effects/random-effects and/or the Two-stage least square (TSLS) regression on small

business finance63

. The fourth section explains the variables and the data employed in

the empirical chapters. The last section provides conclusions.

4.2. A Comparison of the DEA and SFA Approaches

A brief discussion about the evolution of the efficiency measurement revealing the

proposition of Farrel (1957)64

as the first to define technical efficiency, allocative

efficiency (price efficiency) and economic efficiency (overall efficiency) has been

presented in Chapter 3 in the literature review section.

63

The determinants of small business finance is presented on the third empirical chapter of this thesis (see

chapter 7) 64

Farrel (1957) was inspired by the work of Debreu (1951) and Koopmans (1951) to define those

efficiency terms covering technical efficiency, price efficiency, and overall efficiency with taking into

account the multiple inputs.

71

The Farrell‟s concept focuses on the best practices resulted from the data observation

suggesting the term „frontier‟ that all the observation would be compared to it. The

Farrell‟s work has brought to the two developed methodological mainstreams in

constructing the production frontiers, namely parametric (stochastic) approach and non-

parametric (non-stochastic) approach.

Shephard (1970) and Afriat (1972) are among the authors who were then interested in

the frontier concept and advised the usage of mathematical programming methods in

frontier estimation. The concept was first attractive to many authors since Charnes,

Cooper and Rhodes (1978) introduced the term data envelopment analysis (DEA) in

their paper. Since then the DEA approach has started to be widely used by authors

(Coelli et al., 2005).

Charnes, Cooper and Rhodes (1978) first introduced the DEA approach with input

oriented scheme and the constant returns to scale (CRS) for the technological

assumption. The model was widely known as the CCR model and it has been

extensively employed by authors until Banker, Charnes and Cooper (1984) proposed the

variable return to scale (VRS) in their assumption. The model was known as BCC

model and it has been more widely used to date. The idea behind the latter model is

underpinned by the weakness of the CCR model. The CCR model is only appropriate if

all firms are operating at an optimal scale. However, some conditions (i.e. government

regulations, imperfect competition, constraints on finance) could cause a firm not to

operate in the optimal scale. Hence, as the solution, the BCC model was introduced

(Coelli et al, 2005).

On the other hand, regarding the stochastic frontier analysis (SFA) evolution,

Kumbhakar and Lovell (2000) mention some significant contribution of authors [i.e.

Koopmans (1951), Debreu (1951), and Shepard (1953)]. These authors provided the

influential theoretical literature on productive efficiency. Kumbhakar and Lovell (2000)

remark that Koopmans‟ contribution was in the provision of the technical efficiency65

,

while Debreu and Shepard proposed the distance functions as a method of addressing

65

Technical efficiency was defined by Koopmans (1951) as “a producer is technically efficient if and

only if, it is impossible to produce more of any output without producing less of some other output or

using more of some input” (Kumbhakar and Lovel, 2000:6).

72

multiple-output technology, as well as measuring the radial distance of a producer from

a frontier.

The other authors having contributions to the development of SFA are Aigner and Chu

(1968), Seitz (1971), Timmer (1971), Afriat (1972) and Richmond (1974). These

authors estimated a production function with the deterministic method, either by

employing linear programming techniques or modifying the least squares techniques

(Kumbhakar and Lovell, 2000).

However, the significant articles acknowledged as the SFA origination are created by

Meeusen and Broeck (1977) and Aigner, Lovell, and Schmidt (1977), which are then

followed by Battesse and Corra (1977). These two aforementioned papers (MB and

ALS66

) were published almost simultaneously by two different teams of authors from

the two different continents. These three papers contain the composed error structure

and were developed in the context of production function. The inclusion of composed

error structure in the estimation of efficiency is then recognized as one of the important

features of the SFA compared to the other approach.

In general, both approaches (the DEA and SFA) have their own advantages and

disadvantages. DEA is one of the non-parametric techniques that uses a linear

programming concept in the operation and does not account for random errors. Thus, it

is known as deterministic (non-stochastic) method. Conversely, SFA is one of the

parametric techniques that requires functional form and takes into account random

errors. Hence, it is also recognized as a stochastic method.

In the operation, SFA necessitates the assumption for the error term distribution and

employs the functions i.e. production function, cost function, etc. For instance, in the

production function, the left-hand side of the equation (the output of a firm) is a

function of the right-hand side of the equation (it comprises a set of inputs, inefficiency

and random error).

Meanwhile DEA is very popular in the non-parametric mainstream in estimating

efficiency with some advantages as follows (Suzuki and Sastrosuwito, 2011): DEA is a

mathematical linear programming model and more flexible in terms of its ability to take

into accounts for multiple outputs and inputs in estimating the efficiency. Moreover,

66

MB stands for Meeusen and Broeck, and ALS is Aigner, Lovell, and Schmidt.

73

DEA works well with a relatively small sample, and unlike the SFA, it does not

necessitate predetermined assumptions about the distribution of inefficiency and does

not require a specific functional form on the data to determine the most efficient banks

(Fethi et al., 2011).

However, the DEA also has some disadvantages. The weakness of the DEA and non-

parametric methods in general is that the assumption of not allowing for random error

due to luck, data problems or other measurement errors. The problem might arise due to

some errors in one of the units on the efficient frontier can affect the efficiency scores of

other units, especially compared to efficient units (Berger and Humphrey, 1997). In

addition, Coelli et al (2005) emphasized that, because of ignoring statistical noise, the

efficiency estimation resulting from the DEA approach may be biased if the production

process is characterized largely by stochastic elements (Coelli et al., 2005).

On the other hand, SFA have some advantages compared to DEA are: first, it takes into

account statistical noise (random errors) and second, it allows carrying out some

statistical tests (Coelli, et al, 2005). Table 4.1 shows that SFA permits the carrying out

some statistical tests i.e. hypothesis test, variables inclusion test, distributional

assumptions test, and endogeneity bias test.

Nevertheless, SFA also has some drawbacks. Among those weaknesses are the

prerequisites of distributional form in determining inefficiency and the functional form

for estimating production function, cost function, etc. (Coelli, et al, 2005). These

weaknesses led to the SFA method is considered more complicated than the DEA.

Table 4.1 summarizes the comparison between SFA and DEA methods as follows:

Table 4.1.The Comparison between SFA and DEA methods

Conditions SFA DEA

Prior assumptions on production frontier needed Yes No Accommodates multiple inputs and outputs Yes Yes

Accounts for error terms Yes No

Prior assumptions for the error term Yes No Allows for environmental variables Yes Yes

Allows for hypothesis test Yes No

Allows for variables inclusion test Yes No Allows for distributional assumptions test Yes No

Multicollinearity problems Yes No Provides information on peers No Yes

Vulnerability to small number of observations Yes Moderate

Vulnerability to endogeneity bias Yes Yes Allows for endogeneity bias test Yes No

Source: Shaban (2008)

74

The noticeable similarities between DEA and SFA are: both methods accommodate

multiple inputs and outputs and allow for environmental variables in estimating the

efficiency. The same circumstance is also applied to both methods in terms of

vulnerability to endogeneity67

bias.

4.3. Non-parametric Methods: Data Envelopment Analysis (DEA)

Data Envelopment Analysis (DEA) idea was firstly inspired by seminal work of

Farrell‟s (1957) and was subsequently expanded by the works of Charnes et al. (1978)

and Banker et al. (1984). Through the DEA, it is estimated the best practice decision

making units (DMUs) in the frontier and relatively compared the rest of DMUs to the

frontier.

The Data Envelopment Analysis (DEA) approach is one technique that creates a non-

parametric piecewise surface (or frontier) over the data by using the methods of linear

programming. In estimating the efficiency of each observation, the surface becomes a

benchmark(s) where each of them is compared relative to it (Coelli et al., 2005).

Shaban (2008) defines DEA as a non-parametric linear programming technique used to

develop empirical production frontiers and to assess the performance of firms, or

Decision Making Units (DMUs).

Based on those definitions, it is worth noting that DEA uses the mathematical or

deterministic approach particularly the linear programming technique to estimate the

efficient frontier and accordingly the efficient performance of DMUs or firms can be

evaluated.

DEA is widely used to measure the efficiency of economic entities and has been

adopted by many studies. For instances, studies in banking Industry employed this

approach [Fethi et.al (2011), Shin and Kim (2011), Sufian (2010, 2011), Gardener et.al

(2011), Kenjegalieva et al. (2009), Yao et al. (2008), Kao and Liu (2004), Drake and

Hall (2003)].

67

Endogeneity means that there is a significant correlation between the parameter or variable and the

error term.

75

4.3.1. Technical Efficiency (DEA)

As the proposition of Farrell‟s (1957) states that the components of efficiency are

technical efficiency and allocative efficiency. Technical efficiency indicates the

firm‟s/DMU‟s ability to obtain maximal output from a given set of inputs, while

allocative efficiency reflects the firm‟s/DMU‟s ability to use inputs in optimal

proportions, given their respective prices and production technology (Coelli et al, 2005).

In estimating technical efficiency, there has two extended DEA models can be operated.

These are namely the CCR model and BCC model. Brief descriptions about the two

models are presented as follows:

4.3.2. The CCR and BCC Models

CCR Model stands for Charnes, Cooper and Rhodes (1978), the inventors of the model.

The CCR model assumes a constant return to scale (CRS) in the technology of

production and an input orientation. Then, some authors proposed some alternative

assumptions (i.e. Fare, Grosskopf and Logan (1983) and Banker, Charnes and Coopers

(1984) recommended the use of other assumption of the production technology, a

variable return to scale (VRS)).

Before explaining the CCR and BCC model, it is beneficial to discuss the basic concept

of the DEA in general. The DEA efficiency is closely related with the principal concept

of productivity with the notion of ratio of output to input for firm „i‟ as follows (Coelli

et al., 2005):

(4.1)

Where u is a vector of output weights and is the vector of input weights.

Then, the optimal weights are defined by solving the programming problem as follows

(Coelli et al., 2005):

Maximize u, v: (4.2)

Subject to

.

76

In order to maximize the efficiency of firm j, with subject to the restriction that the

efficiencies of all firms are less than or equal to one and all weights are non-negative,

then it requires the optimal weights. The objective function is homogeneous of degree

zero, so that any various weights will produce the same solution, then that condition

need to be treated by a normalization such as with a restriction such as = 1.

By transforming and simplifying that problem, it will produce (Coelli et al, 2005):

Maximize μ, v: (4.3)

Subject to

And for the minimizing form:

Minimize: (4.4)

Subject to

λ

Where: is a scalar representing the input oriented technical efficiency score for i-th

firm, while λ is a vector or constant. Thus, = exhibits the ability of

the firm to reduce inputs in gaining the same output – relatively compared to other firms

in the sample. In the sample, it could be found that some firms will have the efficiency

score of 1.0. This points out that the firms are considered efficient, otherwise,

the higher the score of the firm‟s (the score would be in between 0 and 1 or

), the more efficient the firm. In other words, if , it means that the firm-i is not

efficiently operated, and thus, it consequently requires the reduction of inputs level as

much as to grasp the frontier.

The above formulations are constructed based on the Constant Return to Scale (CRS)

assumption, which is founded by Charnes, Coopers and Rhodes (1978), thus it is well

known as CCR model.

77

For the BCC model, which is proposed by Banker, Charnes and Coopers (1984), it is

recognized as Variable Return to Scale (VRS) assumption. The assumption of VRS can

be applied to above equations by adding a restriction .

This VRS assumption has been applied by many authors in their estimations considering

that the VRS model considers the possibility of a firm to operate not in optimal

condition due to some reasons, i.e. it is caused by some factors: government

regulations, imperfect competition, constraints on finance, etc.

If we are able to find the efficiency scores derived from CRS and VRS assumptions,

then, the „scale efficiency‟ of each firm can be determined by the formula as follows:

(4.5)

Where is scale efficiency, denotes the technical efficiency measure obtained

under the CRS and represents the efficiency score gained by VRS counterpart.

The other alternative outlook of the optimization process is that the output oriented.

This type of the orientation assumes that the output could be increased by utilizing the

same inputs, corresponding with the standard of other firms in the sample. The output

orientation of DEA can be produced by this equation:

Maximize : (4.6)

Subject to

λ

Where denotes the output oriented technical efficiency score for i-th firm.

= exhibit the ability of the firm to maximize outputs obtained by employing the same

inputs, relatively compared to other firms in the sample. The equation was made under

the assumption of CRS.

78

The form of VRS can be applied by adding up the restriction . The measurement

of technical efficiency would be in between 0 and 1 or we can write as follow:

(4.7)

With the calculation, some firms would be identified to be technically efficient, while

the others are not technically efficient in their production.

4.3.3. Nonparametric Productivity Measurement: DEA-based Malmquist

Productivity Index

Through non-parametric DEA method, we could also measure productivity

measurement. Productivity in this case is referring to the concept of the total factor

productivity (TFP) which is accounted for measuring a productivity comprising all

factors of production (multiple inputs) in the process of production to produce multiple

outputs (Coelli et al., 2005).

TFP can be used as the comprehensive measurement of how well the company employ

all factors of production (materials, labour, and capital) to make its outputs (products

and services). TFP can be broken down into technical efficiency change and

technological change. Technical efficiency change entailed to the company related to

some factors such as the improvement of managerial practices, the betterment of

industrial relationships particularly with employees, or the dissemination of the new

technological knowledge across the departments in the company. Whereas,

technological change can be attributed to the introduction of new technology, in which

it guides to a better productivity level (higher outputs made given the same level of

inputs).

As a matter of fact, productivity can also be measured by using partial productivity

indices such as the productivity per labour employed in a factory, the productivity per

land available in farming industry, or the productivity per capital. Since this thesis

79

adopts the concept of transforming multiple inputs into multiple outputs, thus the TFP is

used.

There are three alternatives different indices can be used to evaluate the productivity

changes: the Fisher index, Tornqvist index, and Malmquist index. According to Suzuki

and Sastrosuwito (2011), the Malmquist index has three main advantages relative to the

Fisher and Tornqvist indices. First, it does not require profit maximization, or cost

minimization assumption. Second, it does not require information on the input and

output prices. Third, if researcher has panel data, it allows for the decomposition of

productivity changes into two components (i.e. technical efficiency change or catching

up and technical change or changes in the best practice). Beside its advantages, this

method has also its drawbacks. The major disadvantage is the necessity to compute the

distance functions, but fortunately, the DEA can be used to solve this problem.

4.3.3.1. The Malmquist Index of Productivity

Along with the DEA efficiency estimation, there are some different methods in

calculating productivity index. Those methods are Fisher index, Tornqvist index, and

Malmquist index. In this research, the Malmquist index is employed since the method

has some characteristics as follows (Sufian, 2009): First, it does not require the

assumption of profit maximization or cost minimization. Second, it does not require

information of the input and output prices.

Productivity index is an essential measure to find out the level of productivity and the

change of productivity. In multiple outputs and inputs, Multifactor or Total Factor

Productivity (TFP) is employed to determine the ratio of aggregate outputs produced

relative to aggregate inputs used.

The Malmquist index determines the total factor productivity change (TFP change) by

calculating the ratio of the distance of the observed output and input vectors between

two periods, relative to a reference technology (Coelli et al. 2005).

Färe et al. (1994) specifies the output-oriented Malmquist productivity change index as

expressed as follows:

80

(4.8)

Where:

Mj = Malmquist productivity index

Dj = Distance function

x and y = inputs and outputs, respectively, across time period t to t+1.

That equation exposes the components of the Malmquist productivity index. The ratio

outside the brackets is equal to the technical efficiency change

(EFFCH) between time t and t+1. This ratio represents the change in the relative

distance of the observed production from the maximum potential production. The

component inside the brackets of the Equation is the

geometric mean of the two productivity indices which represents the shift in production

technologies (technical change or TECHCH) between time t and t+1. We can state that

Total Factor Productivity Change (TFP Change) or the Malmquist Productivity change

is the product or the outcome of the two components (EFFCH and TECHCH).

In addition, Technical efficiency change (EFFCH) can be broken down into the two

other components, namely Pure Technical Efficiency Change (PEFFCH) and Scale

Efficiency Change (SECH) as in Fare et al (1994):

(4.9)

(4.10)

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Where:

The ratio = the Pure Technical Efficiency Change (PEFFCH),

The ratio = Scale Efficiency Change (SECH)

Scale efficiency (SECH) is Technical Efficiency Change (EFFCH) divided by Pure

Technical Efficiency Change (PEFFCH).

Thus, from the above equations, we can derive the conclusion that:

MPI = Malmquist Productivity Index

MPI = EFFCH x TECHCH

MPI = PEFFCH x SECH x TECHCH.

4.4. Parametric Methods: Stochastic Frontier Analysis (SFA)

4.4.1. The Stochastic Production Frontier

Stochastic frontier analysis (SFA) has been employed widely in many industries since

Aigner et al (1977) and Meeusen and Van den Broeck (1977) developed it in 1977.

They originally proposed the stochastic frontier form for the production function model

as follows (in the case of cross sectional data):

(4.11)

Where represents the observed outcome of i-th firm (the observed output),

denotes a K x 1 vector consisting of logarithms of inputs, and is a vector of

unknown parameters. Denotes the deterministic part of the frontier while is a

symmetric random error to account for statistical noise ( is a stochastic part of the

frontier and ~ N [0, and is a non-negative random variables associated with

the technical inefficiency. = |U| and U~N[0, . Inefficiency means the quantity

(percentage) by which the frontier (the optimum) could not be attained.

82

The SFA has been extensively used in a large scope of studies encompassing various

models of goal attainment such as production frontier, cost frontier, revenue frontier,

and profit frontier efficiencies. The analysis of SFA technique usually has two stages:

First; it estimates the model parameter. It is mostly done by maximum likelihood

technique. Second; it estimates inefficiency or efficiency based on the model

construction. The result of efficiency estimation for each unit could be employed to

evaluate performance by comparing the efficiency of one unit to another.

4.4.2. Estimating the parameters

4.4.2.1. Ordinary Least Squares (OLS) Estimation

In estimating the parameters, there are some particular assumptions underpinned the

estimation. Some assumptions are as follows (Coelli et al, 2005): is distributed

independently of each and that those both errors are uncorrelated with the

explanatory variables (in ). The other assumptions are zero means ( ,

homoskedastic ( , uncorrelated ( ),

homoskedastic ( , uncorrelated ( ).

Above assumptions seem to be similar with those of the noise component in the

classical linear regression model, unless for since the inefficiency components have

no negative values ( Under those assumptions, the slope coefficients can be

obtained through ordinary least squares (OLS).

The OLS approach estimates the parameter (β) by minimising the sum of squared

deviation between the their means as reflected in the equation:

(4.12)

Through the first-order derivatives to zero, the solution to the first conditions with

respect to β is the OLS estimator:

(4.13)

83

However the OLS estimator of the intercept coefficient is biased downward, it is

suggested to use the other estimation tool namely corrected ordinary least square

(COLS) estimator (Coelli et al., 2005). In addition, many researchers employ maximum

likelihood (ML) estimators since ML estimators can provide the better solution for the

problems of distributional assumptions of the two error terms and have many desirable

large sample (i.e., asymptotic) properties.

4.4.2.2. Maximum Likelihood (ML) Estimation

The ML estimation concept is underpinned by the idea that a definite sample is more

likely to have been generated from some distributions than from others (Coelli et al.,

2005). The ML estimates an unknown parameter by maximizing the probability of

randomly drawing particular sample observations.

The usage of ML in estimating the parameters usually requires the assumption of

which states that errors are independently and identically distributed

normal random variables with 0 means and . Then, the joint density

function for the vector can be written as follows:

(4.14)

This equation articulates the likelihood of observing the sample observations as a

function of β and . The ML estimator of β can be derived by maximising the

logarithm of the likelihood function:

(4.15)

This is to maximise log-likelihood function with respect to β. In the last part of right-

hand side in the equation denotes the sum of squares function which is similar to the

OLS estimation, meaning that maximizing the log-likelihood with respect to β is

equivalent to minimizing the sum of squares (Coelli et al., 2005).

84

4.4.2.3. Estimating Efficiency

After estimating the model parameters, the other aim of SFA technique is to predict the

efficiency level or the degree of efficiency of each observation. The estimation of this

efficiency level is the main interest of the SFA study. There are many advantages to

have the estimate. Through the estimate, we can rank each individual (firm) or unit

within the groups and we can also identify the outperformed unit(s) compared to the

under-performing units.

The common output oriented measure of technical efficiency is the ratio of observed

output to the corresponding stochastic frontier output. Coelli et al. (2005) show it

through this equation:

(4.16)

Where is technical efficiency for i-th firm. The technical efficiency takes a value of

between 0 and 1. The equation measures the output of i-th firm relative to the output of

fully-efficient producing firm using the same input vector. If a firm obtains the value of

1, meaning that the firm is operated in a fully efficient way. Otherwise, if a firm get the

value of below 1, meaning that the firm operates inefficiently.

For the illustration, in the case of output oriented, the value of x 100% would

be the percentage of maximum output that is produced by the firm-i. Hence, If

x 100% = 87%, it means that the firm is producing only 87% of the

maximum possible (frontier) output. It also means that 100% - 87% = 13% is the

percentage of output which is lost due to technical inefficiency of the firm-i.

4.4.2.4. The Stochastic Cost Frontier

Beside technical efficiency in the production function, we could also estimate cost

efficiency by using a cost frontier efficiency function if price data are available. This

cost efficiency information is very important for the management of a company to take

85

a decision making regarding the possibility of the firm to cut down costs (expenses)

while keeping up the same level of output in the operation. The cost frontier efficiency

is also beneficial for the policy makers, such as government bodies and regulators, to

take any decisions and actions after considering the cost performances of all units or

firms under their supervision.

In the case of evaluating banks performance, the stochastic cost frontier efficiency can

be employed to find out, by comparing banks, which bank is outperformed in terms of

doing the operation with the lower cost and which bank is underperformed. We can also

identify the cost efficiency of each bank and the key drivers of the cost efficiency.

The general form of the cost frontier model (in the case of cross-sectional data) is as

follows (Coelli et al., 2005):

(4.17)

Where = the observed cost of the firm 1; = the N-th input price; = the M-th

output; while c(.) is a cost function. This cost function has the characteristics of linearly

homogeneous, non-decreasing, and concave in prices.

The first step to estimate efficiency following the equation (4.17) is determining the

functional form for the cost function. There are two most popular forms can be chosen,

the Cobb-Douglass form and trans-log form.

The Cost frontier model for Cobb-Douglass form is as follows (Coelli et al, 2005):

(4.18)

Where = a symmetric random variable (approximation errors and other statistical-

noise sources); = a non-negative variable (efficiency). If the are non-negative and

fulfil the constraint , the function would be linearly homogeneous, non-

decreasing, and concave in input.

86

By substituting the constraint into the formula (4.18), it results the homogeneity-

constrained Cobb-Douglass cost-frontier model as follows (Coelli et al., 2005):

(4.19)

The same way can be applied to obtain a trans-log model.

The next step is to find cost efficiency through the formula as follows (Coelli et al.,

2005):

(4.20)

Where = cost efficiency for i-firm. Cost efficiency reflects the ratio of minimum

cost to observed cost. The value of CE would always be in between 0 and 1. The value

closes to 1 means more efficient and the value closes to 0 is less-efficient.

Actually, there is also another model besides the technical efficiency (production

frontier) model and cost efficiency (cost frontier) models, and is namely the profit

efficiency (profit frontier) model. However, since this thesis only applies technical

efficiency for DEA approach and cost efficiency model for the SFA approach, thus the

profit efficiency model is not discussed.

4.4.2.5. Stochastic Frontier Models for Panel Data.

The stochastic frontier models can also be applied for panel data sets. Panel data sets

have usually more observations than cross-sectional data sets since they contain various

firms across several years.

Coelli et al. (2005: 275) remarks that panel data frontier models allow us to: relax some

of the strong distributional assumptions that were necessary to disentangle the separate

effects of inefficiency and noise; obtain consistent predictions of technical efficiencies;

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and investigate changes in technical efficiencies (as well as the underlying production

technology) over time.

In addition, Kumbhakar and Lovell (2000) point out that there is a fundamental problem

with cross-section data compared to panel data in the econometric analysis. The

problem is that through the cross section data, we could only observe each firm once, so

that this could severely limit the confidence either for technical or cost efficiency

estimates.

The general form of the panel data model for stochastic frontier production function is

as follows (Aigner, et al, 1977):

(4.21)

The equation is almost the same with the equation (4.11) excepting that we append “t”

after i to denote time. is the observed outcome (goal attainment) of the i-th firm at

time-t; is the deterministic part of the frontier; is the stochastic part and v ~

N[0, ]; while is inefficiency of the i-th firm at time-t. For a stochastic cost frontier

model, would be changed by .

In the estimation purposes, we should determine which structure would be chosen either

time-invariant or time-varying inefficiency models to account for the assumption of

independent distribution of

In the assumptions of time invariant cost-efficiency in the single equation case, the cost

efficiency model for the panel data would be (Kumbhakar and Lovell, 2000):

(4.22)

Where denotes the expenditure incurred by producer (firm)-i at time-t, is the

output of the i-th firm at time-t; is a vector of input prices

faced by firm-i at time-t; is random statistical noise; is time invariant cost-

88

inefficiency. In addition, =1 is applied to ensure the homogeneity of the cost

frontier in input prices.

If we have a panel data set employed, Kumbhakar and Lovell (2000:170) emphasize

that the longer the panel, the less tenable is the assumption that cost efficiency is time

invariant. Thus, all the estimation procedure (Least Square Dummy Variables,

Generalised Least Square, Maximum Likelihood Estimator) can be employed and

modified to accommodate time-varying cost efficiency as well as they were in the

estimation of technical efficiency.

4.4.2.5.1. Time-invariant Inefficiency Models

Time-invariant inefficiency model simply bases the assumption of (Coelli et al, 2005):

(4.23)

Where is treated as either a fixed parameter (in the fixed effects model) or a random

variable (in the random effects model). A standard regression framework can be applied

to estimate the fixed effects model using dummy variables, whilst least-squares or

maximum likelihood techniques can be employed to estimate the random effects model.

Regarding the assumptions of , Pitt and Lee (1981) assumed a half normal

distribution, while Battese and Coelli (1988) regarded the more general truncated

distribution (Coelli et al, 2005).

Pitt and Lee (1981)68

employ the maximum likelihood estimation (MLE) technique to

estimate random effects model over the panel data with time invariant technical

efficiency. The random effects for their panel data are specified as follows:

(4.24)

68

Pitt and Lee (1981) employ panel data on fifty Indonesian weaving firms over the years 1972, 1973 and

1975 in the estimation of a stochastic frontier Cobb-Douglas production function.

89

Where S = +1 for a technical (production) efficiency model, and -1 for a cost efficiency

model. Pit and Lee (1981) assume that the component of inefficiency is to be invariant

with the normal-half normal model:

(4.25)

Battese and Coelli (1988)69

suggested the more general truncated normal distribution for

inefficiency term to be applied on panel data. With this specification, the general

production function for the panel data is similar with the formula (4.26) but the

assumption of inefficiency has become:

(4.26)

This normal-truncated normal model relaxes an implicit restriction in the normal-half

normal model that the mean of the underlying inefficiency variable is zero. Thus, by this

model, it allows , the mean of to be non-zero. The production function of this panel

data model can also be estimated using the MLE technique.

4.4.2.5.2. Time-varying Inefficiency Models

Since time invariant inefficiency models are considered rather restrictive, there could be

the other models that are more relaxed in order managers to be able to take lessons from

experience and the changes of the technical efficiencies over time (Coelli et al, 2005).

The time varying inefficiency models take the basic form:

(4.27)

Where f(.) is a function that decides the time-varying inefficiency. There are two

alternative models (Coelli et al, 2005) f(t) as follows:

69

Battese and Coelli (1988) examine the prediction of the firm-level technical efficiencies with a

Generalised frontier production function over the panel data of the New South Wales and Victorian dairy

industries in Australia over the three financial years, 1978-79, 79-80, and 80-81.

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Kumbhakar (1990): (4.28)

Battese and Coelli (1992) (4.29)

The difference between the Kumbakar (1990) model and Battese and Coelli (1992)

model are as follows (Coelli et al, 2005:278):

Kumbhakar (1990) Battese and Coelli (1992)

The function lies in the unit interval and can be

non-increasing, non-decreasing, concave or convex

depending on the signs and magnitude of α and β.

The function involves only one unknown parameter

and partly as consequence, is less flexible.

It has the properties f(t) ≥0 and f(T)=1 and is either

non-increasing or non-decreasing depending on the

sign . It is also convex for all values of .

In addition, both models can be estimated through fixed effects as well as random

effects framework using maximum likelihood technique under the assumption that

has a truncated normal distribution (Coelli et al., 2005):

(4.30)

4.4.2.5.3. Environmental effects model

In the estimation of efficiency, we can put the only inputs in the calculation process and

it then obtains the results as outputs. However, we can also take into account some

environmental variables considering that those variables could also influence the

outputs. Environmental variables are exogenous variables that could be divided into

stochastic variables and non-stochastic variables.

Coelli et al. (2005) define that non-stochastic variables are observable variables that

exist when the decisions on the key production process are made (e.g. degree of

government regulation, type of firm ownership, age of the labour force, etc.). On the

other hand, the unforeseen stochastic variables are defined as sources of production risk,

such as weather, pest infestations, etc.

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In regard to account for the non-stochastic environmental variables, there have been

different opinions whether to incorporate them directly into the equation of the

production function or exclude them in the function and put those variables in the

second stage analysis.

Coelli et al. (2005) explain that the easiest way to incorporate non-stochastic

environmental variables in the model of efficiency is directly integrating those variables

into the non-stochastic part of the production frontier. For illustration, the form of a

model would be (i.e. in the case of cross sectional data):

(4.31)

Where denotes a vector of environmental variables, while γ is a vector of unknown

parameters. Then we could estimate the technical efficiency of i-th firm by the equation

(Coelli et al., 2005):

(4.32)

Where is a function of and all together. Hence, the outcome (the firm technical

efficiency) would vary in accordance with the common inputs and the environmental

variables.

Some authors allow the inclusion of those environmental variables directly affect the

stochastic component of production frontier. Kumbhakar et al. (1991) and Battese and

Coelli (1993, 1995) are those who supported the model either for cross sectional model

and panel data model, respectively. Through this concept, Kumbhakar et al. (1991)

assume the equations as follows:

(4.33) and

γ, (4.34)

Through these equations, it can be seen that the inefficiency effects have distributions

that vary together with .

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Conversely, a number of authors put environmental variables as the predictors of

technical efficiency using the two-stage approach. For instance, Pitt and Lee (1981)

accounts for those variables in the second stage by examining the relationship between

environmental variables and predicted technical efficiencies. Through this two-stage

approach, they first estimate a frontier model without inserting environmental variables.

Then for the second stage they include the regression of the predicted efficiencies on the

environmental variables.

4.4.2.5.4. Battese and Coelli 1995 Model

There are three popular Battese and Coelli (1988, 1992, and 1995) models in estimating

the SFA efficiency (BC88, BC92, and BC95). The model of BC92 has been briefly

explored in the section of time varying inefficiency model (section 4.4.2.5.2), while

BC88 model has also been presented in the section time-invariant inefficiency models

(section of 4.4.2.5.1).

Battese and Coelli (1995) expand the time varying efficiency model by generalizing in

the panel data models in the BC95 model. Unlike BC92 model, the model accounts for

the observable environmental variables directly influence the stochastic component in

the production function, thus this model employ the only one-stage simultaneously

analysis.

In this model, the non-negative technical inefficiency effects are assumed to be a

function of firm-specific variables and time and to be independently distributed as

truncations of normal distributions. The distribution assumptions of the inefficiency are

also assumed with constant variance, but with means which are a linear function of

observable variables (Battese and Coelli, 1995).

Thus, the technical inefficiency effects in the model of this stochastic frontier are

specified in the equation as follows (Battese and Coelli, 1995):

(4.35)

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Where: represents the observable environmental variables that influence on the

inefficiency . , defined by the truncation of the normal

distribution with zero mean and variance .

Chapter six of this thesis employs SFA method by BC92 model thus the model uses

two-stage analysis comprising efficiency estimation and examining the determinants of

the efficiency.

4.5. Multiple Regression Estimation

Corresponding to some of the research questions set out in this thesis which the two of

them are to find out the determinants of efficiency and the determinants of small

business finance, this thesis uses multiple regressions. A multiple regression model is a

regression model with more than one explanatory variable (Gujarati and Porter, 2010).

The use of this kind of regression is important due to the fact that very few economic

phenomena can be explained by only a single explanatory variable, thus we need some

other explanatory variables included in the model. There are various types of multiple

regressions in practice70

and the type of regression used must be adapted to the

characteristics of the data and the regression techniques71

chosen should also be based

on the prevailing econometric theory.

The section of this thesis explains the basic concept of the multiple regression model as

well as some statistical aspects of the regressions and their applications in the panel data

set in accordance with the research purposes in this thesis.

4.5.1. Multiple Regressions for Panel Data

Panel data set comprises the observations of the same units over a number of periods. In

other words, these data covers the combination between cross-sectional and time-series

aspects of the unit observations.

70

There are multiple regressions for continuous data and regressions for the the limited dependent

variable, such as LOGIT, PROBIT, and TOBIT models, etc. 71

Regression techniques: Ordinary Least Squares, Generalized Least Squares, with fixed-effects/random-

effects, Two-Stage Least Squares, Three-Stage Least Squares, etc.

94

Panel data are commonly the collection of the micro-economic level data consisting of

some individuals, households, or firms in one country but it has become increasingly

familiar to pool individual time series of a number of countries or industries and analyse

them simultaneously (Verbeek, 2012:372).

The advantages of panel data over the cross sectional data or time-series are as follows:

It allows the economists to specify and estimate more complicated and more

realistic models than a single cross section and a single time series would do

(Verbeek, 2012).

It allows the researcher great flexibility in modelling differences in behaviour

across individuals. It provides such a rich environment for the development of

estimation techniques and theoretical results (Greene, 2008).

It gives a richer source of variation which allows for more efficient estimation of

the parameters. It is more informative that one can get more reliable estimates

and test more sophisticated behavioural models with less restrictive assumptions

(Baltagi, 2002).

The standard linear model for the panel data can be written as follows (Verbeek, 2012;

Koop, 2008):

(4.36)

Where denotes the observation of the dependent variable for firm-i at time-t; is

the intercept; is a K-dimensional vector of explanatory variables; is the error

term; this error term varies over individuals and time, and captures all unobservable

factors that affect .

