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STATUS OF THESIS
Title of thesis Development of a Linkage Model to Forecast the Influence of
Construction Sector Towards Malaysian Economy
I _________________________________________________________________________
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Signature of Author Signature of Supervisor
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________________________________ __________________________
________________________________
________________________________
Date : _____________________ Date : __________________
RAZA ALI KHAN
Assoc. Prof Ir. Dr. Mohd. Shahir Liew
Liew
House No A-08
NED University Staff Colony
Gulshan-e-Iqbal, P.O. Box 75300
Abul Asphani Road Karachi, Pakistan.
UNIVERSITI TEKNOLOGI PETRONAS
DEVELOPMENT OF A LINKAGE MODEL TO FORECAST THE INFLUENCE
OF CONSTRUCTION SECTOR TOWARDS MALAYSIAN ECONOMY
by
RAZA ALI KHAN
The undersigned certify that they have read, and recommend to the Postgraduate Studies
Programme for acceptance this thesis for the fulfillment of the requirements for the degree
stated.
Signature: ______________________________________
Main Supervisor: ______________________________________
Signature: ______________________________________
Co-Supervisor: ______________________________________
Signature: ______________________________________
Co-Supervisor:
Signature: ______________________________________
Head of Department: ______________________________________
Date: ______________________________________
Assoc. Prof Ir. Dr. Mohd. Shahir Liew
Dr. Zulkipli Bin Ghazali
Dr. Noor Amila Wan Abdullah Zawawi
Dr. Noor Amila Wan Abdullah Zawawi
DEVELOPMENT OF A LINKAGE MODEL TO FORECAST THE INFLUENCE
OF CONSTRUCTION SECTOR TOWARDS MALAYSIAN ECONOMY
by
RAZA ALI KHAN
A Thesis
Submitted to the Postgraduate Studies Programme
as a Requirement for the Degree of
DOCTOR OF PHILOSOHY
DEPARTMENT OF CIVIL ENGINEERING
UNIVERSITI TEKNOLOGI PETRONAS
BANDAR SERI ISKANDAR,
PERAK
SEP. 2014
DECLARATION OF THESIS
Title of thesis
Development of a Linkage Model to Forecast the Influence of
Construction Sector Towards Malaysian Economy
I _________________________________________________________________________
hereby declare that the thesis is based on my original work except for quotations and citations
which have been duly acknowledged. I also declare that it has not been previously or
concurrently submitted for any other degree at UTP or other institutions.
Witnessed by
________________________________ __________________________
Signature of Author Signature of Supervisor
Permanent address:________________ Name of Supervisor
________________________________ __________________________
________________________________
________________________________
Date : _____________________ Date : __________________
RAZA ALI KHAN
House No A-08 NED
University Staff Colony Gulshan-e-
Iqbal, P.O. Box 75300 Abul Asphani
Road Karachi, Pakistan.
Assoc. Prof. Ir. Dr. Mohd. Shahir Liew
v
DEDICATION
This thesis is dedicated to a very special person of my life, my
beloved wife “Shazia Ali”
vi
ACKNOWLEDGEMENTS
Of course first of all thank to Almighty Allah who help me in all my difficulties and
give me courage to withstand strongly to achieve my objectives.
I would like to express my sincere thanks to my supervisors, Assoc. Prof. Ir. Dr.
Mohd Shahir Liew and Co- supervisors Dr. Zulkipli Ghazali and Dr. Noor Amila
Wan Abdullah Zawawi for their guidance, encouragement and support during this
entire research. Their expertise, moral, spiritual and academic support always
provides me enough strength to conquer all the problems throughout the duration of
this research. Their mentorship have led me to a great sense of confidence in my
abilities, one which I am certain would have achieved otherwise. I gratefully
appreciate their kindness and patience.
I would also like to express special thanks for my beloved wife Shazia Ali and
sons Huzaifa Raza Khan, Hunain Raza Khan and Talal Raza Khan,for their great
supports and countless sacrifices. Without their valuable support, sacrifices,
encouragements, and prayers, I surely would not be where I am today. This research
work would not have been possible without their kind support and cooperation.
There are many people that have encouraged me throughout my life, and who
have supported me to complete my education. My parents, whose prayers always pave
my ways. My father and elder brother whose encouragements, give me confidence
and motivation to which, I owe much of my life success.
Further I would like to extend my gratitude to Assoc. Prof. Sima Jalil who helped
me in improving the language and quality of my thesis. Last but not least I would also
like to thank all my friends and colleagues for their moral support and acted like
family members in Malaysia: Sadaqat Ullah Khan, Tehmina Sadaqat, Sadaf Qasim,
Mohummad Imran, Shujaa Safdar, Muzzamil, and all other my friends and colleagues
at Universiti Teknologi PETRONAS Malaysia and in Pakistan as well, who helped
me during the time span of this research.
vii
Finally, I would like to acknowledge the financial support provided by Universiti
Teknologi PETRONAS Malaysia and NED University of Engineering & Technology
Karachi Pakistan during this study.
viii
ABSTRACT
The construction sector and socioeconomic development are closely associated. The
Infrastructure development is essential to achieve the main development targets such
as urbanization, industrialization, export promotion, equitable income distribution,
and sustainable economic development. It is impossible to develop infrastructure
without the help of the construction sector, and without proper infrastructure a
country cannot be developed. It is, therefore important to conduct a comprehensive
study for the construction sector that discusses the significance, linkage, behavior and
lead lag relationship of the sector. The purpose of this study is to develop vector error
correction model (VECM) equation for construction sector of the Malaysia to analyze
the long run and short run association between the Malaysian construction sector
(MCS) and other major sectors, namely manufacturing, mining and quarrying,
agriculture and forestry and services of the Malaysian economy including gross
domestic product (GDP) of Malaysia. The VECM equation is also used to develop
impulse response functions (IRFs) to analyze the behavior of the MCS against the
positive investment shock produced in other major sectors of the economy and vice
versa. Furthermore the estimated model equation for MCS is used to forecast the
future output of MCS from 2014Q1 to 2020Q4. The quarterly time series data for the
period 1991Q1 to 2010 Q4 and modern econometric time series technique is used to
conduct the analysis. The Pearson correlation test shows that the MCS has strong
correlation with other major sectors and GDP of the Malaysia. The bivariate Granger
causality analysis suggest that the MCS has bidirectional causal links with mining and
quarrying sector and GDP of Malaysia while the uni-directional link with
manufacturing, agriculture and forestry and service sector. Thus the MCS has inter-
sectoral linkage with all major sectors of the economy including GDP. The existence
of co-integration between the MCS and other sectors of the economy confirm that the
long run association exist between MCS and other sectors, the speed of adjustment
of MCS towards long run equilibrium is 49 %. The IRFs shows that the MCS initially
has a negative response against the positive investment shock in other major sectors
of the economy except GDP, while the other major sector reaction against the MCS
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shock is positive. The model validation test confirmed that the estimated model
equation has strong explanatory power with no serial correlation and hetrocedasticity.
The value of R2 is high i.e. 82.45% shows strong explanatory power of the estimated
model. Durban Watson value is close to 2 i.e.1.95 % suggests that the model is free
from autocorrelation and the probability of the F-statistic (0.0000) is significant,
means the estimated model has no technical and statistical problems. Thus the
estimated VECM for MCS is an efficient model and this can be used for forecasting.
It was observed that the mean absolute percentage error between the actual and
forecasted value of MCS output is only 5.34%, with biased and variance proportion
0.00006 and 0.008 percent respectively. The results of the study are informative and
useful for government of Malaysia, ministries of sectors and policy makers, they can
use these information’s in formulating the investment, expenditure plan and
development strategies for the MCS and aggregate economy as well.
x
ABSTRAK
Sektor pembinaan dan pembangunan sosio-ekonomi merupakan dua sektor yang
mempunyai hubung kait antara satu sama lain. Infrastruktur adalah penting untuk
mencapai sasaran pembangunan utama seperti pembandaran, perindustrian, promosi
eksport, pengagihan pendapatan yang saksama, dan pembangunan ekonomi yang
mampan. Pembangunan infrastruktur adalah mustahil tanpa sokongan dari industri
pembinaan, dan dengan ini infrastruktur sesebuah negara tidak boleh membangun.
Tujuan kajian ini adalah untuk membina persamaan “vector error correction module”
atau juga dikenali sebagai persamaan VECM bagi sector pembinaan di Malaysia, bagi
tujuan untuk mengkaji peranan sektor pembinaan Malaysia (MCS) dalam sector
utama yang lain, iaitu pembuatan, perlombongan, kuari, pertanian and perhutanan,
dan juga perkhidmatan ekonomi Malaysia termasuk keluaran masuk dalam negara
kasar (KDNK). Persamaan VECM juga boleh digunakan untuk membina fungsi
“Impulse Response Function” (IRFs) untuk mengkaji tindak balas MCS dengan
kejutan positif dari segi pelaburan di sector ekonomi utama, dan juga kesan
sebaliknya. Tambahan pula, jangkaan dari persamaan untuk MCS boleh digunakan
untuk meramal hasil di masa hadapan, iaitu dari suku pertama tahun 2014 hingga suku
keempat tahun 2020. Bagi tujuan ini, siri masa bagi suku pertama tahun 1991 hingga
suku keempat tahun 2010 digunakan bersama teknik siri masa ekonometrik moden.