To estimate the parameter model using ordinary least square, there have some basic

assumptions that should be fulfilled to achieve efficiency, consistency and unbiased

model. Those assumptions are almost the same with the cross section or time series

data. If it is assumed that complies with the classical assumption, then all results we

obtain under the assumptions will be valid and OLS would be BLUE (best linear

unbiased estimator). But, if we have a case that satisfies the classical assumptions

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except that heteroskedasticity exists, then the Generalised Least Square methods are

applicable (Koop, 2008).

In addition, the problem exists when some basic classical assumptions could not be

fulfilled. Verbeek (2012) gives some illustrations i.e. If and ,

the OLS estimator is consistent for and β under weak regularity conditions. Due to

the fact that we observe the same units repeatedly, it seems unrealistic to assume that

error terms from different period are uncorrelated (Verbeek, 2012). Concerning the

problem, it is worthwhile to account for the models that are commonly used in panel

data analysis, namely REM (random effects model) and FEM (fixed effects model).

4.5.2. Random Effect Model

In estimating the panel data set through multiple regression techniques, we should be

aware to determine the treatment for the possibilities of the individual effect in the

model. Individual effect means that each individual has a different effect. Theoretically,

there are two main individual effects models in the panel data analysis: the fixed effect

model and the random effect model (Koop, 2008).

In panel data model it is assumed that:

(4.37)

Where it is assumed that is time invariant and homoskedastic across individuals;

is also homoscedastic and not correlated over time. The model and assumptions belong

to the formula (4.36 and 4.37) represent the Random Effects Model.

The Random Effect Model (REM), also known as a (one-way) error component model,

is a regression model assuming that all factors affecting the dependent variable, but are

still not included in the model as explanatory variables, can be properly summarized by

a random error term (Verbeek, 2012).

Koop (2008) puts the light on that REM takes the assumption that the individual effect

is a random variable, so that it does not use dummy variables.

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REM can be written as follows:

(4.38)

Where it is assumed that, ; ; which means

that error term comprises two components: an individual specific component which

does not vary over time, and a remainder component is assumed to be uncorrelated over

time (Verbeek, 2012).

We could observe in the model that in the REM, it only has an intercept and k

explanatory variables and it does not require N-dummy explanatory variables like the

fixed-effects model (Koop, 2008).

Regarding the estimation technique in the REM, Koop (2008) asserts that OLS

(ordinary least square) can be employed if the model fulfils the classical assumption.

However, if the classical assumptions are violated, then OLS is no longer BLUE. As the

consequence, we could use GLS (generalised least square) estimation technique or OLS

technique with panel-corrected standard errors.

4.5.3. Fixed Effect Model

Fixed Effect Model (FEM) is a model in the panel data analysis which takes into

account the existing of each individual effect of the observations in the model.

The individual effects exist when we assume that each individual can have different

intercept in the model. As it can be seen in this equation (Koop, 2008):

(4.39)

It is almost similar with the common pooled model. The different lies in , which

varies across individuals. Each intercept for individuals, denotes as a fixed

(individual) effect. As the consequence, each individual can have a different regression

line.

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The fixed effect model employs variables of dummy to account for the individual effect

in the model (Koop, 2008). A dummy variable has a value of either 0 or 1.

Stock and Watson (2012: 396) defines the fixed effect regression as a method for

controlling for omitted variables across entities (firms, states, etc.) but do not change

over time. The fixed effect regression model has different intercepts as many as the total

number of firms (entities).

The fixed effects regression model is as follows (Stock and Watson, 2012):

(4.40)

Where is the value of the first independent variable for the firm-i at time-t (1,

2,…,k = a number of independent variables); = ,…, are firm (entity) specific

intercepts.

Equally, in terms of a common intercept, the fixed effects regression model can also be

presented as follows (Stock and Watson, 2012):

(4.41)

Where =1 if i=2 and =0 otherwise, and so forth.

A problem of the FEM specification is that it will sometimes head to a regression model

with a large numbers of explanatory variables. Hence, it could lead another problem of

uneasy to compute due to the fact that numerous coefficients are being estimated (Koop,

2008).

In conjunction with the application of those REM and FEM in the panel data regression,

the Hausman-test could be employed to choose between them. The idea behind the

importance of the Hausman-test is as follows (Koop, 2008): If (the individual effect

is uncorrelated with any of explanatory variables) is true, then both REM and FEM

estimators are consistent and provide approximately similar result. But, if is false,

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then REM will be inconsistent, whilst FE will be consistent, and those results could be

quite different.

4.5.4. TOBIT Regression

In some cases, we can encounter the model of regression in which the dependent

variable is not continuous but it is quite restrictive in various ways. One of the cases we

may face is for the case of efficiency estimate. Since the efficiency scores derived from

DEA or SFA techniques are in between 0 and 1, it is obvious that the scores are of the

censored dependent variables.

In this section of the thesis, it discusses the fittest model for the censored dependent

variable, especially for the efficiency estimate score which is named the TOBIT

regression.

TOBIT regression model is one of the models that take into account the limited

dependent variables. There are three alternative models considering the limited

dependent variable, they are namely: LOGIT72

, PROBIT73

, and TOBIT74

models.

However, TOBIT model is appropriate to examine the regression on the efficiency

because efficiency scores have properties that correspond to the TOBIT model which

are censored at zero with positive probability but is roughly continuously distributed

over strictly positive values (Wooldridge, 2013)

Koop (2008) specifies that the TOBIT model is a regression model that has the

dependent variable which is censored at zero. The regression equation for the TOBIT

model can be written as follows (Verbeek, 2012; Koop, 2008):

(4.42)

72

LOGIT model is a model for binary responses where the response probability is the LOGIT function

evaluated at a linear function of the explanatory variables (Wooldridge, 2013: 846). 73

PROBIT model is a model for binary responses where the response probability is the standard normal

cumulative distribution function (cdf) evaluated at a linear function of the explanatory variables

(Wooldridge, 2013:849). 74

TOBIT model is a model for a dependent variable that takes on the value zero with positive probability

but is roughly continuously distributed over strictly positive values (Wooldridge, 2013: 853).

99

With the assumption: if and if . is

unobserved or latent variable and is observed dependent variable for observation

unit-i at time-t.

In the TOBIT regression model, we also make some traditional assumptions regarding

parameters and error term. Those assumptions are (Verbeek, 2012): and are i.i.d.

normally distributed, independent of , with the means of zero and variances

of respectively.

Regarding the estimator, Koop (2008) stipulates that OLS method is not appropriate for

the TOBIT regression since the OLS estimator is biased for the censored observations.

The more points we have in the censored observations, the worse the bias will be.

Hence, the parameters of the TOBIT regression model could be properly estimated by

the estimator taking into account the censored nature of the dependent variable. The

estimator is the Maximum likelihood method.

The likelihood function for TOBIT regression model can be written as follows

(Verbeek, 2012):

(4.43)

Where and is given by:

If ,

If (4.44)

For the case of examining the determinants of efficiency, the TOBIT regression is the

fittest one since the assumption used if and if is

appropriate and reflect the circumstances of the efficiency values.

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4.5.5. Two-Stage Least Squares (TSLS) Regression

Two-stage least squares (TSLS) is applied in the regression estimation when there is a

problem of endogeneity. Endogeneity means that there is a correlation between the

right-hand side regressors and the error terms (disturbances). Endogeneity occurs due to

some possible reasons i.e. measurement error, the omission of relevant variables,

sample selectivity, or other reasons (Baltagi, 2005).

Baltagi (2005) and Koop (2008) point out that Endogeneity causes inconsistency of the

usual OLS estimates or the OLS would be biased, and then for curing the problem it

requires instrumental variable (IV) methods i.e. TSLS to obtain consistent parameter

estimates. Koop explains instrumental variable (or instrument) is a random variable that

has a correlation with the explanatory variable but is not correlated with the regression

error.

Gujarati (2006) explains that the TSLS method involves two successive applications of

OLS in the process. The principal idea underpinning the TSLS is to substitute the

explanatory variable which has a correlation with the error term of the equation by a

variable that is not so correlated. Such a variable is named a proxy or instrumental

variable. Through this process, the parameter estimators resulted from the TSLS are

consistent estimators.

In practice, to create instrumental variables that they are then employed to replace

endogenous variables, it firstly requires running a regression on the reduced form of the

right-side endogenous variables. Then, or fitted values form those reduced

regressions can be used as instrumental variables (Studenmund, 2011).

4.6. Data and Variables

This thesis uses data of all commercial banks in Indonesia, which is provided at the

Central Bank of Indonesia (BI) and data provided by PT. Ekofin Konsulindo with the

criteria as follows :

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The sample is all commercial banks, which consist of conventional banks and

sharia banks in Indonesia.

Only banks which have complete data (about the bank's financial statements

over the period 2002-2010) to be included in the observation data, except for

sharia banks. As the number of sharia banks is relatively very small compared to

conventional banks, then the data of Islamic banks established in the period

2002-2012 are also included.

The data are presented in annual basis for the period of 2002-2010.

After the data are collected and sorted out, there have been 116 banks fullfiling the

criteria comprising 109 conventional banks and 7 islamic banks. All these 109

conventional banks consist of 4 State-owned banks (SOB), 55 Private banks (PB), 26

provincial/Local-government owned banks (LGOB), 15 joint-venture banks (JVB) and 9

foreign-owned banks (FB). The total of islamic banks is 7 banks and are all privately

owned banks.

4.6.1. The Variables Specification and Definition

In estimating the technical efficiency of a bank through the DEA method, there has two

prominent approaches can be used to determine the inputs and outputs. They are namely

the production approach and the intermediation approach. The production approach

treats banking industry similar to any kinds of industries that produce goods and

services. Therefore it observes material, capital and human resources as production

factors to provide outputs. The inputs for the production approach are usually general

and administrative expenses, labor cost, price of capital, etc and the output are deposits,

loans and others.

On the other hand, the intermediation approach considers bank as financial intermediary

between depositors and lenders, so that deposits are placed as input beside other

resources used and loans and any other income are put as the outputs.

While in the SFA estimation, the objective function is cost efficiency (CE) which stems

from the Total Costs of banks (operating costs and non operating costs). Total operating

102

costs consist of interest expenses and non-interest expenses (for conventional banks) or

profit-sharing expenses and non-profit sharing expenses (for islamic banks). Non-

operating costs comprise all expenses except for the operating costs (e.g. labour

expenses, rent expenses, depreciation expenses, etc.).

4.6.1.1. The Inputs and Outputs Specification

For the first empirical chapter where the DEA method is used as the main estimation

technique, this thesis employs 3 models in estimating bank technical efficiency with the

specifications are as follows:

Model 1, using total finance, securities and investments, other income as outputs and

general and administrative expenses, fixed assets, total deposits as inputs.

Model 2, using small business finance, other finance, securities and investments, other

income as outputs and general and administrative expenses, fixed assets, and total

deposits as inputs.

Model 3, using other finance, securities and investments, other income as outputs and

general and administrative expenses, fixed assets, and total deposits as inputs.

Model 1 assumes that all commercial banks ignore the compositions of their finance,

whether it derives from small business or other sectors of finance, so that the ultimate

goals of the banks are to service all customers without viewing customers‟ background.

Model 2 decomposes the finance into small business finance and other finance in order

to recognize of the loan potfolios. This model takes into account small business finance

as one of the important goals in providing services to customers. Contrasted with the

preceding models, the last model (model 3) does not include small business finance in

the components of outputs so that it only reflects other finance and securities and

investments in the outputs. For all models, securities and investments have also become

the output, since those activities are also important in gaining profit.

In addition, for the second empirical chapter (SFA estimate), it employs a couple of

outputs (total finance, securities and investments for the Model 1) and (small business

finance, other finance, securities and investments for the Model 2). Alongside the

103

outputs, it is also employed 3 prices of inputs (price of funds, price of labour and price

of capital)

4.6.1.2. Inputs and Outputs Definition

The definitions of the components of outputs and inputs in the models used are as

follows:

Output components (for DEA and SFA):

Total loans (total finance): Total loans (finance) on all sectors of each commercial bank

at the end of year position.

Securities and investments: Total investment in securities and all placements of each

commercial bank at the end of year position.

Small business loans (small business finance): Total small business loans (finance) of

each commercial bank at the end of year position.

Other loans (other finance): Total other loans (finance) outside small business finance

of each commercial bank at the end of year position.

Other income: Other income generated by each commercial bank at the end of year

position.

Cost component (for SFA):

Total costs (total expenses) : operating costs and non-operating costs.

Input components (for DEA):

General and administrative expenses: Total general and administrative expenses

occurred and experienced by each of commercial bank for the certain period i.e. over

one year (in the income statement).

Fixed assets: Total net fixed assets that each commercial bank has as of the particular

position i.e. at the end of year (in the balance sheet).

Total deposits: total deposits in the particular position (i.e. at the end of year) that each

commercial bank obtains from depositors (in the balance sheet).

Price of Inputs components (for SFA):

Price of funds (pf): total deposits expenses divided by total deposits.

104

Price of labour (pl): total labour expenses divided by total assets.

Price of capital (pc): total cost for the banks equity divided by bank‟s equity.

4.6.2. Environmental and Bank specific variables

There are several variables included in the analysis comprising of some bank specific

variables and some environmental variables which reflect the macroeconomic condition.

4.6.2.1. Bank specific variables

LNTA: Natural logarithm of total assets (LNTA) as a proxy for bank size.

ROA: Return on assets (ROA) is assigned to represent bank profit.

CAR: Capital adequacy ratio is assigned to embody bank capital. Capital adequacy

ratio (CAR) is measured by calculating bank capital divided by risky-assets.

LDR: Loans to deposits ratio (LDR) is allocated to demonstrate bank liquidity. Bank

liquidity means the ability of a bank to cover its short-term liabilities.

NPL : Non-performing loans (NPL) is assigned to represent bank risk.

4.6.2.2. Environmental variables

INFL: Annual inflation rate. This measures the overall percentage increase in the

consumer price index for all goods and services n Indonesia

GDPGR: Annual real GDP (gross domestic product) growth rate. The growth of

Indonesia‟s total goods and services adjusted for inflation.

UNEMP: Annual unemployment rate in Indonesia.

MKTINDEX: Indonesian Composite Index in Indonesia Stock Exchange.

USDRATE: United States Dollar (USD) exchange-rate against Indonesian Rupiahs

(IDR)

4.7. Software:

DEAP 2.1 and LIMDEP 10 software were employed to generate efficiency scores for

non-parametric method (DEA). The parametric efficiency estimation was also achieved

by LIMDEP 10 and FRONTIER4.1. The descriptive statistics as well as some other

graphs were obtained and displayed through STATA 12 and Microsoft Excel 2007.

105

5. Chapter Five: Small Business Finance and Indonesian Commercial Banks’

Technical Efficiency: DEA Approach

5.1. Introduction

This Chapter aims to assess the performance of commercial banks in Indonesia using

the popular linear programming non-parametric technique Data Envelopment Analysis

(DEA) to estimate banks‟ technical efficiency and total factor productivity index. The

analysis is extended by examining the determinants of Indonesian banks‟ efficiencies

during the period 2002-2010. The determinants observed in this study are the specific

factors of Indonesian commercial banks and macroeconomic indicators during the

period.

The analysis covers 116 commercial banks in Indonesia composed of 109 conventional

and 7 Islamic banks. Those conventional banks comprise 4 state-owned banks (SOB),

55 private banks (PB), 26 provincial/Local-government owned banks (LGOB), 15 joint-

venture banks (JVB) and 9 foreign-owned banks (FB), whilst the 7 Islamic banks are all

also of private banks (PB).

The intermediation approach is adopted in estimating the DEA technical efficiency. The

study uses input-oriented approach since that the factors of production are more

controllable for managers in a highly regulated industry. Three models are estimated

using different classifications in the outputs components: model 1 uses total finance

(TF), securities and investment (SI), and other income (OI); model 2 uses small

business finance (SBF), other finance (OF), securities and investment (SI), and other

income (OI); model 3 only uses other finance (OF), securities and investment (SI), and

other income (OI); The inputs employed in the study are total deposits (TD), fixed

assets (FA) and general and administrative expenses (GA).

The organisation of this chapter are as follows: The first section presents the descriptive

statistics of the data sample with their analysis to portray the characteristics of the

Indonesian banking industry as well as the macroeconomic indicators over the study

period. The second section shows the non-parametric efficiency and productivity of the

commercial banks along with the comparison of their group of banks‟ performance

106

during the period. The third section discusses the determinants of commercial banks‟

efficiency over the period. Finally, the last-section gives the conclusion.

5.2. Descriptive Statistics

This section discusses the descriptive statistics of the data covering two purposes:

estimating efficiency and predicting the determinants of efficiency. The data required for

obtaining the efficiency estimate are: the inputs and outputs components (employed in

estimating DEA efficiency). While the data needed for revealing the determinants of the

efficiency are: bank specific variables and macroeconomic variables. The characteristics

of the data are explored in this section.

5.2.1. Data of Inputs and Outputs

Table 5.1 shows the data of inputs and outputs employed in estimating the technical

efficiency of Indonesian commercial banks over the period 2002-2010. The study

estimates technical efficiency by adopting the intermediation approach, hence including

total deposits as one of the inputs.

Table 5.1. Inputs and Outputs

IDR Millions

Total

Finance

Small

Buss.

Finance

Other

Finance

Securities

&

Investments

Other

Income

General

& Admin

Fixed

Assets

Total

Deposits

Mean 8,170,709 1,005,995 7,164,714 5,498,728 256,670 463,405 225,000 12,294,227

Std Dev 22,376,636 4,376,527 19,388,371 16,975,907 761,355 1,260,603 656,037 35,659,342

Maximum 246,968,128 75,374,673 207,555,181 137,260,529 8,529,607 15,645,936 5,290,384 332,727,856

Minimum 1,229 0.001 53 5,981 36 2,060 112 156

Sources: Data observed

Table 5.1 summarises key discriptive statistics for the inputs and outputs used in the

analysis. There is disparity of banking operation in Indonesia from the small-scale

banks and the large-banks. The average finance of each commercial bank for a year was

around IDR 8.2 trillion where IDR 1.0 trillion was allocated to finance the small

business sector in Indonesia during the period of 2002-2010.

107

One important information that could be revealed from the data is that there are still

some banks that had zero75 outstanding balance in small business finance over the period

of 2002-2010. In other words, when the Central Bank of Indonesia (BI) relaxed the

regulation of minimum small business finance (SBF) portion at the particular

percentage and adjusted the SBF proportion to each bank‟ condition and willingness76

,

some commercial banks tended to disregard the finance for small businesses. The third

one, the average of SBF was still far below the average of securities and investments

(IDR 1.0 trillion vs. IDR 5.5 trillion). This implies that out of the deposits collected

from customers, commercial banks have more preferred to invest in securities and

investments rather than in SBF.

The high standard deviation for general and administrative expenses (GA) reflects the

high disparity in operations across the banks. The average total deposits collected from

depositors were about IDR 12.3 trillion, but the total finance was only about IDR 8.2

trillion. It suggests that the financial intermediary function was not optimally run. This

low portion of total finance over the deposits indicates that Indonesian banks‟ managers

applied a very prudent lending policy over the period.

Table 5.2. The Proportion of Small Business Finance

Type 2002 2003 2004 2005 2006 2007 2008 2009 2010 Avg.

FB 0.00% 0.00% 0.00% 0.00% 0.00% 0.20% 0.27% 0.05% 0.28% 0.09%

JVB 3.01% 3.46% 1.79% 1.62% 1.11% 1.39% 1.23% 1.04% 1.56% 1.80%

LGOB 43.73% 39.94% 34.39% 31.29% 23.19% 19.60% 17.84% 16.93% 16.85% 27.09%

PB 19.64% 18.66% 18.23% 16.67% 15.81% 14.42% 12.47% 13.04% 11.16% 15.57%

SHARIA 19.21% 16.79% 13.76% 11.84% 30.80% 32.78% 27.02% 30.41% 27.58% 23.35%

SOB 29.00% 26.78% 25.85% 24.43% 25.47% 25.76% 24.62% 24.96% 26.47% 25.93%

Avg. 19.10% 17.61% 15.67% 14.31% 16.06% 15.69% 13.91% 14.41% 13.98% 15.64%

FB is foreign-owned banks; JVB is joint venture banks; LGOB is local government-owned banks; PB is private banks; SHARIA is

Islamic banks; SOB is state-owned banks.

75

For the small business finance item, some banks in the sample do not provide finances to small

businesses a fixed value of 0.001 is added to the variable all over the sample to be able to estimate the

models. This is a common procedure in the literature in order to avoid the loss of data. 76

The regulation about the minimum SBF requirements for Indonesian commercial banks (The Package

of January 1990) was revoked by BI Regulation Number 3/2/PBI/2001 about Small Business Finance on

January 4, 2001. The regulation states that the provision of small business finance is readjusted based on

the ability of each bank. In other words, commercial banks are not compelled to provide the particular

percentage of their finance portfolios devoted to small businesses. See chapter 2 in details.

108

Table 5.2 demonstrates some particular information regarding small business finance of

Indonesian banks during 2002-2010. The average proportion of SBF out of the total

finance of all Indonesian commercial banks was about 15.64 percent over the period. It

indicates that on average, the proportion of SBF was still below 22.5 percent during the

period. It means that the change of bank regulation (by the GOI) to relax the minimum

threshold of small business finance proportion (adjusting to each bank condition)77

has

influenced the decision of Indonesian banks to finance small businesses over the period.

It also remarks that the focus of the Indonesian bank financing during the period was

still on the other finance, which consisted of corporate finance and consumer finance. It

is unfortunate that, data about the portfolios for both corporate finance and consumer

finance could not be found in banks‟ financial statements.

The lower portion of SBF created by Indonesian commercial banks during the period

signifies the impression that on average, management of the commercial banks in

Indonesia are still reluctant to finance the small business sector. This could strengthen

the argument that they mostly perceive small business sector as high-risk investment

because of fundamental weaknesses adhering to small business sector, particularly in

terms of lack of collateral and information asymmetry.

The local government owned banks (LGOB), Islamic banks (SHARIA) and state-owned

banks (SOB) could still provide more than 22.5 per cent of their lending portfolio to

small businesses given the fact that the regulatory of minimum SBF threshold was

scrapped in 2001. The LGOB on average are the highest provider of SBF as proportion

of their loan portfolio. They are primarily located in the province with their branches

spread in every district (regency). This gave them the advantage to exploit their location

efficiently in providing financing for the small business sector. However, in terms of

lending propensity, the LGOB‟s portfolio financing for small businesses decreased over

the period.

In addition, the SOB are recorded as the second highest group of banks that provided

financing small businesses for over 22.5 per cent during the period. One of the main

reasons for such a record is that the high performance and reputation of PT. Bank

77


109

Rakyat Indonesia (BRI). BRI has been recognized as one of the largest and most

successful microfinance institutions in the world and recognized more resilient across

the crises [Patten, et al., (2001), Seibel et al. (2010)]. BRI has more than four thousands

branches of which around 324 large branches with 3,694 small branches that provide

microbanking system covering the Indonesian whole country (Paten et al., 2001). It is

also worth noting that SOB was the only one group of banks which could constantly

maintain the proportion of SBF of over 22.5 per cent in every year.

Foreign-owned banks (FB) and joint-venture banks (JVB), each had a very low average

SBF portfolio during the period 2002-2010, 0.09 per cent and 1.80 per cent, respectively.

It seems that they disregarded SBF in their lending portfolio and concentrated their

financing mostly on corporate and consumer sectors over the period.

5.2.2. Data of Bank-Specific Variables and Macroeconomic Variables

The data of bank specific factors and macroeconomic indicators are very important to

be explored in regards to identify which variables explained the variation of the

Indonesian banks average efficiency during the period 2002-2010. The data descriptive

of those variables are presented in Table 5.3 as follows:

Table 5.3. Bank-Specific and Macroeconomic Variables

Variable Obs. Mean Std. Dev. Min Max

lnta 1011 14.9074 1.8291 9.7722 19.83

roa 1011 0.0228 0.0640 -0.829 0.32

car 1011 0.2420 0.1875 -0.223 3.494

ldr 1011 0.7698 0.3734 0.02 3.35

npl 1011 0.0224 0.0297 0.00 0.529

infl 1011 8.0658 3.9359 2.80 17.1

gdpgr 1011 5.3920 0.6446 4.50 6.3

unemp 1011 9.1770 1.1954 7.10 11.2

usdrate 1011 9340 499 8576 10398

LNTA is the natural logarithm of total assets; ROA is return on assets; CAR is capital adequacy ratio; NPL is

non-performing loans; INFL is annual inflation rate; GDPGR is Gross Domestic Product at real prices growth

rate; UNEMP is annual unemployment rate; USDRATE is United States Dollars (USD) exchange-rate in

terms of Indonesian Rupiahs (IDR).

The natural logarithm of total assets (LNTA) represents the size of bank. The expected

result in the hypothesis testing is the bigger the size of a bank, the more efficient the

bank will be, and vice versa, the smaller the size of the bank, the less efficient the bank.

110

This hypothesis is related to the structure conduct performance theory which states that

as the larger the size of the company, the more efficient the utilisation of the company‟s

resources, and it then improves the company‟s performance.

ROA (return on assets) is an indicator for bank profitability which is expected that the

more profitable the bank, the more efficient the bank will be. During the study period,

the average ROA of 116 commercial banks in Indonesia was 2.28 percent meaning that

the commercial banks in Indonesia enjoyed the profit during the period 2002-2010 after

suffering from the worst crisis: 1997-1998. The minimum ROA score was attained by

one of the closed banks during the period.

Capital adequacy ratio (CAR) is a proxy for the bank capital and it is expected that the

higher the capital of a bank, the more efficient the bank operated. The average CAR of

Indonesian commercial banks for the period 2002-2010 was 24.20 percent. It is

considerably sound performance since it was higher than the minimum of 8 percent

CAR requirement. The higher CAR also reflects the strong condition of the bank in

facing financial and business risks.

Loans to deposits ratio (LDR) is a representative for bank liquidity. It is also expected to

have a positive sign on bank efficiency. It means that as the higher the LDR, the bigger

the opportunity of a bank to generate profit and attain better efficiency, and vice versa.

The average LDR of 76.98 percent of Indonesian banks during the period 2002-2010

indicates that the function of financial intermediary has increased over the period

although it had not reached the optimal level yet.

Non-performing loans (NPL) as an indicator for bank risk. It is presumed to have a

negative sign on bank efficiency. The higher the NPL of a bank, the higher the equity of

the bank would be reduced to cover the loss experienced by the bank. The NPL of

Indonesian banks during the period 2002-2010 was 2.24 percent. It was extremely good,

since it was still below 5 percent. The more prudent lending policy adopted by banks

seems to have a good impact on the lower non performing loans during the period.

111

Three macroeconomic factors are employed in this study: inflation (INF), Gross

Domestic Product growth (GDPGR) and Unemployment rate (UNEMP), Stock market

index/Indonesia composite index (MKTINDEX), USD exchange rate against

Indonesian Rupiah/IDR (USDRATE).

The study employed some macroeconomic indicators adding to bank specific factors as

explanatory variables in the multiple regression models examined. This aims to

investigate the influence of environmental or external variables on Indonesian bank

efficiency during the period. The macroeconomic factors included in this study are:

inflation (INF), Gross Domestic Product growth (GDPGR) and Unemployment rate

(UNEMP), Stock market index/Indonesia composite index (MKTINDEX), and USD

exchange rate against Indonesian Rupiah/IDR (USDRATE).

Inflation rate (INF) is one of the important macroeconomic variables reflecting price

stability in a country. The rate of inflation is presumed to have a negative association

with bank efficiency. The higher the inflation rate, the lower the efficiency of a bank,

and vice versa. GDP growth (GDPGR) is a measurement of the level of an economic

growth in a country. It is also an indicator of the economic activities in the country.

GDP based economic growth is presumed to have a positive effect on bank efficiency.

The unemployment rate (UNEMP) is a measure of the level of unemployment in a

country. This indicator is also important to be observed to identify the employment

absorption rate of the labour force in the country. The association between UNEMP and

bank efficiency is assumed to have a negative sign.

USD exchange rate against IDR (USDRATE) is the price of one US Dollar currency

against Indonesian Rupiahs (IDR). If IDR weakens, more rupiah amounts should be

prepared to be exchanged with the USD. For instance, if this number increases (USD 1

= IDR 9,000 to USD 1 = IDR 10,000), or IDR weakens, it is expected that bank

efficiency will decrease, and vice versa, if IDR increases, bank efficiency will also

increase.

112

5.3. Efficiency and Productivity of Indonesian Commercial Banks

This section reveals the technical efficiency of Indonesian commercial banks that are

derived by non-parametric technique (the DEA approach) during the period 2002-2010.

This section also explains the productivity level of Indonesian commercial banks as well

as the productivity change. The efficiency and productivity level of the Indonesian

banks reflect the real performance of Indonesian banking Industry during the period.

5.3.1. Technical efficiency of Indonesian commercial banks

5.3.1.1. Overal performance of Indonesian banks

Figure 5.1 ilustrates the trend of technical efficiency of Indonesian banks during the

period 2002-2010. The three lines represent the estimated technical efficiency (TE)

from the three models (Model 1, 2 and 3 as explained in section 5.1.).

Figure 5.1. Average technical efficiency of the Indonesian banks (Model 1,2 and 3)

Source: Data Observed

There is a declining trend in Indonesian bank technical efficiency over the period.

However, due to the different specifications of the three models there is a disparity

between the average efficiency obtained from these models. The average efficiency

2002 2003 2004 2005 2006 2007 2008 2009 2010

TE-Model 1 0.6922 0.6812 0.6962 0.6884 0.6708 0.6639 0.5786 0.5194 0.5330

TE-Model 2 0.8075 0.7964 0.7755 0.7773 0.7275 0.7290 0.6707 0.6392 0.6403

TE-Model 3 0.6563 0.6481 0.6472 0.6575 0.6361 0.6304 0.5496 0.4845 0.4983

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Ave

rage

Eff

icie

ncy

Sco

re

Average Technical Efficiency (Model 1, 2, 3)

113

obtained from model 2 is the highest amongst the models78

. This may imply the effect

of economies of scope; hence in model 2 the study splits the total finance into two

outputs, small businesses finance and other finance. There could be two explanations of

the different levels of efficiency obtained from the models. One, that the main

contributor to the difference on average efficiency is the effect of economies of scope.

The second is the unobserved technical issues related to the DEA method, that is the

more outputs or inputs might be included in the model, the higher the efficiency scores

generated. Hence, it is not clear which of these effects rather prevail over the other, we

opt to consider the differences as economies of scope effects. Nonetheless, these three

models confirming the declining trends in Indonesian banks efficiency were also

supported by the finding of Suzuki and Sastrosuwito, (2011) and Hadad et.al (2011b).

Thus, the results imply that Indonesian banks need to minimise inputs given the level of

outputs they produce in order to improve their technical efficiency.

The results provide insight on the effect of diversifying loan portfolio on bank

performance. Technical efficiency gained due to diversification may result in using the

banks‟ resources more efficiently in engaging in small businesses finance79

. This

approach might have a positive impact on the banks performance in terms of

improvement in technical efficiency. However, it might also augment the risk associated

with banks‟ operation by allocating high proportion of the loan portfolio to small

businesses due to information asymmetry problems. It is important to further investigate

the effect of loans and risk on banks‟ efficiency using a second stage analysis. The last

section of this chapter provides such an analysis in order to understand the determinants

of banks‟ efficiency.

Financial Crisis and efficiency:

It is evident from Figure 5.1 that the decline in banks technical efficiency became more

steeper post the financial crisis on 2007. Nonetheless, the decline in performance was

not sharp as was the case for other countries, i.e. European and Australian banks

(Alzubaidi and Bougheas, (2012), Forughi and De Zoysa (2012). The smoother decline

78

Average efficiency of those models over the period 2002-2010 are 0.6352 for model 1, 0.7285 for

model 2, and 0.6001 for model 3.

79

Diversification of products could reduce the unit cost, since only the component of variable cost

changes while the total fixed cost does not.

114

in efficiency can be explained as a reflection of the resilience and experience of

Indonesian banks in dealing with the crisis. The Indonesian banks suffered dramatically

during the crisis in 1997 compared to the slight decline in performance post 2007 crisis.

Similar to many emerging markets, Indonesian banks were more capitalised compared

to European banks during the crisis in 2007 (Fethi et al, 2012). The high level of

capitalisation has played an important role in absorbing the effect of the crisis. Post the

crisis of 1997, the BI has implemented various reform programmes that aim to increase

the resilience of Indonesian banks, by increasing capital, improving competition and

improving banks‟ efficiency (see more details in section 2.2.3 and 2.2.4).

Another factor that could have helped banks absorb the shock of the financial crisis in

2007 is the diversification of loan portfolio. One of the main criticisms to the

Indonesian banks post the 1997 crisis was the higher exposure to corporate loans. These

corporate were highly vulnerable to the financial crisis contributed to the default risk

because of the high level of bankruptcy risk associated. Despite the lifting of BI

regulation that imposed at least 20% of banks‟ loan portfolios to be allocated to small

businesses; many banks‟ groups maintained a relatively high proportion of their loan

portfolios to small businesses.

5.3.1.2. Ownership and Banks’ efficiency

It is commonly perceived in both the theoretical and empirical literature that state-

owned banks tend to underperform private and foreign banks in terms of efficiency. The

perception stems from the high agency problem and lack of incentive to improve

performance associated with state-owned enterprise managers. In the case of Indonesian

banks, the results reveal that state-owned banks seem to be the most efficient banks in

the banking sector (Table 5.4). The state-owned banks ranked first followed by foreign

and joint venture banks. Local government banks and private banks ranked as the least

efficient according to the results obtained from all models.

These results provide interesting evidence that despite those foreign banks has almost 0%

of small business loans they still attain a higher level of efficiency compared to local

115

government banks, the highest provider of small business loans. This implies that the

efficiency of foreign banks comes from other sources rather than loan portfolio

diversification. For instance, they tend to have lower investment in branches and

number of employees (i.e. factors of production) compared to SOBs, LGOBs and PBs.

The private banks represent a controversial performance in the Indonesian banking

sector. One would expect higher performance for private banks unlike what the result

implies. Perhaps the technology of FBs and the sheer size of SOBs are factors that

played an important role in clustering the performance of banks as seen in Table 5.4.

The results coincide with the evidence provided by Fethi et al. (2011) that find state-

owned banks are the most efficient banks in Egypt.

The result contradicts the findings of Yildirim (2002) and Chen et al. (2005) for the case

of Turkish and Chinese banks respectively. They found that state-owned banks are less

efficient in Turkey and China respectively.

Another classification of banks in the sample is obtained by clustering the banks into

conventional and Sharia banks (Islamic banks). As seen from the Table 5.4 that Sharia

banks seem to underperform conventional banks according to the results from model 1

and 3. According to the efficiency scores obtained from model 2 by splitting the loans

into two types of outputs namely small business finance and other finance, Sharia

banks tend to outperform conventional banks. This result implies the positive effect of

loan portfolio diversification in the Sharia banks on their technical efficiency. This

deduction can be further verified from the high growth in small business finance

performed by Sharia banks (Shaban et al., 2014).

The ranking of the Indonesian banks efficiency based on the three criteria (ownership,

operation, and FE exposure) for the three consecutive models are provided in the table

5.4 as follows:

116

Table 5.4. Ranking of Indonesian Banks Efficiency (2002-2010)

Ownership: Efficiency

Model 1

Ranking Efficiency

Model 2

Ranking Efficiency

Model 3

Ranking

SOB 0.966 1 0.989 1 0.861 2

FB 0.912 2 0.914 2 0.914 1

JVB 0.773 3 0.785 3 0.770 3

PB 0.574 4 0.678 5 0.535 4

LGOB 0.545 5 0.706 4 0.500 5

Operation: Efficiency

Model 1

Ranking Efficiency

Model 2

Ranking Efficiency

Model 3

Ranking

CONV 0.636 1 0.725 2 0.604 1

SHARIA 0.595 2 0.834 1 0.465 2

FE Operation: Efficiency

Model 1

Ranking Efficiency

Model 2

Ranking Efficiency

Model 3

Ranking

FEB 0.663 1 0.742 1 0.632 1

NFEB 0.534 2 0.678 2 0.482 2

For the ownership, there are state-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private

banks (PB), and local government-owned banks (LGOB). For the operation, there are conventional banks (CONV)

and islamic banks (SHARIA). For the foreign exchange (FEB) operation, there are also two groups of banks, namely

foreign exchange banks (FEB) and non foreign exchange banks (NFEB).

Based on ownership criteria, for model 1 and model 2, SOBs have been the most

efficient banks with the average efficiency scores of 0.966 and 0.989. While in the

model 3, SOBs were only posed in the second position below the FBs, with an average

score of 0.861. These results are very sensible considering that in the model 1 and 2, the

calculation of efficiency took into account the total finance (model 1) and its

decomposition (model 2) in the output components. This is important since SOBs were

undergoing various financing schemes for both the corporate level as well as for small

and medium businesses, so that their exposures on various financing schemes could

enhance bank efficiency. Whereas when the model only considered other finance (OF)

and abandoned SBF in the output components, the most efficient one was foreign banks

(FBs). This result is also sensible because FB did not provide adequate exposure for

SBF and they seemed to concentrate on other finance in their lending exposures. Those

results have confirmed the results of previous studies (Suzuki and Sastrosuwito, 2011;

Haddad, 2011a) particularly about the most efficiency‟s bank group in Indonesia.

In addition, both in model 1 and 2, FBs have constantly occupied the second position

except for model 3 where they successfully occupied the top position. JVBs were also

117

consistent in the third place in all models provided. The least efficient banks in terms of

ownership during the period were private banks (PB) and local government-owned

banks (LGOB) which had an average efficiency of below 0.750. Private Banks were still

better than the LGOBs since they ranked the fourth in the two models.

In terms of their operations, conventional banks (CONV) were more efficient in two

models, while Islamic banks (SHARIA) could outperform in model 2. For the Sharia

banks, we could find that when they considered small business finance into their

accounts in the estimation of efficiency, they obtained higher efficiency scores and

outperformed the conventional banks. In model 2, Sharia banks reached 0.834 on their

average efficiency but in model 3, when small business finance was excluded in the

estimation, their average efficiency dropped to 0.465. It is quite interesting to explore

why Sharia banks had a better efficiency score when they paid more attention to

providing finance for small businesses in Indonesia during the period 2002-2010 (in

model 2). This following figure seems to answer the question:

Figure 5.2. Numbers of sharia banks and Efficiencies Model 2


Figure 5.2 demonstrates that the number of sharia banks in Indonesia has increased

within the period 2002-2010 and that the increasing number of banks tends to have an

association with the attainment of their efficiency score. Their efficiency scores were

below 0.800 for the period 2002-2005 when the number of sharia banks was only two

banks. Since the number of banks increased in 2005, average efficiency has also

increased and they tend to get their best performance within 2006-2008. The

phenomenon is quite interesting before the decline in the year 2009.

0.632

0.801

0

1

2

3

4

5

6

7

8

0.000

0.200

0.400

0.600

0.800

1.000

1.200

2002 2003 2004 2005 2006 2007 2008 2009 2010

No. of SHARIA Banks

CONV

SHARIA

118

Another figure (figure 5.3) could also explain the phenomenon of raising the

performance of sharia banks when they involved more in small business finance.

Figure 5.3. SBF percentage and Efficiencies Model 2


The figure above displays the average percentage of small business finance within their

portfolios for sharia and conventional banks. Small business finance percentage

(SBFPEC) for conventional banks tend to decrease from 21.82 percent in 2002 to 10.86

percent in 2010 with the average SBFPEC at 15.52 percent, while SBFPEC for sharia

banks tend to increase even though a decline occurred in 2003-2005. The average

SBFPEC for Sharia banks in 2002 was 19.21 percent and it has considerably increased

in 2006 and since then the percentage has always been above 25 percent, with the

average of 23.35 percent within the period. That figure also shows that when Sharia

banks added the proportion of small business finance in 2006, efficiency scores also

increased until the drop occurred in 2009 and 2010. Based on the figure, we could

suppose that the efficiency score of sharia banks tends to increase when they increase

the proportion of their small business finance. Another finding from the figure is that the

decrease in small business finance proportion of the conventional banks has also caused

a decline in their efficiency within the period.

On the last criteria, in terms of foreign exchange services provided, foreign exchange

banks (FEB) have consistently gained higher efficiency scores than those of non foreign

exchange banks (NFEB). We can conclude that banks which provided foreign exchange

transactions in their operation could get benefit from the transactions, not only to

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

0

0.2

0.4

0.6

0.8

1

1.2

2002 2003 2004 2005 2006 2007 2008 2009 2010

CONV_SBFPEC SHARIA_ SBFPEC CONV_EFF SHARIA_EFF

119

increase their profit but also to enhance their efficiency.

The graphs of the evolution of the Indonesian banks‟ technical efficiency among the

different group of ownership during 2002-2010 can be observed in figure 5.4, 5.5., and

5.6. (See the Appendices 3, 4, and 5).

5.3.1.2. Size and Banks’ efficiency

In terms of the size of bank, the average technical efficiency levels of the Indonesian

banks are presented in Figure 5.4, 5.5, and 5.6 for the three consecutive models. This

thesis classifies the banks into four bank peer groups based on asset size. Bank peer

group 1 are those which each has total assets of less than IDR 10 trillion. Bank peer

group 2 consists of banks which have more than IDR 10 trillion until IDR 50 trillion.

Bank peer group 3 are those with each has the asset size of greater than IDR 50 trillion

and less than and equal to IDR 100 trillion. The last group (Bank peer group 4) are those

banks which each has more than IDR 100 trillion in asset size.

Table 5.5. Distribution of Technical Efficiency By Bank Peer Group

Peer Group 1 2 3 4 TOTAL

Average Technical Efficiency of each group (MODEL 1) 0.5654 0.7842 0.8891 0.9835 0.6352



Number of banks in Peer group (as of 2010) 70 33 6 7 116

Peer Group 1 (Total Assets <= IDR 10 Trillion);

Peer Group 2 (Total Assets > IDR 10 Trillion and <= IDR 50Trillion);

Peer Group 3 (Total Assets > IDR 50 Trillion and <= IDR 100 Trillion); Peer Group 4 (Total Assets > IDR 100 Trillion);

120

Table 5.5 reveals the results that the largest banks (peer group 4) are the most technical

efficient banks in Indonesia over the period 2002-2010 under the model 1, 2 and 3 with

an average technical efficiency of 0.9835, 0.9883, and 0.9614, respectively. These

results suggest the positive impact of the economies of scale on bank efficiency given

the fact that the largest banks tend to be more efficient than other peer groups. These

results are supported in practice by the very large number of customers served by the

large banks operated in Indonesia i.e. PT. Bank Mandiri, PT. Bank BCA, and PT Bank

BNI with sophisticated bank technology including cash machines and internet banking

facilities. These findings coincide with the studies of Shaban (2008), Demir et al (2005)

and Isik and Hassan (2003b) in the case of Egypt and Turkey. They found that large

banks had a better performance compared to the other size of banks.

The second highest efficient banks are peer group 3 and the least efficient banks are the

smallest banks (peer group 1). The smallest banks experienced the least efficient

operated in Indonesia during the period 2002-2010. These results are also sensible given

the fact that the smallest banks are majority of the private banks (PBs) which find some

difficulty in obtaining customers‟ confidence post-the crisis 1997-98. As known, the

crisis of 1997-98 testified the considerable funds‟ movement from PBs to state-owned

banks.

Figure 5.7. Average Technical Efficiency By Bank Peer Group (Model 1)


Figure 5.7 exhibits the evolution of their technical efficiency levels attained by four

different groups for model 1. Almost all bank peer groups except peer group 4

experienced the decline pattern of the average technical efficiency over the period 2002

0.0000

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Eff

icie

ncy

Sco

re

Average Technical Efficiency Model 1 by Bank Peer Group

Peer Group 1 (Total Assets <=IDR 10 Trillion)

Pee Group 2 (Total Assets > IDR10 Trillion and <= IDR 50Trillion)

Peer Group 3 (Total Assets > IDR50 Trillion and <= IDR 100Trillion)

Peer Group 4 (Total Assets > IDR100 Trillion)

121

to 2010. The largest banks (bank peer group 4) emerge to be the most stable banks

attaining a technical efficiency level of around 1.000. This constant performance has

delivered the banks to become the most technical efficient banks in Indonesia during the

period. It is also worthwhile to state that the crisis of 2007-08 had a lower impact on the

largest banks (bank peer group 4) than those of their peers of group 1, 2 and 3. This is

also evidence that the largest banks‟ group in Indonesia was more resilient to the current

world economic crisis. Almost similar findings obtained by the other models can be

found in the figure 5.8 and 5.9 (See Appendix 6 and 7).

5.3.2. The Productivity of Indonesian commercial banks

The productivity of Indonesian commercial banks during the period 2002-2010 can be

observed through Table 5.6 as follows:

Table 5.6. Malmquist Index Summary over the period 2002-2010

Year EFF.CH TECH.CH PE.CH SE.CH TFP.CH

Model 1 0.954 1.043 0.963 0.990 0.995

Model 2 0.964 1.026 0.968 0.996 0.989

Model 3 0.951 1.043 0.961 0.990 0.991

EFF.CH is the efficiency change; TECH.CH is the technological change; PE.CH is pure efficiency

change; SE.CH is scale efficiency change; TPF.CH is total factor productivity change.

In general, Indonesian commercial banks underwent the decreasing productivity growth

(productivity regress) over the period 2002-2010. All models witness the productivity

regress over the period. Model 1, 2, 3 observe the regress of 0.05%, 1.1%, and 0.9% on

average annually. This productivity regress was caused by the non-existence of

efficiency change although there had a progress in the technology change. The

technology change experienced a progress of 4.3%, 2.6%, and 4.3% yearly on average

for the three consecutive models respectively over the study period. The positive

technological change implies an improvement in technology adopted by banks over the

period 2002-2010. These results provide strong evidence of the success of Indonesian

banks in terms of facilitating the newest technology including internet banking and text

message banking in their services.

122

While the evidence that there was no efficiency change in Indonesian banks during the

observed period, has supported the findings of the downward trend of technical

efficiency experienced by Indonesian banks over the period. This led Indonesian banks

to attain the productivity regress over the period. Hence, the recommendation for

Indonesian banks to improve their technical efficiency by minimising the inputs given

the level of outputs they produce is also very relevant to enhance their productivity level

(see section 5.3.1.1).

Table 5.7. presents in details on annual productivity progress/regress over the period for

the model 1.

Table 5.7. Malmquist Index Summary of Annual Means (Model 1)


2002/2003 0.959 0.969 0.989 0.970 0.930

2003/2004 1.055 0.949 1.030 1.025 1.002

2004/2005 1.021 0.988 0.992 1.030 1.009

2005/2006 0.948 1.083 0.961 0.987 1.027

2006/2007 0.846 1.207 0.989 0.855 1.021

2007/2008 0.846 1.160 0.832 1.017 0.981

2008/2009 0.916 1.066 0.903 1.014 0.976

2009/2010 1.065 0.953 1.027 1.037 1.015

Mean 0.954 1.043 0.963 0.990 0.995

EFF.CH is the efficiency change; TECH.CH is the technological change; PE.CH is pure efficiency

change; SE.CH is scale efficiency change; TPF.CH is total factor productivity change.

In model 1, during the covered period, on average the productivity growth decreased by

0.5 % annually. We can clearly identify that productivity growth has declined for a

couple of years: the year 2002/2003, 2007/2008 and 2008/2009. The decline for the

period of 2002/2003 was due to the fact that commercial banks had been still in a

recovery period after the big crisis of 1997-98, whilst the consecutive decline in the

period of 2007/2008 and 2008/2009 seem to be affected by the global financial crisis in

2007/2008. This decline of productivity growth of Indonesian banks that occurred

during the crises of 2007-2008 is similar with the findings of the previous study (Suzuki

and Sastrosuwito, 2011). In addition, the productivity growth of Indonesian banks

during the period 2002-2010 was basically supported by the technological change that

grew 4.3% annually and there was no efficiency change within the period.

123

Meanwhile, the annual productivity progress/regress over the same period for model 2

and model 3 can be seen at the Table 5.8and 5.9 (at the Appendix 8 and 9).

5.4. Determinants of Efficiency

This section reveals the determinants of technical efficiency of Indonesian banks during

the period 2002-2010. There are three models examined. The dependent variable for

Model 1 (TE-1) is the technical efficiency obtained by the DEA approach considering

total finance, and securities and investment as outputs. The dependent variable for

Model 2 (TE-2) is the technical efficiency obtained by the DEA approach considering

small business finance, other finance, securities and investment as outputs. The last one,

the dependent variable for Model 3 (TE-3), is the technical efficiency obtained by the

DEA approach which takes into account only other finance, and securities and

investment as outputs. In addition, all models use the same components of inputs,

namely general and administrative expenses, fixed assets, total deposits as inputs. The

explanatory variables included in the regression models are some specific factors of

Indonesian banks as well as the country macroeconomic Indicators.

The TOBIT-regression is employed to estimate the determinants of Indonesian bank

efficiency since the efficiency scores are constrained between zero and one. A lot of

previous studies use the same kind of regression in revealing the determinants of

efficiency [(Chang et.al (1998); Nikiel and Opiela (2002); Casu and Molyneux (2003);

Havrylchyk (2005); Grigorian and Manole (2006); Ariff and Can (2008); Sufian (2010);

Gardener et.al (2011)].

The standard TOBIT-regression model is defined as follows:

Where is a latent variable, is technical efficiency score obtained from DEA

approach. is the set of parameters to be estimated. is the error term.

The equation of the regression is specified as follows:

124

= b0 + b1 + b2 + b3 + b4 + b5 + b6 +

b7 + b8 + b9 + b10 .

Where:

The technical efficiency of i-th bank in period t obtained by the DEA

approach.

Natural logarithm of total assets (LNTA) as a proxy for bank size.

Return on assets (ROA) is assigned to represent bank profit.

Capital adequacy ratio has been assigned to embody bank capital.

Capital adequacy ratio (CAR) is calculated by the formula of bank

capital divided by risk-weighted assets.

Bank liquidity means the ability of a bank to cover its short-term

liabilities. Loans to deposits ratio (LDR) is supposed to demonstrate

bank liquidity.

Non-performing loans (NPL) is assigned to represent the bank risk.

Annual inflation rate. This measure reflects the overall percentage

increase in the consumer price index for all goods and services in

Indonesia.

Annual real GDP (gross domestic product) growth rate. The growth of

Indonesia‟s total goods and services produced in Indonesia in annual

term.

Annual unemployment rate in Indonesia.

Indonesian Stock Composite Index in the Indonesia Stock Exchange.

US Dollar exchange rate, in terms of IDR (Indonesian Rupiah).

Error term

All variables employed in the model are to reveal the determinants of efficiency in the

Indonesian banking industry. The total banks included in this study are 116 banks which

covers around 96 per cent of the population, within the 9-year study period from 2002-

2012, resulting in 1011 total observations.

The correlation matrix amongst independent variables is presented as follows:

125

Table 5.10. Correlation Matrix of Independent Variables-1 lnta roa car ldr npl infl gdpgr unemp mktindex

lnta 1

roa 0.0742 1

car -0.2228 0.0426 1

ldr -0.0116 0.0331 0.1126 1

npl -0.1030 -0.4565 0.0877 0.0831 1

infl -0.0523 0.0095 -0.0525 -0.0061 0.0346 1

gdpgr 0.1477 -0.048 0.0275 0.1198 -0.1518 0.3182 1

unemp -0.1646 0.0756 -0.0521 -0.1345 0.0784 0.4866 -0.1168 1

mktindex 0.2200 -0.069 0.0615 0.1461 -0.168 -0.3461 0.5662 -0.6426 1

usdrate 0.1147 -0.027 0.0051 0.1062 -0.0829 0.1306 -0.1054 -0.2222 0.1913

LNTA is the natural logarithm of total assets; ROA is return on assets; CAR is capital adequacy ratio; NPL is non-performing loans;

INFL is annual inflation rate; GDPGR is Gross Domestic Product at real prices growth rate; UNEMP is annual unemployment rate;

MKTINDEX is the market index, a composite index in Indonesia stock exchange; USDRATE is United States Dollars (USD)

exchange-rate in terms of Indonesian Rupiahs (IDR).

Since the variable MKTINDEX has a high correlation either with GDPGR (0.5662) and

UNEMP (-0.6426), it is decided to drop the MKTINDEX in the model. The significant

test for the correlations can be observed on appendix 26. Thus, the correlation matrix

between the variables incorporated in the model is presented on the following table:

Table 5.11. Correlation Matrix of Independent variables-2

lnta roa car ldr npl infl gdpgr unemp usdrate

lnta 1.0000

roa 0.0361 1.0000

car -0.2060 -0.1484 1.0000

ldr -0.0115 0.0504 0.1104 1.0000

npl -0.1031 -0.3794 0.3160 0.0831 1.0000

infl -0.0524 0.0156 -0.0481 -0.0061 0.0346 1.0000

gdpgr 0.1478 -0.0175 0.0388 0.1198 -0.1518 0.3182 1.0000

unemp -0.1646 0.0354 -0.0686 -0.1345 0.0784 0.4866 -0.1168 1.0000

usdrate 0.1147 -0.0008 -0.0029 0.1062 -0.0829 0.1306 -0.1054 -0.2222 1.0000



USDRATE is United States Dollars (USD) exchange-rate in terms of Indonesian Rupiahs (IDR).

Table 5.11 shows that there is no significant association between each independent

variable (all the correlation coefficients are below 0.5). Therefore, we conclude that

there is no multicollinearity in the model. The correlation coefficient between each

variable is very important to be observed since a strong association between each of the

explanatory variables would demolish the model and it is considered not a BLUE (best,

linear, unbiased estimator) model.

126

The determinants of Indonesian banks‟ technical efficiency over the period 2002 to

2010 are presented in table 5.10 as follows:

Tabel 5.12. Determinants of Indonesian banks’ technical efficiency (2002-2010)

VARIABLES TE-1 TE-2 TE-3

lnta 0.0953*** 0.0766*** 0.0965***

(0.00453) (0.00500) (0.00482)

roa 0.114 0.206 0.0906

(0.130) (0.140) (0.140)

car 0.492*** 0.334*** 0.582***

(0.0497) (0.0531) (0.0535)

ldr 0.0988*** 0.131*** 0.0731***

(0.0219) (0.0248) (0.0233)

npl 0.703** 0.226 0.801**

(0.326) (0.351) (0.349)

infl 0.00779*** 0.0114*** 0.00791***

(0.00262) (0.00288) (0.00281)

gdpgr -0.0801*** -0.112*** -0.0749***

(0.0143) (0.0158) (0.0153)

unemp 0.0545*** 0.0367*** 0.0539***

(0.00829) (0.00908) (0.00889)

usdrate -0.000118*** -0.000127*** -0.000113***

(1.71e-05) (1.87e-05) (1.83e-05)

Constant -0.00188 0.798*** -0.129

(0.233) (0.256) (0.250)

No. of Observations 1,011 1,011 1,011

Pseudo R-square 0.6223 0.3802 0.5235

Standard errors in parentheses

*** p<0.01, ** p<0.05, *p<0.1

The dependent variable for Model 1 (TE-1) is the DEA technical efficiency scores obtained considering total finance, securities and

investment as outputs. The dependent variable for Model 2 (TE-2) is the DEA technical efficiency scores obtained considering

small business finance, other finance, securities and investment as outputs. The dependent variable for Model 3 (TE-3) is the DEA

technical efficiency scores obtained with takes into account only other finance, and securities and investment as outputs. All models

use the same components of inputs, namely general and administrative expenses, fixed assets, total deposits.

The explanatory variables are: LNTA is the natural logarithm of total assets; ROA is return on assets; CAR is capital adequacy

ratio; NPL is non-performing loans; INFL is annual inflation rate; GDPGR is Gross Domestic Product at real prices growth rate;

UNEMP is annual unemployment rate; USDRATE is United States Dollars (USD) exchange-rate in terms of Indonesian Rupiahs

(IDR).

Based on table 5.10, it is observed that the regression on model 1 has been the fittest

model reflected by the Pseudo_R-square of 0.6223. This means the 62.23% variation of

the efficiency scores can be explained by the explanatory variables in the model.

Meanwhile, the lowest R-square of about 38.02 percent is presented by the regression

on model 2.

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LNTA as the proxy for bank size has been a significant factor for Indonesian banks‟

efficiency with a positive sign. LNTA is significant for all models with a confidence

level of 99 percent. These results suggest that the bigger the size of a bank, the bank

operates more efficiently. These findings also confirmed the Structure Conduct

Performance hypothesis that if the size of a company increases, the company will

operate more efficient (due to the benefit of economies of scale), and it would finally

enhance the company‟s profit. In terms of the case of Indonesian banks, these results

also witnessed the right policy adopted by the Indonesian banks‟ authority (BI) during

the period by merging some commercial banks. The purposes of the mergers

programme during the period were to have bigger, strong and sturdy banks and become

more resilient to crises that may occur. In addition, these findings are consistent with

other results [see e.g. Andries (2011), Vu and Turnel, (2011), Yildirim and Philippatos

(2007)].

CAR has also affected Indonesian bank efficiency with the positive direction. CAR is

significant across the three models with the confidence level of 99 percent. These

findings suggest that it is improved for banks to add their capital considering that as the

capital adequacy increases, the efficiency of bank also increases. The CAR of

Indonesian commercial banks over the study period was 24.20 per cent. Their high level

of capital adequacy reflects high compliance of Indonesian banks to the regulation

regarding the anticipation to encounter all bank risks including credit risk and solvency

risk. This findings coincide with other literatures [Yildirim and Philippatos (2007),

Andries (2011); Thangavelu and Findlay, C. (2013)].

LDR has also a significant impact on the Indonesian bank efficiency with the positive

association. This LDR is also significant at a confidence level of 99 percent. This result

suggests that as lending or finance exposure increases, the efficiency of bank also

increases. In other words, the additional proportion of credit or finance over deposit can

enhance bank efficiency. The average LDR across all Indonesian commercial banks

during the period was 76.98 per cent. This means that there are more chances for

Indonesian commercial banks to enhance their lending exposure (since the average LDR

was less than 80 per cent) to generate more profit and obtain a higher efficiency level.

Obviously, the management of banks should also practise more prudential principle in

implementing the policy of expanding their lending portfolios.

128

The NPL variable has no conclusive results for the three models. NPL affects banks‟

efficiency in model-1 and model-3 with the confidence level of 95 percent, but it failed

to significantly affect bank efficiency in model-2. The insignificant effects of the NPL

on banks‟ efficiency is similar with the findings of previous literatures [Andries (2011),

Thangavelu and Findlay (2013)].

Surprisingly, the ROA variable as the proxy for bank profitability has no significant

effect on Indonesian bank efficiency in the three models although the relation between

the variable with efficiency is positive. It means that the more profitable the bank

operates, the more efficient the bank will be. However, the positive relationship is not

significant. These results coincide with the findings of Andries (2011).

All macroeconomic variables, namely INFL, GDPGR, UNEMP and USDRATE have

also significantly affected Indonesian banking efficiency during the period 2002-2010.

All macroeconomic indicators affected Indonesian bank efficiency with an alpha of one

percent.

In general, we can conclude that from all the models examined, the variable of LNTA,

CAR, and LDR are the bank-specific variables that consistently affected Indonesian

bank efficiency over the period 2002-2010. These results could also be used as an

important recommendation for all management of the banks that the size of bank,

capital adequacy, and bank liquidity are the three important keys for enhancing

efficiency. These findings are also important for policy makers, particularly for the

Indonesian commercial bank authority to create the regulations which are empirically

beneficial for commercial banks.

5.5. Conclusions

The results of the study show that the average technical efficiency of commercial banks

in Indonesia has tended to decline over the period 2002-2010. State-owned banks were

the most efficient banks in Indonesia when considering small business finance (SBF) as

an output in the calculation of efficiency, but when SBF was excluded, Foreign-owned

banks were the most efficient ones. Sharia banks outperformed conventional banks in

129

one model when the total finance was splitted to small business finance and other

finance in the output components.

The productivity growth of Indonesian banks has decreased during the study period. No

change in the average banks‟ efficiency levels, has led to their decreased productivity

growth attained, although there was an improvement in technological change. These

findings suggest the importance of Indonesian banks to minimise their inputs given the

outputs to enhance their efficiency level as well as their productivity.

Across all models, it is verified that LNTA, CAR and LDR are bank specific variables

that consistently affect Indonesian banks‟ technical efficiency. Meanwhile all

macroeconomic variables in this study [INFL (inflation rate), GDPGR (Gross Domestic

Product Growth), UNEMP (unemployment rate) and USD RATE (USD rate)] have also

significantly affected Indonesian banks‟ technical efficiency during the period 2002-

2010.

The implications of these results are considered as suggestions for the policy makers in

Indonesia as follows: First, it is not a guarantee that privatisation is beneficial. The case

of Indonesian banks has indicated that state-owned banks (SOBs) are much more

efficient than their private counterparts. Second, small business finance (SBF) is also an

important factor in enhancing bank efficiency. Third, bank size, bank capital, and bank

liquidity as well as bank lending/finance portfolio proportions are confirmed to be of the

banks‟ specific variables that significantly affected the Indonesian banks‟ efficiency. All

these findings suggest that the merger policy, the provision to maintain the capital

adequacy, and the provision to manage adequate liquidity as well as lending portfolio

have become very crucial aspects in managing commercial banks. Fourth, although

there was a productivity regress of Indonesian banks due to the non-existence of their

efficiency change, the technological change implemented in Indonesian commercial

banks has increased the productivity level of Indonesian commercial banks during the

study period. This suggest that extensive new technologies (including the

implementation of cash machines, phone-banking, internet banking, etc) have

beneficially improved the level of productivity of Indonesian commercial banks during

the period.

130

6. Chapter Six: Small Business Finance and Indonesian Commercial Banks’ Cost

Efficiency: SFA Approach

6.1. Introduction

This chapter examines the performance of Indonesian commercial banks using the most

popular technique in the parametric method, namely the stochastic frontier analysis

(SFA) to estimate banks‟ cost efficiency over the period 2002-2010. This chapter

focuses only on the cost efficiency estimates given the fact the cost efficiency model is

more widely used than the SFA method in the banking industry. This chapter also

provides an extensive analysis by examining the determinants of Indonesian bank cost

efficiency over the study period observing bank-specific factors and macroeconomic

indicators as explanatory variables.

The study includes 111 commercial banks in Indonesia consisting of 109 conventional

banks and 2 Islamic banks during the study period. In terms of their ownership, these

banks are composed of 4 State-owned banks (SOB), 55 private banks (PB), 26

provincial/Local-government owned banks (LGOB), 15 joint-venture banks (JVB), 9

foreign-owned banks (FB). All banks are conventional banks except the two banks

which are classified as Islamic banks (SHARIA). These Islamic banks are also private

banks (PB).

In estimating cost efficiency, this study uses a translog function for the total cost as an

objective of the function along with some outputs and prices of inputs variables. The

study presents three different models classified by different components in outputs.

model 1 uses total finance (TF), securities and investment (SI), and other income (OI);

model 2 use small business finance (SBF), other finance (OF), securities and

investment (SI), and other income (OI); model 3 uses other finance (OF), securities and

investment (SI), and other income (OI). The input prices employed in this study are

price of fund (PF), price of labour (PL), and price of capital (PC). All variables are

presented in a logarithm form.

The organisation of this chapter is as follows: The first section demonstrates the

descriptive statistics of the data sample completed with a brief analysis of each variable.

131

The second section presents the parametric SFA cost efficiency estimates of Indonesian

banks as well as the comparison of their groups of banks‟ performance over the study

period. The third section discusses the determinants of Indonesian banks‟ cost efficiency

over the period. Finally, the last section provides the conclusion.

6.2. Descriptive Statistics

This section exhibits the descriptive statistics of the data sample employed in this thesis

to estimate Indonesian banks’ SFA cost efficiency as well as to predict the determinants

of bank cost efficiency. To estimate banks‟ cost efficiency we exploit the data of total

costs, outputs and the prices of inputs, while in examining the determinants of banks‟

cost efficiency, we use bank specific variables and macroeconomic indicators as

explanatory variables.

6.2.1. Data for Estimating Cost Efficiency

Table 6.1 exposes the data of outputs employed in estimating the cost efficiency of

Indonesian commercial banks over the period 2002-2010.

Table 6.1. Output Components

IDR Millions

Total Finance

Small Buss.

Finance

Other

Finance

Securities &

Investments Other Income

Mean 8,239,970 1,005,694 7,234,276 5,554,995 259,357

Std. Dev 22,500,000 4,401,679 19,500,000 17,100,000 765,516

Minimum 1,229 0.001 53 5,981 36

Maximum 247,000,000 75,400,000 208,000,000 137,000,000 8,529,607

Source: Data processed

The above table summarises the key descriptive statistics for the outputs used in the

analysis. There is a disparity of banking operation in Indonesia ranging from the small-

scale banks to the large-banks. The average annual finance of each commercial bank

during the period 2002-2010 was around IDR 8.2 trillion where IDR 1.0 trillion of the

number was allocated to financing the small business sector in Indonesia. In percentage

term, the proportion of small business finance was below 22.5 percent.

132

As explained in chapter five, a number of banks had zero80

outstanding balance in their

small business finance (SBF) over the study period. This circumstance occurred over

time during the period 2002-2010 after the Central Bank of Indonesia (BI) did not

impose all commercial banks to maintain the minimum threshold of SBF proportion at

the particular percentage rather than readjusting the proportion to each bank‟s condition

and willingness in 200181

.

Meanwhile, the characteristics of total cost and the price of inputs are presented at the

table as follows:

Table 6.2. Total Cost and Price of Inputs

IDR Millions

Total Costs

(IDR Million)

Price of

funds

Price of

Labour

Price of

Capital

Mean 1,417,452.00 0.0672 0.0208 3.2566

Std Dev 3,682,207.00 0.0300 0.0174 4.5489

Minimum 1,438.00 0.0070 0.0020 0.0752

Maximum 35,000,000.00 0.2950 0.3360 29.8478


Table 6.2 summarizes the data of total costs and prices of inputs. Total costs cover all

expenses incurred by all commercial banks i.e. operating expenses and non-operating

expenses. Operating expenses cover total interest expenses for conventional banks or

operating expenses from profit sharing for Islamic banks, provision for losses on

earnings assets, provision for losses on commitments and contingencies, general &

administrative expenses, salaries and employee benefits, losses from decline in fair

value of trading account securities, losses on foreign exchange, etc. The average annual

total costs for a bank over the study period was IDR 1.42 trillion with a minimum of

IDR 1.44 billion and a maximum of IDR 35 trillion. The disparity of the total costs of

Indonesian banks represents the difference of the scale of the operations over large

banks and small banks in Indonesia.

80

For the small business finance items, some banks in the sample do not provide finance to small

businesses, and then a fixed value of 0.001 is added to the variable all over the sample to be able to

estimate the models. This is a common procedure in the literature in order to avoid loss of data.

81

The regulation about the minimum SBF requirements for Indonesian commercial banks (The Package of

January 1990) was revoked by BI Regulation Number 3/2/PBI/2001 about Small Business Finance on

January 4, 2001. See chapter 2 for details.

133

Price of funds (PF) is calculated by total interest expenses divided by total deposits. It

can be observed that the average price of funds is around 6.72% out of total deposits

collected. Price of labour (PL) is calculated by personnel expenses divided by total

assets and price of capital (PC) is calculated by non-interest expenses divided by fixed

assets. On the average, price of labour of Indonesian banks was around 2.08% of the

total assets. For calculating the price of capital, the components of non-interest expenses

are calculated by total expenses minus interest expenses and personnel expenses. The

maximum data for PF, PL and PC are derived from the data of one commercial bank

which was closed down at the end of the study period.

In terms of banks‟ ownership and operations, the total cost of Indonesian commercial

banks over the study period is presented in the following table:

Table 6.3. Total Costs to Total Assets (2002-2010)

Types 2002 2003 2004 2005 2006 2007 2008 2009 2010 Avg.

Ownership:

FB 0.0993 0.0651 0.0553 0.0683 0.0847 0.0804 0.0761 0.0614 0.1868 0.0864

JVB 0.1020 0.0780 0.0678 0.0571 0.0747 0.0646 0.0699 0.0647 0.1086 0.0764

LGOB 0.1138 0.1080 0.0919 0.0863 0.0865 0.0884 0.0948 0.0948 0.1033 0.0964

PB 0.1361 0.1168 0.0927 0.1014 0.1167 0.0960 0.1252 0.1059 0.1179 0.1121

SOB 0.1301 0.1144 0.0879 0.0922 0.1029 0.0851 0.0835 0.0846 0.0799 0.0956

Operational

System:

CONV 0.1163 0.0964 0.0791 0.0811 0.0931 0.0829 0.0899 0.0823 0.1193 0.0934

SHARIA 0.0867 0.0947 0.0866 0.0977 0.1102 0.1016 0.1004 0.0998 0.0832 0.0957

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB), local government-

owned banks (LGOB), conventional banks (CONV) and islamic banks (SHARIA).