Teknik fungsi hubungkait Pearson menunjukkan bahawa MCS mempunyai hubungan
yang baik dengan sector utama yang lain and juga KDNK dalam negara. Analisis
menggunakan teknik “bivariate Granger casuality” menunjukkan bahawa MCS
membunyai hubungan sebab dan akibat dua-hala dengan sector perlombongan, kuari
and KDNK dalam negara, manakala hubungan sehala pula dikaitkan dengan sector
pembuatan, pertanian, perhutanan, dan perkhidmatan. Oleh itu, MCS mempunyai
hubungan antara sektor dengan semua sektor ekonomi yang lain. Integrasi bersama
yang wujud antara MCS dan sektor-sektor lain menunjukkan pelarasan jangka
panjang pada kadar 49%. Manakala IRFs pula menunjukkan bahawa MCS pada
mulanya memberi tindak balas negatif terhadap kejutan positif dalam sektor-sektor
utama yang lain, kecuali KDNK, manakala tindak balas sektor utama yang lain
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terhadap kejutan positif dalam MCS adalah positif. Ujian pengesahan modul
mengesahkan bahawa persamaan modul anggaran mempunyai kuasa penjelasan yang
kukuh tanpa sebarang korelasi bersiri dan “hetrocedasticity”. Nilai R2 menunjukkan
peratusan yang tinggi, iaitu 82.45%, dan ini menunjukkan hubungan yang kukuh
dengan modul jangkaan. Angka Durban Watson adalah hampir kepada 2, iaitu 1.95 %
dan ini menunjukkan bahawa modul ini adalah bebas dari korelasi automatik,
manakala kebarangkalian “F-statistic” (0.0000) adalah ketara dan bermakna bahawa
modul jangkaan tidak mempunyai masalah teknikal dan statistik. Oleh itu, jangkaan
VECM untuk MCS boleh menjadi modul yang baik untuk membuat jangkaan masa
hadapan. Ia juga diperhatikan bahawa peratusan ralat mutlak minimum di antara nilai
sebenar dan hasil nilai yang diramalkan oleh MCS adalah hanya 5.34 %, dengan berat
sebelah dan varians kadar 0.00006 dan 0.008 peratus. Keputusan kajian ini amat
berguna untuk sektor kementerian dan penggubal dasar, di mana maklumat yang
diperoleh boleh digunakan dalam membina pelaburan, menggubal perbelanjaan dan
strategi pembangunan untuk MCS dan juga sektor ekonomi secara keseluruhan.
xii
In compliance with the terms of the Copyright Act 1987 and the IP Policy of the
university, the copyright of this thesis has been reassigned by the author to the legal
entity of the university,
Institute of Technology PETRONAS Sdn Bhd.
Due acknowledgement shall always be made of the use of any material contained
in, or derived from, this thesis.
© Raza Ali Khan, 2014
Institute of Technology PETRONAS Sdn Bhd
All rights reserved.
xiii
TABLE OF CONTENT
STATUS OF THESIS ............................................................................................. 1
DECLARATION OF THESIS ............................................................................... iv
DEDICATION ......................................................................................................... v
ACKNOWLEDGEMENTS .................................................................................... vi
ABSTRACT .........................................................................................................viii
ABSTRAK ............................................................................................................... x
LIST OF ABBREVIATIONS ............................................................................xxiii
LIST OF NOMENCLATURE ............................................................................. xxv
CHAPTER 1 INTRODUCTION ............................................................................. 1
1.1 Background of Study ......................................................................................... 1
1.1.1 Significance of Construction sector in Economic Development .......... 3
1.1.2 Need for Development in Construction Sector ..................................... 4
1.2 Problem Statement ............................................................................................. 5
1.3 Objectives of the Study ...................................................................................... 7
1.4 Scope of Study ................................................................................................... 7
1.5 General Methodology ........................................................................................ 8
1.6 Chapter Scheme ................................................................................................. 9
1.7 Chapter Summary ............................................................................................ 12
CHAPTER 2 ECONOMIC DEVELOPMENT AND CONSTRUCTION
SECTOR .......................................................................................................... 13
2.1 Introduction...................................................................................................... 13
2.2 Definition of Development .............................................................................. 13
2.2.1 Economic Development ...................................................................... 14
2.2.2 Social Development ............................................................................ 14
2.3 Inter-sectoral Linkages Theory of Development ............................................. 16
2.3.1 Backward Linkage ............................................................................... 17
2.3.2 Forward Linkage ................................................................................. 17
2.3.3 Backward and Forward Linkages ........................................................ 18
2.4 Tools for Measuring Strength of Linkages ...................................................... 18
2.4.1 Granger Causality Econometric Approach.......................................... 20
2.4.2 Vector Auto Regression (VAR) Model ............................................... 20
xiv
2.4.3 Vector Error Correction Model (VECM) ............................................ 20
2.5 What is Construction Sector? .......................................................................... 22
2.6 Construction Sector and National Development ............................................. 25
2.7 Construction and Economic Growth ............................................................... 26
2.7.1 Gross Fixed Capital Formation ........................................................... 30
2.7.2 Value added ......................................................................................... 31
2.7.3 Employment Generating Potential ...................................................... 31
2.7.4 Highlights of Key Studies over Construction and Economic Growth 33
2.7.5 Research gap ....................................................................................... 39
2.8 Chapter Summary ............................................................................................ 39
CHAPTER 3 AN OVERVIEW ON FACTS, PROBLEMS AND PROSPECTS
OF MCS .......................................................................................................... 41
3.1 Introduction...................................................................................................... 41
3.2 Overview of Malaysia Economic Development .............................................. 41
3.3 Malaysian Economic growth and development policies ................................. 43
3.3.1 New Economic Policy (NEP) 1970-1990 ........................................... 44
3.3.2 New Development Policy (NDP) 1991-2000 ...................................... 46
The NDP has a ten year time period in which three, five years plans,
Sixth, Seventh and Eighth were launched ....................................... 47
3.3.3 National Vision Policy (NVP) 2001-2010 .......................................... 47
3.3.4 Economic Transformation Program (ETP) ......................................... 48
3.3.5 Structural Transformation and Major Sectors of Malaysian
Economy .......................................................................................... 49
3.3.6 Inflation and Unemployment .............................................................. 50
3.3.7 Balance of Trade ................................................................................. 51
3.4 Malaysian Construction Sector (MCS) ............................................................ 52
3.4.1 MCS Output and GDP Malaysia ......................................................... 53
3.4.2 MCS Output as a Percentage of GDP Malaysia .................................. 55
3.4.3 MCS and GDP Growth ....................................................................... 56
3.4.4 MCS and Major Sectors of Malaysian Economy (Comparison) ......... 58
3.4.5 The Performance of Subsectors of MCS ............................................. 61
3.4.6 Employment Contribution of MCS ..................................................... 65
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3.4.7 Employment by Category (Full Time Paid Employees) ..................... 66
3.4.8 Registered Contractor, Sub-Contractor, and Suppliers ....................... 67
3.4.9 Registered Consultants ........................................................................ 69
3.4.10 MCS workforce ................................................................................. 70
3.4.11 Productivity of MCS Worker ............................................................ 71
3.4.12 Role of Public and Private Sector in Malaysian Construction
Industry ............................................................................................ 73
3.5 MCS and Global Market .................................................................................. 73
3.6 Problems, Issues and Challenges of MCS ....................................................... 77
3.6.1 Construction Approach ....................................................................... 78
3.6.2 Comprehensive Integrated Solution Provider ..................................... 78
3.6.3 Error Free Construction ....................................................................... 79
3.6.4 Timely Adequate Financing ................................................................ 80
3.6.5 Long Chain and Large Number of Contractors ................................... 80
3.6.6 Fragmentation in the Industry ............................................................. 80
3.6.7 Fair and Transparent Bidding Process ................................................ 81
3.6.8 Construction Sector Payment System and Adjudication Act .............. 82
3.6.9 Public Private Partnership Projects and Land Issues........................... 82
3.6.10 Educated, Trained and Skilled Manpower ........................................ 83
3.6.11 Research and Development (R&D) ................................................... 83
3.7 Summary .......................................................................................................... 84
CHAPTER 4 RESEARCH METHODOLOGY .................................................... 85
4.1 Introduction...................................................................................................... 85
4.2 Paradigm of Research ...................................................................................... 86
4.3 Variables of Interest ......................................................................................... 87
4.4 Data Size and Sources ..................................................................................... 87
4.5 Empirical Analysis........................................................................................... 88
4.5.1 Unit Root Test ..................................................................................... 89
4.5.2 Identification of Order of Integration .................................................. 90
4.5.3 Optimal Lag Order .............................................................................. 91
4.5.4 Co-integration and Rank Tests ............................................................ 92
4.5.4.1 Trace Test................................................................................. 93
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4.5.4.2 Maximum Eigenvalue Test ...................................................... 93
4.5.4.3 Johansen’s Co-integration Methodology ................................. 94
4.5.5 Bivariate Granger Causality ................................................................ 94
4.5.6 Vector Error Correction Model (VECM) ............................................ 95
4.5.7 The Coefficient of Determination (R2) ................................................ 97
4.5.8 The Durbin Watson (D.W) .................................................................. 97
4.5.9 The F- Statistics ................................................................................... 98
4.5.10 Model Structure Stability Test .......................................................... 98
4.5.11 Impulse Response Functions (IRFs) ................................................. 99
4.5.12 Forecasting ...................................................................................... 100
4.5.12.1 Forecasting Error ................................................................. 100
4.5.13 Theil’s Inequality Coefficient ......................................................... 101
4.6 Summary ........................................................................................................ 102
CHAPTER 5 INTER-SECTORIAL LINKAGES OF MCS ............................... 103
5.1 Introduction.................................................................................................... 103
5.2 Relationship between Construction and Other Sectors of Malaysian
Economy ..................................................................................................... 103
5.3 Measurement of MCS Inter-sectorial Linkages ............................................. 106
5.3.1 Unit Root (Stationarity) Test ............................................................. 106
5.3.2 Hypothesis for Unit Root Test .......................................................... 107
5.3.3 Unit Root Test Results ...................................................................... 108
5.3.4 Optimal Lag Length .......................................................................... 109
5.3.5 Pair wise Granger Causality Analysis ............................................... 109
5.3.6 Hypothesis Testing ............................................................................ 111
5.3.7 Pair wise Causality Hypothesis Test Result ...................................... 112
5.3.8 Linkage Direction .............................................................................. 113
5.4 Graphical Models for Pair Wise Granger Causality ...................................... 114
5.5 Summary ........................................................................................................ 117
CHAPTER 6 VECM, IRF AND FORECASTING FOR MCS ........................... 119
6.1 Introduction.................................................................................................... 119
6.2 Vector Error Correction Model (VECM) ...................................................... 119
6.3 Co-integration Examination........................................................................... 120
xvii
6.4 VECM for Construction Sector ..................................................................... 122
6.5 Long Run and Short run Causality Coefficients ............................................ 124
6.6 Explanatory Power and Efficiency of Equation 6.9 for MCS Model 6.1 ...... 126
6.7 Validation of the Estimated Equation 6.9 for MCS Model 6.1 ..................... 127
6.8 Residual Graph .............................................................................................. 128
6.9 Serial Correlation Test for Residual .............................................................. 129
6.10 Residual Hetroskedasticity Test .................................................................. 130
6.11 Residual Correlogram .................................................................................. 130
6.12 Residual Normality Distribution Test .......................................................... 131
6.13 Structure Stability Test for Model Equation 6.9 .......................................... 132
6.14 Impulse Response Functions (IRFs) ............................................................ 134
6.15 Forecasting and Validation of Forecasted Values ....................................... 141
6.16 Summary ...................................................................................................... 145
CHAPTER 7 CONCLUSIONS AND RECOMMENDATIONS ........................ 147
7.1 Introduction.................................................................................................... 147
7.2 Conclusions ................................................................................................... 147
7.2.1 Summary of Empirical Finding of the Model ................................... 148
7.3 Policy Implications ........................................................................................ 151
7.4 Contribution and significance of Study ......................................................... 152
7.5 Novelty of Study ............................................................................................ 152
7.6 General Recommendation ............................................................................. 153
7.7 Recommendations for Future Studies ............................................................ 154
REFERANCE ...................................................................................................... 156
PUBLICATIONS & ACHIEVEMENTS ............................................................ 165
APPENDIX A JOHANSEN CO-INTEGRATION TEST RESULTS ............... 167
APPENDIX B CO-INTEGRATING EQUATIONS ........................................... 168
APPENDIX C VECM SYSTEM EQUATIONS (M1-M6)................................. 169
APPENDIX D COEFFICIENTS FOR VECM - 1 .............................................. 171
APPENDIX E COEFFICIENTS FOR VECM - 2 ............................................... 172
APPENDIX F COEEFICIENTS FOR VECM -3 ................................................ 173
APPENDIX G COEFFICIENTS FOR VECM - 4 .............................................. 174
APPENDIX H COEFFICIENTS FOR VECM -5 ............................................... 175
xviii
APPENDIX I COEFFICIENTS FOR VECM -6 ................................................. 176
APPENDIX J VECM SYSTEM EQUATIONS RESULTS (M1-M6) ............... 177
APPENDIX K SHORT RUN COEFFICIENT SIGNIFICANCE TEST (M 1) .. 179
APPENDIX L SHORT RUN MCS COEFFICIENTS SIGNIFICANCE TEST . 181
APPENDIX M IRF FOR ALL STUDY VARIABLES ...................................... 183
xix
LIST OF FIGURES
Figure 1.1 Study flowchart .......................................................................................... 11
Figure 2.1: Structure of Construction Sector ............................................................... 24
Figure 2.2: S- Shape Relationship (Source: Turin, 1978) ............................................ 27
Figure 2.3: The Bon Curve (Source: Bon, 1992) ......................................................... 28
Figure 2.4: Globle Construction Output Distribution .................................................. 32
Figure 2.5: Global Construction Employment Distribution ....................................... 33
Figure 3.1 Malaysia Map ............................................................................................. 42
Figure 3.2: Construction output and GDP (1990-2010) .............................................. 55
Figure 3.3: Contribution of Construction output and GDP growth (%) ...................... 56
Figure 3.4: Yearly construction sector and GDP growth ............................................ 57
Figure 3.5: Key sectors average contribution (%) to GDP in various plans ................ 60
Figure 3.6: Key sectors average contribution (%) to GDP in various plans ............... 61
Figure 3.7: Subsectors output level, ............................................................................ 62
Figure 3.8: Subsectors contribution, ............................................................................ 63
Figure 3.9: Average contribution (1985-2010) %........................................................ 64
Figure 3.10: Employment contribution ........................................................................ 65
Figure 3.11: Construction personnel by trade category % (2010) ............................... 66
Figure 3.12: Composition of grade 1-7 and foreign contractors (%), .......................... 67
Figure 3.13: Composition of consultant....................................................................... 70
Figure 3.14: Foreign and local worker ......................................................................... 71
Figure 3.15: Construction output per employee, ......................................................... 72
Figure 3.16: Construction value added per employee ................................................. 72
Figure 3.17: Investment trend (public and private sector) ........................................... 73
Figure 3.18: Value of overseas projects (2001-2010) .................................................. 76
Figure 3.19: Number of overseas projects (2001-2010) .............................................. 77
Figure 4.1 Analysis flow chart ..................................................................................... 86
Figure 5.1 Linkage Model at 5 % level of significance ............................................. 115
Figure 5.2 Linkage Model at 10 % level of significance ........................................... 116
Figure 6.1 Residual Graph for VECM Equation 6.9 ................................................. 129
Figure 6.2 Residual correlogram model M1 .............................................................. 131
xx
Figure 6.3 Residual Distribution Graph ..................................................................... 132
Figure 6.4 CUSUM Test for Structural Stability ....................................................... 133
Figure 6.5 CUSUM Square Test ................................................................................ 134
Figure 6.6 Response of MANF to CONS .................................................................. 135
Figure 6.7 Response of CONS to MANF .................................................................. 136
Figure 6.8 Response of MINQ to CONS ................................................................... 136
Figure 6.9 Response of CONS to MINQ ................................................................... 137
Figure 6.10 Response of AGRF to CONS ................................................................. 137
Figure 6.11 Response of CONS to AGRF ................................................................. 138
Figure 6.12 Response of SERV to CONS ................................................................. 138
Figure 6.13 Response of CONS to SERV ................................................................ 139
Figure 6.14 Response of GDP to CONS .................................................................... 139
Figure 6.15 Response of CONS to GDP .................................................................... 140
Figure 6.16 Response of MCS to its own shock ........................................................ 140
Figure 6.17 Forecasted line ........................................................................................ 144
Figure 6.18 Comparison between Original and Forecasted values ............................ 144
xxi
LIST OF TABLES
Table 1.1: Chapter Scheme .......................................................................................... 10
Table 2.1 Important studies over construction and economic development ................ 34
Table 3.1 NEP outcome over the period 1970 -1990................................................... 45
Table 3.2 NDP Outcome .............................................................................................. 47
Table 3.3 Contribution to GDP (%) ............................................................................. 50
Table 3.4 Inflation and Unemployment rate (%) 1971-2010 ....................................... 50
Table 3.5Malaysia Trade Balance 1970-2010 ............................................................. 51
Table 3.6: Average output of construction Sector (6th to 9th plan) ............................ 54
Table 3.7: Average construction output percentage of GDP in various MPs .............. 56
Table 3.8: Average growth in various MP Malaysia ................................................... 58
Table 3.9: Sectoral share to GDP ................................................................................. 59
Table 3.10: Employment contribution ......................................................................... 59
Table 3.11: Contribution (%) of construction subsectors in gross out of Construction
...................................................................................................................................... 64
Table 3.12: Categories of Contractors by CIDB ......................................................... 68
Table 3.13: Contractor categorization for registration at PKK .................................... 69