Table 6.3 illustrates the profile of expenditures experienced by commercial banks in

Indonesia during the period 2002-2010. Based on types of ownership, joint venture

banks (JVB) and foreign banks (FB) were the first and second lowest expenditure banks‟

group in Indonesia in terms of average total costs to total assets (TCTA). They attained

an average TCTA of 7.64% and 8.64% respectively. These results support the belief that

foreign banks or joint venture banks operate more efficiently than their local peers

(domestic banks). These facts are very sensible considering that they employ fewer

employees for similar roles compared to domestic banks. In addition, they could also

minimize the expenses of the usage of fixed assets by employing less branches and

134

offices in supporting their operations. All these conditions reduced the total cost of their

operations. Meanwhile, private banks (PB) witnessed the least efficient among all

groups in terms of TCTA with a proportion of total expenditures over total assets

reaching more than 10 percent. These results are also sensible given the fact that they

spend more resources to win consumer confidence compared to other groups. Since the

crisis of 1997-98, PBs obtained less trustworthiness from customers compared to their

peers. Since then they have applied a number of strategies to attract customers i.e.

giving more gifts to depositors and employing more sales force to promote their deposit

and lending products.

In terms of their operational system, conventional banks (CONV) consumed lower costs

on average than their peers from Islamic banks (SHARIA). They experienced 9.34%

and 9.57% of TCTA on average respectively over the period. These results are also

reasonable for the period since conventional banks benefited from their long

establishment in the Indonesian banking Industry compared to their peers of Islamic

banks. This profile coincides with Shaban et al. (2014) stating that Islamic banks

experienced overpricing behaviour during the period.

In addition, Table 6.4 exhibits the decomposition of the total cost experienced by

Indonesian banks by ownership and operational system during the period 2002-2010.

Table 6.4. Expenses of Indonesian banks (2002-2010)

OWNERSHIP Types: OPERATIONAL

Systems:

SOB PB JVB LGOB FB Conv. Sharia

Obs 36 513 135 234 81 981 18

Avg Total Costs

(IDR Million) 15,300,000 1,125,210 385,052 492,589 1,479,997 1,423,883 1,066,982

Avg Interest Exp82

(IDR Million) 7,923,090 597,617 173,957 235,385 488,748 714,684 492,020

Avg Non-Interest

Exp (IDR Million)

4,580,650

344,037

149,970

127,932

769,566

455,378

399,177

Avg Personnel Exp

(IDR Million) 2,820,548 185,049 61,459 129,272 223,231 254,775 175,786

Avg Total

Costs/Assets 0.0956 0.1121 0.0764 0.0964 0.0864 0.0934 0.0957

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB), local

government-owned banks (LGOB), conventional banks (CONV) and islamic banks (SHARIA).

82

Interest expense is applied to conventional banks; meanwhile for sharia (Islamic banks), operating

expenses on profit or revenue sharing contract is applied.

135

State Owned Banks (SOBs) experienced the highest average expenditure for all three

kinds of expenses (interest expenses, non-interest expenses, and personnel expenses)

incurred in their banks. These results are very sensible given the facts that they are

mainly from the largest banks in terms of total assets. They also employ a considerable

amount of employees across their branches all over Indonesia. JVBs experienced the

lowest expenditures for the components of interest expenses and personnel expenses

whilst the lowest expenditures on non-interest expenses were experienced by the

LGOBs. Overall, conventional banks underwent higher average expenditures than those

of their peers from Islamic banks. This result is different with the findings of the TCTA

terms since this is measured in IDR currency.

The following three tables reveal the costs for each of the collected funds, employed

labours and consumed capital assets. The price of funds indicates the average cost of

funds as the main cost for banks in their operation.

Table 6.5. Price of Funds (2002-2010)

2002 2003 2004 2005 2006 2007 2008 2009 2010 Avg.

Ownership Type:

FB 0.0833 0.0382 0.0316 0.0502 0.0800 0.0641 0.0661 0.0626 0.0462 0.0580

JVB 0.0645 0.0424 0.0313 0.0501 0.0763 0.0590 0.0621 0.0517 0.0657 0.0559

LGOB 0.0750 0.0680 0.0439 0.0435 0.0513 0.0523 0.0528 0.0562 0.0583 0.0557

PB 0.1184 0.0880 0.0562 0.0668 0.0879 0.0680 0.0731 0.0719 0.0659 0.0774

SOB 0.1255 0.0993 0.0593 0.0615 0.0778 0.0563 0.0518 0.0565 0.0455 0.0704

Operation system:

CONV 0.0991 0.0739 0.0481 0.0575 0.0769 0.0626 0.0658 0.0643 0.0622 0.0678

SHARIA 0.0565 0.0515 0.0505 0.0595 0.0670 0.0470 0.0500 0.0555 0.0420 0.0533

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB), local government-owned

banks (LGOB), conventional banks (CONV) and islamic banks (SHARIA).

LGOBs, JVBs and FBs experienced the lowest price of funds in the cost structure of

Indonesian banking over the period of 2002-2010. These results depict the success of

the LGOBs to gain the cheapest funds benefiting from their locations. LGOBs are

mainly situated in provinces and districts all over Indonesia. For the JVBs and FBs,

these results are not quite surprising considering that they are known as superior banks

in interest-rate management. Conversely, the PBs spent more to pay interest to their

depositors. These results are very sensible given the fact that they found difficulties in

obtaining more funds from depositors due to less trustworthiness perceived by

customers compared to their peers from foreign banks and state-owned banks. In

136

addition, Islamic banks offered lower prices on their funds collected compared to their

peers from the conventional banks.

Table 6.6. Price of Labour (2002-2010)

2002 2003 2004 2005 2006 2007 2008 2009 2010 Avg.

Ownership Type:

FB 0.0162 0.0137 0.0120 0.0113 0.0124 0.0136 0.0111 0.0112 0.0118 0.0126

JVB 0.0157 0.0209 0.0181 0.0121 0.0125 0.0105 0.0104 0.0123 0.0209 0.0148

LGOB 0.0261 0.0263 0.0285 0.0244 0.0223 0.0241 0.0295 0.0292 0.0294 0.0266

PB 0.0180 0.0196 0.0212 0.0219 0.0215 0.0194 0.0222 0.0239 0.0332 0.0223

SOB 0.0140 0.0168 0.0185 0.0195 0.0183 0.0180 0.0170 0.0148 0.0145 0.0168

Operation system:

CONV 0.0193 0.0209 0.0218 0.0203 0.0197 0.0188 0.0214 0.0223 0.0285 0.0214

SHARIA 0.0190 0.0150 0.0145 0.0160 0.0155 0.0155 0.0145 0.0155 0.0155 0.0157

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB), local government-

owned banks (LGOB), conventional banks (CONV) and islamic banks (SHARIA).

In terms of price of labour, FBs and JVBs experienced the lowest costs on their

personnel employment. These results are very sensible considering that they can operate

more efficiently in using their employees compared to other peer groups. On the other

hand, LGOBs spend more money on paying salaries and other related expenses to their

employees. These might happen due to higher salaries applied to their employees and or

more employees were hired for a similar position compared to other peers.

Unfortunately, the data concerning the number of employees from all banks is not

available. However, the possibility of hiring more employees might be the main factor

of their high cost of labour. In addition, based on their operational system, Islamic banks

experienced lower expenses on their human resources compared to their peers from

conventional banks.

Table 6.7. Price of Capital (2002-2010) 2002 2003 2004 2005 2006 2007 2008 2009 2010 Avg.

Ownership Type:

FB 6.40 7.34 9.43 13.78 14.68 14.19 15.80 14.94 16.62 12.58

JVB 5.97 7.00 6.99 5.97 5.37 5.45 4.24 3.21 7.65 5.76

LGOB 1.99 1.83 1.75 1.98 2.23 2.27 2.26 2.03 2.68 2.11

PB 1.40 1.51 1.56 1.52 1.60 1.86 1.90 1.89 2.29 1.72

SOB 1.73 1.18 1.43 1.55 1.97 1.77 2.48 3.22 3.97 2.15

Operation system:

CONV 2.61 2.79 3.00 3.23 3.34 3.43 3.44 3.20 4.35 3.27

SHARIA 0.70 2.48 1.31 2.96 3.00 4.07 4.00 3.55 3.02 2.79

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB), local

government-owned banks (LGOB), conventional banks (CONV) and islamic banks (SHARIA).

137

The last component of the right-hand side of the equation in the estimation models for

Indonesian banks‟ cost efficiency is the price of capital. This ratio is achieved by total

non-interest expenses divided by fixed assets. The data indicates that PBs are the most

efficient banks while FBs are the least efficient ones. These results are contrasted with

the preceding results on price of funds and price of labour. These results might be

caused by a lower level of fixed assets employed by the FBs to the PBs. On the other

hand, Sharia banks turned out to experience lower average price of capital than those of

conventional ones.

6.2.2. Small Business Finance of Indonesian commercial banks

The three different models examined in this thesis reside in the output components

where in model 2, total finance is broken down in to small business finance (SBF) and

other finance (OF). Table 6.8 presents the profile of small business finance undertaken

by Indonesian commercial banks classified based on ownership and operation:

Table 6.8. Banks’ Small Business Finance by Different Ownership & Operation

(2002-2010)

OWNERSHIP Types: OPERATIONAL Systems:

SBF-SOB SBF-PB SBF-JVB SBF-LGOB SBF-FB SBF-Conv SBF-Sharia

Obs. 36 513 135 234 81 981 18

Mean

(IDR Million) 16,800,000 485,770 27,689 615,106 22,691 997,652 1,443,998

Std Dev

(IDR Million) 15,900,000 1,344,016 73,873 961,757 111,866 4,437,158 1,493,210

Minimum

(IDR Million) 2,883,722 235 0 7,526 0 0 174,114

Maximum

(IDR Million) 75,400,000 13,100,000 558,277 7,280,370 676,037 75,400,000 5,506,198

Avg SBFPCT 0.2593 0.1564 0.0180 0.2709 0.0009 0.1552 0.1766

SBF-SOB: small business finance of State-owned banks; SBF-PB: small business finance of private banks; SBF-JVB: small

business finance of joint-venture banks; SBF-LGOB: small business finance of local government-owned banks; SBF-FB:

small business finance of foreign-owned banks; SBF-Conv: small business finance of conventional banks; SBF-Sharia: small

business finance of Islamic banks.

LGOBs and SOBs successfully maintained their lending (finance) portfolio devoted to

small businesses of around 27.09% and 25.93% respectively over the period 2002 to

138

2010 although the GOI had revoked the regulation of SBF threshold in 200183

. Their

achievements imply their success in utilizing their vast network coverage over the

provinces and districts in Indonesia. Moreover, this also reflects the high involvement of

state-owned banks in providing financing to small businesses in Indonesia. As known

that PT. Bank Rakyat Indonesia (Bank BRI) is one of the state-owned banks that has

given significant contributions to the development of small businesses in Indonesia by

providing financing to micro and small businesses.

In terms of the operational system, Islamic banks (SHARIA) allocated their financing

portfolios to small businesses well above the achievement of their peers from

conventional banks. Islamic banks benefited from the murabaha84

contract in their

financing. The advantage of this scheme compared to the working capital or investment

loans of conventional banks is that in the murabaha contract, a small business client

does not need to provide any collateral in advance whilst conventional banks prevail

collateral as the main pre-condition for loans (Shaban et al., 2014).

Figure 6.1 presents the propensity of Indonesian banks to finance small businesses

across different groups of banks.

Figure 6.1. Banks’ Small Business Finance Proportions (2002-2010)

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks (PB),

local government-owned banks (LGOB), and Islamic banks (SHARIA).

83

The prevailing regulations during the period (BI Regulation Number 3/2/PBI/2001 about Small

Business Finance on January 4, 2001) does not oblige all commercial banks in Indonesia to finance small

businesses a minimum of 22.5 percent anymore, rather it is readjusted based on the ability of each bank.

84

Murabaha (baia or sale) contract is a transaction where a bank buys an asset on behalf of the client and

selling it to the client at a mark-up price.

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

2002 2003 2004 2005 2006 2007 2008 2009 2010

SBF

SBF Percentage (Indonesian Commercial Banks Period 2002-2010)

FB

JVB

LGOB

PB

SOB

SHARIA

139

Although LGOBs are recorded as the largest providers of small businesses over the

period, their SBs financing tended to decline over time during the period 2002 to 2010.

SOBs emerged to be the only one group that constantly provides SBF of above 22.5%

over time during the period. This attainment reflects their high commitment to support

small businesses development in Indonesia over the period. On the other hand, JVBs

and FBs seem to be still reluctant to finance small businesses. They achieved their SBF

of 1.80% and 0.09% on average over time respectively. This might be caused by some

reasons. First, they have less experience in providing financing to small businesses.

Second, small business finance require specific lending technology i.e. relationship

lending which is for some banks considered more costly. Third, as Owualah (1990) and

Petersen and Rajan (1994) explained that the characteristics embedded in small

businesses (asymmetry of information, lack of collateral, and inexperienced

management) have been the major impediments to obtain external financings including

from commercial banks.

Sharia banks are recorded as a group of banks which had an increasing trend of

financing small businesses in Indonesia particularly in the last five years of the study

period. They recorded their financing to small businesses above 20% in 2010 with an

annual average financing of 17.66% over the period 2002 to 2010. The condition

coincides with the study undertaken by Shaban (2014) which states that sharia banks

(Islamic banks) were inclined to finance small businesses in Indonesia during the same

period.

6.2.3. Data of Bank-Specific Variables and Macroeconomic Variables

Bank specific factors are derived from the observed data of 109 commercial banks

consisting of 107 conventional banks and 2 sharia banks in Indonesia for the period

2002-2010, whilst the macroeconomic Indicators were collected from the Bank

Indonesia, Statistics of Indonesia, Department of National Planning, Government of

Indonesia as well as the Asian Development Bank (ADB). Table 6.9 presents the

characteristics of the Indonesian bank specific variables as well as the macroeconomic

Indicators included in the analysis in this chapter.

140

Size of bank is represented by natural logarithm of total assets (LNTA). This variable is

adopted to determine whether a bank could take benefit from the economic of scale of

its operation. It is hypothesised that the bigger a bank the more efficient the bank will be.

The result of the hypothesis testing would also testify whether the wave of mergers

among commercial banks is a right decision to enhance bank efficiency. In addition,

return on assets (ROA) represents banks‟ profitability. On the observed dataset, average

ROA of 109 commercial banks in Indonesia was 2.24 percent over the study period.

This is regarded a successful achievement after they suffered during the crisis of 1997-

98. During the crisis, the majority of banks experienced losses in their operations.

Table 6.9. Bank-Specific and Macroeconomic Variables

Variable Obs. Mean Std. Dev. Min Max

lnta 999 14.9090 1.8381 9.7722 19.8264

roa 999 0.0229 0.0643 -1.53 0.32

car 999 0.2401 0.1584 -0.2230 1.0900

ldr 999 0.7669 0.3741 0.0001 3.3500

npl 999 0.0225 0.0298 0.0001 0.5290

infl 999 8.08 3.94 2.80 17.10

gdpgr 999 5.39 0.64 4.50 6.30

unemp 999 9.19 1.19 7.10 11.20

mktindex 999 1,714 1,020 425 3,704

usdr 999 9,338 498 8,576 10,398

LNTA is the natural logarithm of total assets; ROA is return on assets; CAR is capital

adequacy ratio; NPL is non-performing loans; INFL is annual inflation rate; GDPGR is

Gross Domestic Product at real prices growth rate; UNEMP is annual unemployment rate;

MKTINDEX is the market index, a composite index in Indonesia stock exchange;

USDRATE is United States Dollars (USD) exchange-rate in terms of Indonesian Rupiahs

(IDR).

Capital adequacy ratio (CAR) is a representative for bank capital and this reflects the

ability of the bank to cover all risks including financial and solvency risks. The average

CAR of those 109 banks was 24.01 per cent meaning a strong condition of Indonesian

banks‟ equity over the period 2002 to 2010. In addition, loan to deposits ratio (LDR) is a

proxy for bank liquidity as well as bank lending exposure. The average LDR of

Indonesian banks over the period was 76.69 percent, indicating that the condition of the

banks was quite liquid and the financial intermediary function of the banks increasingly

improved during the period 2002 to 2010 after suffering from the Asian financial crisis

of 1997-98.

141

Non-performing loans (NPL) as an indicator of the bank risk. The average NPL of

Indonesian banks during the period was 2.25 percent which is considered a good

performance85

. This achievement reflects sound and better loan management undertaken

by Indonesian bank management post the crisis of 1997-98.

As these are also applied in the first empirical chapter (chapter five), this chapter also

adopts some macroeconomic indicators namely inflation (INF), Gross Domestic Product

growth (GDPGR), unemployment rate (UNEMP), Indonesia stock market index

(MKTINDEX), United State Dollars against Indonesian Rupiahs (USDRATE). These

factors are considered as the environmental factors. The inclusion of these factors in the

regression models is to investigate their association with Indonesian banks‟ cost

efficiency during the period 2002 to 2010.

6.3. Cost Efficiency of Indonesian Commercial Banks

6.3.1. Average Cost Efficiency of Indonesian Commercial Banks

This section presents an analysis of the cost efficiency of Indonesian commercial banks

over the period 2002-2010. The analysis provides two estimation methods namely

standard pooled estimation (pooled) and Battese and Coelli 1992 (BC92) methods. The

pooled estimation method is a standard-model using pooled data in estimating the

efficiency scores in the stochastic frontier approach (SFA) with the assumption of time

variant. Meanwhile, BC92 is an estimation method to obtain efficiency estimates using

time variant with different assumption of inefficiency (μ is assumed to be truncated

normal distribution).

85

Indonesian bank Authority (BI) determines 5% NPL as cut-off point for a commercial bank to be

classified as a well-performing bank in managing its loans.

142

6.3.1.1. Overall Performance of Indonesian Banks’ Cost Efficiency

Figure 6.2 exhibits the tendency of cost efficiency of Indonesian banks during the

period 2002-2010. The estimated average cost efficiency of the banks across the three

models are represented by the three lines inside the figure.

Figure 6.2. Average Cost Efficiency of Indonesian Banks (Pooled-Model 1,2,3)

CEPOOL1: Average SFA cost efficiency scores obtained by standard pooled method for model 1;

CEPOOL2: Average SFA cost efficiency scores obtained by standard pooled method for model 2;

CEPOOL3: Average SFA cost efficiency scores obtained by standard pooled method for model 3.

The three lines depict a slightly increasing trend of Indonesian banks‟ cost efficiency

over the period 2002 to 2010. There is a disparity in the average cost efficiency obtained

across the three models. These results are caused by different specifications at the

output components of the three models. The average cost efficiency obtained from

model 1 (cepool1) is the highest amongst the three models86

. Model 1 estimates cost

efficiency with the specifications in the output components where total finance was used.

In addition, model 3 records the lowest average cost efficiency. This result is very

sensible considering that the model only accounts for other finance and disregards small

business finance.

It is worth noting that despite that there was an incline tendency of the average cost

efficiency attained by Indonesian banks, they recorded declines over the year 2003,

2004, 2008 and 2010 compared to the preceding year respectively. The decline that

occurred in the period 2003-2004 might have been because of the economic conditions

during the time where the average banks‟ lending rate were trending to decrease from

86

Average cost efficiency of the three models over the period 2002-2010 are 0.8464, 0.8392, and 0.8115

for the model 1, 2 and 3 respectively.

2002 2003 2004 2005 2006 2007 2008 2009 2010

cepool1 0.8394 0.8331 0.8324 0.8415 0.8557 0.8558 0.8556 0.8611 0.8429

cepool2 0.8287 0.8243 0.8253 0.8325 0.8502 0.8491 0.8451 0.8533 0.8445

cepool3 0.8009 0.7956 0.7953 0.8061 0.8232 0.8244 0.8203 0.8273 0.8100

0.7600 0.7800 0.8000 0.8200 0.8400 0.8600 0.8800

Effi

cie

ncy

Sco

re

Cost Efficiency - Pooled - Model 1,2,3

143

18.76% in 2002 to 16.48% and 14.68% in 2003 and 2004 respectively. The decrease of

the lending rate seems to affect bank income as well as bank cost efficiency to decline

during the time. Meanwhile, the decline occurred in 2008 was mainly caused by the

global economic crisis where Indonesian economic growth shrunk from 6.30% in 2007

to 6.00% in 2008. In addition, the decline which occurred in 2010 was caused by the

overheated economy in 2010 reflected by the increasing in the inflation rate from 2.80%

in 2009 to 7.00% in 2010.

Figure 6.3. Average Cost Efficiency of Indonesian Banks (BC92-Model 1,2,3)

BC92_1: Average SFA cost efficiency scores obtained by Battese-Coelli 1992 (BC92) method for model 1;

BC92_2: Average SFA cost efficiency scores obtained by Battese-Coelli 1992 (BC92) method for model 2;

BC92_3: Average SFA cost efficiency scores obtained by Battese-Coelli 1992 (BC92) method for model 3.

Interesting results are obtained from the Battese and Coelli 1992 (BC92) estimation

method (see figure 6.3). This estimation also provides an increasing trend over a period

similar to the standard pooled estimation method. The increasing pattern of SFA cost

efficiency estimate could be observed by the sign of Eta ( )87

which has a positive sign

over all BC2 method (See appendices 19, 21 and 23).

The differences between the BC92 and standard pooled estimation are as follows: first,

the average cost efficiency estimation under BC92 constantly increased over time

during the period. Second, model 2 which accounts for small business finance and other

87 Eta ( ) is the only one parameter estimated in the BC92 approach. Eta reflects the improvement or

decline of the efficiency attainment by observed companies (banks) over time during the study. A positive

Eta constitutes that there has an improvement of the efficiency attainment by the companies (banks) (in

this case in terms of cost efficiency attainment) on average.

2002 2003 2004 2005 2006 2007 2008 2009 2010

bc92_1 0.6616 0.6758 0.6896 0.7029 0.7158 0.7284 0.7405 0.7522 0.7634

bc92_2 0.7437 0.7607 0.7768 0.7920 0.8064 0.8199 0.8326 0.8446 0.8558

bc92_3 0.5844 0.5998 0.6150 0.6298 0.6443 0.6584 0.6722 0.6856 0.6987

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

Effi

cie

ncy

Sco

re

Cost Efficiency - BC92 - Model 1,2,3

144

finance in their outputs becomes the highest average cost efficient amongst the models88

under BC92. This latter result coincides with the findings of the first empirical chapter,

stating that model 2 achieved the highest average technical efficiency amongst the

models (see chapter 5 for details).

The achievement of model 2 to outperform other two models has empirically confirmed

the effect of diversifying the loan portfolio on bank performance in terms of cost

efficiency. The cost efficiency obtained due to diversification might have resulted from

more efficient usage of bank resources related to small business finance. However, it is

important to investigate whether loans and risks affected banks‟ cost efficiency. For the

empirical evidence, the last section of this chapter provides an analysis of the

determinants of banks‟ cost efficiency where loans and risks are adopted as some

explanatory variables in the regression models.

Overall, all estimations obtained using both the standard pooled and BC92 estimation

methods provide similar findings that Indonesian banking cost efficiency enjoyed an

increasing trend over the period 2002 to 2010. These findings supported the argument

that Indonesian banks have taken lessons from past Asian Financial Crisis of 1997-98

and become more resilient over the study period.

Financial Crisis and Cost Efficiency:

It is clearly observed from figure 6.2 and 6.3 that Indonesian banks enjoyed an

increasing trend of cost efficiency levels over the period 2002 to 2010. Those figures

also illustrate Indonesian bank cost efficiency levels attained were relatively stable

during the years 2007-2009. The very tiny decline occurred in 2008. These results

verified the better performance of Indonesian banks over the global financial crisis of

2008 compared to other countries i.e. European and Australian banks (Alzubaidi and

Bougheas, (2012), Forughi and De Zoysa (2012).

The smoother decline in the cost efficiency levels could also be a reflection of the

resilience of Indonesian banks in dealing with the crisis of 2007. These findings also

88

Average cost efficiency of the three models derived from BC92 estimation over the period 2002-2010

are 0.7145 for model 1, 0.8036 for model 2, and 0.6431 for model 3.

145

reflect that Indonesian banks took lessons learnt from the dramatic suffering of the

previous crisis of 1997-98. Emerging markets including the Indonesian banks were

more capitalised compared to European banks during the crisis in 2007 (Fethi et al,

2012). The high level of capitalisation has played an important role in absorbing the

effect of the crisis. Due to the success of the GOI in implementing the numbers of

various programmes [by increasing bank capital, improving bank competition and

efficiency (see the section 2.2.3 and 2.2.4)], these have resulted in the resilience of

Indonesian banks to successfully get through the financial crisis of 2007.

6.3.1.2. Ownership and Banks’ Cost Efficiency

Figure 6.4 displays the different performance of Indonesian banks cost efficiency across

different ownership based on the standard pooled (pool) and Battese & Coelli 1992

(BC92) estimation methods for model 1, 2, and 3 respectively. It seems that the

disparity of the performance is more observable on the BC92 method rather than those

of the pooled method. For instance, for model 1, the range of the efficiency score for

BC92 is 0.2285 (from 0.8903-0.6618) while for the pooled method is 0.0237 (from

0.8606-0.8370). Similar results are also applied for models 2 and 3. These different

results are possible due to different methods applied. This study adopts both methods in

the analysis of the findings and conclusions.

Figure 6.4. Cost Efficiency of the Indonesian Banks: By Different Ownership

CEPOOL1: Average SFA cost efficiency scores under standard pooled method for model 1; CEPOOL2: Average

SFA cost efficiency scores under standard pooled method for model 2. CEPOOL3: Average SFA cost efficiency

scores under standard pooled method for model 3. BC92_1: Average SFA cost efficiency scores under Battese-Coelli

1992 (BC92) method for model 1; BC92_2: Average SFA cost efficiency scores under Battese-Coelli 1992 (BC92)

method for model 2; BC92_3: Average SFA cost efficiency scores under Battese-Coelli 1992 (BC92) method for

cepool1 cepool2 cepool3 bc92_1 bc92_2 bc92_3

FB 0.8606 0.8445 0.8365 0.8903 0.8626 0.8907

JVB 0.8561 0.8228 0.8389 0.8237 0.8108 0.8119

LGOB 0.8546 0.8521 0.8083 0.7017 0.8205 0.5696

PB 0.8370 0.8354 0.8025 0.6618 0.7813 0.5970

SOB 0.8590 0.8593 0.8004 0.7436 0.8517 0.5890

- 0.2000 0.4000 0.6000 0.8000 1.0000

Effi

cie

ncy

Sco

re

Average Cost Efficiency - Model 1,2,3 (By Ownership)

146

model 3. Banks’ Ownership: State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB),

private banks (PB), local government-owned banks (LGOB).

The ranking of Indonesian banks‟ cost efficiency based on their ownership and

operations during the study period is presented in the following table:

Table 6.10. Ranking of Indonesian Banks Cost Efficiency (2002-2010)

Ownership: POOL1 Rank POOL2 Rank POOL3 Rank BC92_1 Rank BC92_2 Rank BC92_3 Rank

FB 0.8606 1 0.8445 3 0.8365 2 0.8903 1 0.8626 1 0.8907 1

JVB 0.8561 3 0.8228 5 0.8389 1 0.8237 2 0.8108 4 0.8119 2

LGOB 0.8546 4 0.8521 2 0.8083 3 0.7017 4 0.8205 3 0.5696 5

PB 0.8370 5 0.8354 4 0.8025 4 0.6618 5 0.7813 5 0.5970 3

SOB 0.8590 2 0.8593 1 0.8004 5 0.7436 3 0.8517 2 0.5890 4

System

Operation: POOL1 Rank POOL2 Rank POOL3 Rank BC92_1 Rank BC92_2 Rank BC92_3 Rank

CONV 0.8469 1 0.8394 1 0.8124 1 0.7155 1 0.8033 2 0.6449 1

SHARIA 0.8190 2 0.8289 2 0.7629 2 0.6576 2 0.8182 1 0.5491 2

FE Operation: POOL1 Rank POOL2 Rank POOL3 Rank BC92_1 Rank BC92_2 Rank BC92_3 Rank

FEB 0.8473 1 0.8392 2 0.8152 1 0.7218 1 0.8079 1 0.654 1

Non FEB 0.8432 2 0.8394 1 0.7978 2 0.6879 2 0.7882 2 0.6039 2

For the ownership, they compose of state-owned banks (SOB), foreign-owned banks (FB), joint-venture banks

(JVB), private banks (PB), and local government-owned banks (LGOB). For the system operation, they consist of

conventional banks (CONV) and islamic banks (SHARIA). For the foreign exchange (FE) operation, they include

two groups of banks, namely foreign exchange banks (FEB) and non foreign exchange banks (Non FEB).

Based on ownership criteria, the results expose that foreign banks (FBs) emerge as the

most cost efficient banks in the Indonesian banking sector (Table 6.10). The FBs ranked

first at four criteria [the standard pooled method for model 1 (pool1), and BC92 method

for model 1, 2, and 3 respectively (bc92_1, bc92_2, and bc92_3)]. It is then followed by

SOBs and JVBs which ranked first at one criterion (pool2 and pool3) respectively.

Lastly, private banks (PBs) ranked as the least cost efficient according to the results

obtained from all criteria evaluated. They recorded the lowest rank at three criteria

(pool1, bc92_1 and bc92_2) and the fourth rank at two criteria (pool2 and pool3).

The success of FBs to be the most cost-efficient banks coincides with some empirical

evidence that FBs tend to outperform other group of banks in terms of efficiency [See

Bonin et al (2005) for the case of eleven transition countries in Europe and Gardener et

147

al (2011) for the case of Southeast Asian banking]. It is worthwhile to explain that the

sound performance of FBs does not come from their activities in loan portfolio

diversification given the fact that they only posed at the third rank at the standard

pooled method for model 2 (pool2)89

. These results also coincide with the findings in

chapter 5 of this thesis. Their highest ranked performance seems to be derived from

their lower investment in branches and number of employees (i.e. factors of production)

as well as their competitive cost of funds compared to SOBs, LGOBs and PBs.

In addition, SOBs posed the first rank at one criterion (the standard pooled method for

model 2). This result suggests that their deeply involvement in small business finance

has resulted to be the most cost efficient banks in that criterion. These findings coincide

with the results of the DEA technical efficiency (see chapter five) as well as the

evidence provided by Fethi et al. (2011) which found that state-owned banks were the

most efficient banks in Egypt. Meanwhile, private banks experienced a controversial

performance by achieving the lowest SFA cost efficiency on average. These findings

dissatisfy the belief that private banks operate more professionally than their peers from

state-owned banks. These findings are different from the study results of Margono, et al

(2010).

In terms of their system operations, conventional banks (CONV) were more efficient in

all criteria (models and methods) evaluated, except for the BC92 method of model 2

(bc92_2) where Islamic banks (SHARIA) achieved a slightly higher cost efficient than

their peers of CONV. These findings are very sensible considering that conventional

banks have been more established than Islamic banks in terms of their experience in the

industry. These results coincide with the findings of Hamiltona et al (2010) that Islamic

banks were less cost efficient in the Jordanian banking sector.

Finally, in terms of foreign exchange services, foreign exchange banks (FEB) constantly

obtained higher efficiency scores than their peers from non-foreign exchange banks

(NFEB), except for the pooled method for the model 2 (pool2). Hence, we can derive

the conclusion that foreign exchange banks benefited from the economies of scope by

89

Model 2 splits total finance into small business finance and other finance in outputs for the calculation

of cost efficiency estimates.

148

providing foreign exchange transaction in addition to domestic currency one in their

operations.

The Evolution of Indonesian Banks’ Cost Efficiency over the Period 2002-2010:

The evolution of average cost efficiency attained by different groups of ownerships and

different operational systems across Indonesian commercial banks during the period

2002 to 2010, can be observed in the figure as follows:

Figure 6.5. The Evolution of Indonesian Banks’ Cost Efficiency – Pooled - Model 1

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks

(PB), local government-owned banks (LGOB), conventional banks (CONV) and islamic banks

(SHARIA).

Figure 6.5 demonstrates the fluctuation of their cost efficiency over the period 2002 to

2010. All banks underwent fluctuations with an average cost efficiency was over 0.800.

FBs and SOBs experienced the highest average cost efficiency during the period with an

average of 0.861 and 0.859 respectively. FBs‟ cost efficiency reached their peak

performance of 0.907 in the year 2009 but turned out to be steeply down in 2010. SOBs

did very well in the first two years, but systematically deteriorated on average in the

following years. Although PBs recorded attaining the lowest cost efficiency level on

average, they fortunately had an upward trend in their cost efficiency attainment over

time during the period. It is worthwhile to note that all banks experienced a slight

decrease in 2008 except the LGOBs and SOBs. These reflect that all government-owned

banks (local and central government banks) in Indonesia performed better and more

2002 2003 2004 2005 2006 2007 2008 2009 2010

FB 0.8808 0.8447 0.8764 0.8336 0.8736 0.8776 0.8678 0.9077 0.7835

JVB 0.8537 0.8501 0.8222 0.8712 0.8779 0.8697 0.8668 0.8610 0.8327

LGOB 0.8310 0.8365 0.8419 0.8419 0.8582 0.8631 0.8708 0.8764 0.8720

PB 0.8293 0.8223 0.8217 0.8336 0.8470 0.8462 0.8444 0.8469 0.8411

SOB 0.8920 0.8756 0.8628 0.8587 0.8400 0.8456 0.8469 0.8599 0.8493

CONV 0.8388 0.8340 0.8338 0.8424 0.8558 0.8564 0.8562 0.8617 0.8430

SHARIA 0.8732 0.7835 0.7585 0.7964 0.8486 0.8241 0.8221 0.8305 0.8344

0.6500

0.7000

0.7500

0.8000

0.8500

0.9000

0.9500

Co

st E

ffic

ien

cy

Cost efficiency of Model 1: Pooled

149

resilient during the crisis of 2007-08.

In addition, conventional banks experienced a slight increase in the trend of their cost

efficiency and they performed better in terms of cost efficiency performance compared

to Sharia banks over the period. This means that their management successfully

managed in employing their experiences in the industry to increase their cost efficiency

level.

Figure 6.6. The Evolution of Indonesian Banks’ Cost Efficiency – BC92 - Model 1

State-owned banks (SOB), foreign-owned banks (FB), joint-venture banks (JVB), private banks

(PB), local government-owned banks (LGOB), conventional banks (CONV) and islamic banks

(SHARIA).

In contrast to the data pooled estimation, the BC92 generates the results that all groups

experienced an increasing trend in their average cost efficiency levels over time during

the period. FBs are the most cost efficient banks and were followed by JVBs and SOBs

in the second and third places. Similar to the previous results, PBs demonstrated the

lowest cost efficiency level although the trend was positive. In addition, Conventional

banks experienced better average cost efficiency compared to Sharia banks over the

period.