Table 3.14: List of Overseas Countries/Projects That Malaysian Contractors Venture
as of 2010 ..................................................................................................................... 75
Table 5.1 Sectoral correlation .................................................................................... 104
Table 5.2 Unit Root Test Results ............................................................................... 108
Table 5.3 Lag Length Selection Table ....................................................................... 109
Table 5.4 Null and Alternate Hypothesis ................................................................... 112
Table 5.5 Empirical Results of Granger Causality .................................................... 113
Table 5.6 Direction of linkage ................................................................................... 114
Table 6.1 Co-integration Examination Results Based on JJ Rank (Trace) Statistics 121
Table 6.2 Co-integration Examination Results Based on JJ Rank (Eigenvalue)
Statistics ..................................................................................................................... 121
Table 6.3, 5 Co-integrating Equations (Normalized Coefficient) .............................. 122
Table 6.4, Coefficients Value and Probabilities of Model Equation 6.7 ................... 125
xxii
Table 6.5, Results of Equation 6.9 for MCS model 6.1 ............................................. 127
Table 6.6 Breusch-Godfrey Serial Correlation LM Test .......................................... 130
Table 6.7, Results of Hetroskedasticity ..................................................................... 130
Table 6.8 Comparison between Original and Forecasted Value ................................ 141
Table 6.9 Future Forecast of Construction Output .................................................... 145
xxiii
LIST OF ABBREVIATIONS
Terms Abbreviation
Dangerous, Dirty, and Difficult 3Ds
Augmented Dickey Fuller ADF
Advance Industrial Countries AICs
Akaike Information Criteria , AIK
Board of Architect Malaysia BAM
Board of Engineers Malaysia BEM
Schwartz Bayesian Information Criteria , BIC
Board of Quantity Surveyor Malaysia BQSM
Constrction Industrial Development Board CIDB
Construction Industry Payment and Adjudication Act CIPAA
Cumulative Sum CUSUM
Dicky-Fuller DF
Department of Statistics DOS
Durbin Watson DW
Error Correction Model ECM
Explained Sum of Squares ESS
Financial Prediction Error FPE
Gross Domestic Product GDP
GrossFixed Capital formation GFCF
Gross National Product GNP
Hannan Quinn HQ
International Labour Office ILO
Impulse Response Functions IRFs
Information Technology IT
Less Develop Countries LDCs
Likelihood Ratio LHR
Long Run LR
Mean Absolute Error MAE
xxiv
Mean Absolute Percentage Error MAPE
Master Builders Association Malaysia MBAM
Malaysian Construction sector MCS
Mean error ME
Malaysian Plan MP
Malaysia productivity corporation MPC
Mean Square Error MSE
Newly Industrialized Country
New Economic Policy
National Development Policy
National Vision Policy
NIC
NEP
NDP
NVP
Ordinary Least Square OLS
Pusat Khidmat Kontraktor PKK
Phillips Perron PP
Public Private Partnership PPP
Research and Development R & D
xxv
LIST OF NOMENCLATURE
𝜆𝑖’ is eigenvalue
τ is coefficient of lag series in unit root test
Π is the long run parameter matrix
Γ is the coefficient of matrix deterministic trend
Φ isthe matrix of specific variable
𝜇𝑡 is white noise residual series
𝜀𝑡 is error term
𝜔 is restrictive residual
d is Durbin Watson statistics
Di is matrix of deterministic trend
k’ is number of parameters
m is the optimal number of lag length
N is number of observations
R2 is the coefficient of determination
Sm is the squared recursive residual
T is the time trend
Wm is the systematic movement of coefficient
U is the Theil inequality coefficient
Yt is time series variable
Yf is forecasted observations
1
CHAPTER 1
INTRODUCTION
1.1 Background of Study
The construction sector and construction activities are considered to be one of the
major sources of economic growth, social development and economic activities,
particularly in developing economies. During an economic takeoff, construction
provides shelter and the required infrastructure for the sustainable development and
the quality of life of the society. Some examples can be found in Pakistan, India,
China and Malaysia. The construction sector is an important sector of the economy in
terms of its contribution to gross domestic products (GDP), domestic gross fixed
capital formation (GFCF) and employment generation (Hillebrandt, 2000). The
construction sector has become a highly dynamic sector and one of the most complex
industries (Memon, 2010). The activities of the sector have great significance in the
achievement of national socio-economic development goals in providing
infrastructure, income generation and employment creation. It includes public and
private buildings and structure such as hospitals, schools, townships, offices, houses
and other buildings; urban infrastructure (including water supply, sewerage,
drainage); highways, roads, ports, railways, airports; power systems; irrigation and
agriculture systems; telecommunications. It deals with all economic activities directed
to the creation, renovation, repair or extension of fixed assets in the form of buildings,
land improvements of an engineering nature. Besides, the construction sector
generates substantial employment and provides a growth impetus to other sectors
through backward and forward linkages.
The role of construction in the national economy has been addressed by a number
of researchers. But empirical evidence pointed to the significant role of the
2
construction sector in economic development was first researched by D. A. Turin
(1960). Since then, over the past 50 years, many such studies by other scholars have
been conducted to provide a clearer understanding of the relationship between
construction and economic development (Pheng, 2011).
Strout (1958) provided a comparative inter-sectoral analysis of employment
effects with an emphasis on construction. Ball (1965) and Ball (1981) addressed the
employment effects of the construction sector as a whole. Park (1989) has confirmed
that the construction industry generates one of the highest multiplier effects through
its extensive backward and forward linkages with other sectors of the economy (Park,
1989; Rameezdeen et al, 2006). Field and Ofori (1988) stated that construction makes
a noticeable contribution to the economic output of a country; it generates
employment and incomes for the people and therefore the effects of any changes in
the construction industry occur at all levels and in virtually all aspects of life (Chen,
1998; Ofori, 1988). The construction sector contributes to the economic growth of a
country through the share of its own value-added output and moreover as a significant
employer of the labor forces (Bynoe, 2009).
The most comprehensive study on the role of construction sector in development was
conducted by Turin in the year 1973. His observations are summarized as follows:
I. The value added contribution of the construction sector was 3 to 5 percent of
GDP in developing countries and 5-8 percent for developed countries;
II. The capital formation of construction sector for developing economies was 6
to 9 percent of GDP and 10 to15 percent for developed economies and the
aggregate for all countries was 45 to 60 percent;
III. Construction sector purchased approximately 50 to 60 percent of inputs from
other sectors of the economy; and
IV. Developing countries use 30 to 55 percent of total budget of construction
sector in civil engineering projects, whereas the developed countries allocated
25 to 30 percent (Ofori, 1990; Turin, 1973)
The World Bank (1984) and Wells (1985) studies suggested that the relationship
between construction output level and stages of development established by Turin did
not change from 1970 to 1980, ( Wells, 1985; World Bank, 1984)
3
1.1.1 Significance of Construction sector in Economic Development
Construction activities and their outputs are an integral part of a country’s national
economy and industrial development. The construction sector is often seen as a driver
of economic growth, especially in the developing countries. The sector can mobilize
and effectively utilize local human and material resources in the development and
maintenance of housing and infrastructure to promote local employment and improve
economic efficiency (Amponsah, 2007).
The significant role of the construction sector in the national economy has been
highlighted by Turin (1969). He argued that there is a positive relationship between
construction output and economic growth based on a cross section of data from a
large number of countries at various levels of development. Furthermore, as
economies grow, construction output grows at a faster rate, assuming a higher
proportion of GDP (Hua, 1995; Turin, 1969; Wells, 1986). A number of studies have
shown that the interdependence between the construction sector and other economic
sectors is not static, but changes as the nation’s economy grows and develops (Bon,
1992).
Hirschman (1958) first defined the concept of ‘linkage’ in his work “The Strategy
of Economic Development”. He emphasized the significance of ‘unbalanced’ growth
among supporting sectors of the economy as opposed to a balanced development of
all interrelated economic activities (Lean, 2001). Park (1989) confirmed that the
construction sector generates one of the highest multiplier effects through its
extensive backward and forward linkages with other sectors of the economy (Park,
1989). Many studies, like Fox, (1976), Bon and Pietroforte, (1993), Pietroforte and
Bon, (1995) use the strong direct and total linkage indicator to explain the leading role
of the construction sector in the national economy.
Thus, policymakers frequently use the sector as a trend indicator because it is as a
sector present in every development activity. It is a sector that contributes to
economic development through output generation, employment creation, income
generation and redistribution of resources in the society. This sector impacts directly
on communities and the public at large and its improved efficiency and effectiveness
will enhance quality, productivity, health, safety, environmental outcomes and value
4
for money to society. It also plays a key role in generating income in both formal and
informal sectors. It supplements the foreign exchange earnings derived from trade in
construction material and engineering services. Therefore, it is important to measure
the contribution and impact of the construction sector in economic development and
to motivate research in it.
1.1.2 Need for Development in Construction Sector
The World Bank and its allied agencies and institutions have acknowledged the
significance of the construction sector after World War II. They started to think about
the financing and monitoring of development projects in less developed countries and
newly sovereign states of Asia and Africa. According to World Bank statistics, 44%
of allocated amount of assistance was used in construction work during a three year
period i.e. 1980-1982. It proposed to member countries a set of measures
(technological development) for all levels of construction activities to improve the
efficiency and productivity of this important sector of national economy (Lopes,
1997).
The most common problems of the construction sector in developing countries are
inadequate capacity, heavy cost due to low level of technological development in
local industry, shortage of material, equipment and plant locally, financial constraints
and lack of skilled construction personnel at technical, managerial and labour level.
Therefore construction sector is highly importing dependent in these countries
(Fadhlin et al., 2004). Developing countries can benefits from the development of the
construction sector if, they can find ways to optimize and effectively utilize its full
potential in the economy. A comprehensive research study is essential to develop a
sound and compact policy for the construction sector, by which it can efficiently play
a strong role in the economy. In this way it can realize its potential to create jobs in all
parts of the country as well as stimulate business activities in other sectors of the
economy. The construction sector is highly integrated with other sectors of economy
through both backward and forward linkages. This integration combined with high
value added to output ratio indicates that construction provides a substantive stimulus
throughout the economy and vice versa (Geadah, 2003).
5
There is a need for research in construction management and economics that must
be focused at three levels - the firm, the sector and the economy. There would be
different policies and measures required for development of the construction sector in
different economies as there is no typical unique format for the development of the
sector due to different social, economic, cultural, historical and political influencing
factors which hit the industry.
The purpose and contribution of this study is to examine the behavior of the
Malaysian construction sector (MCS) in economic expansion and development of
Malaysia, looking at parameters, i.e. major sectors of Malaysian economy, such as
manufacturing (MANF), mining and quarrying (MINQ), agriculture and forestry
(AGRF) and service (SERV) sector including GDP versus construction sector. This
study will provide valuable information regarding the role of construction sector in
socio- economic development of Malaysia and estimate econometric model equations
for the Malaysian construction sector (MCS) to investigate the linkages between
construction and other key sectors of Malaysian economy including GDP. This
important information will be useful for Malaysian policy makers, planners and key
players of construction sector in developing future strategies about the sector to make
it more efficient sector for social economic development of Malaysia, especially to
transform Malaysia into a prosperous, competitive, dynamic, robust and resilient
country by the year 2020.
1.2 Problem Statement
Malaysia is regarded as one of the most successful non-western country, whose socio-
economic performance in economic growth and development is highly impressive by
any standards. By the year 1990, Malaysia had achieved the title of newly
industrialized country [NIC] (Aziz, 2011). Now it is considered as an upper middle
income country and it is rushing towards achieving the status of a developed and
industrialized nation with a strong economy by the year 2020 as envisaged in Vision
2020. The Malaysian government will have to take strong and correct measures for
economic uplift and socio-economic development of the country.
6
It was observed that the economic development and future prospects of a country
are closely associated with the growth and development of the construction sector as
mentioned previously. The construction sector plays a significant role in socio-
economic and infrastructure development of the country and makes a substantial
contribution to gross domestic product (GDP), capital formation, and employment
(Hillebrandt, 2000). It provides great support in the development of other sectors like
agriculture, manufacturing, transport, tourism, water and power, mineral and mining
and service sectors [education, health, communication] (Naidu, 1998).
Malaysia must find ways to take advantages of the special features of the
construction sector, which offer unique opportunities for socio economic development
of the country. The government of Malaysia should focus on the development of the
construction sector and design sound policies for optimal utilization of the potential of
the sector which can help to achieve its national goal such of a developed nation by
the year 2020.
However, attempts to formulate strategies for the sector to fulfill future demand
and challenges would require a reliable understanding and an adequate knowledge of
the past and present scenario of the sector (Fadhlin, 2004). It would also require
knowledge about the reliable methods and tools for linkage and forecasting such as
input output analysis, time series analysis, econometric forecasting methods,
judgmental methods and artificial intelligence methods. Therefore, it is important
before making any future strategy regarding the construction sector and the aggregate
economy to analyze and understand the role and behavior of MCS in the economic
development of Malaysia. It is necessary to know how the construction sector of
Malaysia is linked to other sectors and how its response when an investment level
change in major sectors of the economy, how one unit change in the output level of
other sector effect to MCS output and vice versa.
The purpose of this study is to analyze the behavior, linkage direction, short and
long run relationship of MCS with aggregate economy and long run forecast for MCS
output. The VECM equation for MCS is developed in this regard that helps to
understand the relationship and response of construction sector against the positive
investment shock produced in other sectors and the behavior of other sector if shock
7
produced in the construction sector. The VECM model is beneficial in many ways,
particularly in predicting the future value therefore this study uses the estimated
VECM for forecasting of MCS output.
1.3 Objectives of the Study
The prime objective of this study is to develop VECM for MCS to analyze the
relationship, linkages and behavior of the construction sector in the economic
development of Malaysia. In order to achieve this main objective, the following
objectives have been developed in the study presented here in:
I. To establish the causal link between MCS and other major sector of the
Malaysian economy and identify whether there is unidirectional or
bidirectional causal relationship between construction sector and other sector
of the economy including GDP.
II. To develop a vector error correction model (VECM) equation for MCS that
can be used to analyze the long run and short term association between MCS
and other major sector of the Malaysian economy.
III. To develop impulse response functions (IRFs) for MCS to assess the behavior
of MCS against the shock from other sectors and the response of other sectors
if shock is produced in MCS.
IV. To forecast MCS outputs level from 2014 to 2020 and validate the estimated
model equation and forecasted values through suggested statistical methods
such as residual analysis, mean absolute percentage error (MAPE) between
actual and forecasted values.
1.4 Scope of Study
As discussed in section 1.3 of this chapter, the main objective of the study is to
develop VECM for MCS and analyze the contribution and behavior of MCS in the
8
economic development of Malaysia. Therefore, the study reviewed the available
literature that presents a clear and precise understanding of the role, importance,
contribution and impact of construction sector in economic development of Malaysia.
In addition it discusses the linkages between construction and aggregate economy.
This study focuses on the causal link between MCS and other major sectors,
which are the main variables of the study, namely manufacturing, mining and
quarrying, agriculture and forestry, services and in addition, the GDP of the
Malaysian economy. Therefore, only the information about these variables based on
value of the output were highlighted.
Quarterly time series data from 1991-2010 is used for analysis and development
of econometric model equations for MCS in which MCS is used as a dependent
variable. The VECM approach is used to investigate the long run and short run
association between concerned variables. The Engel Granger causal model is used to
measure the bivariate causal relationship and linkage between construction and other
major sectors of Malaysian economy including GDP.
Johansen, co-integration rank techniques, trace and maximum likelihood are
applied to assess’ the number of co-integrating equations in the data set. The IRFs are
developed only for MCS as a targeted sector is MCS.
1.5 General Methodology
The methodology adopted to achieve the objectives of the study is briefly discussed
below:
Phase 1: A comprehensive literature review was conducted, with the research problem
identified and objectives of the study were set.
Phase2: Research methodology was developed and quarterly time series data of
concerned variables for the study period (1991 to2010) was collected from published
documents and converted into same base index price (2000, index) .
9
Phase 3: Descriptive and econometric analyses were conducted to satisfy the
objectives of the study and the results and discussions were made. The major steps in
phase -3 are:
a) Augmented Dickey Fuller (ADF) test was conducted to examine the unit root/
stationarity problem in the data series and to know the order of integration of series.
b) Johansen, co-integration rank tests trace and maximum eigenvalue was used to
measure the co-integration equations in the data set.
c) Granger causality model was used to examine the causal link between construction
and other key sectors of the Malaysian economy.
d) VECM equation for MCS was estimated to determine the long run and short run
adjustments of MCS.
e) Tests were conducted for strength, efficiency, correctness and validation of the
estimated model.
f) Developed IRFs for MCS to examine the behaviour of MCS against the positive
shock from the other sectors and vice versa.
g) Estimated model equation used for forecasting for MCS output on the basis of
regression analysis and validate the output value.
Phase 4: Conclusions and recommendations were made.
1.6 Chapter Scheme
As shown in Table 1.1 this study comprises seven chapters, which covers the
background of the study and discusses the role of construction sector in social
economic development of a country. The problem statement is developed in the light
of Malaysia Vision 2020. Based on the problem statement, the objectives of the study
are set. In order to be focused and precise, the study scope, general methodology and
chapter scheme is defined.
10
The literature review is covered in two chapters (chapter 2 and 3) in which the
most important previous studies, their approach and findings are discussed. These
chapters also review the various theories and models for selecting the best model and
the concerned variables for this study. Chapter 3 focuses on MCS, its status, structure,
problems and future prospects.
Chapter 4 illustrates the complete methodology adopted to attain the defined
objectives of the study. This chapter explains the paradigm of research, data, methods
and techniques that were used to analyze the data; the procedures that were adopted to
estimate the model equation and to validate it.
The results and discussion are presented under chapter 5 and chapter 6. Actually,
these chapters articulate the major findings of the study. The inter-sectoral linkages
and their directions for Malaysian construction sector are determined and discussed in
chapter 5. The VECM model equation for MCS is estimated and validated in chapter
6. In addition this chapter predicts the future output value of MCS till 2020.
Finally, the conclusion of the study drawn from study analysis is presented in
chapter 7. This chapter summarizes the whole study and outlines the major findings
and valuable outcome of the study. Furthermore, it mentions the future research work
for optimizing the benefit from this important sector of economy
Table 1.1: Chapter Scheme
Serial
Number
Chapter Title Remarks
Chapter 1 Introduction
Chapter 2 Economic Development and Construction Sector Literature
Review Chapter 3 MCS Facts, Problems and Prospects
Chapter 4 Research Methodology
Chapter 5 Inter-sectoral Linkages of MCS Results and
Discussion Chapter 6 Vector Error Correction Model, IRFs and
Forecasting for MCS
Chapter 7 Conclusion and Recommendation
11
The procedure of conducting this study is shown in Figure 1.1 under the title study
flow chart that is self-explanatory.
Figure 1.1 Study flowchart
Conclusion & Recommendations
Results &Discussion
Econometric Analysis Descriptive Analysis
Finalizing the Data
Through
Documents Secondary Data Data
Acquisitio
n
Research Methodology
Literature Review
Objectives
Research Problem
Phase-1
Phase-2
Phase-3
Phase-4
12
1.7 Chapter Summary
There are no two opinions about the significance and pivotal role of the construction
sector in the socio economic development of a country. The economic development
and social well-being of a country can be judged by the construction sector.