The evolutions of Indonesian banks‟ cost efficiency under model 2 and 3 respectively

are presented on figures 6.7, 6.8, 6.9, and 6.10 (see the appendices 10-13).

2002 2003 2004 2005 2006 2007 2008 2009 2010

FB 0.8664 0.8730 0.8794 0.8854 0.8912 0.8967 0.9019 0.9069 0.9116

JVB 0.7908 0.7997 0.8082 0.8166 0.8246 0.8324 0.8399 0.8471 0.8541

LGOB 0.6453 0.6605 0.6752 0.6894 0.7032 0.7165 0.7294 0.7418 0.7537

PB 0.6005 0.6169 0.6328 0.6482 0.6633 0.6778 0.6920 0.7056 0.7188

SOB 0.6933 0.7069 0.7201 0.7328 0.7451 0.7569 0.7682 0.7791 0.7896

CONV 0.6629 0.6770 0.6907 0.7040 0.7169 0.7294 0.7414 0.7531 0.7643

SHARIA 0.5942 0.6111 0.6277 0.6437 0.6592 0.6743 0.6888 0.7029 0.7164

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

Co

st E

ffic

ien

cy

Cost efficiency of Model 1: BC92

150

6.3.1.3. Size and Banks’ Cost Efficiency

This section presents the average cost efficiency of Indonesian banks under the standard

pooled and BC92 methods for the three models over the period 2002 to 2010. Banks are

classified into four bank peer groups based on their asset size. Bank peer group 1 covers

banks‟ asset size of less than IDR 10 trillion. Bank peer group 2 includes banks with

their asset size of more than IDR 10 trillion and less than and equal to IDR 50 trillion.

Bank peer group 3 composes of banks with asset size of greater than IDR 50 trillion and

less than and equal to IDR 100 trillion. Bank peer group 4 consists of banks with asset

size of more than IDR 100 trillion. The distribution of the group is presented in the table

6.11.

Table 6.11. Distribution of Average Cost Efficiency By Bank Peer Group


Average Cost Efficiency (Pooled) MODEL 1 for each group 0.8459 0.8468 0.8443 0.8539 0.8464

Average Cost Efficiency ( bc92_1) MODEL 1 for each group 0.7049 0.7439 0.7104 0.7358 0.7145






Peer Group 1 (Total Assets <= IDR 10 Trillion); Peer Group 2 (Total Assets > IDR 10 Trillion and <= IDR 50Trillion);

Peer Group 3 (Total Assets > IDR 50 Trillion and <= IDR 100 Trillion); Peer Group 4 (Total Assets > IDR 100 Trillion).

Bank peer group 4 (the large banks) experienced the highest average cost efficiency

under the pooled method for model 1, 2, and 3 with an average cost efficiency of 0.8539,

0.8446, and 0.8258 respectively, while bank peer group 3 suffered the lowest average

cost efficiency during the period 2002 to 2010. These results reflect the success story of

the largest banks in Indonesia in managing their operation benefiting from the

economies of scale. PT. Bank Mandiri, PT. Bank BNI, PT. Bank BRI, and PT. Bank

BCA are those of the group members that are consistently marked sound performance

over the period. These results also coincide with the findings of Shaban (2008), Demir

et al (2005) and Isik and Hassan (2003b) in the case of Egypt and Turkey. They found

that large banks were more efficient compared to other sizes of bank.

151

On the other hand, there is a disparity of the results obtained from the BC92 method

revealing that bank peer group 2 recorded the most cost efficient banks for model 1 and

3, whilst for model 2, bank peer group 3 ranked first during the period.

Figure 6.11. Cost Efficiency – Pooled - Model 1 (by Bank Peer Group)


Figure 6.11 displays the evolution of their cost efficiency levels attained by their groups

for model 1. Bank peer group 3 and 4 experienced a declining trend of the average cost

efficiency while bank peer group 2 and 1 underwent an increasing trend over the period

2002-2010. However, bank peer group 4 came to be more stable with the attainment of

their average cost efficiency level of above 0.8400. This made the group become the

most cost-efficient banks during the period.

Figure 6.12. Cost Efficiency – BC92 - Model 1 (by Bank Peer Group)


Meanwhile, under the BC92 estimation method, peer group 1, 2 and 4 enjoyed the

0.7800

0.8000

0.8200

0.8400

0.8600

0.8800

0.9000

0.9200

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Co

st E

ffic

ien

cy

Average Cost Efficiency Pooled-Model 1 by Bank Peer Group

Peer Group 1 (Total Assets <= IDR 10Trillion)

Pee Group 2 (Total Assets > IDR 10Trillion and <= IDR 50Trillion)

Peer Group 3 (Total Assets > IDR 50Trillion and <= IDR 100 Trillion)

Peer Group 4 (Total Assets > IDR 100Trillion)

0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Co

st E

ffic

ien

cy

Average Cost Efficiency BC92-Model 1 by Bank Peer Group

Peer Group 1 (Total Assets <= IDR10 Trillion)




152

increasing trend of their average cost efficiency over the period whilst peer group 3

experienced a decline trend. Bank peer group 2 reached the highest average cost

efficiency level of 0.7439 while the lowest average cost efficiency level was undergone

by bank peer group 1 with an average level of 0.7049.

The other results obtained for models 2 and 3 are presented in figure 6.13, 6.14, 6.15,

and 6.16 (see the appendices 14-17).

6.4. Determinants of Cost Efficiency

This section reveals the determinants of cost efficiency of Indonesian banks during the

period 2002-2010. There are three models (model 1, model 2 and model 3) examined

with the two different methods (the pooled data method and the BC92 method). The

cost efficiency estimates obtained from the two methods are dependent variables which

are then regressed with banks‟ specific factors as well as macroeconomic Indicators as

their explanatory variables.

This thesis uses TOBIT-regression to reveal the determinants of Indonesian banks‟ cost

efficiency given the fact efficiency scores are constrained between zero and one. The

preceding studies also use this method to examine the determinants of bank efficiency.

[See i.e. Chang et.al (1998); Nikiel and Opiela (2002); Casu and Molyneux (2003);

Havrylchyk (2005); Grigorian and Manole (2006); Ariff and Can (2008); Sufian (2010);

Gardener et.al (2011)].

The standard Tobit-regression model is defined as follows:

Where is a latent variable, is cost efficiency estimate obtained by SFA parametric

approach. is the set of parameters to be estimated. is the error term.

The equation of the regression is specified as follows:

153

= b0 + b1 + b2 + b3 + b4 + b5 + b6 +

b7 + b8 + b9 + b10 .

Where:

The dependent variable is which denotes the SFA cost efficiency estimate of i-th

bank in period t. is the natural logarithm of total assets, a proxy for bank size.

is return on assets, a proxy for bank profitability. is capital adequacy

ratio, represents bank capital. This ratio also reflects bank provisions to cover bank

solvency risk. is loan deposit ratio, a proxy of bank intermediation as well as

bank liquidity. is non-performing loans, represents bank risk. is an

annual inflation rate. This reflects the overall percentage increase in the consumer price

index for all goods and services in Indonesia. is gross domestic product at real

price growth rate. This is a sign of the growth of Indonesia‟s total goods and services

produced in Indonesia in annual term. is an annual unemployment rate in

Indonesia. is the stock composite index in the Indonesia Stock Exchange

(ISE). is United State Dollar exchange rate, in terms of Indonesian Rupiah

(IDR). is error term.

The sample dataset covers 109 banks consisting of around 93 per cent of the population

of Indonesian banking Industry, with the 9-year study period from 2002-2012, resulting

999 total observations. The criteria for bank inclusion in this study are those banks

which have been in existence over the period 2002 to 2010 and their annual financial

statements data are complete over the period.

Table 6.12. Correlation Matrix of Independent Variables-1

lnta roa car ldr npl infl gdpgr unemp mktindex

lnta 1

roa 0.0742 1

car -0.2228 0.0426 1

ldr -0.0116 0.0331 0.1126 1

npl -0.1030 -0.4565 0.0877 0.0831 1

infl -0.0523 0.0095 -0.0525 -0.0061 0.0346 1

gdpgr 0.1477 -0.048 0.0275 0.1198 -0.1518 0.3182 1

unemp -0.1646 0.0756 -0.0521 -0.1345 0.0784 0.4866 -0.1168 1

mktindex 0.2200 -0.069 0.0615 0.1461 -0.168 -0.3461 0.5662 -0.6426 1

usdrate 0.1147 -0.027 0.0051 0.1062 -0.0829 0.1306 -0.1054 -0.2222 0.1913



154

MKTINDEX is the market index, a composite index in Indonesia stock exchange; USDRATE is United States Dollars (USD)

exchange-rate in terms of Indonesian Rupiahs (IDR).

Table 6.12 presents the correlation matrix amongst independent variables. This aims to

justify that all explanatory variables have low correlation between each other. In other

words, this is an effort to avoid a multicollinearity problem. It is observed from the table

that the variable MKTINDEX has a high correlation both with GDPGR (0.5662) and

with UNEMP (-0.6426). The significant test for the correlations can be observed on

appendix 26. To solve the problem of this multicollinearity, the MKTINDEX variable

is then dropped from the model.

Table 6.13. Correlation Matrix of Independent variables-2 lnta roa car ldr npl infl gdpgr unemp usdrate

lnta 1.0000

roa 0.0361 1.0000

car -0.2060 -0.1484 1.0000

ldr -0.0115 0.0504 0.1104 1.0000

npl -0.1031 -0.3794 0.3160 0.0831 1.0000

infl -0.0524 0.0156 -0.0481 -0.0061 0.0346 1.0000

gdpgr 0.1478 -0.0175 0.0388 0.1198 -0.1518 0.3182 1.0000

unemp -0.1646 0.0354 -0.0686 -0.1345 0.0784 0.4866 -0.1168 1.0000

usdrate 0.1147 -0.0008 -0.0029 0.1062 -0.0829 0.1306 -0.1054 -0.2222 1.0000

LNTA is the natural logarithm of total assets; ROA is return on assets; CAR is capital adequacy ratio; NPL is non-

performing loans; INFL is annual inflation rate; GDPGR is Gross Domestic Product at real prices growth rate; UNEMP is

annual unemployment rate; USDRATE is United States Dollars (USD) exchange-rate in terms of Indonesian Rupiahs

(IDR).

The table 6.13 constitute the results that there is no significant association between these

independent variables, hence, it can be concluded thta there is no multicollinearity in the

model. All the correlation coefficients are below 0.5. The determinants of Indonesian

banks‟ cost efficiency of the model 1 is presented in table. 6.14.

The regression models present CEPOOL1 (Cost efficiency estimates obtained from the

standard-pooled data method for Model 1) and BC92-1 (cost efficiency estimates under

Battese-Coelli 1992 method for Model 1) as dependent variables. In general, the results

of the first regression (CEPOOL1) suggest that ROA, CAR, LDR, and NPL are of the

bank specific factors which significantly determine the cost efficiency of Indonesian

banks over the period 2002-2010. Meanwhile, INFL, GDPGR, and USDR are of the

macroeconomic indicators which recorded as significant factors for the Indonesian

banks‟ cost efficiency over the period.

155

Tabel 6.14. Determinants of banks cost efficiency – Model 1 (1) (2)

VARIABLES cepool1 bc92_1

lnta 0.000636 0.00890***

(0.00161) (0.00155)

roa 0.275*** 0.00105

(0.0260) (0.00981)

car 0.0414*** -0.000495

(0.0123) (0.00517)

ldr 0.0289*** 0.00952***

(0.00554) (0.00241)

npl -0.126** 0.0472**

(0.0607) (0.0236)

infl -0.00134*** -0.00291***

(0.000502) (0.000195)

gdpgr 0.00627** 0.0320***

(0.00280) (0.00127)

unemp 0.00244 -0.00576***

(0.00159) (0.000606)

usdr 1.37e-05*** 3.08e-05***

(3.33e-06) (1.40e-06)

Constant 0.628*** 0.189***

(0.0463) (0.0247)

sigma_u 0.0274*** 0.124***

(0.00242) (0.00838)

sigma_e 0.0445*** 0.0163***

(0.00106) (0.000387)

Observations 999 999

Number of id 111 111


*** p<0.01, ** p<0.05, * p<0.1

The dependent variables are: CEPOOL1: SFA cost efficiency scores

obtained by the standard pooled data method for Model 1; and BC92-1:

SFA cost efficiency scores obtained by Battese-Coelli 1992 method for

Model 1.

The explanatory variables are: LNTA is the natural logarithm of total

assets; ROA is return on assets; CAR is capital adequacy ratio; NPL is

non-performing loans; INFL is annual inflation rate; GDPGR is Gross

Domestic Product at real prices growth rate; UNEMP is annual

unemployment rate; USDRATE is United States Dollars (USD) exchange-

rate in terms of Indonesian Rupiahs (IDR).

On the other hand, the findings derived from the BC92 regression reveal that LNTA,

LDR, and NPL significantly affect the cost efficiency of Indonesian banks over the

period. In addition, all macroeconomic variables (INFL, GDPGR, UNEMP, and USDR)

also impact on the cost efficiency of Indonesian banks over the period.

Size of bank which is represented by LNTA has a positive impact on Indonesian banks‟

cost efficiency in the second regression (BC92) with a confidence level of 99%. This

156

result suggests that the bigger the size of a bank, the more efficient the bank will be.

This finding complies with the SCP hypothesis, stating that as the size of a company‟s

operation increases, the company will operate more efficiently due to the economies of

scale and benefit more profit. This result also verifies the strategic policies exerted by

the Indonesian bank authority to merge commercial banks in Indonesia in regards to

establish many sturdy banks which are more resilient to crises. LNTA has also a positive

association with cost efficiency under the first regression (CEPOOL1) although the

association is not significant. The positive association between bank size and bank

efficiency coincides with the previous results (Yildirim and Philippatos (2007), Andries

(2011), Vu and Turnel, (2011).

ROA as the proxy for bank profitability has a positive impact on Indonesian banks‟ cost

efficiency in the first regression with a confidence level of 99%. This implies that the

profitability gained by Indonesian banks has a significant contribution on the

enhancement of the cost efficiency level of the banks during the period. Meanwhile in

the second regression, ROA has also a positive impact on Indonesian bank cost

efficiency although the the relationship is not significant. This finding is in opposite to

the findings of Yildirim and Philippatos (2007), Andries (2011), and Thangavelu and

Findlay, C. (2013). They did not find a significant association between ROA and bank

efficiency.

Bank capital which is represented by CAR has also affected Indonesian banks‟ cost

efficiency with a positive direction in the first regression. This variable is significant

with an alpha of 1%. This implies that capital adequacy is very important for banks to

support their operation to be more cost efficient. It is worthwhile to explain that the high

compliance of Indonesian banks in terms of capital adequacy fulfilment has a positive

impact on the attainment of their cost efficiency level90

. This finding coincides with

previous studies [Yildirim and Philippatos (2007), Andries (2011); Thangavelu and

Findlay, C. (2013)].

90

The average CAR of Indonesian commercial banks over the study period was 24.01 per cent (it was

higher than the minimum CAR imposed by the Indonesian bank authority of 8 per cent).

157

Meanwhile bank liquidity as represented by LDR has also a significant impact on

Indonesian bank cost efficiency with a positive association. It occurred impressively in

both approaches. These findings suggest the importance on bank liquidity as well as the

degree of lending exposure maintained by banks in enhancing cost efficiency. These

findings witnessed the success of Indonesian banks to manage their expenses when their

loan portfolio increased over the study period. Average LDR across all Indonesian

commercial banks during the period was 76.69 per cent. There is still a chance for

Indonesian commercial banks to enhance the finance proportion in their investment

portfolios since the average LDR during the period was less than 80 percent. It is also

important to state that an increase in lending exposure should always be accompanied

by the implementation of prudential principles in processing and monitoring the loans in

order to guarantee the performing loan quality. Loan quality management is very critical

for bank management.

NPL as a proxy for bank credit risk has also an impact on Indonesian bank cost

efficiency in both regressions, although the directions of associations are different. NPL

has a negative impact on bank cost efficiency in the first regression with a confidence

level of 95%, but it significantly affected bank cost efficiency in the second regression

with a positive sign. Based on the value of the coefficient of the regression of that

variable, it is obvious that the results in the first regression are stronger than those of the

second. These findings are different to the findings of Andries (2011), Thangavelu and

Findlay (2013) in that they did not find a significant association between non-

performing loans and bank efficiency.

It is also worth noting that from the macroeconomic indicators, INFL, GDPGR, and

USDR (USD rate in terms of IDR/Indonesian Rupiahs) are significant factors across the

two methods. INFL has a negative association with cost efficiency, while GDPGR and

USDR have positive associations with the cost efficiency of Indonesian banks. These

findings are very sensible, since if the inflation rises, bank would experience an increase

in costs or expenses, and it finally makes the bank less efficient, and vice versa.

GDPGR as the indicator of economic growth has a positive association with cost

efficiency. This means that the higher the economic growth in Indonesia, the

commercial banks in the country experience more cost efficient, and vice versa.

158

In general, it can be concluded that from the regressions of the model 1 across the two

methods examined, the variables LNTA, ROA, CAR, LDR, and NPL are bank specific

factors that significantly affected Indonesian bank efficiency over the period 2002-2010

at least in one regression. These results could also be used as an important

recommendation for all management of banks that the size of a bank, the capital

adequacy, the liquidity or the finance expansion indicator, and the credit risk

management are the important keys for enhancing efficiency. These findings are also

important for policy makers, particularly for the Indonesian commercial bank authority

in setting regulations regarding those significant factors91

.

Table 6.15 demonstrates the determinants of Indonesian banks‟ cost efficiency over the

model 2. The table witnessed ROA and LDR as the significant factors determining the

cost efficiency of Indonesian banks over the period 2002-2010. Meanwhile, INFL,

GDPGR, and USDR (of the macroeconomic indicators) also determine Indonesian bank

cost efficiency over the period. These results are derived from the pooled data method.

On the other hand, for the BC92 method, LNTA and LDR significantly determine the

cost efficiency of Indonesian banks over the study period. Meanwhile (of the

macroeconomic variables), all factors (INFL, GDPGR, UNEMP, and USDR) are

significant in predicting the cost efficiency of the Indonesian banks over the period.

Similar to model 1, LNTA as the proxy for the size of bank has been a significant factor

for Indonesian bank efficiency with a positive sign in the second regression (BC92

method). LNTA is significant for the second regression with a confidence level of 99%.

This result implies that the bigger the size of a bank, the bank will be more cost efficient.

This finding complies with the SCP hypothesis that if the size of a company‟s operation

increases, the company will benefit from economic of scale and exhibit more efficient.

This result also fits with the current policy of the Indonesian bank authority to merge

commercial banks in regards to establishing strong and sturdy banks and also be

resilient to crises. LNTA has also a positive association with cost efficiency in the first

regression (the Pooled data method) albeit it is not significant.

91

The policies that relate to those factors i.e. bank mergers and acquisitions, capital adequacy requirement,

and bank liquidity and risk management.

159



lnta 0.000920 0.0122***

(0.00209) (0.00186)

roa 0.308*** -0.00695

(0.0314) (0.0121)

car 0.0219 -0.00454

(0.0150) (0.00636)

ldr 0.0233*** 0.0102***

(0.00678) (0.00297)

npl -0.0806 -0.0182

(0.0732) (0.0290)

infl -0.00166*** -0.00321***

(0.000605) (0.000239)

gdpgr 0.00977*** 0.0346***

(0.00339) (0.00155)

unemp 0.00129 -0.00509***

(0.00191) (0.000746)

usdr 1.27e-05*** 3.33e-05***

(4.02e-06) (1.72e-06)

Constant 0.627*** 0.192***

(0.0561) (0.0282)

sigma_u 0.0368*** 0.102***

(0.00307) (0.00691)

sigma_e 0.0534*** 0.0201***

(0.00127) (0.000477)




*** p<0.01, ** p<0.05, * p<0.1




Model 2.







Also, similar to the results of model 1, ROA affected bank cost efficiency in the first

regression (the pooled data method) with a positive sign. It implies the importance of

banks to obtain profit from the operation since the higher profitability of a bank would

lead that bank to be more cost efficient.

Different with the findings of model 1, CAR is not recorded as a significant factor

affecting Indonesian banks‟ cost efficiency during the period. These results are

160

interesting given the fact that in model 2 the output components were broken down to

be small business finance and other finance. This might be caused by the advantage of

the diversifications which lead banks‟ management not necessary to provide more

capital since the risk embedded on the small business finance could be considered lower.

LDR has impressively affected the Indonesian bank cost efficiency in both regressions

with a positive association. These findings imply the importance of lending exposure on

the enhancement of bank cost efficiency. In other words, the additional proportion of

credit or finance over the deposit could enhance bank cost efficiency. Average LDR

across all Indonesian commercial banks during the period was 76.69 percent. There is

still a chance for Indonesian commercial banks to enhance the finance proportion in

their investment portfolios since the average LDR during the period was less than 80 per

cent. It is also worth noting that banks should also maintain the quality of their finance

portfolios in order to achieve low non-performing loans (NPL less than 5%). The

average NPL for this period was 2.25% which is considered good. Unfortunately, NPL

was not recorded as significant factors affecting banks‟ cost efficiency in this model

although the direction of the association is in accordance with the hypothesis. The

association between NPL with cost efficiency is negative for both regressions.

Similar to the first model, from the macroeconomic variables, INFL, GDPGR, and

USDR (USD rate in terms of IDR/Indonesian Rupiahs) are significant factors across the

two approaches. INFL has a negative association with the cost efficiency, while GDPGR

and USDR have positive associations with the cost efficiency of Indonesian banks.

These findings are very sensible, since if inflation rises, banks would exert increasing

costs or expenses, and it finally makes the bank less efficient, and vice versa. GDPGR

as the indicator of economic growth has a positive association with cost efficiency. It

means that with a higher level of the economic growth, banks would also undergo the

higher efficiency due to the growing economic activities in the real sectors. Meanwhile

UNEMP has significantly affected the cost efficiency of the second approach (BC92)

with the negative sign.

It is concluded from the regressions of model 2, that the variable of LNTA, ROA, and

LDR are of bank specific factors that significantly affected the Indonesian bank cost

efficiency over the period 2002-2010 at least in one regression. These results lead to the

161

important recommendation either for banks‟ management as well as the policy makers

that the size of a bank, the profitability of a bank, and the liquidity or the finance

expansion indicator are the important keys for enhancing cost efficiency particularly for

the model considering the diversifications of the financing or loans.

Table 6.16 depicts the determinants of Indonesian bank cost efficiency over the model 3

when the model only accounts for other finance as one of the output and disregards

small business finance.

For the standard pooled data method, ROA, CAR and LDR are recorded as the

significant factors determining the cost efficiency of Indonesian banks over the period

2002-2010 from the bank specific factors. Meanwhile, of the macroeconomic indicators,

INFL, GDPGR, and USDR each has also an impact on Indonesian bank cost efficiency

over the period. On the other hand, for the BC92 method, it is verified that LNTA, LDR,

and NPL each has a significant impact on the cost efficiency of Indonesian banks over

the study period. While of all the macroeconomic indicators, all factors (INFL, GDPGR,

UNEMP, and USDR) each has an impact on the cost efficiency of the Indonesian banks

over the period.

LNTA has a positive impact on Indonesian bank cost efficiency with a positive sign in

the second regression (BC92 method). LNTA is significant for the second regression

with a confidence level of 99%. This result also supports the SCP hypothesis that the

company would experience more efficiency in operation when the size of the company

increases. LNTA has also a positive association with cost efficiency in the first

regression (the Pooled data method) although the association is not significant.

ROA has also an impact on the bank cost efficiency in the first regression (the pooled

data method) with a positive sign. This finding suggests the importance of the

enhancement of the ROA attainment since this will lead banks to be more cost efficient.

In the second regression (the BC92 method), ROA has also a positive association with

cost efficiency, but the relationship is not significant.

162



lnta 0.000466 0.00993***

(0.00229) (0.00169)

roa 0.254*** 0.00472

(0.0313) (0.0106)

car 0.0711*** 0.00193

(0.0153) (0.00559)

ldr 0.0137** 0.0115***

(0.00696) (0.00261)

npl 0.0109 0.0707***

(0.0733) (0.0255)

infl -0.00188*** -0.00323***

(0.000602) (0.000210)

gdpgr 0.0121*** 0.0357***

(0.00341) (0.00137)

unemp 0.00287 -0.00677***

(0.00190) (0.000655)

usdr 1.80e-05*** 3.44e-05***

(4.02e-06) (1.52e-06)

Constant 0.526*** 0.0588**

(0.0565) (0.0277)

sigma_u 0.0427*** 0.153***

(0.00339) (0.0103)

sigma_e 0.0530*** 0.0176***

(0.00126) (0.000418)




*** p<0.01, ** p<0.05, * p<0.1




Model 3.







CAR has also affected Indonesian bank efficiency with a positive direction in the first

regression. This result implies the importance of capital in the cost efficiency attainment

when banks ignored small business finance in their portfolio financing. CAR has been

significant for the first regression with a confidence level of 99%. CAR also has a

positive association with cost efficiency in the second regression albeit not significant.

163

LDR has significantly affected Indonesian bank cost efficiency with a positive

association. This occurred impressively in both methods with a confidence level of 95%

on the first regression and 99% on the second. These findings mean that as much as the

finance exposure increases, the cost efficiency of a bank would also increase, and vice

versa. Average LDR across all Indonesian commercial banks during the period was

76.69 per cent. The average LDR of 76.69 percent is not satisfactory during the period

(less than 80%) and bank managers have the opportunity to increase the proportion of

LDR in regards to stimulate the financial intermediary function of commercial banks.

In addition, NPL has significantly affected bank cost efficiency in the second regression

(BC92 method), with a positive signed association. This result coincides with the

second regression of model 1.

From the macroeconomic indicators, INFL, GDPGR, and USDR are significant factors

across the two regressions. INFL has a negative association with cost efficiency, while

GDPGR and USDR have positive associations with the cost efficiency of Indonesian

banks. These findings are very sensible, since if the inflation rises, banks would exert

increasing costs or expenses that lead to be less cost efficient. GDPGR as the indicator

of economic growth has a positive impact on the banks cost efficiency. This implies that

higher level of the economic growth would lead Indonesian banks to be more cost

efficient. Meanwhile, UNEMP has affected bank cost efficiency in the second

regression with a negative association. This implies an increasing number of

unemployed in the country would lead commercial banks to be less cost efficient.

It is concluded from the regression results across the two methods in the model 2 that

the variables of LNTA, ROA, CAR, LDR, and NPL are of bank specific factors that

significantly affected Indonesian bank efficiency over the period 2002-2010 at least in

one regression. These results could also be used as an important recommendation for all

managers of banks that the size of a bank, the bank profitability, the capital adequacy,

the liquidity or finance expansion, and the credit risk management are the important

keys for enhancing efficiency. These findings are also important for policy makers,

particularly for the Indonesian commercial bank‟s authority to set the proper regulations

that are considered relevant to the factors (i.e. the policies of mergers and acquisitions,

capital adequacy requirement, etc).

164

In general, we can conclude that from all models examined, LDR has emerged as the

only one variable from bank specific factors that constantly affected Indonesian bank

cost efficiency over the study period. This implies that bank loan proportions is the most

important factor affecting cost efficiency of Indonesian banks over the study period.

Meanwhile, in terms of macroeconomic indicators, some variables which consistently

affected Indonesian bank cost efficiency across the three different models are inflation

rate (INFL), GDP Growth (GDPGR), and USD rate (USDR).

6.5. Conclusions

The average cost efficiency of Indonesian commercial banks are likely to increase over

the period 2002-2010. These findings are derived by three models (model 1, model 2,

and model 3) and two different efficiency estimation methods (the Pooled-data standard

method and BC92 method).

In general, FBs (foreign-owned banks) were constantly on the top position across the

three different models in terms of cost efficiency. They were the most efficient banks in

the two methods in model 1 (the Pooled data method and BC92), one method in model

2 (BC92) and one method in model 3 (BC92). SOBs (state-owned banks) experience the

most efficient in one method in model 2 (the Pooled data method), and JVBs (joint

venture banks) were the most efficient also in one method in model 3 (the Pooled data

method).

In model 2, when considering SBF (small business finance) as an output in the

calculation of efficiency, SOBs were the most efficient banks in Indonesia in one

method ( the Pooled data method). This finding strengthens the argument that SOBs

which have PT Bank BRI as the market leader of SBF were successful in employing

their SBF in enhancing their cost efficiency. However, when SBF was excluded (in

model 3), JVBs were the most efficient ones in the same method. CONV (conventional

banks) outperformed SHARIA (Islamic banks) except for the only in one method (BC92)

in model 2 when total business finance was broken down into small business finance

and other finance in the output components. These findings indicate the success of

165

conventional banks using their experience in the industry and confirm the importance of

small business finance experienced by sharia banks. FEB (foreign exchange banks)

were more efficient than Non FEB (Non foreign exchange banks) in all methods across

the three models except for one method (the pooled data method) in model 3.

Large banks (peer group 4) experienced the highest average cost efficiency under the

pooled method for models 1, 2, and 3 during the period 2002 to 2010. These results

reflect the success of the largest banks in Indonesia in managing their operation

benefited from the economies of scale. PT. Bank Mandiri, PT. Bank BNI, PT. Bank BRI

and PT. Bank BCA are those of the group members that are consistently marked sound

performance over the period. These results coincide with the findings of Shaban (2008),

Demir et al (2005) and Isik and Hassan (2003b) for the case of Egypt and Turkey. They

found that large banks were more efficient compared to other sizes of banks.

The findings of examining the determinants of Indonesian commercial banks‟ cost

efficiency over the period 2002-2010 reveal that in general, some variables which

consistently affected Indonesian bank cost efficiency across the three different models

during the period 2002-2010 are Loans to deposits ratio (LDR) as a proxy from the bank

specific factors, and inflation rate (INFL), GDP Growth (GDPGR), and USD rate

(USDR) as the proxies for the macroeconomic variables.

The implications of those results as considered suggestions for policy makers in

Indonesia are as follows: First; it is not a guarantee that privatisation is beneficial. The

case of Indonesian banks has indicated that SOBs (state-owned banks) are much more

efficient than the private ones. Second; Small business finance (SBF) is still very

important to be maintained by commercial banks, considering that it does not only have

the social effects, but also it can enhance the banks‟ cost efficiency. SOBs and Sharia

banks are of the cases that enjoy the success of enhancing efficiency by means of

providing small business finance in their portfolio financing. Third; Liquidity ratio

(LDR) has proven to be the most consistent factors that determine the cost efficiency of

Indonesian banks. These imply that the policies of managing adequate liquidity as well

as loan expansions are very beneficial in the improvement of bank performance in terms

of cost efficiency.

166

7. Chapter seven: The Lending Propensities of Indonesian Commercial Banks over

the Period 2002-2010

7.1. Introduction

This chapter specially discusses the lending propensity of Indonesian banks in terms of

the trend of the total loans (TL) and small business loans (SBL) experienced by

Indonesian commercial banks over the period 2002-2010. The outputs of the study

reveal the propensity of lending facilities provided by Indonesian banks in terms of their

ownership and their size. This study also provides an extensive analysis by exposing the

significant factors affecting bank lending propensity.

The study about the lending propensity of Indonesian banks is motivated by three

reasons. First, the topic of bank lending propensity has been currently one of the

important issues in banking and financial research [See McNulty (2013), Berger and

Black (2011), Rraci (2010) Laderman (2008), Frame et al. (2004), Akhavein et al.

(2004), Berger et al (1998, 2007)]. Second, lending or financing is one of the most

essential activities in Indonesian commercial banks considering that the Indonesian

economy is still very dependent on bank financing. Third, studies about the lending

propensity of commercial banks in emerging economies particularly in Indonesia are

still scarce to date. Hence, this is the first study to examine bank lending propensity in

Indonesia.

The analysis covers 109 commercial banks in Indonesia where these banks are

categorized as conventional banks over the period 2002-2010. Islamic banks (Sharia

banks) are excluded in this study considering that Islamic banks have different nature of

the financing scheme compared to the conventional ones92

.

This chapter is organized as follows: The first section discusses previous studies on

lending propensity in commercial banks and the importance of lending propensity in

commercial banks‟ activities. The second section presents the methodology adopted.

92

Islamic banks do not provide lending facilities with an interest instrument charged to their customers,

rather than using financing facilities with the scheme of profit or revenue sharing.

167

The third section reveals the empirical findings of lending propensity of Indonesian

banks. Finally, the last section concludes the chapter.

7.2. Previous studies

Empirical studies on examining bank lending propensity are growing fast in the

academic literatures. For details, see among others [Strahan and Wetson (1998), Peek

and Rosengren (1998), Goldberg and White (1998), Berger et al. (2001, 2007),

Akhavein et al. (2004), Laderman (2008), Shen et al. (2009), Berger and Black (2011),

McNulty (2013)].

The study about the lending propensity of commercial banks mostly discusses the

tendency of banks to provide small business loans as the main point of view. This is

applied due to the fact that the type of the loan is quite unique93

, hence not all

commercial banks are able to serve small businesses in the their provision of loans.

Strahan and Wetson (1998) examined the impact of bank size and level of complexity of

the organizational structure of the bank and the banking consolidation on bank ability to

provide small business loans. They employed sample dataset on commercial banks in

the U.S. during the period 1993 to 1994. The findings of their study reveal that the

amounts of small business loans has increased for both small banks and large banks

over the study period but the growth of the loans has tended to decrease for large banks.

The complexity of the organizational structure94

has nothing to do with a bank's small

business loans. Mergers and acquisitions (M & A) as a form of consolidation among

smaller banks have a positive influence on the increase in small business lending, while

the opposite result occurs when the M & A experienced by the large banks and small

banks.

93

It is rather different with commercial and corporate loans in terms of lending technologies applied.

Commercial and corporate loans usually apply financial statement analysis and credit scoring systems in

the loan evaluation process, whilst the majority of small business loans apply the relationship lending

technology in the process.

94

The complexity of organizational structure of the banks is reflected by the form of their business entity

whether they are a sole bank company or a holding bank company.

168

Peek and Rosengren (1998) evaluated the impact of bank mergers on the lending

propensity of small businesses in the US banking industry over the period 1993-1996.

Their research was motivated by a huge wave of bank mergers which occurred in the

US over the period 1985-1995. The findings suggest that for around half of merger

occasions, the small business lending of acquiring banks before mergers was higher than

those of the acquired banks and they tended to increase their small business lending

portfolio after mergers. Their findings also advocate that small acquiring banks have a

propensity to more actively lend to small businesses than those of the large acquiring

banks.