Infrastructure development is essential element to achieve the national development
goal such as industrialization, urbanization, capital formation and export promotion.
The infrastructure development is impossible without a strong construction sector.
The construction sector is highly integrated with other major sector of economy
through its backward and forward linkages. It is involved in most of the economic
activities and generates employment opportunities for both skill and unskilled people.
Therefore construction sector consider as driver of country economy. The purpose of
this study is to examine the role and significance of the Malaysian construction sector
(MCS) in the socio economic development of Malaysia.
13
CHAPTER 2
ECONOMIC DEVELOPMENT AND CONSTRUCTION SECTOR
2.1 Introduction
This chapter describes the role of construction sector in the socio-economic
development of a country. The chapter is divided into two parts. The first part
discusses the meaning of the development and significance of inter-sectoral linkages
for socio- economic development of a country. In addition measuring techniques of
linkages are discussed in detail. The second part is focused on the construction sector
and its significance for the economic development of a country.
2.2 Definition of Development
The term development’ is generally used in many other indistinguishable
terminologies such as growth, improvement, welfare, secular change, and progress.
Actually, it is difficult to present any precise, specific, and clear definition of
development. But in view of its importance and increasing popularity as a scientific
study a practical definition of the term seems to be quite essential. In specific words
development means improvement, either in the general situation of the system, or in
some of its components. Broadly speaking the term development is multidimensional
in its nature, therefore various interpretations have been presented by the social
scientists for defining the concept of development as Kurt Martin (1967) said it is a
multi-dimensional process of economic, political, social, institutional and cultural
change (Martin, 1967). The meaning and objectives of development include the
provision of basic needs, reducing inequality, raising living standards through
14
appropriate economic growth, improving self-esteem in relation to the developed
countries, and expanding freedom of choice in the market and beyond, that can be
categorized as economic development, social development, sustainable development
and territorial development. Here focus on economic and social development is made.
2.2.1 Economic Development
A simple and precise definition of economic development is given by Prof. Meier and
Baldwin (1964) “Economic development is a process whereby an economy’s real
national income increases over a long period of time” (Meier, 1964). The various
scholars like Buchanan, Ellis, Baran, Buttrick and Williamson are also considered that
the economic development means an increase in per capita income or output.
In 1970s economic development came to be redefined, in terms of economic
welfare or in term of the satisfaction of the basic necessities of the people. In the light
of this new perception, economic development is that process by which poverty,
unemployment and inequality of income are reduced as Prof. Colin Clark defines
economic development from the perspectives of economic welfare. In his own words
“economic progress can be defined simply as an improvement in economic welfare”
(Malhotra, 2009).
2.2.2 Social Development
The early definition of social development was emphasized on the social
infrastructure to help economic development. During the 1970s and 1980s social
development canvas is extended to basic necessities of human being. In 1990s The
United Nations Development Program (UNDP) promoted the concept of human
development where the focus is put on the improvement of the various dimensions
affecting the well-being of individuals and their relationships with the society like
education, health, empowerment, entitlements, capabilities, etc. Furthermore the
concept of human development broadens to freedom of people’s choice, which is
closely related to the foundation of social development. Corresponding to these
15
developments, the concept of social development became refined, and its importance
was confirmed globally in the Social Summit in 1995, (Sakamoto, 2003).
According to Morris (2010) social development is the bundle of technologies,
subsistence, organizational, and cultural accomplishments through which people feed,
clothe, house, and reproduce themselves, explain the world around them, resolve
disputes within their communities, extend their power at the expense of other
communities, and defend themselves against others’ attempts to extend power
(Morris, 2010).
In the light of the above discussion we can conclude that the socio-economic
development is a continuous process that has to be extended for a long period of time
to improve the quality of all human life, to get rid from the vicious circle of poverty,
to overcome the unemployment and to provide a better standard of living to the
masses. In short the socioeconomic development has three important aspects,
sustenance (rising people living level), self-esteemed (creating environment that
promote human dignity and respect) and people freedom (enlarging the range of
choices). A bulk quantity of literature is available on socio-economic development
and the number of theories and models have been developed in this regards to
understand it. These theories and models seek to explain and predict how economies
develop (or not) over time, how barriers to economic development can be identified
and overcome and how government of any country can induce sustained and
accelerate growth rate with appropriate development policy.
The most popular theories and models of economic development are classical
growth theory, endogenous growth theory, comparative advantage theory, Rostow’s
stage theory, Harrod- Domar growth model, Lewis model (surplus labor model),
Harris Todaro model (two sector with unemployment) and Solow growth model.
During the last 60 years after independence of a number of colonies in Asia and
Africa, the question of development received special attention and the theory of inter-
sectoral linkage has created much interest and has become the primary subject of
development economics. The idea of linkages of industries for economic development
was introduced by Hirschman, (1958) under the theory of unbalanced growth. The
following section discusses on the inter-sectoral linkage theory of development.
16
2.3 Inter-sectoral Linkages Theory of Development
The linkage among various sectors of the economy is one of the significant sources of
economic development in a modest world. The evaluation of the strength and
direction of the relationship that exists among the various sectors of the economy
determines the importance of a particular sector to an economy (Bynoe, 2009).
The sectoral configurations and the linkage among various sectors of the economy
and their mutual impact on economic development and GNP, has developed multiple
theories of economic growth (Wild, 2008). The theory of unbalanced growth
presented by Hirschman recommends that the economic development policy should
emphasize on accelerating the growth of leading sectors. This would result in growth
effect being shifted from the leading sectors to the other supporting sectors
(Hirschman, 1958). The enhancement in leading sector activities will significantly
affect the production, employment level and per capita income of other sectors due to
strong backward and forward linkage, thus have a multiplier effect on aggregate
economy. It is therefore important that an economic activity that has the ability to
stimulate and drive others in the growth process should be given greater consideration
(Saka and Lowe, 2010).
German economist, Friedrich List, highlighted the importance of production
capabilities that activate development process and pursue certain industrial activities
for enhancing production capacity of other sectors of the economy (Wild, 2008). It is
observed that a country in which a rapid growing sector is relatively large will
experience more economic growth than a country in which a slow-growing sector is
relatively large (Hoen, 2002). Overall economic growth depends on the sectoral
growth rates, which are influenced by the linkages between the sectors. Inter sectoral
linkages thus play a crucial role in the industrialization and socioeconomic
development of a country. They provide opportunities for future economic activities
and development of new sectors of the economy.
Therefore, it is necessary to understand the strength and direction of
interdependent linkages among the sectors. An in-depth knowledge of inter-sectoral
linkage is very useful for policy makers so that effective and efficient long term
17
policies could be formulated in order to attain comprehensive development and
sustainable growth in the economy.
The study of inter linkages is very important for developing countries like
Malaysia, Pakistan, India, so that positive growth stimuli among sectors can be
identified and nurtured to sustain the economic growth momentum. This would go a
long way in re-address various socio-economic problems such as poverty,
unemployment and inequality (Kaur, 2009). Linkages thus play a crucial part in the
industrialization of a country and generate further production activities in an
economy.
The sectoral linkages are developed as a result of each sector's role as a supplier
and receiver of inputs from other sectors of the economy. The manners in which two
sectors link with each other is known as backward and forward linkages between the
sectors.
2.3.1 Backward Linkage
Backward linkage of a sector shows the relationship between the activity in the sector
and its purchases. The backward linkage of a particular sector is defined as the change
in the gross output of all sectors in an economy if the final demand for that particular
sector increases by a unit (Dasgupta, 2005). An economic sector or industry has
significant backward linkages when its final product demands extensive input from
other sector or industry. For instance construction sector uses many products of
manufacturing and mining and quarrying sector such as cement, gravel, marble, steel
,electrical items, plastic and paints.
2.3.2 Forward Linkage
Forward linkage occurs when the finished goods of one industry or sector are used as
the input of another industry or sector. For example cement, steel, plumbing and
electrical items are the final products of the manufacturing sector, but construction
18
sector uses these items as an input for its production. Forward linkage shows the
relationship between the total output of a sector and the sale of its output as an
intermediate input to other sectors. The measure of forward linkages in demand led
model is defined as the row-sums of the Leontief inverse, i.e. forward linkage of a
particular sector shows the change in the total output of the sector if the final demand
of each sector increases by one unit (Dasgupta, 2005).
2.3.3 Backward and Forward Linkages
Backward and forward linkages and its indices reveal the relative linkage strength of a
particular sector (Dasgupta, 2005). Polenske and Sivitanides (1989) found that in all
countries the backward linkage of construction was higher than or equal to the
average backward linkage estimated. They also deduced that regardless of the stage of
economic development, construction activities possess one of the highest backward
linkages among economic sectors (Bynoe, 2009).
2.4 Tools for Measuring Strength of Linkages
In an economy, there are three (3) analytical tools, which are widely used for
measuring the strength of the linkage, sectoral economic performance and production
interdependence. These three (3) analytical tools are:
(i) Leontief’s (1936) Input–output analysis
(ii) Statistical technique, involving analysis of causality among the sectors.
(ii) The new econometric modeling.
Leontief (1936) pioneered the use of the input-output analysis to gauge the
backward and forward linkages between the sectors of the economy. The Input-
Output technique is an important analytical tool to understand and grasp the nature
and the degree of integration of an economy. Only an input-output frame of reference
can provide the picture of interdependence of sectors within an economy. This
19
concept is very useful for assessing the impact associated with the growth of a
particular sector (Dasgupta, 2005).
Bon (1988) is one of the few researchers who applied the concept of Leontief
input-output matrix to the construction industry. He considered the input–output
technique to be ideal, since it provides a framework with which to study both direct
and indirect resource utilization in the construction sector and industrial
interdependence. He also found that the input–output tool can be used for studies of
the construction sector in three broad aspects: employment creation potential, role in
the economy, and identification of major suppliers to the construction industry (Lean,
2001). Another study conducted by Rameezdeen et al, (2006), also used input-output
table to analyze the significance of construction in a developing economy and its
relationships with other sectors of the national economy.
The popular studies on application of the input-output concept to the construction
sector are Bon and Minami, (1986), Bon and Pietroforte, (1990), Bon (1991; 1992),
Pietroforte, (1995), Bon and Yashiro, (1996), Pietroforte and Gregori, (2003),
Rameezdeen and Ramachandra, (2008). The input–output technique has been
considered as an efficient framework to study the direct and indirect resource
utilization in the construction sector and its interdependence with other sectors ( Bon,
1988).
The determination of causality among different variables is one of the most
important and yet difficult tasks in economic theories. The number of research studies
over linkages highlighted that the causality claim on the basis of simple statistical
regression analysis is spurious or weaken (Freedman, 2007). However, sophisticated
statistics and econometric methodology are now available for measuring the strength
of linkages among economic variables. One approach that is most popular to examine
causality empirically among variables is the Granger causality econometric analysis.
It is used to analyze the causality link between the concerned variables. It helps to
determine whether there is a uni directional or bi directional relationship between the
variables.
20
2.4.1 Granger Causality Econometric Approach
Granger causality concept was developed in 1969 by Engel Granger and has been
widely accepted and used to understand the causal relationship between the variables
since that time. The Granger causality approach is based on prediction. According to
Granger analysis a variable Y is considered Granger cause another variable Z then
past values of variable Y should contain information that helps to explain the current
level of Z given above and beyond all other information (Granger, 1969). When the
number of causality equation increase (more than one) the Granger causality
discussion automatically leads to the topic of vector auto regression (VAR) model,
because the two or more causality equations deal under VAR system.
2.4.2 Vector Auto Regression (VAR) Model
The basic property of the VAR model is it has more than one dependent variable,
resulting in it has more than one equation. Each equation uses as its explanatory
variables lags of all the concerned variables or studied variables under study (Koop,
2005). The major reason for using the VAR model is it provides a complete
framework for investigating causal link between each set of variables. Furthermore, it
has an excellent forecasting ability.
However the one drawback of VAR model is that it can be worked under the
assumption of stationary time series only if the data variable series is non-stationary
then VAR cannot be an appropriate model. In this situation Econometrician
recommends vector error correction model (VECM) to examine the causal link
between the non-stationary variables.
2.4.3 Vector Error Correction Model (VECM)
The error correction model (ECM) is first introduced into the inter-sectoral linkage
theory by Sargan (1964) and popularized by David son et. al (1978) as a viable
alternative to the VAR system. The recent revival in the popularity of the VECM has
21
been based on the Granger Representation Theorem that if the two variables Y and Z
are integrated order one and are co-integrated then the association between them can
be expressed as a ECMs (Maddala and Kim, 1998).
Like VAR model the VECM also has one equation for each concerned variable
included in the model. It expresses both long term and short term association between
the variables. The long term association captures through co-integrating equation,
while short term is partially captured by the error correction term and further captured
as a coefficient of explanatory variables. The good thing about VECM is that it is
non-spurious regression model. Non-spurious regression refers to unbiased fair
reliable regression analysis.
With the popularity of this new econometric methodology presented by Engle
and Granger, many modeling studies related to economic, financial and development
issues have applied this new technique to analyze economic linkage between
variables.
Green (1997) applied the Granger causality test to determine the relationship
between GDP and residential and non-residential investment, using quarterly national
income and gross domestic product data for the period 1959–1992. This study
conclude that residential investment effect the GDP, but it is not caused by GDP,
while non-residential investment does not affect the GDP, but it is caused by GDP. He
concluded that housing leads and other types of investment lag the business cycle
(Green, 1997). Tse and Ganesan’s (1997) applied econometric techniques to
determine causal relationships between construction flows and GDP uses Hong Kong
quarterly time series data from 1983 to 1989. Their result showed that the GDP leads
construction flow and not the other way round (Tse and Ganesan, 1997). Chan (2001)
uses econometric models to assess construction linkages with sectors of Singaporean
economy. The result shows that some causal relationships are bi-directional, including
those between the construction sector and other sectors; and the construction sector
and the GDP (Lean, 2001).
A study conducted by Saka, and Lowe, (2010) under the title “An assessment of
the linkages between the construction sector and other sectors of the Nigerian
economy”, indicates that construction sector significantly leads many sectors and
22
virtually all economic sectors feedback into the construction sector. Hence mutual
interdependence of construction with sectors of the economy is created (Saka and
Lowe, 2010). However, in available literature, there is no study that discusses and
develops an econometric model for analyzing the behavior, response, reaction and
lead lag short and long run relationship of construction sector such as VECM.
The prime objective of this study is to examine the role of construction sector in
economic development of Malaysia and investigates the inter-sectoral linkage of MCS
with other major sectors of the Malaysian economy. In the next section and
subsequent sections of this chapter discussion is made about the construction sector its
importance and its contribution to economic development.
2.5 What is Construction Sector?
The construction sector may defined be in various ways. A construction sector is one
which makes the process that comprise of construction or erection of building,
infrastructure, preparation of a site for any project, alteration, maintenance, repair,
demolition of any structure or building and other real property work. National
construction council of Tanzania (2004) defines the construction industry as:
“It is a sector of the economy that transforms various resources into constructed
physical economic and social infrastructure necessary for socio economic
development. It clinches the process by which the said infrastructure is planned,
designed procured, constructed, altered, repaired maintained and demolished. The
industry consists of organizations and persons who include companies, firms and
individuals working as consultants, contractors, sub-contractors, material and
components producers, plant, equipment suppliers, builders and merchants. The
industry has close relationship with clients and financers. The government is involved
in the industry as purchaser (client), financers, regulator and operator” (Tanzania
Council, 2004).
23
A comprehensive picture of the construction sector is painted by the United
Nation under the International Standard Industrial Classification (1968) definition.
According to this definition construction industry includes:
“..constructing, altering ,repairing and demolishing of buildings; constructing,
altering and repairing, highways and streets and bridges; viaducts, culverts, sewers,
and water gas and electricity mains; railway roadbeds, sub-ways and harbor and
water ways; piers, airports and parking areas; dams, drainage, irrigations, flood
control and water power projects and hydroelectric plants; pipe lines; water wells;
athletic fields, golf courses, swimming pools and tennis courts; communication system
such as telephone and telegraph lines; marine construction, such as dredging and
under water rock removal; pile driving, land draining and reclamation; and other
type of heavy construction…mining services such as preparing and constructing
mining sites and drilling crude oil and natural gas wells…specialist trade contractors
activities…”
“The assembly and installation on site of prefabricated, integral parts into
bridges, water tanks, storage and warehouse facilities, railroad and elevated right-of-
way, lift and escalator, plumbing, sprinkler, central heating, ventilating and air
conditioning, lighting and electrical wiring etc. Systems of buildings and all kind of
structures… departments or other units of the manufacturers of the prefabricated
parts and equipment which specialize in this work and which it is feasible to treat as
separate establishments, as well as business primarily engaged in the activity are
classified in this group.” (United Nations, 1968).
The similar definition is adopted by Malaysian Industrial Classification 1972
(updated 1978). According to Malaysia CIDB ACT 38 (2000) construction industry
means
“the broad conglomeration of industries and sectors which add value in the
creation and maintenance of fixed assets within the built environment” (Fadhlin,
2004).
In short, the term construction deals with all economic activities directed to the
creation, renovation, repair or extension of fixed assets in the form of buildings, land
24
improvements of an engineering nature and the activities of creation of physical
infrastructures, superstructures and related facilities (Wells, 1985).
In the light of above discussion the construction sector can be defined as the
combination of activities such as supply chain of construction material and related
goods, mining and manufacturing of construction materials, professional services
such as design, architecture, supervision and management of construction site and
construction work, arrangement of utilities etc. The Figure 2.1 summarizes the scope
of the construction sector holistically. The Figure 2.1 is comprised of three columns.
First column depicts to supply chain activities, second column covers construction
activities at site and third column represents to professional services. All activities of
construction sector collectively play important role in built environment and
socioeconomic development of a country.
Figure 2.1: Structure of Construction Sector
Quarrying of
construction material
Manufacturing of
construction
products,materials &
assemblies
Sale of construction
materials and
assemblies
Planning & designing
Preparation of site
Construction, creation
&erection Fixing, assembly &
installation
Repair, maintenance,
alteration &extension
Demolition
Built
Environment
Professional
Services &
Management
25
2.6 Construction Sector and National Development
The construction sector is a critical element of any country due to its influential
control over national development. This sector is one of the key sectors of an
economy that governs a large share of financial resources and can be played role as a
driving force towards improved social well-being in the country. The sector helps to
accelerate social and economic development and fight against poverty and
unemployment. The construction sector therefore considers as a driver of economic
growth, especially in developing economies such as India, Pakistan, Vietnam,
Indonesia, and Malaysia.