Regarding the wave of mergers which took place in the US banks within the 1990s

decade, Goldberg and White (1998) also raised concerns whether widespread

consolidation through mergers had an impact on the lower bank availability to lend to

small businesses. Their concerns were implemented by their study to make a

comparison between de novo banks‟ small business lending with those of similarly sized

incumbent banks95

. They employed the dataset from Federal Deposit Insurance

Corporation (FDIC) call reports over the period 1987-1994. The results show that the

small business lending portfolios of de novo banks‟ were significantly higher than those

of similar portfolios of incumbents. These results suggest the possibility of novo banks

to become one of the solutions to the problem of less availability of small business loans

from the consolidated banks.

Berger et al. (2001) investigated the effect of bank size and foreign ownership on

lending propensity to small businesses in Argentina. They employed 115 commercial

banks during the period 1998. They found that major and foreign-owned banks did

experience some difficulties in lending relationships with small businesses opaque.

These situations are caused by the higher costs incurred by banks. Banks spent more

expenses in lending relationships to small businesses‟ customers. In addition, the

circumstance was magnified by a delinquency of small businesses in the payment of

their debts. However, another finding suggests that the distressed banks did not

95

A de novo bank is a bank that has received a charter and started operations within am aximum of three

years from the year of observation. A bank that has been in operation for longer than three years from the

year of observation is treated as an incumbent (Goldberg and White, 1998). They focused on banks with

an asset size of $5 million to $100 million.

169

experience significant matters in lending to small businesses. This is supported by the

fact that small businesses are also likely to borrow from other banks to reduce their risks

of having a relationship with the distressed banks.

Another study exploring banks‟ small business lending was done by Akhavein et al.

(2004). They examined the lending propensity of small banks to small firms in the

agricultural industry in the US over the period 1987-1994. The study focused only on

small banks with an asset size of greater than $ 5 million and less than $100 million.

The findings of the study suggest that the relationships, represented by the length of

tenure of farm operators, had a positive impact on bank lending. De novo banks96

were

inclined positively to lend to small businesses. However, de novo banks experienced

some difficulties in obtaining new customers that had longer and stronger relationships

with other banks. Another finding suggests that for the category of small banks, small

business lending to small farms tended to decrease when bank size increased.

Rao et al. (2006) analysed the propensity of banks‟ lending to small scale industry in

India over the period 1992-2003. They also examined the impact of the size and the

performance of banks on loans to small scale industry in India. They employed the

dataset of 97 commercial banks which are classified into four groups: State Bank of

India and its associates, nationalised banks, foreign banks and other commercial banks.

Those banks are also widely categorized into three size classes (small, medium, and

large banks) based on the total assets as on March 31, 2003. The results reveal that loans

for small scale industry have a lower annual growth compared to loan growths of all

industries and all sectors over the study period (11.4 percent compared to 14.4 percent

and 16.3 percent, respectively). The findings also suggest that across different groups,

the growth rates of loans for small-scale industries are lower than those of non-small

scale industry. The findings also suggest that the size of a bank has a negative impact on

small scale industry loans for public-sector banks, while the opposite result occurred to

their peers from the private banks. In addition, the performance of banks represented by

the return on assets has a positive effect on small scale industry loans for public and

private banks albeit significant only for private banks.

96

They employ the definition from Goldberg and White (1998) that de novo banks are banks that operate

no more than three years of age.

170

Meanwhile, Laderman (2008) investigated whether the presence of a bank‟s branch

affect its propensity on small business lending in the United States. They employed data

on small business lending flow from the Community Reinvestment Act reports97

over

the year 2004 in the US. The findings suggest that the majority of small business

lending was coming from the banks which have a local branch. This reflects that the

proximity is advantageous (conducive) to lending. She also found that only around 10

percent of small business lending came from banks with no branch in the local market

and around half of these seemed to be from a bank with branches in the same country.

These results also support the role of proximity in small business lending. The last

findings reveal that the average size of the loans were almost similar between out-of

market and in-market98

and those were likely to be backed up by commercial real estate

as collateral.

Shen, et al. (2009) examined the impact of bank size on the lending propensity of

commercial banks to small medium enterprises (SMEs) in China. They employed a

dataset covering commercial banks of 79 counties in 12 provinces in China over the

period 2001-2004. The study found that the size of the banks do not have an impact on

SMEs lending. Some of the variables i.e. the relationship of wages and loan quality,

competition, and institutional arrangements significantly affect SME lending. In other

words, if loan quality is connected with a manager‟s wage, if there is a greater

competition, and if an institution has more self-loan approval right, then financing to

SMEs will be higher. In addition, law enforcement has also a positive impact on SMEs

lending.

Chernykh and Theodossiou (2011) examined the determinants of banks‟ propensity to

provide long-term business loans in Russia. They focused on long-term business loans99

since they assume that the total loans and in particular long-term loans are the main

97

The reports include data containing 5,000 banks delivered in accordance with the Community

Reinvestment Act (CRA). He used the CRA reports to only focus on small business loans (loans under $

1 million for businesses with revenue under $ 1 million).

98

She defines out-of-market as for banks that make loans in the market (metropolitan statistical area or

MSA) but do not have branches there. Conversely, in-market loans mean that the banks provide loans

with a physical presence (branches) in the MSA. 99

They define long-term business loans as loans with a term at least three years to maturity.

171

factors affecting economic development and economic growth in emerging markets,

including Russia. The study employed a cross-sectional dataset of 881 commercial

banks operating in Russia during the year 2007. The findings suggest that the Russian

banks‟ propensity to lend long-term to companies were influenced by their size, capital,

and the availability of their long-term liabilities (deposits) as their sources of funding.

Bank size, bank capital, and bank long-term liabilities had a positive impact on the

propensity of banks to make long-term loans in Russia throughout the year.

A recent study by Berger and Black (2011) shed light on the investigation whether the

current paradigm in the study of small business lending is applied for their observed

data. The paradigm suggests that small banks are benefiting by financing smaller, less

transparent businesses (employing „soft information100

‟), whilst large banks are likely to

finance relatively large companies because of their transparent information (using the

„hard information101

‟). They made a comparison between large and small banks‟

advantages in using their lending technologies. The results suggest that the predictions

of the current paradigm do not apply because large banks do not have the same benefits

of using hard technology102

in their loans than small banks. In addition, the advantages

of large banks do not increase monotonically following an increase in the size of the

bank103

. Other findings show that small banks have a comparative advantage in

relationship lending technology; however these kinds of advantages can also be

implemented to finance large companies.

The most recent study outlining the determinants of small business lending was

conducted by McNulty, et al. (2013). They examined the effect of bank size on banks‟

100

Soft information means qualitative information which is actually difficult to quantify and process by

loan officers i.e. the circumstances of the firm, the owners of the company, and its management. 101

Hard information means quantitative information processed by loan officers, i.e. financial ratios over

audited financial statements, collateral values, and credit scores. 102

The hard lending technology applied in this case is using financial statement as the main tools for

evaluating loan application (financial statement lending technology).

103

The assumption suggests that “large banks‟ comparative advantage in using hard-information lending

technologies should be monotonically increasing in the size of the firm. As firms increase in size, they

tend to have higher-quality financial statements, yielding an implied increasing advantage in hard

technologies.” (Berger & Black, 2011:725). Therefore, the results are contrary to the assumption.

172

propensity on small business loans in 5537 banks in the United States during the period

1993-2006. Their findings suggest that there was a negative relationship between bank

size and the proportion of small business loans. These imply that the tendency to lend to

small businesses has declined as the size of the bank increases. Small business loan

growth did not keep pace with the growth of the size of the bank. The results also show

that small banks were recorded to be more inclined to lend to small businesses.

7.3. The Importance of Banks’ Lending Propensities

The study about bank lending propensity is very essential to be investigated. The

findings of the study could be used as indicators of banks‟ willingness to finance all

business sectors (reflected by total loans) and small businesses (represented by small

business loans).

The discussion about bank lending propensity became much more interesting when

Berger et al. (2007) questioned the use of the small business loans to total assets

(SBLTA) as the proxy for representing bank‟s lending propensity on small businesses.

They doubt the majority findings of the previous research regarding the large banks

experienced lower ratio of SBLTA than those of their peers from small banks over the

study period. They argue that there was a possibility that large banks had a lower ratio

of SBLTA just because the denominator was expanded and not because of the

contraction in the numerator. In addition, they also explain that the expansion of the

denominator originated from some activities (i.e. making large business loans or other

investments) that magnify their assets, while at the same time, small banks were not

able to expand their loan portfolios on large credits due to the problems of the legal

lending limit or highly large corporate lending risks.

Corresponding to Berger et al.‟s opinion, McNulty et al (2013) provided the alternative

solutions to represent bank lending propensities issued by offering another ratio namely

small business loans to total loans and leases (SBLTLL) in their article. They suggest

that the lending propensities of a bank to small businesses is still very important issue to

date to explore considering that commercial banks‟ lending is still an important source

of supporting small businesses.

173

This thesis adopts six variables to represent the bank lending propensity of Indonesian

commercial banks. The first three variables reflect banks‟ total lending (total loans)

propensity [these are namely a natural logarithm of total loans (LNTL), total loans to

total assets (TLTA) and total loans to total deposits (TLTD)] and three other variables

represent bank lending propensity only on small businesses [namely natural logarithm

of Small business loans (LNSBL), small business loans to total assets (SBLTA) and

small business loans to total loans (SBLTF)104

].

7.4. Methodology

7.4.1. Demand and Supply Model of Small Business Finance

The main research question of this study is to examine the impact of bank size on the

supply of total loans (TL) and small business loans (SBL) provided by Indonesian

commercial banks. However, in economic theory, the supply and demand models are

mainly related to one variable, namely price. In other words, both the quantity

demanded and the quantity of the supply of loans, each of which is a function of a single

explanatory variable, called, price (McNulty et al., 2013). Since the propensity of banks

to provide those types of loans reflects the supply quantity of the total loans and total

small business loans on each, the models should take into account price variable.

McNulty et al. (2013) as well as Peek et.al (2003) argue that the demands for loans from

companies are also associated with the demands for the companies‟ products. Hence,

taking into account the aggregate demand factors, it is also important to control the loan

demand function. Therefore, in the demand model specification, loan demand by

businesses is a function of the price of the loans ( and economic variables that

affect the productivity of business entities or enterprises ( . The structural form

of the demand function can be written as follows:

(7.1)

104

The use of SBLTA and SBLTF is to address the previous studies undertaken by many authors

including Berger et al. (2007) and McNulty et al (2013).

174

Where: is a constant, are coefficients of the explanatory variables and is

the error term. For controlling the aggregate demand function, they proposed the

variables representing the economic factors ( : the number of establishments in

the bank‟s county domicile, the unemployment rate in the county, and the year-end

value for the industrial production index. They employed the data of county economic

factors adjusted with the data covered.

This study uses a similar model as above with the adjustment in the macroeconomic

factors employed. Since the dataset employed in this study covers all commercial banks

in the country of Indonesia, this study uses three variables that represent the Indonesian

macroeconomic conditions, namely (1) GDP Growth (GDPGR) as proxy for Indonesian

economic activity, (2) Inflation rate (INFL) as a proxy of price stability in Indonesia,

and (3) Unemployment rate (UNEMP)105

as a proxy for Indonesian prevalence for

unemployment. These variables are employed as the control variables for the function of

loan demanded by debtors as well as the supply of loans by banks. Peek et.al (2003)

state that using such variables i.e. GDP growth, inflation and unemployment rates, as a

part of forecast errors could be more effective ways to control for loan demand.

Referring to some previous studies106

examining the loans supply, it is specified that the

loan supply structural model is as follows (McNulty et al., 2013):

(7.2)

Where denotes the quantity of supplied loans (in this case, total loans, and small

business loans), is loan rate or price at time-t, is the size of each bank at time-

t, is the bank capital of each bank at time-t, and is the bank

deposit of each bank at time-t, and is the error term.

This study also employs a similar model with the dataset of the Indonesian banks as

follows is total loans (TL) and small business loans (SBL) provided by

commercial banks. Banks‟ propensity on total loans is represented by natural logarithm

105

Unemployment rate is calculated by dividing the number of unemployed individuals by all individuals

currently in the labor force. The data obtained from the Statistics of Indonesia. http://www.bps.go.id.

106

See for example: Berger et al. (1998), Kishan and Opiela (2000), Akhavein et al. (2004), Berger and

Black (2011), among others.

175

of total loans (LNTL), total loans total loans to total assets (TLTA) and total loans to

total deposits (TLTD) while banks‟ propensity to lend to small businesses is represented

the three variables, namely natural logarithm of small business loans (LNSBL), small

business loans to total assets (SBLTA) and small business loans to total loans

(SBLTF). Meanwhile, is the price of loans (lending rate data from the Central Bank

of Indonesia). The data of the price of loans are the average lending rates for different

groups of banks operated in Indonesia over the period 2002-2010. Since there is no

difference between the loan rates for the two types of loans, the data of the price of

loans are used for both models. Bank size ( ) is represented by the natural

logarithm of bank’ total assets; bank capital ( is represented by the bank‟

capital to total assets ratio; and bank deposit ( ) is represented by the natural

logarithm of bank’ total deposits.

7.4.2. Two-Stage Least Square (TSLS) Technique

Most of the previous studies examined use a two-stage regression technique of least

squares (TSLS) to estimate the supply of loans. This technique is taken as a response to

and treatment in accordance with the natural conditions around the model, namely the

simultaneity problem. The problem is characterized by the simultaneity condition due to

feedback or a simultaneous relationship between the supply of loans and price variables.

In other words, these two variables affect each other. The problem is also considered by

the econometric terms as endogeneity, because there is an endogenous variable (in this

case, price) between the explanatory variables in determining the supply of the loans

that have a high correlation with the error term. As a consequence, the resulting

estimates of OLS will be biased and inconsistent.

TSLS is applied to handle the problem thus there is no more endogeneity and the

estimation will be unbiased and consistent. Studenmund (2011) states that TSLS is the

most widely used technique to mitigate this problem. He added that the TSLS is

operated by running a regression on the reduced form on the right hand side endogenous

variable that needs to be replaced, and then the (the fitted values resulted from the

176

reduced-form regression) is used as an instrumental variable to replace the endogenous

variable in the main equation.

Murray (2006) stated the advantages of the TSLS method. He explained that the TSLS

estimator does not suffer measurement error bias due to two special natures of the

TSLS. Both of these properties are: 1) Fitted values resulted from OLS regressions are

always uncorrelated with the related residuals; 2) Each fitted value in TSLS is a linear

combination of explanatory variables from the first stage regression. Hence, each fitted

value in the second stage is not correlated with all miss measurement conditions caused

by employing fitted values replacing the actual values in the second stage of the

TSLS107

.

The example of the equations that reflect the TSLS can be observed as follows [Gujarati

(2006), Studenmund (2011), Wooldridge (2013)]:

(7.3)

(7.4)

We have one main equation in (7.3) that is a function of and . Since is

also affected by , thus there is a problem of simultaneity and become endogenous

variable as well as . To mitigate the problem, we regress the equation (7.4) until we

obtain the prediction value for , that is hese fitted values are then used as the

instrumental variable to replace in the main equation (7.3).

For the identification purposes, especially for the case of the loan supply equation (7.2),

we can identify that the variable of price ( seems to be endogenous variable in the

place of right-hand side of the equation. It occurs since is not only explaining

the , but also is explaining the . The mutual influences reflect that both are

endogenous variable. Then, as the consequence, we should find the instrumental

107

See Murray (2006:591) for more details.

177

variable to replace in the (7.2). The instrumental variable is originated by finding the

fitted values through the regression equation as follows:

(7.5)

From this regression of this equation, we can obtain the values of . Then, the (as

the instrumental variable) can be used in the equation (7.2) to replace the .

(7.6)

The regression estimation resulted from this equation would be unbiased and consistent

to mitigate the problem of endogeneity and simultaneity in this case [See McNulty et al.

(2013) and Gujarati (2006)].

7.5. Data

This study employs data of all commercial banks in Indonesia, which are provided by

the Central Bank of Indonesia and the data provided by PT. Ekofin Konsulindo with the

following criteria: (1) the sample data are all commercial banks, particularly

conventional banks operating in Indonesia during the period 2002-2010. (2) Each of the

banks‟ financial statements should be available completely within the period from 2002

to 2010. There have 109 banks to meet the criteria. It comprises of 4 state-owned banks

(SOB), 55 private banks (PB), 26 provincial / local government-owned banks (LGOB),

15 joint venture banks (JVB) and 9 foreign-owned banks (FB).

7.6. Empirical results

7.6.1. Descriptive Statistics

This section exhibits the descriptive statistics of the data sample employed to examine

the lending propensity of Indonesian commercial banks during the period 2002-2010. It

comprises two important parts. First, it describes the characteristics of some key

variables used to estimate the lending propensity of Indonesian commercial banks.

Second, it provides the classification of the data by peer group based on asset size. The

178

classification is very beneficial to provide more insights on the attitude of banks in

terms of their lending (finance) propensity based on the size of each bank during the

sample period.

7.6.1.1. Descriptive Statistics of the key variables.

The descriptive statistics of the key variables is presented in table 7.1. There is a wide

variation of banking operations in terms of asset size of commercial banks in Indonesia.

There is a bank that operates with only IDR 17.5 billion of assets whilst another bank

operates its asset up to IDR 408 trillion. An average asset size of Indonesian banks

included in this study is around IDR 16.9 trillion over the period.

Table 7.1. Descriptive statistics of the key variables

Variable N Mean Std. Dev. Minimum Maximum

Total Assets (TA) (in IDR Million) 981 16,100,000 44,900,000 17,540 408,000,000

Total Loans (TL) (in IDR Million) 981 8,231,294 22,700,000 1,229 247,000,000

Total Loans to Total Assets (TLTA) 981 0.5375 0.1852 0.0055 1.0810

Total Loans to Total Deposits (TLTD) 981 0.7866 0.4777 0.0090 3.4329

Small Business Loans (SBL) (in IDR Million) 981 997,652 4,437,158 0 75,400,000

Small Business Loans to Total Assets

(SBLTA) 981 0.0789 0.1015 0 0.6005

Small Business Loans to Total Loans

(SBLTL) 981 0.1552 0.1950 0 0.9994

Price of Loans (P) 981 0.1459 0.0211 0.1039 0.1891

Total Deposits (DEP) (in IDR Million) 981 12,500,000 36,200,000 1,503 333,000,000

Capital Ratio (CAPRATIO) 981 0.1419 0.1180 - 0.2749 1.0274

Source: Data observed

In terms of total loans achieved by the observed commercial banks in Indonesia over the

study period, they could achieve IDR 8.23 trillion on average, with ratios of total loans

to total assets (TLTA) and total loans to total deposits (TLTD) are 53.75 per cent and

78.66 percent respectively. The ratio of TLTD is also known as loans to deposits ratio

(LDR). The percentage achievement of TLTD or LDR reflects that Indonesian

commercial banks have managed well in operating the intermediary function over the

study period108

. This achievement could have also contributed to the average GDP

108

Bank Indonesia Regulation No. 15/7/PBI/2013 on the reserve requirement states that the LDR target of

each bank should reach 78-100 percent in position before December 1, 2013 and 78-92 percent on

December 2, 2013 and thereafter.

179

growth over the period which reached 5.39 percent.

It is important to explain that in terms of small business loans, some banks do not have

lending exposure to small businesses, whilst in reverse some banks put their lending

portfolios majority on small businesses. An average small business loan to total assets

(SBLTA) is 7.89 percent while the average small business loan to total loans (SBLTL) is

15.52 percent. It is worthwhile to state that the change of the banking regulation by

eliminating the minimum particular percentage threshold of SBLTL for each bank109

has

affected the ratio of SBLTL over the period 2002-2010.

Price of loans (P) is the average lending rate for different groups of banks imposed on

commercial banks in Indonesia during the period. The lending rate prevailed at the

commercial banks in Indonesia over the period was between 10 percent and 19 percent

with an average of about 14.59 percent. In addition, total deposits (DEP) collected by

commercial banks in Indonesia vary with an average of IDR 12.5 trillion. Capital ratio

(CAPRATIO) also ranges with an average ratio of 14.19 percent.

7.6.1.2. Distribution by Peer group

In accordance with the main purpose of this study which is to examine the impact of

bank size on the lending propensity of Indonesian commercial banks, the information

contained in the table 7.2 assists us to understand the condition of loan propensity of

Indonesian banks associated with their asset size.

Table 7.2 presents the distribution of some of the key variables across 4 (four) banks‟

groups classified by asset size. The classification of the banks is as follows: Peer group

1 comprises banks with the asset size of less than or equal to IDR 10 trillion (TA <=

109

The regulations about the minimum threshold of small business loans for each commercial bank in

Indonesia (20 per cent on the package of January 1990 and 22.5 per cent on The Directors of BI Decree

No. 30/4/KEP/DIR on April 1997) are then revoked by BI Regulation Number 3/2/PBI/2001 about Small

Business Finance on January 4, 2001. The regulation states that the provision of small business loans is

then readjusted based on the ability of each bank. In other words, since then, commercial banks are not

compelled to provide a minimum particular percentage of their finance portfolios devoted to small

businesses.

180

IDR10 trillion). Peer group 2 consists of banks with asset size of more than IDR 10

trillion and less than or equal to IDR 50 trillion (IDR 10 trillion < TA <= IDR 50

trillion). Peer group 3 includes the banks with asset size of more than IDR 50 trillion

and less than or equal to IDR 100 trillion (IDR 50 trillion < TA <= IDR 100 trillion).

The last, Peer group 4 covers banks with asset size of more than IDR 100 trillion. The

number of banks varies across the years since some banks changed their classifications

into different groups in subsequent years. However, as shown in the table below, as of

the year 2010, there are 65, 31, 6, and 7 banks, classified in the peer groups 1, 2, 3, 4,

respectively.

Table 7.2. Distribution by Peer group


Average Total Deposits (IDR Million) 1,621,552 15,887,459 49,176,734 166,000,000 232,685,745

Peer Group average deposits as a proportion of all

peer groups' average deposits 0.70% 6.83% 21.13% 71.34% 100.00%

Average Total Assets (IDR Million) 2,230,373 21,520,513 66,363,032 205,900,000 296,013,918

Peer Group average assets as a proportion of all

peer groups' average assets 0.75% 7.27% 22.42% 69.56% 100.00%

Average Total Loans (IDR Million) 1,221,311 11,386,730 38,976,264 98,463,083 150,047,388

Peer Group average TL as a proportion of all peer

groups' average TL 0.81% 7.59% 25.98% 65.62% 100.00%

Average Total Loans/Total Assets (TLTA) 0.5430 0.5235 0.5871 0.4786 0.5331

Average Total Loans/Total Deposits (TLTD) 0.8110 0.7352 0.7991 0.6003 0.7364

Average Small Business Loans (IDR Million) 162,812 1,042,671 5,001,519 13,191,781 19,398,783

Peer Group average SBL as a proportion of all peer

groups' average SBL 0.84% 5.37% 25.78% 68.00% 100.00%

Average Small Business Loans/Total Assets

(SBLTA) 0.0884 0.0488 0.0690 0.0668 0.0682

Average Small Business Loans/Total Loans

(SBLTL) 0.1743 0.0980 0.1182 0.1241 0.1287


Peer Group 1 (Total Assets <= IDR 10 Trillion); Peer Group 2 (Total Assets > IDR 10 Trillion and <= IDR 50Trillion);

Peer Group 3 (Total Assets > IDR 50 Trillion and <= IDR 100 Trillion); Peer Group 4 (Total Assets > IDR 100 Trillion).

Average total deposits collected by the largest banks (peer group 4) were accounted for

71.34% while the smallest banks (peer group 1) only accounted for 0.70%. Peer group 2

and 3 enjoyed average deposits of about 6.83% and 21.13% respectively. These figures

reflect that the concentration of deposit accounts of commercial banks in Indonesia is

located on the large banks.

Similar conditions are also reflected in terms of asset size. The proportion of average

181

assets was also dominated by the largest banks which accounted for 69.56% out of an

average asset of all peer groups. The smallest banks simply enjoyed average assets of

0.75%. These results indicate that large banks in Indonesia have greater opportunities to

use their resources in terms of assets to lend to their customers. However, the magnitude

of the tendency of banks to provide loans is depended on the willingness of their

management and management's perception of the risk of their lending portfolios.

In terms of total loans, the largest banks accounted for 65.62% of all average loans

provided by Indonesian commercial banks over the study period. It valued an IDR 98.46

trillion on average. Nonetheless, in terms of average TLTA and TLTD, the smallest

banks had a better portion of the total loans than those of the largest ones. The small

group accounted for an average of 54.30% and 81.10% for TLTA and TLTD, compared

to the largest peer which only accounted for 47.86% and 60.03%. It is worth noting that

in the perspective of the financial intermediary, the smallest banks performed superior

than those of other peers with an average TLTD achievement of 81.10%.

Meanwhile, in terms of small business loans, the average SBL of the smallest banks

accounted for only 0.84%t which is much lower than the largest banks which accounted

for 68%. These seem to be because of the different magnitude of their assets. However,

these results indicate that large banks in Indonesia also give contributions to the

development of small businesses by allocating their portfolio financing devoted to small

businesses. As known, a number of large banks employ their extensive network of

branches to increase their absolute value of small business loans. A state-owned bank

namely PT. Bank BRI, is one of the large banks that uses a large number of branches

throughout Indonesia to provide small business loans to its customers. In addition, PT.

Bank Danamon Indonesia, a private bank in the large category as well, has also

expanded their financing for small businesses by establishing a large number of small

lines of business namely Danamon Savings and Loans (DSP)110

. The combination

between the two contributors along with other banks has delivered the group to reach an

average SBLTL and SBLTA of about 12.41% and 6.68% respectively over the study

110

PT. Bank Danamon Indonesia through one of its lines of business, namely Danamon Savings and

Loans/ Danamon Simpan Pinjam (DSP), specifically to serve and help the development of micro and

small enterprises, especially those in the surrounding communities and the market, such as market traders,

retailers, and others. DSP was first established in 2004.

182

period. Of course, their achievement is still below the attainment of the smallest banks

in terms of SBLTL and SBLTA which recorded 17.43% and 8.84%. These results

coincide with the findings of Shaban et al (2014) that Indonesian large banks were

generally less inclined to provide small business loans over the study period.

In addition, the achievement of the smallest banks to have higher SBLTL and SBLTA

than those of their large peers indicate that on average, the smallest banks‟ propensity to

finance small businesses are higher than those of the large banks. These findings

coincide with the study results of McNulty, et al (2013) which suggests that small banks

have a higher propensity to lend to small businesses compared to those of the large

banks.

The evolution of total lending exposures (total loans) among different groups of banks

during the period 2002-2010 can be observed in some of the following figures (figure

7.1, 7.2, and 7.3):

Figure 7.1. Average TLTA across different groups


Figure 7.1 demonstrates the total lending propensity of commercial banks in Indonesia

in terms of TLTA over the period 2002-2010. It shows an upward trend of total bank

loans from under 50 per cent in 2002 to more than 60 per cent in 2010. The smallest

banks (peer group 1) posed at the top spot over the first five years, whilst peer group 3

overtook the position for the rest of the following years.

The large banks recorded the lowest performance on average, but the slope of their

credit curve looks steeper than their counterparts. The lowest attainment of their TLTA

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Total Loans to Total Assets (TLTA) by Bank Peer Group

Peer Group 1 (Total Assets<= IDR 10 Trillion)

Pee Group 2 (Total Assets >IDR 10 Trillion and <= IDR50Trillion)

Peer Group 3 (Total Assets >IDR 50 Trillion and <= IDR100 Trillion)

Peer Group 4 (Total Assets >IDR 100 Trillion)

183

in 2002 is due to fact that the year was of the beginning revival period after the Asian

financial crisis of 1997-1998. Meanwhile, the global crisis of 2008 did not appear to

significantly affect the achievement of loans in 2009-2010.

Figure 7.2. Average TLTD across different groups


Figure 7.2 shows the lending propensity of Indonesian banks in terms of total loans to

total deposits (TLTD) over the period 2002 to 2010. This figure is very important to be

evaluated since TLTD is as a representative for the function of banks‟ financial

intermediary111

. The figure shows the upward trend of the ratio meaning that the

intermediary function of the banks increased over the period. The smallest banks

recorded high willingness to provide loans to small businesses over the period compared

to their peers. Meanwhile, the largest banks experienced the lowest average ratio over

the period albeit the slope of the curve is steeper than those of other peers.

111

TLTD is similar with the loans deposits ratio (LDR), the most commonly used ratio term applied in

banking to reflect the financial intermediary functions of commercial banks. The banks‟ intermediary

functions are associated with activities of collecting funds (deposits) from depositors and granting credit

(loans) to borrowers.

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Total Loans to Total Deposits (TLTD) by Bank Peer Group



Peer Group 3 (Total Assets >IDR 50 Trillion and <= IDR 100Trillion)


184

Figure 7.3. Average TL across different groups


From the perspective of the average amount of the total loans across the groups, figure

7.3 illustrates that the largest banks showed a high increase of average loans over time

across the years, whilst other peers tended to keep maintaining the loans constant in

magnitude. The increase in the nominal average loans of the large banks (peer group 4)

significantly contributed to the increase of their attainment on average TLTA and TLTD.

In addition, the evolution of small business loans achieved by a different group of bank

during the period 2002-2010 can be observed by the following figures (figure 7.4, 7.5,

and 7.6):

Figure 7.4. Average SBLTL across different groups


-

20,000,000

40,000,000

60,000,000

80,000,000

100,000,000

120,000,000

140,000,000

160,000,000

20

02

20

03

20

04

20

05

20

06

20

07

20

08

20

09

20

10

IDR

Mill

ion

Year

Average Total Loans (TL) by Bank Peer Group

Peer Group 1 (TotalAssets <= IDR 10Trillion)

Pee Group 2 (TotalAssets > IDR 10 Trillionand <= IDR 50Trillion)

Peer Group 3 (TotalAssets > IDR 50 Trillionand <= IDR 100 Trillion)

Peer Group 4 (TotalAssets > IDR 100Trillion)

-

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

0.3500

0.4000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Average Small Business Loans to Total Loans (SBLTL) by Bank Peer Group



Peer Group 3 (Total Assets > IDR50 Trillion and <= IDR 100Trillion)


185

Figure 7.4 illustrates that, in general, commercial banks in Indonesia tend to reduce their

financing portfolios to small businesses. A regulatory change in 2001112

appears to have

a significant impact on the propensity of Indonesian banks‟ lending to small businesses

with a downward direction during the period 2002-2010. In particular, the sharp decline

in small business lending occurs in peer groups 1 and 3 where they dropped their

proportion of small business financing to below 20 per cent after years of 2003-2004.

Fortunately, peer group 4 (largest banks) shows the opposite performance. They tend to

increase the proportion of small business financing to nearly 15 percent on average in

2010. It seems that they have considered the potential advantages of financing the small

business sector. In practice, one of the largest banks, PT. Danamon Indonesia

successfully formed a special business unit working on micro and small business loans

conducted in 2004. The efforts of the bank along with PT. Bank BRI and PT. BNI113

appear to have a considerable contribution to the attainment of the largest banks in

financing small businesses.

Figure 7.5. Average SBLTA across different groups


Similar patterns are shown by the above figure. In terms of SBLTA, the downward trend

experienced by peer group 1 and 3, while the opposite results achieved by peer groups 4

and 2.

112


113

PT. Bank Rakyat Indonesia (BRI) and PT. Bank Negara Indonesia (BNI) are two state-owned banks

that constantly provide small business loans in a sizeable proportion among the other large banks during

the period 2002-2010.

-

0.0200

0.0400

0.0600

0.0800

0.1000

0.1200

0.1400

0.1600

0.1800

0.2000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Year

Average Small Business Loans to Total Assets (SBLTA) by Bank Peer Group





186

Figure 7.6. Average SBL across different groups


Meanwhile, in terms of the absolute amount of the average small business loans

achieved by commercial banks in Indonesia over the period 2002-2010, it appears that

there was a sharp rise shown by Peer Group 4. They reached the average SBL above

IDR 20 trillion in 2010. The SBL of the Peer Group 3 has fluctuated up to IDR 5 trillion

in 2010. SBF of banks in Peer group 1 and 2 are still in the range of about IDR 1 trillion

and less. These results make sense given the fact that small banks have a restricted

capacity of financing due to asset size. However, when assessed by the proportion of

SBF, the banks included in Peer Group 1 and 2 achieved higher SBF ratios than those of

the large banks.

7.6.2. Regression Results

This section presents the regression results obtained from the two-stage least squares

regression on the data of the Indonesian commercial banks to estimate the supply of

total loans (TL) and total small business loans (SBL) over the period 2002-2010. Fixed

effects panel data regression is employed in this study. The result of the Hausman-test

indicates that fixed effects are more appropriate to apply. Since the focus of the

discussion of this section is the determinants of the lending propensity of Indonesian

commercial banks, especially to investigate the association between asset size and total

loans as well as small business loans, the first regression is not described. However the

first regression results reveal that the F-statistics and t-statistics of the reduced form

equations are statistically different from zero, meaning that the shift variables are

-

5,000,000

10,000,000

15,000,000

20,000,000

25,000,000

2002 2003 2004 2005 2006 2007 2008 2009 2010

IDR

Mill

ion

Year

Average Small Business Loans (SBL) by Bank Peer Group



Peer Group 3 (Total Assets > IDR50 Trillion and <= IDR 100 Trillion)


187

statistically significant.

The results of the regressions encompass the three models. Model 1, 2, and 3 present the

results when each uses gross domestic product growth (GDPGR), inflation rate (INFL),

and unemployment rate (UNEMP) as the shift variable for the demand function in the

reduced equations respectively.

Table 7.3 shows the regression results where the lending propensity of Indonesian

commercial banks are expressed by the natural logarithm of total loans (LNTL), the

ratio of total loans to total assets (TLTA), and the ratio of total loans to total deposits

(TLTD).

Table 7.3. TSLS Regression Results 1 MODELS: 1 2 3 1 2 3 1 2 3

VARIABLES: LNTL LNTL LNTL TLTA TLTA TLTA TLTD TLTD TLTD

p -13.53*** 1.221 -10.99** -3.657** 0.611 -4.752*** -9.729** 0.714 -13.26***

-5.12 -1.581 -4.576 -1.576 -0.516 -1.55 -4.247 -1.345 -4.251

lnta 0.558*** 0.765*** 0.593*** -0.0731** -0.0132 -0.0885*** 0.963*** 1.110*** 0.914***

-0.104 -0.0725 -0.0967 -0.0321 -0.0237 -0.0328 -0.0864 -0.0617 -0.0899

capratio -0.896** -0.0398 -0.749** -0.239** 0.00894 -0.302*** -1.647*** -1.040*** -1.851***

-0.374 -0.228 -0.344 -0.115 -0.0743 -0.116 -0.31 -0.194 -0.319

lndep 0.370*** 0.416*** 0.378*** 0.0663*** 0.0795*** 0.0629*** -1.132*** -1.100*** -1.143***

-0.0745 -0.0669 -0.0713 -0.0229 -0.0218 -0.0242 -0.0618 -0.0569 -0.0662

Constant 2.588 -3.434*** 1.554 1.230* -0.513** 1.677*** 4.562*** 0.299 6.002***

-2.118 -0.716 -1.896 -0.652 -0.234 -0.642 -1.756 -0.609 -1.762

Observations 981 981 981 981 981 981 981 981 981

Number of id 109 109 109 109 109 109 109 109 109


*** p<0.01, ** p<0.05, * p<0.1

Model 1 applied GDPGR (Gross Domestic Products Growth) as a shift variable for the demand function

Model 2 applied INFL (inflation rate) as a shift variable for the demand function

Model 3 applied UNEMP (unemployment rate) as a shift variable for the demand function

The Dependent variables are: the natural logarithm of total loans (LNTL), the ratio of total loans to total assets (TLTA), and ratio

of total loans to total deposits (TLTD) for the three consecutive models. All reflects the lending propensities of Indonesian commercial banks over the study period. The Explanatory variables are: Price of loans (P) is the average lending rate for different

groups of banks imposed at commercial banks in Indonesia during the period. LNTA is the natural logarithm of bank‟s total assets.

LNDEP is the natural logarithm of bank‟s total deposits. Capital ratio (CAPRATIO) is the ratio of bank‟s capital over bank‟s total assets.

188

The relationship between bank size and total loans is positive and significant in terms of

LNTL and TLTD as dependent variables. They are all significant at the 1% level. The

positive coefficient means that as bank size increases, the propensity of Indonesian

banking loans also increases. In terms of LNTL models, the positive coefficients on the

LNTA across the three models are below 1.00 (i.e. 0.56, 0.76, and 0.59 for the three

models respectively). This means that an increase in their total loans does not keep pace

with the growth size of the bank. These results reflect the growth in the bank size is

higher than the lending growth. These results confirm the findings of McNulty et al

(2013). Meanwhile, the association between asset size and the TLTA is negative and

significant in the two models (model 1 and 3). These results imply that the lower growth

of bank loans compared to the growth of asset size has led a relationship between these

variables to be negative.

The variable of bank deposits has also a significant impact on commercial banks‟ loans

tendency in Indonesia over the study period. In terms of LNTL and TLTA as the

dependent variables, the variable natural logarithm of deposits (LNDEP) has a positive

and significant relationship with LNTL and TLTA respectively. These results indicate

that the increase in total bank deposits has caused the total volume of loans to increase

as well. Similar results are also responding to the ratio of total loans to total assets.

However, a LNDEP coefficient of less than 1.0 in all models for the LNTL and TLTA

regression results means that the total increase from both variables is less than

proportional to the growth of the banks‟ deposits. The inverse findings occurred for the

TLTD models. The difference between the lending and deposit growths seems to be the

reason for the negative association between those variables.

In addition, the variable of capital ratio (CAPRATIO) is statistically significant with

negative signs in 7 out of 9 models presented in terms of total loans (LNTL, TLTA, and

TLTD variables). These findings mean as bank capital increases, the size of bank

lending decreases and vice versa. McNulty et al (2013) pointed out that if there is a

negative association found between the capital ratio and bank lending, it seems that

those banks are willing to pursue risky loans by increasing lending although the bank

capital decrease. These results confirmed the study findings of McNulty et al (2013) in

189

the LNSBL114

models.

Table 7.4 presents the regression results where the lending propensity of Indonesian

banks to small businesses are represented by the natural logarithm of total small

business loans (LNSBL), small business loans to total assets (SBLTA) and small

business loans to total loans (SBLTL).

Table 7.4. TSLS Regression Results 2 MODELS: 1 2 3 1 2 3 1 2 3

VARIABLES: LNSBL LNSBL LNSBL SBLTA SBLTA SBLTA SBLTL SBLTL SBLTL

p -52.94 2.737 -44.68 1.305* 0.0809 0.391 3.448** 0.0744 1.2

-36.25 -11.46 -33.22 -0.679 -0.217 -0.598 -1.391 -0.445 -1.214

lnta -1.496** -0.714 -1.380** -0.0239* -0.0411*** -0.0367*** -0.0116 -0.0590*** -0.0432*

-0.737 -0.526 -0.702 -0.0138 -0.00994 -0.0127 -0.0283 -0.0204 -0.0257

capratio 0.0843 3.316** 0.564 0.0374 -0.0336 -0.0156 0.0688 -0.127** -0.0617

-2.648 -1.649 -2.495 -0.0496 -0.0312 -0.0449 -0.102 -0.0641 -0.0911

lndep 2.317*** 2.490*** 2.343*** 0.0282*** 0.0244*** 0.0253*** 0.0107 0.000274 0.00376

-0.527 -0.485 -0.518 -0.00987 -0.00917 -0.00932 -0.0202 -0.0188 -0.0189

Constant 5.416 -17.32*** 2.042 -0.171 0.329*** 0.202 -0.34 1.037*** 0.577

-14.99 -5.189 -13.77 -0.281 -0.0981 -0.248 -0.575 -0.202 -0.503

Observations 981 981 981 981 981 981 981 981 981

Number of id 109 109 109 109 109 109 109 109 109


*** p<0.01, ** p<0.05, * p<0.1

Model 1 applied GDPGR (Gross Domestic Products Growth) as a shift variable for the demand function

Model 2 applied INFL (inflation rate) as a shift variable for the demand function

Model 3 applied UNEMP (unemployment rate) as a shift variable for the demand function

The Dependent variables are: the natural logarithm of small business loans (LNSBL), the ratio of small business loans to total assets

(SBLTA), and ratio of small business loans to total loans (SBLTL) for the three consecutive models. All reflects the lending propensities of Indonesian commercial banks over the study period. The Explanatory variables are: Price of loans (P) is the average

lending rate for different groups of banks imposed at commercial banks in Indonesia during the period. LNTA is the natural

logarithm of bank‟s total assets. LNDEP is the natural logarithm of bank‟s total deposits. Capital ratio (CAPRATIO) is the ratio of bank‟s capital over bank‟s total assets.

In terms of LNSBL, SBLTA, and SBLTL as dependent variables, the majority findings

of the three models (Model 1, 2 and 3) specify that the variable of asset size has a

significant impact on small business loans with negative sign. The coefficient of

negative means that as asset size increases, the size of small business loans decreases.

114

Natural logarithm of small business loans (LNSBL).

190

The results supported the figures above concerning that the propensity of Indonesian

commercial banks to lend to small businesses has decreased over the period. It seems

that the change of the regulation to eliminate minimum threshold of small business

loans of each bank in 2001115

has reduced the propensity of Indonesian banks to lend to

small businesses.

Negative coefficient of greater than 1.0 for both models in the LNSBL regressions

(model 1 and model 3) indicate that as banks increase in asset size, their total volume of

small business finance decreases with a magnitude higher than their increasing level of

the asset size. However, in terms of SBLTA and SBLTL, the negative coefficients are

not large enough although they are significant in 5 out of 6 models presented. The

negative association between bank size and small business loans is similar with the

study findings of McNulty et al (2013) especially for a model of SBLTLL116

.

All models show a positive and highly significant117

relationship between LNDEP and

small business loans in terms LNSBL and SBLTA as dependent variables. This suggests

that the increase in total deposits is associated with an increase in small business loans.

A high relationship is reflected in the models of LNSBL with coefficients above 1.0.

These results also confirm the findings of McNulty et al (2013) in the model of LNSBL.

The relationship between LNDEP with SBLTL is also positive in all three models

although not significant.

In addition, the association between capital ratios (CAPRATIO) with the small business

loans is not significant in the majority models except for model 2 of the LNSBL and

SBTL. These findings also confirmed the study results of McNulty et al (2013).

7.7. Conclusions

This chapter investigates the effect of bank size and other variables on the lending

propensity of Indonesian commercial banks during the period 2002-2010. Proxies for

115


116

SBLTLL stands for small business loans to total loans and leases.

117

It is significant at 1% level.

191

banks‟ lending propensity are the total loans (LNTL, TLTA, and TLTD) and small

business loans (LNSBL, SBLTA, and SBLTL). Two-stage least square regression with

fixed-effects is adopted to overcome the simultaneity problem caused by the

relationship between supply and demand equations that are both associated with or each

is a function of the price variable.

In the section of descriptive statistics, it is explained that in terms of the average amount

of total loans (TL), the largest banks (peer group 4) dominate the share of loans which

reached 65.62% of average loans granted by commercial banks in Indonesia over the

study period. However, in terms of average TLTA and TLTD, the smallest group (peer

group 1) has a better portion of the total loans than those of the largest one. The group

accounted for an average of 54.30% and 81.10% for TLTA and TLTD, compared to the

largest banks which only accounted for 47.86% and 60.03% respectively. It is worth

noting that in the perspective of the financial intermediary, the smallest bank group is

better than other peers by achieving an average of 81.10% TLTD.

In terms of the average amount of small business loans (SBL), the largest bank group

(Peer group 4) also dominates the share of small business loans across all peer groups

which accounted for 68% compared to the smallest peers which only accounted for

0.84% on average. This large amount of SBL achieved by the largest group is the result

of the efforts of some large banks that use an extensive network of branch offices

throughout Indonesia i.e. PT. Bank BRI, PT. Bank BNI and PT. Bank Danamon

Indonesia). However, in terms of the ratio of small business loans (SBLTA and

SBFTL), the smallest bank group (peer group 1) exceeds the largest bank group with the

attainment of 8.84% and 17.43% for SBLTA and SBLTL (compared to the largest

banks which only reach 6.68% and 12.41% respectively). These results indicate that on

average, the propensity of small banks to lend to small businesses are higher than those

of the large banks. These findings are consistent with McNulty, et al (2013).

In general, by observing the evolution of total lending exposures (total loans) among

different group of banks over the period 2002-2010, it is concluded that all commercial

banks tended to increase the average loan exposures to their customers. The upward

propensity of total commercial bank loans in Indonesia indicate the revival period has

192

happened after hitting by the Asia financial crisis in 1997-98. This also means that the

financial intermediation function of commercial banks in Indonesia operated well

during the period.

On the other hand, from the perspective of the development of small business loans, it is

concluded that commercial banks in Indonesia tend to reduce their portfolios to small

business loans during the period. A regulatory change in 2001118

appears to have a

significant impact on the trend of Indonesian bank loans to small businesses with a

downward direction during the period 2002-2010.

Meanwhile, on the regression results, which employs six variables: LNTL, TLTA, and

TLTD (each as a proxy for total banks‟ lending propensities) and LNSBL, SBLTA, and

SBLTD (each as a proxy for banks‟ lending propensities to small businesses), it is

concluded that the relationship between bank size and total loans is positive and

significant in terms of LNTL and TLTD as dependent variables. They are all significant

at the 1% level. A positive coefficient means that as bank size increases, the propensity

of Indonesian banking loans also increases. However, the increase in their total loans

does not keep pace with the growth of bank size because the coefficient is under 1.0.

These results confirm the findings of McNulty et al (2013). Meanwhile, the relationship

between asset size and the TLTA is negative and significant in two models (model 1 and

3). These results seem to occur resulting from the lower growth of bank lending

compared to the growth of asset size has led to the relationship between these variables

is negative.

The variable of bank deposits also has a significant impact on the lending propensities

of commercial banks in Indonesia. In terms of LNTL and TLTA as the dependent

variables, the variable natural logarithm of deposits (LNDEP) has a positive and

significant relationship with LNTL and TLTA respectively. These results indicate that an

increase in the amount of bank deposits, the total volume of loans increases as well.

Similar results are also applied to the ratio of total loans to total assets. However, the

LNDEP coefficient of less than 1.0 in all models for the LNTL and TLTA regression

118


193

results means that the total increase from both variables are less than proportional to the

growth of bank deposits. Meanwhile, inverse findings occurred for the TLTD models.

The difference between loans and deposits growth seems to be the reason for the

negative relationship between the variables.

In addition, the variable of capital ratio (CAPRATIO) is statistically significant with a

negative sign on 7 of 9 models presented in terms of total loans (LNTL, TLTA, and

TLTD) as the dependent variables. These findings mean as bank capital increases, the

size of bank loans decreases and vice versa. McNulty et al (2013) pointed out that a

negative association between capital ratio and bank loans is caused by the banks that are

willing to pursue risky loans by increasing lending despite a decline in bank capital.

These results confirmed the findings of McNulty et al (2013) in the LNSBL119

models.

On the other hand, in the case of LNSBL, SBLTA, and SBLTL as the dependent

variables, the findings of the majority of the three models (Model 1, 2 and 3) determine

that the variable of asset size has a significant impact on small business loans with a

negative sign. The negative coefficient means that as the size of the asset increases, the

size of the small business loans declines. The results justified some figures concerning

small business loans (figure 7.4 and 7.5) that the propensity of Indonesian commercial

banks to lend to small businesses has declined over the period. A regulatory change to

eliminate the 20 per cent minimum threshold of small business loans for each bank in

2001120

seems to have reduced the propensity of Indonesian banks to lend to SBs.

The negative coefficient of greater than 1.0 for both models in the LNSBL regressions

(model 1 and model 3) indicate that when bank assets grow, the total volume of small

business loans decrease with a higher magnitude than the increase in asset size.

However, in the case of SBLTA and SBLTL, there is a significant negative coefficient

on 5 of the 6 models presented. The negative relationship between bank size and small

business loans is similar to the findings of McNulty et al (2013) especially for the

SBLTLL model121

.

119


120


121


194

All models showed a significant122

positive relationship between LNDEP and small

business loans in terms LNSBL and SBLTA as the dependent variable. This suggests

that the increase in total deposits is associated with an increase in small business loans.

A high relationship is also reflected in the model LNSBL with coefficients above 1.0.

These results also confirm the findings of McNulty et al (2013) in the model LNSBL.

The relationship between LNDEP with SBLTL is also positive in all three models

although not significant. While the relationship between bank capital ratios

(CAPRATIO) with small business loans is not significant in the majority of the models

except for model 2 of the LNSBL and SBTL. This finding also confirms the study

results of McNulty et al (2013).

The implication of the findings of the study as a suggestion for the policy makers in

Indonesia is as follows: First, Indonesia's banking supervisory agency123

should

continue to monitor the achievement of commercial banks in providing loans to their

customers so that economic activities in Indonesia operate as expected. However, such

monitoring should also include the development of credit quality given that low quality

of credit will not only reduce bank assets but also the bank's capital. Second, the

Indonesian government's decision to re-enact the minimum threshold of lending to small

businesses in 2012124

[although the new regulations is dedicated to small and medium

enterprises (SME)] is right, given that when the government relaxed small business loan

portfolios during the period from 2002 to 2010, the commercial banks tended to lower

the portion of the small business loans in their portfolios. By implementing the new

regulations, it is expected that the role of Indonesian commercial banks in providing

loans or financing for small businesses or SMEs, will continue to rise in support of the

economic development of Indonesia.

122

Significant at 1% level,

123

As stipulated by Indonesia‟s Government Law No. 21 of 2011 on the Financial Services Authority, as

of December 31, 2013, the task of banking regulation and supervision of the Bank Indonesia has been

transferred to the Financial Services Authority (FSA or OJK).

124

BI Regulation No.14/22/PBI/2012 on 21 December, 2012 regarding the credit or financing for micro,

small, and medium enterprises by commercial banks and technical assistance for the development of these

businesses. This regulation imposed on all commercial banks in Indonesia to finance SMEs gradually up

to reaching 20 percent in the end of 2018.

195

8. Chapter eight: Conclusion

This chapter summarizes the empirical findings obtained in the pursuit to answer the

research questions identified in Chapter 1 of this thesis. This thesis applied two

prominent methodologies, namely the Stochastic Frontier Analysis (SFA) and Data

Envelopment Analysis (DEA) to estimate the efficiency of Indonesian banks during the

period 2002-2010. The thesis also investigated the impact of bank size and other

variables on Indonesian banks‟ propensity to provide lending to their borrowers.

To the best of my knowledge, this is the first study that applies the two methodologies:

non-parametric DEA and parametric SFA for estimating Indonesian commercial banks‟

efficiency, especially using bank production function through DEA and cost function

through SFA over the study period. The thesis also examines bank specific factors and

macroeconomic factors that determine the efficiency of commercial banks in Indonesia.

The thesis also contributes to the literature by examining small business finance as one

of the output components in estimating bank efficiency in one of the models presented.

The last, this thesis is also the first in the literature that evaluates the lending propensity

of Indonesian banks over the period 2002-2010.

This chapter is organized as follows: the first section provides a summary of the

findings obtained from the three empirical chapters. The next section provides a number

of policy implications that might be of considerations for policy makers. Finally, the last

section provides conclusions with the limitations and recommendation for future

research.

8.1. Empirical Findings

In general, the efficiency of Indonesian commercial banks in terms of DEA technical

efficiency tended to decline over the period 2002-2010. The decline tendency is justified

by the declining records during the years of 2006, 2008 and 2009. The global financial

crisis of 2008 seems to have a little effect on the performance of the Indonesian banking

sector especially in terms of DEA technical efficiency as well as SFA cost efficiency.

These results coincide with the findings of previous studies carried out by Suzuki and

196

Sastrosuwito (2011) and Hadad et.al (2011).

Across all groups of banks by ownership, in general, state-owned banks (SOB)

experienced the most efficient banks in the majority of the models presented in the DEA

efficiency section. These results also confirmed the findings of Suzuki and Sastrosuwito

(2011) and Hadad et al (2011). These results suggest that SOBs were financially more

resilient in facing the global crisis of 2008. The results also imply the traditional

reliance from Indonesian households to SOBs as the most trustworthy place for

depositing their money across the period, similar to the condition which occurred during

the Asian financial crisis of 1997-98125

. The exemption was only happened on one

model when the outputs neglect small business finance, where the foreign owned banks

(FOB) overtook the top position ahead of the SOBs.

Meanwhile, in terms of the different operational system offered to customers,

conventional banks (CONV) are more efficient than Islamic banks (SHARIA) in the

majority of the models examined. These results indicate the success of the banks in

employing their long experience to operate more efficiently during the period. However,

Islamic banks could outperform conventional banks in the model two where the

efficiency estimation takes into accounts small business finance as one of the important

outputs in operation. These results also strengthen the findings that Islamic banks have a

higher propensity to finance small businesses compared to their peers from conventional

banks.

Across all models examined, it is found that that the productivity growth of Indonesian

commercial banks tended to decrease over the study period. These results were

empowered by the decrease over the following periods: 2002/2003, 2007/2008, and

2008/2009. The decline for the period of 2002/2003 occured due to the fact that

commercial banks were still in the beginning years of the recovery period after suffering

from the Asian crisis of 1997-98, whilst the consecutive declines over the period

2007/2008 and 2008/2009 seem to be caused by the effects of the global financial crisis

in 2007/2008. The decline of Indonesian banks‟ productivitivty growth over the periods

125

The crisis of 1997-1998 testifies the huge movement of funds from private commercial banks to state-

owned banks in Indonesia during the period.

197

coincides with the findings of the previous study (Suzuki and Sastrosuwito, 2011). In

addition, productivity growth of Indonesian banks over time during the period 2002-

2010 was mainly caused by technological change and there was no efficiency change

over the period. The findings were evidenced by the consistent findings across the three

models for the Malmquist Index Summary of annual means.

It is obtained from DEA efficiency regressions, that across all models examined, the

variables of LNTA, CAR, and LDR were of the bank-specific variables that consistently

affected the Indonesian bank efficiency over the period 2002-2010. All three variables

have positive signs associated with banks‟ efficiency. These imply that the increase in

assets, capital, and the volume of loans would definitely enhance the efficiency of the

Indonesian banks. These findings could also be used as an important recommendation

for all the banks‟ management that they should highlight those factors (the size of bank,

the capital adequacy, and the bank liquidity/the bank lending exposure) in enhancing

efficiency. The policies of merging these banks, managing their adequate capital, and

managing bank liquidity/lending exposure seem to be important factors to enhance

Indonesian banks‟ efficiency. In addition, all macroeconomic indicators included in this

study [INFL (inflation rate), GDPGR (Gross Domestic Product Growth), UNEMP

(unemployment rate) and USD RATE (USD rate)] significantly affected Indonesian

bank efficiency during the study period. These suggest the importance of the

government of Indonesia to maintain the stability of the macroeconomic indicators to

guarantee the efficiency of Indonesian commercial banks to be in the best level.

In the case of the SFA cost efficiency estimation, the results of the study show that in

general, the average cost efficiency of Indonesian commercial banks across the three

majority models tended to increase over the period 2002-2010. Foreign-owned banks

(FB) appeared to be the most efficient banks across the three different models in terms

of cost efficiency. They were most efficient in the two methods in model 1 [Pooled Data

and Battese and Coelli 1992 (BC92)], one method in model 2 (BC92) and one method

in model 3 (BC92). Meanwhile, state-owned banks (SOB) and joint venture banks (JVB)

have been the most efficient ones in only one method respectively (model 2 of the

Pooled Data method for SOB and model 3 of the Pooled Data method for JVB). In

addition, when considering small business finance (SBF) as one of the outputs in the

198

estimation of efficiency, SOBs were the most efficient banks in Indonesia in the Pooled

Data method. Meanwhile, when SBF was excluded (in model 3), JVBs were the most

efficient ones in the Pooled Data method as well. Conventional banks are more efficient

in almost all models compared to Islamic banks (SHARIA) except in model 2 of the

BC92 method where the model considered small business finance.

From the regression results of the SFA cost efficiency models, it is found that in general,

LDR was the only one from the bank specific factors which consistently affected

Indonesian banks‟ cost efficiency over the period 2002-2010. These findings could

become some important recommendation for all the banks‟ management in Indonesia

that bank lending exposure or bank liquidity is the most important factor in enhancing

bank cost efficiency. Again, the policies of managing bank lending portfolio and or bank

liquidity seem to be the crucial aspects to enhance Indonesian banks‟ cost efficiency.

Meanwhile, of the macroeconomic indicators, inflation rate (INFL), GDP Growth

(GDPGR), and USD rate (USDR) were also factors that significantly impacted on

Indonesian banks‟ cost efficiency over the period.

The findings of the study on examining the impact of bank size and other variables on

Indonesian banks‟ lending propensity over the period 2002-2010, show that Indonesian

commercial banks‟ lending tended to increase over time during the study period. The

upward propensity of Indonesian banks‟ total loans implies that the recovery period post

the Asia financial crisis of 1997-98 has occurred during the study period. This also

means that the function of bank as financial intermediary has begun to operate well

during the period (particularly in terms of the lending provision). In reverse, the

Indonesian banks‟ lending propensity in terms of small business loans tended to

decrease over time during the study period. A regulatory change in 2001126

appears to

have a significant impact on the declining trend of Indonesian banks‟ loans to small

businesses over the period. It is worth noting that government regulations on banking

would significantly affect banks‟ management in Indonesia. However, this controversy

policy adopted over the 2001-2012 was rectified by the new regulation issued in 2012

where all commercial banks in Indonesia should be returned back to finance small

126


199

businesses as they are considered one significant element of micro, small and medium

enterprises127

.

In addition, the findings of the lending propensity regression models, which employ six

variables: LNTL, TLTA, and TLTD128

(each as a proxy for total banks‟ lending

propensity) and LNSBL, SBLTA, and SBLTD129

(each as a proxy for banks‟ lending

propensity to small businesses), it is found that the relationship between bank size and

total loans is positive and significant in terms of LNTL and TLTD as dependent

variables. They are all significant a 99% confidence level. Positive coefficient of the

variable means that as bank size increases, the propensity of Indonesian banking loans

also increases. However, the increase in their total loans was not similar in magnitude

with the growth of bank size because the coefficient is under 1.0. These results coincide

with the findings of McNulty et al (2013) in the case of the US banks. Meanwhile, the

relationship between asset size and the TLTA is negative and significant in two models

(model 1 and 3).

The variable of bank deposits has also a significant impact on the lending propensity of

commercial banks in Indonesia. In terms of LNTL and TLTA as the dependent variables,

the variable natural logarithm of deposits (LNDEP) has a positive and significant

relationship with LNTL and TLTA respectively. These results imply that an increase in

the amount of the banks‟ deposits caused the total volume of their loans to increase as

well. Similar results are also occurred to the ratio of the total loans to total assets.

However, the LNDEP coefficient of less than 1.0 in all models for the LNTL and TLTA

regression results means that the total increase in both variables (LNTL and TLTA) are

less than proportional to the growth of the bank deposits. Meanwhile, the opposite

results are found for the TLTD models. The difference between the growth of loans and

deposits seems to be the reason for the negative relationship between the variables.

127

BI Regulation No.14/22/PBI/2012 on 21 December, 2012 regarding the commercial banks‟ loans or

financings for micro, small and medium enterprises (MSMEs), has compelled the Indonesian commercial

banks to provide minimum 20% of their financing allocated to MSMEs. See more details in the chapter

two.

128

LNTL (natural logarithm of total loans), TLTA (total loans to total assets), and TLTD (total loans to

total deposits).

129

LNSBL (natural logarithm of small business loans, SBLTA (small business loans to total assets), and

SBLTD (small business loans to total deposits).

200

In addition, the variable of capital ratio (CAPRATIO) is statistically significant with a

negative sign on 7 of 9 models presented in terms of total loans (LNTL, TLTA, and

TLTD) as the dependent variables. These findings mean as bank capital increases, the

size of bank loans decrease and vice versa. McNulty et al (2013) pointed out that a

negative association between capital ratio and bank loans is caused by the banks that are

willing to pursue risky loans by increasing lending despite a decline in bank capital.

These results confirmed the findings of McNulty et al (2013) in the LNSBL130

models.

On the other hand, in the case of LNSBL, SBLTA, and SBLTL as the dependent

variables, the majority findings of the three models (Model 1, 2 and 3) demonstrate that

the variable of asset size has a significant impact on small business loans with a

negative sign. The negative coefficient means that as the size of the asset increases, the

size of the small business loans decline. The results justified some figures concerning

small business loans (figure 7.4 and 7.5) that the propensity of Indonesian commercial

banks to lend to small businesses has declined over the period. A regulatory change to

eliminate the minimum threshold of small business loans for each bank in 2001131

seems to have a significant impact on the declining propensity of Indonesian banks to

lend to small businesses.

The negative coefficient of greater than 1.0 for both models in LNSBL regressions

(model 1 and model 3) indicate that when bank assets grew, the total volume of small

business loans decreased with a higher magnitude than an increase in asset size.

However, in the case of SBLTA and SBLTL, there is a significant negative coefficient

on 5 of the 6 models presented. The negative relationship between bank size and small

business loans is similar to the findings of McNulty et al (2013) especially for the

SBLTLL model in the case of the US banks132

.

130


131


132


201

All models show a significant133

positive relationship between LNDEP and small

business loans in terms of LNSBL and SBLTA as the dependent variables. These

findings suggest that the increase in total deposits is associated with an increase in small

business loans. A high relationship between those variables is also reflected in the model

LNSBL with the coefficients above 1.0. These results also coincide with the findings of

McNulty et al (2013) in the model LNSBL. The relationship between LNDEP with

SBLTL is also positive in all three models albeit not significant.

Meanwhile, the association between capital ratios (CAPRATIO) with small business

loans is not significant in the majority of models except for model 2 of the LNSBL and

SBTL. These findings also coincide with the study results of McNulty et al (2013) for

the case of the US banks. Meanwhile, the relationship between bank capital ratios

(CAPRATIO) with small business loans is not significant in the majority models except

for model 2 of the LNSBL and SBTL. The findings coincide with the study results of

McNulty et al (2013).

8.2. Policy implications

The findings of this thesis imply that the policies undertaken by the Government of

Indonesia (GOI) has succeeded in increasing the cost efficiency of Indonesian banks but

failed to enhance their technical efficiency over the period. These achievements are

stemmed from the fact that the period was the revival phase after suffering from the

Asian financial crisis of 1997-98. It is also worth noting that the majority of commercial

banks in Indonesia seem to have implemented the cost efficiency programme in their

operations as the internal management policies to avoid failure in their operations post

the crisis period.

Meanwhile, in terms of the input-output process in the operations, it seems that the

majority of commercial banks were successful in collecting funds but failed to enhance

earning assets. The prudential policies implemented in their lending activities (reflected

by the low attainment of loans deposits ratio), although it has increased over time during

133

Significant at 1% level,

202

the period, it could not offset the attainment of funding activities. The results of the

productivity study of the DEA Malmquist index supported the evidence that the

productivity of Indonesian commercial banks tended to decrease over time during the

period.

The policy of GOI released in 2001 which relaxed the compulsory threshold of a

minimum particular percentage of banks financing to small business finance has also

affected the performance of Indonesian banks over the period. This policy has affected

the downward trend (declining path) of small business loans in commercial banks in

Indonesia over the period 2002 to 2010. Although the total lending propensity (in terms

of the total loans) has increased, it could not have enhanced the technical efficiency of

the banks over the period.

The GOI policy to empower state-owned banks (SOB) by merging some particular

banks after the crisis period is also successful in maintaining customer trust. The policy

has also resulted that SOBs is the most efficient banks in terms of technical efficiency,

although in terms of cost efficiency, foreign-owned banks (FOB) performed better.

Some particular reasons that could be traced to SOBs in terms of their cost efficiency

performance are that the overstaffing, the highly fixed assets employment and the

market segmentations. These factors as well as any other factors (endogenous and

exogenous) should be importantly considered by GOI to enhance the performance of the

SOBs. However, the high performance of the SOBs in terms of their technical efficiency

should be maintained over time in the future.

In addition, the importance of small business lending (finance) should be of

considerations to the policy makers given the fact that in some cases explained in this

thesis the involvement of small businesses has increased the attainment of bank

efficiency. The performance of SOBs to outperform all other bank groups in all majority

models in DEA and in one model in SFA (when considering small business finance)

witnessed the importance of the small business finance in the banks‟ financing

portfolios. The success of Sharia banks to outperform conventional banks in the model

of taking into account small business finance in the output components is also very

interesting to be considered by the policy makers.

203

On the other hand, although foreign banks (FB) did better performances in terms of cost

efficiency, they failed to be the best performer in the technical efficiency. The reluctance

to expand their business in providing small business lending seems to be one of the

major reasons to support the circumstances. Lack of experience in small business

lending technologies as well as lack of expertise in dealing with customers in the

segment of small businesses seem to be the main factors they have not deeply involved

in the financing of small businesses. The collaborations with some particular domestic

banks that have successful experiences in managing small business loans can be one of

the solutions to begin providing lending facilities to this segment in their portfolios.

Similar circumstances are also applied to joint venture banks (JVB).

One of the influential factors for enhancing bank technical efficiency performance is

bank size. The findings of the study in this thesis reveal the positive relationship

between bank size and efficiency. It seems that the policy of Bank Indonesia in the new

architecture of Indonesian banking to establish a number of anchor banks that could

compete in the global market is relevant with the findings of the study. Establishing the

large size of banks through mergers and acquisitions seem to be the alternative solution

to reach the objectives. This is most appealing given the fact that Indonesia would face

the ASEAN free trade area in 2015. Two other important factors in determining bank

technical efficiency are loans to deposits ratio (LDR) and capital adequacy ratio (CAR).

Meanwhile, in terms of cost efficiency, the most important determinant is loans to

deposits ratio (LDR). These imply the importance of policy makers (banking

supervisory and authority board) to set policies regarding capital adequacy management

as well as lending exposure management to guarantee the sound performance of all

commercial banks in Indonesia.

In addition, in terms of enhancing bank productivity, the findings of the study reveal

that technological change implemented in commercial banks has supported the

productivity level of Indonesian commercial banks during the study period. This implies

the extensive new technologies (including the implementation of cash machines, phone-

banking, internet banking, etc.) application has a significant contribution to the attained

level of productivity of Indonesian commercial banks. Considering the fact, it is very

advantageous for bank managers to expand their operations by implementing new

technologies that could affect to the enhancement of their productivity.

204

From the last empirical chapter examined, there are two recommendations to policy

makers regarding the lending facilities provided by Indonesian commercial banks. First,

Indonesia's banking supervisory board134

should continue to monitor the achievement of

commercial bank lending to support the economy of Indonesia. The monitoring process

should also consider the quality of each lending facility given that the low quality of

credit will not only reduce a bank's assets but also a bank's capital. Second, the

Indonesian government's decision to re-enact the minimum threshold of lending to small

businesses in 2012135

[although the new regulations is dedicated to micro, small and

medium enterprises (MSMEs)] is absolutely important, to support the enhancement of

commercial banks‟ involvement in providing loans or financing to small businesses as

well as micro and medium enterprises in Indonesia.

8.3. Limitations

The limitations of the results of this thesis can be summarised as follows. More detailed

data on the observed banks would help expand the analysis with the possible various

models to examine. Some data which are not available are the number of employees, the

number of branches, and the individual interest rate for each of type of lending facilities

provided. The availability of the numbers of employees‟ data would be beneficial to be

used as one of the input components (as the labour input). The number of branches data

could also be used to understand the effect of excessive branches on bank performance

included as control variables. The individual interest rate for each type of loans could

also be utilized to expand the various analyses in the last empirical chapter.

This thesis uses the Battese and Coelli 1992 (BC92) in estimating SFA cost efficiency

(in the second empirical chapter) since the study aims to adopt two-stage effects model

134

As stipulated by Indonesia‟s Government Law No. 21 of 2011 on the Financial Services Authority, as

of December 31, 2013, the task of banking regulation and supervision of Bank Indonesia has been

transferred to the Financial Services Authority (FSA or OJK).

135

BI Regulation No.14/22/PBI/2012 on 21 December, 2012 regarding the credit or financing to micro,

small, and medium enterprises by commercial banks and technical assistance for the development of these

businesses. This regulation imposed on all commercial banks in Indonesia to finance SMEs gradually

started in 2012. It is then expected that by the end of 2018 all commercial banks should be maintaining

the SMEs finance minimum of 20 percent.

205

and only account for an average estimation over the period. However, this method is

less flexible compared to the BC1995 method in terms of capturing the yearly rank

order over time.

8.4. Future Research

This thesis results have brought to some recommendations that could be undertaken as

the potential research in the future as follows:

It is very beneficial to compare the performance of Indonesian banks in terms of

operational systems (conventional banks and sharia banks) considering that the number

of sharia banks has been increasing and the share of their assets are also growing to date.

It is also very challenging to compare bank performance across the Association of South

East Asian Nations (ASEAN) countries considering that the region could be facing a

free trade area agreement in 2015 for its members (Indonesia, Malaysia, Thailand, the

Philippines, Singapore, Brunei Darussalam, Laos, Cambodia, Vietnam, and Timor

Leste). The research about the comparison performance both in bank efficiency and

bank lending propensity of the ASEAN‟s commercial banks would help map a

constellation of the competition among the banks operating across the region. In

addition, the study results could be of considerations to explore the possible cooperation

among commercial banks across the region.

In terms of the estimation of SFA cost efficiency, it is recommended for further research

to use the Battese and Coelli 1995 (BC95) considering that the method is more flexible

and has an advantage in terms of its ability to observe the yearly rank order over the

observations. It is also beneficial for further research to conduct profit efficiency of

commercial banks given the fact that that profit efficiency considers not only expenses

but also revenues, thus the results could be more comprehensive in explaining the entire

efficiency of the bank compared to cost efficiency.

206

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218

APPENDICES:

Appendix 1. Figure 1.1. Thesis Objectives

Appendix 2. Table 2.6. Indonesian Government Policy Responses in 1998 and

2008

The 1998 Crisis The 2008 Crisis

1.

Monetary policy: extremely strict. Bank of Indonesia

increased interest rate levels to very high levels.

Deposit account interest rates reached 60 per cent in the peak crisis period. As regards liquidity, the government

implemented a liquidity squeeze.

1. Monetary policy: Bank Indonesia interest rate was

reduced by 300 basis points from 9.5 per cent to 6.5

per cent. Liquidity was relaxed.

2. Fiscal policy: to being with these was a budget surplus then this was revised by permitting a large budget

deficit.

2. Fiscal policy: the stimulus was implemented, the budget deficit enlarged, taxes reduced.

3. Banking health: Prudential banking regulations were extremely weak. NPLs reached 27 per cent. LDR

became more than 100 per cent.

3. Banking health: Prudential banking regulations were relatively tight. NPL less than 4 per cent,

LDR 77 per cent, CAR around 17 per cent.

4. Response toward banking: closure of sixteen banks, which then led to rushes.

4. Response towards banking: deposit insurance increased from Rp.100 million to Rp.2 billion per

account.

5. Policies focused towards structural reform by carrying out economic liberalizations, getting rid of monopolies

and licensing.

5. Safeguarded relatively open trade regime.

6. Exchange rate regime: managed floating. Economic players were not used to exchange rate risk changes and

did not carry out Hedging.

6. Exchange rate regime: flexible. Economic players start to become used to exchange rate risk changes.

Source: Basri (2011)

219

Appendix 3. Figure 5.4. Average technical efficiency of Indonesian banks (model 1)


Appendix 4. Figure 5.5. Average technical efficiency of Indonesian banks (model

2)


Appendix 5. Figure 5.6. Average technical efficiency of Indonesian banks (model

3)


0.000

0.200

0.400

0.600

0.800

1.000

1.200

2002 2003 2004 2005 2006 2007 2008 2009 2010

eff

icie

ncy

year

Average technical efficiency by ownership (model 1)

SOB

PB (FEB)

PB (NFEB)

LGOB

JVB

FOB

0.000

0.200

0.400

0.600

0.800

1.000

1.200

2002 2003 2004 2005 2006 2007 2008 2009 2010

eff

icie

ncy

year

Average technical efficiency by Ownership (model 2)

SOB

PB (FEB)

PB (NFEB)

LGOB

JVB

FOB

0.000

0.200

0.400

0.600

0.800

1.000

1.200

2002 2003 2004 2005 2006 2007 2008 2009 2010

eff

icie

ncy

year

Average techical efficiency by ownership (model 3)

SOB

PB (FEB)

PB (NFEB)

LGOB

JVB

FOB

220





Appendix 8. Table 5.8. Malmquist Index Summary of Annual Means (Model 2)


2002/2003 0.972 0.968 0.987 0.984 0.941

2003/2004 0.972 1.024 0.973 0.999 0.996

2004/2005 1.020 0.976 1.004 1.016 0.995

2005/2006 0.919 1.085 0.943 0.974 0.997

2006/2007 0.872 1.154 0.991 0.880 1.006

2007/2008 0.896 1.095 0.875 1.023 0.981

2008/2009 1.005 0.990 0.946 1.062 0.995

2009/2010 1.072 0.935 1.028 1.043 1.002

Mean 0.964 1.026 0.968 0.996 0.989


-

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

200220032004200520062007200820092010

Ave

rage

Eff

icie

ncy

Sco

re


Peer Group 1 (Total Assets<= IDR 10 Trillion)


Peer Group 3 (Total Assets> IDR 50 Trillion and <= IDR100 Trillion)

-

0.2000

0.4000

0.6000

0.8000

1.0000

1.2000

Ave

rage

Eff

icie

ncy

Sco

re


Peer Group 1 (TotalAssets <= IDR 10 Trillion)

Pee Group 2 (Total Assets> IDR 10 Trillion and <=IDR 50Trillion)

Peer Group 3 (TotalAssets > IDR 50 Trillionand <= IDR 100 Trillion)

221

Appendix 9. Table 5.9. Malmquist Index Summary of Annual Means (Model 3)


2002/2003 0.921 0.989 0.980 0.939 0.910

2003/2004 1.059 0.938 1.012 1.046 0.993

2004/2005 1.036 0.978 1.018 1.018 1.013

2005/2006 0.969 1.076 0.966 1.003 1.043

2006/2007 0.837 1.224 0.986 0.849 1.025

2007/2008 0.840 1.145 0.825 1.018 0.962

2008/2009 0.909 1.079 0.892 1.020 0.981

2009/2010 1.066 0.946 1.027 1.038 1.008

Mean 0.951 1.043 0.961 0.990 0.991


Appendix 10. Figure 6.7. Evolution of Indonesian Banks’ Cost Efficiency – Pooled

- Model 2


Appendix 11. Figure 6.8. The Evolution of Indonesian Banks’ Cost Efficiency –

BC92 - Model 2


0.6500

0.7000

0.7500

0.8000

0.8500

0.9000

0.9500

2002 2003 2004 2005 2006 2007 2008 2009 2010

Co

st E

ffic

ien

cy

Cost efficiency of Model 2 : Pooled

FB

JVB

LGOB

PB

SOB

CONV

SHARIA

-

0.2000

0.4000

0.6000

0.8000

1.0000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Co

st E

ffic

ien

cy

Cost efficiency of Model 2 : BC92

FB

JVB

LGOB

PB

SOB

CONV

222

Appendix 12. Figure 6.9. Evolution of Indonesian Banks’ Cost Efficiency – Pooled

- Model 3


Appendix 13. Figure 6.10. The Evolution of Indonesian Banks’ Cost Efficiency –

BC92 - Model 3


-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Co

st E

ffic

ien

cy

Cost efficiency of Model 3 : Pooled

FB

JVB

LGOB

PB

SOB

CONV

SHARIA

-

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Co

st E

ffic

ien

cy

Cost efficiency of Model 3: BC92

FB

JVB

LGOB

PB

SOB

CONV

SHARIA

223

Appendix 14. Figure 6.13. Cost Efficiency – Pooled - Model 2 (by Bank Peer

Group)

Appendix 15. Figure 6.14. Cost Efficiency – BC92 - Model 2 (by Bank Peer Group)

0.7000

0.7500

0.8000

0.8500

0.9000

0.9500

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Co

st E

ffic

ien

cy






0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

0.9000

1.0000

2002 2003 2004 2005 2006 2007 2008 2009

Ave

rage

Co

st E

ffic

ien

cy






224

Appendix 16. Figure 6.15. Cost Efficiency – Pooled - Model 3 (by Bank Peer

Group)

Appendix 17. Figure 6.16. Cost Efficiency – BC92 - Model 3 (by Bank Peer Group)

0.7200

0.7400

0.7600

0.7800

0.8000

0.8200

0.8400

0.8600

0.8800

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Co

st E

ffic

ien

cy






0.0000

0.1000

0.2000

0.3000

0.4000

0.5000

0.6000

0.7000

0.8000

2002 2003 2004 2005 2006 2007 2008 2009 2010

Ave

rage

Co

st E

ffic

ien

cy






225

Appendix 18. Table 6.17. Coefficient of Cost Efficiency Estimation Variables –

Pooled Model 1 -----------------------------------------------------------------------------

Limited Dependent Variable Model - FRONTIER

Dependent variable LTC

Log likelihood function 190.44549

Estimation based on N = 999, K = 23

Inf.Cr.AIC = -334.9 AIC/N = -.335

Model estimated: Sep 10, 2013, 07:30:47

Stochastic frontier based on panel data

Estimation based on 111 individuals

Variances: Sigma-squared(v)= .02913

Sigma-squared(u)= .18854

Sigma(v) = .17067

Sigma(u) = .43421

Sigma = Sqr[(s^2(u)+s^2(v)]= .46655

Gamma = sigma(u)^2/sigma^2 = .86618

Var[u]/{Var[u]+Var[v]} = .70167

Stochastic Cost Frontier Model, e = v+u

LR test for inefficiency vs. OLS v only

Deg. freedom for sigma-squared(u): 1

Deg. freedom for heteroscedasticity: 0

Deg. freedom for truncation mean: 0

Deg. freedom for inefficiency model: 1

LogL when sigma(u)=0 39.52751

Chi-sq=2*[LogL(SF)-LogL(LS)] = 301.836

Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------

| Standard Prob. 95% Confidence

LTC| Coefficient Error z |z|>Z* Interval

--------+--------------------------------------------------------------------

|Deterministic Component of Stochastic Frontier Model

Constant| 3.43482*** .62279 5.52 .0000 2.21416 4.65547

LTF| .57733*** .09170 6.30 .0000 .39760 .75705

LSI| -.18000* .09591 -1.88 .0605 -.36798 .00797

LOI| .38607*** .08289 4.66 .0000 .22361 .54853

LTFSQ| .14138*** .01228 11.51 .0000 .11731 .16546

LSISQ| .22873*** .01937 11.81 .0000 .19076 .26669

LOISQ| .09386*** .00618 15.18 .0000 .08174 .10598

LPFPC| .23626* .12782 1.85 .0645 -.01426 .48679

LPLPC| .66082*** .10880 6.07 .0000 .44758 .87405

LPFPCSQ| .16672*** .02343 7.11 .0000 .12079 .21265

LPLPCSQ| .07845*** .01641 4.78 .0000 .04629 .11061

LTFSI| -.12279*** .01257 -9.76 .0000 -.14743 -.09814

LTFOI| -.02044*** .00618 -3.31 .0009 -.03255 -.00834

LSIOI| -.08518*** .01027 -8.30 .0000 -.10530 -.06506

LPFCPLC| -.11586*** .01876 -6.18 .0000 -.15263 -.07908

LTFPFPC| .03339** .01363 2.45 .0143 .00666 .06011

LTFPLPC| -.00482 .01014 -.48 .6345 -.02469 .01505

LSIPFPC| .00824 .01539 .54 .5923 -.02193 .03841

LSIPLPC| -.00055 .01380 -.04 .9683 -.02759 .02649

LOIPFPC| -.02459** .00997 -2.47 .0136 -.04412 -.00506

LOIPLPC| -.02206** .01044 -2.11 .0346 -.04252 -.00160

|Variance parameters for compound error

Lambda| 2.54414*** .71292 3.57 .0004 1.14685 3.94143

Sigma(u)| .43421*** .07484 5.80 .0000 .28754 .58089

--------+--------------------------------------------------------------------

Note: ***, **, * ==> Significance at 1%, 5%, 10% level.

-----------------------------------------------------------------------------

ltc = Log (tc/pc); ltf = Log(tf); lsi = Log(si);loi = Log(oi); ltfsq = .5*log(tf)^2; lsisq = .5*log(si)^2; loisq =

.5*log(oi)^2; lpfpc = Log(pf/pc); lplpc = Log(pl/pc); lpfpcsq = .5*log(pf/pc)^2; lplpcsq =.5*log(pl/pc)^2; ltfsi =

log(tf)*log(si); ltfoi = log(tf)*log(oi); lsioi = Log(si)*log(oi); lpfcplc = log(pf/pc)*log(pl/pc); ltfpfpc =

log(tf)*log(pf/pc); ltfplpc = log(tf)*log(pl/pc); lsipfpc = log(si)*log(pf/pc); lsiplpc = log(si)*log(pl/pc); loipfpc

= log(oi)*log(pf/pc); loiplpc = log(oi)*log(pl/pc)

226


BC92 Model 1 -----------------------------------------------------------------------------





Inf.Cr.AIC = -390.3 AIC/N = -.391






Sigma(v) = .16672

Sigma(u) = .31544

Sigma = Sqr[(s^2(u)+s^2(v)]= .35679


Var[u]/{Var[u]+Var[v]} = .56538


Battese-Coelli Models: Time Varying uit

Time dependent uit=exp[-eta(t-T)]*|U(i)|








Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------



--------+--------------------------------------------------------------------


Constant| 3.18476*** .72487 4.39 .0000 1.76405 4.60547

LTF| .47467*** .10062 4.72 .0000 .27745 .67189

LSI| -.08646 .11049 -.78 .4339 -.30302 .13010

LOI| .42440*** .09468 4.48 .0000 .23884 .60996

LTFSQ| .15213*** .01164 13.07 .0000 .12932 .17495

LSISQ| .22072*** .02218 9.95 .0000 .17725 .26419

LOISQ| .10024*** .00671 14.93 .0000 .08709 .11340

LPFPC| .11411 .13291 .86 .3906 -.14639 .37462

LPLPC| .79846*** .10715 7.45 .0000 .58845 1.00846

LPFPCSQ| .13804*** .02531 5.45 .0000 .08844 .18763

LPLPCSQ| .06890*** .01819 3.79 .0002 .03325 .10455

LTFSI| -.12015*** .01390 -8.65 .0000 -.14739 -.09291

LTFOI| -.02413*** .00618 -3.91 .0001 -.03624 -.01203

LSIOI| -.08852*** .01207 -7.33 .0000 -.11218 -.06486

LPFCPLC| -.09788*** .02025 -4.83 .0000 -.13756 -.05820

LTFPFPC| .03605*** .01329 2.71 .0067 .01001 .06209

LTFPLPC| -.00625 .01098 -.57 .5691 -.02777 .01527

LSIPFPC| .01034 .01500 .69 .4903 -.01905 .03973

LSIPLPC| -.00577 .01260 -.46 .6472 -.03047 .01894

LOIPFPC| -.02349** .01150 -2.04 .0411 -.04603 -.00095

LOIPLPC| -.02083* .01097 -1.90 .0576 -.04233 .00068


Lambda| 1.89206*** .06778 27.92 .0000 1.75922 2.02490

Sigma(u)| .31544*** .00639 49.37 .0000 .30292 .32796

|Eta parameter for time varying inefficiency

Eta| .05574*** .00799 6.98 .0000 .04008 .07139

--------+--------------------------------------------------------------------


----------------------------------------------------------------------------- ltc = Log (tc/pc); ltf = Log(tf); lsi = Log(si);loi = Log(oi); ltfsq = .5*log(tf)^2; lsisq = .5*log(si)^2; loisq =

.5*log(oi)^2; lpfpc = Log(pf/pc); lplpc = Log(pl/pc); lpfpcsq = .5*log(pf/pc)^2; lplpcsq =.5*log(pl/pc)^2; ltfsi =

log(tf)*log(si); ltfoi = log(tf)*log(oi); lsioi = Log(si)*log(oi); lpfcplc = log(pf/pc)*log(pl/pc); ltfpfpc =

log(tf)*log(pf/pc); ltfplpc = log(tf)*log(pl/pc); lsipfpc = log(si)*log(pf/pc); lsiplpc = log(si)*log(pl/pc); loipfpc

= log(oi)*log(pf/pc); loiplpc = log(oi)*log(pl/pc)

227


Pooled Model 2

-----------------------------------------------------------------------------





Inf.Cr.AIC = -351.3 AIC/N = -.352






Sigma(v) = .17768

Sigma(u) = .25202

Sigma = Sqr[(s^2(u)+s^2(v)]= .30836


Var[u]/{Var[u]+Var[v]} = .42232









Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------



--------+--------------------------------------------------------------------


Constant| 3.69130*** .65914 5.60 .0000 2.39941 4.98319

LSBF| .14775*** .01798 8.22 .0000 .11252 .18299

LOF| .41616*** .08166 5.10 .0000 .25612 .57621

LSI| -.30155*** .10875 -2.77 .0056 -.51469 -.08841

LOI| .59995*** .08794 6.82 .0000 .42760 .77231

LSBFSQ| .00992*** .00094 10.53 .0000 .00807 .01176

LOFSQ| .13127*** .00554 23.71 .0000 .12041 .14212

LSISQ| .21010*** .01901 11.05 .0000 .17285 .24736

LOISQ| .11595*** .00812 14.27 .0000 .10003 .13187

LPFPC| .20906 .16767 1.25 .2124 -.11956 .53769

LPLPC| .54858*** .13824 3.97 .0001 .27763 .81952

LPFPCSQ| .14580*** .02715 5.37 .0000 .09259 .19901

LPLPCSQ| .11596*** .02567 4.52 .0000 .06564 .16628

LSBFOF| -.00858*** .00113 -7.58 .0000 -.01080 -.00636

LSBFSI| -.00412** .00191 -2.16 .0310 -.00787 -.00038

LSBFOI| .00386*** .00124 3.11 .0018 .00143 .00629

LOFSI| -.08483*** .01005 -8.44 .0000 -.10453 -.06513

LOFOI| -.04336*** .00452 -9.60 .0000 -.05221 -.03451

LSIOI| -.09520*** .01236 -7.70 .0000 -.11943 -.07097

LPFCPLC| -.13185*** .02179 -6.05 .0000 -.17457 -.08913

LSBFPFPC| .00913*** .00260 3.52 .0004 .00404 .01421

LSBFPLPC| -.00505* .00284 -1.78 .0757 -.01062 .00052

LOFPFPC| .03913** .01655 2.36 .0181 .00668 .07157

LOFPLPC| -.00388 .01573 -.25 .8051 -.03471 .02695

LSIPFPC| -.01020 .01595 -.64 .5224 -.04147 .02107

LSIPLPC| .02010 .01523 1.32 .1870 -.00976 .04996

LOIPFPC| -.02436* .01260 -1.93 .0533 -.04906 .00034

LOIPLPC| -.02782** .01270 -2.19 .0285 -.05272 -.00292


Lambda| 1.41838*** .21623 6.56 .0000 .99458 1.84218

Sigma(u)| .25202*** .03158 7.98 .0000 .19014 .31391

--------+--------------------------------------------------------------------


-----------------------------------------------------------------------------

ltc = Log (tc/pc); lsbf = Log(sbf); lof = Log(of); lsi = Log(si); loi = Log(oi); lsbfsq = .5*log(sbf)^2; lofsq =

.5*log(of)^2; lsisq = .5*log(si)^2; loisq = .5*log(oi)^2; lpfpc = Log(pf/pc); lpfpc = Log(pf/pc); lplpc = Log(pl/pc);

lpfpcsq = .5*log(pf/pc)^2; lplpcsq =.5*log(pl/pc)^2; lsbfof = log(sbf)*log(of); lsbfsi = log(sbf)*log(si); lsbfoi =

log(sbf)*log(oi); lofsi = log(of)*log(si); lofoi = Log(of)*log(oi); lsioi = Log(si)*log(oi); lpfcplc =

log(pf/pc)*log(pl/pc); lsbfpfpc = log(sbf)*log(pf/pc); lsbfplpc = log(sbf)*log(pl/pc); lofpfpc = log(of)*log(pf/pc);

lofplpc = log(of)*log(pl/pc); lsipfpc = log(si)*log(pf/pc); lsiplpc = log(si)*log(pl/pc); loipfpc =

log(oi)*log(pf/pc); loiplpc = log(oi)*log(pl/pc)

228


BC92 Model 2 -----------------------------------------------------------------------------





Inf.Cr.AIC = -394.4 AIC/N = -.395






Sigma(v) = .17189

Sigma(u) = .18957

Sigma = Sqr[(s^2(u)+s^2(v)]= .25589


Var[u]/{Var[u]+Var[v]} = .30650











Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------



--------+--------------------------------------------------------------------


Constant| 3.29788*** .76292 4.32 .0000 1.80258 4.79318

LSBF| .15162*** .01817 8.35 .0000 .11602 .18723

LOF| .36598*** .10582 3.46 .0005 .15857 .57339

LSI| -.16410 .12004 -1.37 .1716 -.39937 .07117

LOI| .56679*** .10672 5.31 .0000 .35763 .77596

LSBFSQ| .00909*** .00087 10.45 .0000 .00739 .01079

LOFSQ| .13672*** .00603 22.68 .0000 .12491 .14853

LSISQ| .20059*** .02083 9.63 .0000 .15976 .24142

LOISQ| .11771*** .00825 14.26 .0000 .10153 .13388

LPFPC| .02381 .18162 .13 .8957 -.33217 .37979

LPLPC| .81010*** .13924 5.82 .0000 .53720 1.08301

LPFPCSQ| .11784*** .03061 3.85 .0001 .05784 .17784

LPLPCSQ| .09303*** .02367 3.93 .0001 .04664 .13942

LSBFOF| -.00799*** .00116 -6.91 .0000 -.01025 -.00572

LSBFSI| -.00400** .00190 -2.10 .0353 -.00772 -.00028

LSBFOI| .00294** .00131 2.24 .0248 .00037 .00551

LOFSI| -.08769*** .01301 -6.74 .0000 -.11318 -.06219

LOFOI| -.04108*** .00516 -7.96 .0000 -.05120 -.03096

LSIOI| -.09446*** .01366 -6.92 .0000 -.12123 -.06769

LPFCPLC| -.10602*** .02345 -4.52 .0000 -.15198 -.06005

LSBFPFPC| .00637** .00274 2.32 .0203 .00099 .01175

LSBFPLPC| -.00267 .00282 -.95 .3434 -.00820 .00286

LOFPFPC| .04736*** .01619 2.92 .0034 .01562 .07910

LOFPLPC| -.01698 .01500 -1.13 .2578 -.04638 .01243

LSIPFPC| .00321 .01689 .19 .8494 -.02990 .03631

LSIPLPC| .00526 .01658 .32 .7511 -.02724 .03776

LOIPFPC| -.03323** .01382 -2.41 .0162 -.06031 -.00615

LOIPLPC| -.01593 .01314 -1.21 .2253 -.04167 .00982


Lambda| 1.10283*** .07160 15.40 .0000 .96249 1.24317

Sigma(u)| .18957*** .00170 111.43 .0000 .18623 .19290


Eta| .08354*** .01172 7.13 .0000 .06057 .10650

--------+--------------------------------------------------------------------


----------------------------------------------------------------------------- ltc = Log (tc/pc); lsbf = Log(sbf); lof = Log(of); lsi = Log(si); loi = Log(oi); lsbfsq = .5*log(sbf)^2; lofsq =

.5*log(of)^2; lsisq = .5*log(si)^2; loisq = .5*log(oi)^2; lpfpc = Log(pf/pc); lpfpc = Log(pf/pc); lplpc = Log(pl/pc);

lpfpcsq = .5*log(pf/pc)^2; lplpcsq =.5*log(pl/pc)^2; lsbfof = log(sbf)*log(of); lsbfsi = log(sbf)*log(si); lsbfoi =

log(sbf)*log(oi); lofsi = log(of)*log(si); lofoi = Log(of)*log(oi); lsioi = Log(si)*log(oi); lpfcplc =

log(pf/pc)*log(pl/pc); lsbfpfpc = log(sbf)*log(pf/pc); lsbfplpc = log(sbf)*log(pl/pc); lofpfpc = log(of)*log(pf/pc);

lofplpc = log(of)*log(pl/pc); lsipfpc = log(si)*log(pf/pc); lsiplpc = log(si)*log(pl/pc); loipfpc =

log(oi)*log(pf/pc); loiplpc = log(oi)*log(pl/pc)

229


Pooled Model 3

-----------------------------------------------------------------------------





Inf.Cr.AIC = -64.9 AIC/N = -.065






Sigma(v) = .19112

Sigma(u) = .60568

Sigma = Sqr[(s^2(u)+s^2(v)]= .63512


Var[u]/{Var[u]+Var[v]} = .78493







LogL when sigma(u)=0 -157.81325


Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------



--------+--------------------------------------------------------------------


Constant| 4.38918*** .52530 8.36 .0000 3.35962 5.41874

LOF| .51971*** .08301 6.26 .0000 .35702 .68241

LSI| -.12576 .10187 -1.23 .2170 -.32543 .07390

LOI| .31507*** .10090 3.12 .0018 .11731 .51284

LOFSQ| .12751*** .00748 17.05 .0000 .11285 .14216

LSISQ| .20676*** .01560 13.25 .0000 .17618 .23733

LOISQ| .08634*** .00716 12.07 .0000 .07232 .10037

LPFPC| .19458 .13804 1.41 .1587 -.07597 .46514

LPLPC| .80901*** .12921 6.26 .0000 .55576 1.06225

LPFPCSQ| .12640*** .02881 4.39 .0000 .06994 .18285

LPLPCSQ| .03854* .02210 1.74 .0812 -.00478 .08186

LOFSI| -.11462*** .00937 -12.23 .0000 -.13299 -.09626

LOFOI| -.01935*** .00573 -3.38 .0007 -.03057 -.00813

LSIOI| -.07214*** .01138 -6.34 .0000 -.09444 -.04985

LPFPCPLP| -.07568*** .02476 -3.06 .0022 -.12421 -.02715

LOFPFPC| .05602*** .01077 5.20 .0000 .03492 .07713

LOFPLPC| -.03571*** .01052 -3.39 .0007 -.05633 -.01509

LSIPFPC| .00258 .01441 .18 .8578 -.02566 .03082

LSIPLPC| .00401 .01360 .30 .7679 -.02263 .03066

LOIPFPC| -.03727*** .01220 -3.05 .0023 -.06117 -.01336

LOIPLPC| -.00727 .01174 -.62 .5356 -.03027 .01573


Lambda| 3.16917*** .91135 3.48 .0005 1.38296 4.95538

Sigma(u)| .60568*** .09762 6.20 .0000 .41436 .79700

--------+--------------------------------------------------------------------


-----------------------------------------------------------------------------

ltc = Log(tc/pc); lof = Log(of); lsi = Log(si); loi = Log(oi); lofsq = .5*Log(of)^2; lsisq = .5*Log(si)^2; loisq =

.5*Log(oi)^2; lpfpc = Log(pf/pc); lplpc = Log(pl/pc); lpfpcsq = .5*Log(pf/pc)^2; lplpcsq = .5*Log(pl/pc)^2; lofsi =

Log(of)*Log(si); lofoi = Log(of)*Log(oi); lsioi = Log(si)*log(oi); lpfpcplp = Log(pf/pc)*Log(pl/pc); lofpfpc =

Log(of)*Log(pf/pc); lofplpc = Log(of)*Log(pl/pc); lsipfpc = Log(si)*Log(pf/pc); lsiplpc = Log(si)*Log(pl/pc); loipfpc

= Log(oi)*Log(pf/pc; loiplpc = Log(oi)*Log(pl/pc)

230


BC92 Model 3 -----------------------------------------------------------------------------





Inf.Cr.AIC = -129.6 AIC/N = -.130






Sigma(v) = .18641

Sigma(u) = .42530

Sigma = Sqr[(s^2(u)+s^2(v)]= .46436


Var[u]/{Var[u]+Var[v]} = .65416









LogL when sigma(u)=0 -157.81325


Kodde-Palm C*: 95%: 2.706, 99%: 5.412

--------+--------------------------------------------------------------------



--------+--------------------------------------------------------------------


Constant| 3.95999*** .75833 5.22 .0000 2.47369 5.44630

LOF| .40880*** .09808 4.17 .0000 .21657 .60104

LSI| -.01354 .12656 -.11 .9148 -.26160 .23452

LOI| .33506*** .10910 3.07 .0021 .12122 .54890

LOFSQ| .13934*** .00881 15.82 .0000 .12208 .15660

LSISQ| .19508*** .01898 10.28 .0000 .15789 .23227

LOISQ| .09064*** .00703 12.89 .0000 .07686 .10442

LPFPC| .03145 .14309 .22 .8260 -.24899 .31189

LPLPC| .92927*** .12787 7.27 .0000 .67865 1.17990

LPFPCSQ| .10420*** .03136 3.32 .0009 .04274 .16566

LPLPCSQ| .03500 .02271 1.54 .1232 -.00950 .07950

LOFSI| -.11067*** .01000 -11.07 .0000 -.13027 -.09106

LOFOI| -.02173*** .00662 -3.28 .0010 -.03469 -.00876

LSIOI| -.07403*** .01210 -6.12 .0000 -.09775 -.05031

LPFPCPLP| -.06411** .02605 -2.46 .0139 -.11517 -.01304

LOFPFPC| .06151*** .01140 5.40 .0000 .03917 .08385

LOFPLPC| -.03478*** .01071 -3.25 .0012 -.05577 -.01380

LSIPFPC| .00349 .01493 .23 .8151 -.02577 .03275

LSIPLPC| -.00065 .01338 -.05 .9611 -.02688 .02558

LOIPFPC| -.03572*** .01326 -2.69 .0071 -.06170 -.00973

LOIPLPC| -.00670 .01225 -.55 .5844 -.03072 .01732


Lambda| 2.28151*** .05544 41.15 .0000 2.17285 2.39018

Sigma(u)| .42530*** .01355 31.38 .0000 .39874 .45186


Eta| .05400*** .00520 10.38 .0000 .04380 .06420

--------+--------------------------------------------------------------------


----------------------------------------------------------------------------- ltc = Log(tc/pc); lof = Log(of); lsi = Log(si); loi = Log(oi); lofsq = .5*Log(of)^2; lsisq = .5*Log(si)^2; loisq =

.5*Log(oi)^2; lpfpc = Log(pf/pc); lplpc = Log(pl/pc); lpfpcsq = .5*Log(pf/pc)^2; lplpcsq = .5*Log(pl/pc)^2; lofsi =

Log(of)*Log(si); lofoi = Log(of)*Log(oi); lsioi = Log(si)*log(oi); lpfpcplp = Log(pf/pc)*Log(pl/pc); lofpfpc =

Log(of)*Log(pf/pc); lofplpc = Log(of)*Log(pl/pc); lsipfpc = Log(si)*Log(pf/pc); lsiplpc = Log(si)*Log(pl/pc); loipfpc

= Log(oi)*Log(pf/pc; loiplpc = Log(oi)*Log(pl/pc)

231

Appendix 24. FIRST REGRESSIONS IN COMPLETION TO TABLE 7.3 (THE

TSLS REGRESSION RESULTS 1)

xtivreg lntl lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg lntl lnta capratio lndep ( p= infl ) , fe small first

xtivreg lntl lnta capratio lndep ( p= unemp ) , fe small first

F test that all u_i=0: F(108, 868) = 4.79 Prob > F = 0.0000 rho .70754853 (fraction of variance due to u_i) sigma_e .01575868 sigma_u .02451156 _cons .3941417 .0138213 28.52 0.000 .3670145 .4212688 gdpgr -.4414485 .0903354 -4.89 0.000 -.6187498 -.2641472 lndep -.0027363 .0029095 -0.94 0.347 -.0084468 .0029742 capratio -.051727 .0091682 -5.64 0.000 -.0697215 -.0337325 lnta -.0119081 .0030424 -3.91 0.000 -.0178794 -.0059368 p Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84

overall = 0.0731 max = 9 between = 0.0250 avg = 9.0R-sq: within = 0.3257 Obs per group: min = 9

Group variable: no Number of groups = 109Fixed-effects (within) regression Number of obs = 981

First-stage within regression

F test that all u_i=0: F(108, 868) = 6.66 Prob > F = 0.0000 rho .76261529 (fraction of variance due to u_i) sigma_e .01396157 sigma_u .02502423 _cons .3574351 .0123695 28.90 0.000 .3331574 .3817127 infl .1890166 .0115404 16.38 0.000 .1663662 .2116671 lndep -.0033422 .0025769 -1.30 0.195 -.0083999 .0017155 capratio -.048485 .0080627 -6.01 0.000 -.0643097 -.0326603 lnta -.011505 .0026721 -4.31 0.000 -.0167495 -.0062605 p Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02




F test that all u_i=0: F(108, 868) = 4.69 Prob > F = 0.0000 rho .6930807 (fraction of variance due to u_i) sigma_e .01572521 sigma_u .02363068 _cons .3330471 .0196571 16.94 0.000 .294466 .3716282 unemp .2758586 .0524321 5.26 0.000 .17295 .3787673 lndep -.0040865 .0029084 -1.41 0.160 -.0097949 .0016218 capratio -.0480258 .0092554 -5.19 0.000 -.0661913 -.0298603 lnta -.0098214 .0031097 -3.16 0.002 -.0159248 -.003718 p Coef. Std. Err. t P>|t| [95% Conf. Interval]

corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




232

xtivreg tlta lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg tlta lnta capratio lndep ( p= infl ) , fe small first

xtivreg tlta lnta capratio lndep ( p= unemp ) , fe small first


corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84





corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02





corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




233

xtivreg tltd lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg tltd lnta capratio lndep ( p= infl ) , fe small first

xtivreg tltd lnta capratio lndep ( p= unemp ) , fe small first


corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84





corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02





corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




234

Appendix 25. FIRST REGRESSIONS IN COMPLETION TO TABLE 7.4 (THE

TSLS REGRESSION RESULTS 2)

xtivreg lnsbl lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg lnsbl lnta capratio lndep ( p= infl ) , fe small first

xtivreg lnsbl lnta capratio lndep ( p= unemp ) , fe small first


corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84





corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02




corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




235

xtivreg sblta lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg sblta lnta capratio lndep ( p= infl ) , fe small first

xtivreg sblta lnta capratio lndep ( p= unemp ) , fe small first


corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84





corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02





corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




236

xtivreg sbltl lnta capratio lndep ( p= gdpgr) , fe small first

xtivreg sbltl lnta capratio lndep ( p= infl ) , fe small first

xtivreg sbltl lnta capratio lndep ( p= unemp ) , fe small first


corr(u_i, Xb) = -0.8219 Prob > F = 0.0000 F(4,868) = 104.84





corr(u_i, Xb) = -0.8013 Prob > F = 0.0000 F(4,868) = 193.02





corr(u_i, Xb) = -0.8068 Prob > F = 0.0000 F(4,868) = 106.21




237

Appendix 26. Correlation Test

0.0000 mktindex 0.5662 1.0000 gdpgr 1.0000 gdpgr mktindex

. pwcorr gdpgr mktindex,sig

0.0000 mktindex -0.6426 1.0000 unemp 1.0000 unemp mktindex

. pwcorr unemp mktindex,sig

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