The construction sector can mobilize and effectively utilize local human and
material resources in the development and maintenance of housing and infrastructure
to promote local employment and improve economic efficiency (Amponsah, 2007). It
has great attraction and provides a wide range of opportunities to supplier for business
like building materials, labor and professional services, energy, transport and other
suppliers.
The importance of construction sector in the national economy has been
highlighted by a number of researchers and quantum of literature in the form of
books, research papers, reviews and article available on this issue. This sector
includes all types of works that are predominantly of civil engineering nature. Every
single project of civil engineering involves construction; a structure needs to be built,
a mine or tunnel needs to be excavated, a road or highway to be placed, and a dam to
be constructed. These projects can be categorized into four major areas namely
building construction, infrastructure construction, industrial construction and
construction for services and utilities.
Building construction covers all types of buildings, including residential and non-
residential, industrial and recreational and the buildings which are used for health,
education and religious purposes. Infrastructure construction includes the roads,
highways, bridges, reservoirs, distributary canals, dams, ports, air-ports, harbors and
other heavy and mega structures. Other special structures were designed and
constructed throughout history, such as ancient pyramids in Egypt, Great Wall of
China, world highest bridge in France (Millau-viaduct-bridge) and longest channel
26
tunnel between England and France etc. Industrial or commercial construction covers
the projects which are mostly associated with factories, mills and plants that are
produce goods and services for business such as steel mills, chemical plants, textile
mills, fertilizer plant, machine tool factory and atomic reactor plant, etc. Construction
for utilities covers all types of services that are provided by the stakeholders or state
authorities to individuals of society such as water supply lines, electricity and
telecommunication supply line, sewerage and drainage system (Bo, 2006). All of
these construction activities are essential for local and national development.
The relationship between a country’s stage of development and the level of
activity in the construction sector is one which has received great attention at the
macroeconomic level for a number of years. It has historically been linked with the
process of industrialization and urbanization, particularly since the advent of the
Industrial Revolution. Transport infrastructures facilitated trade and co-operation
between countries and also the diffusion of technical innovations from the most
advanced to the less advanced areas of the globe (Lopes and Oliveira, 2011). Thus the
construction sector has an important and powerful role on national development as
well as social and economic growth of the society. There is no doubt that behind the
socio-economic growth and developments which are improving the standard of living
of the people by providing goods and services silently hide the construction sector
that can be called as the backbone of national development.
2.7 Construction and Economic Growth
Economic growth means an improvement in the level of production of goods and
services by a country over a period of one year. It is usually brought through
technological advancement and positive peripheral forces. In an early stage of
development, economic growth seem to be increased by a higher rate because of
infrastructure development, process of urbanization and capital formation in which
construction and manufacturing sector play a significant role. It is observed that an
average contribution of construction sector to capital formation in developing
countries is 55%.
27
The construction sector is an important part of developed and developing
economies through effective planning, economical designing; appropriate
maintenance and good operation transform various resources into constructing
physical infrastructures that are not only important but also essential for social and
economic development of a country. The significant role of the construction sector in
the national economy has been highlighted by a number of scholars in their studies
such as Turin (1969), who had argued that there is a positive relationship between
construction output and economic growth on the basis of cross sectional data from a
large number of countries at various levels of development. In 1978 he found “S”
shape relationship as shown in Figure 2.2 between construction value added
percentage and gross domestic product (GDP) per capita furthermore, he calculated
the proportion of the value added by construction products as a percentage to gross
domestic product was around 3-5% for developing economies and 5-8% for advanced
economies over the period of ten years i.e. 1955-1965 (Turin, 1978). This finding was
later on confirmed by other studies like Miles, (1984), Wells, (1985), Wells, (1986),
Ofori, (1988) and Chen, (1998).
Figure 2.2: S- Shape Relationship (Source: Turin, 1978)
The changing role of construction sector at various stages of development is
discussed by Bon (1992) and presented the idea of an inverted U relationship between
LDC
s
NICs
AICs
Per capita GDP
Co
nst
ruct
ion v
alue
added
%
28
construction activity and the level of income per capita. He used 50 years period data
of Finland, Italy, Ireland, Japan, United Kingdom and the United State of America for
this analysis, and concluded that the proportion of construction in gross national
product (GNP) first rises and reaches its maximum and declines thereafter along with
the level of economic development. Because at an early stage of development,
industrialization and urbanization process required new infrastructure that increase the
construction output rapidly. But the once economy is developed and the
infrastructure is placed than a larger share of construction output is coming from
repair and maintenance work in which productivity is relatively low, resulting in
construction output decreases in later stages of development (Fadhlin, 2004). Bon
(1992) study outcome is different from Turin (1978) that is the reason for different
shape of the curves in two studies given by the Bon is the sample size and its nature.
He argued that the sample size used by Turin is ruled by less developed countries
(LDCs) and newly industrialized countries (NICs) so that the trends individualities of
advanced industrialized countries (AICs) were weakened and failed to express their
full impact (Bon, 1992). According to the study undertaken by the CIB project group,
after allowing for cyclical fluctuations, the general trend in construction activity in
very developed countries is for construction activity to be in relative decline
(Carassus, 2004) as shown in Figure 2.3.
Figure 2.3: The Bon Curve (Source: Bon, 1992)
NICs
AICs LDC
GNP per Capita
Shar
e of
Const
ruct
ion i
n G
NP
29
It is quite clear and understandable that the construction industry plays pivotal
role in infrastructure development. Construction of relevant infrastructure between
rural and urban areas like roads and highways, improve the trade; create employment
opportunities for skilled as well as unskilled people that have a significant impact on
the national distribution of wealth. For instance wide-ranging road networks in South
Korea led to the rapid growth of vegetable production and other cash crops produced
for the urban markets. Experience from Japan and Korea has proven that the
infrastructure development is the most significant part of public policy in developing
countries.
Field and Ofori (1988) stated that the construction makes a noticeable
contribution to the economic output of a country; it generates employment and
incomes for the people and therefore the effects of changes in the construction
industry in the economy occur at all levels and in virtually all aspects of life (Chen,
1998; Rameezdeena and Ramachandra, 2008). The contributions of the construction
industry come from the linkages between the construction sector and the economy as
a whole, and the inter-sectoral linkages between construction and other sectors as well
(Giang and Pheng, 2011). It stimulates a sizeable amount of economic growth through
backward and forward linkages. Backward linkage is the demand generated by one
sector for the output of another sector. Construction's demand for goods and services
from other sectors of the economy are considerable; the development of the
construction industry therefore encourages these subsidiary industries thus boosting
further economic growth. Moreover, its multiplier effect on other sectors of the
economy, a systematic rebuilt and maintenance program of building and
infrastructure, well planned community service infrastructure can provide viable
support to improve economic growth and save the natural and built environments.
Furthermore its labour intensive nature allows it to create new jobs and these new jobs
generated further demand for the goods and services of other economic sectors.
The major contribution of the construction sector in economic growth can be seen
through its contribution to gross fixed capital formation, value added to GDP and
potential to generate employment in the economy.
30
2.7.1 Gross Fixed Capital Formation
Capital formation is a very important element of developing economies like Malaysia.
It plays a significant role in economic development by either increasing the physical
capital stock in the local economy or stimulating the technology indirectly. The
producing output capacity of any economy largely depends on the capital formation,
i.e. the more is the capital accumulation; the higher would be the productive capacity.
It is also used to determine economic growth and used as a pre requisite for the
formulation of future development programs, therefore the formation of the fixed
capital investment is a great concern for the state of the nation as it represents an
investment in the future of the economy of the country. Fixed investment usually
contains houses and infrastructures in public and private sectors like roads, highways,
bridges, dams, power plants industrials and commercial buildings etc. as well as the
business investment in plant and machinery of all industries. Investment in the
construction sector can be defined as construction-related to the Gross Fixed Capital
Formation (GFCF). The GFCF is an expenditure on fixed assets either for replacing or
adding to the stock of fixed assets. These fixed assets are repeatedly or continuously
used in the production process (Ganesan, 2000). The underlying idea is that a machine
or building continues to yield the same contribution to output each year regardless of
its age, until it reaches the limit of its useful life, when this contribution falls to zero
and it is scrapped (Gruneberg, 2000).
The role of construction sector cannot be ignored in GFCF. It is estimated that an
average contribution of the construction sector in capital formation is 55% (Geadah,
2003), varying between 40 to 70 percent (Hillebrandt, 1993). The major reason for
deviation in GFCF contribution percentage is in the stage of the development. The
developed countries with relatively higher growth and high GNP per capita have a
relatively low contribution in capital formation from the construction sector because
major infrastructure has already been placed, therefore new investment opportunities
are less and the larger portion of the investment is allocated for repair and
maintenance work in which productivity is low. Whereas the developing countries
have greater opportunities of investment in construction of infrastructure that increase
the contribution of construction in GFCF is towards the upper end of this range.
31
2.7.2 Value added
Value added or value addition means an increase in the realizable value by converting
the intermediate goods to finish product. It is a measure of output and is the principle
difference of revenue and non-labour cost of inputs. In other words increase in the
value of goods and services as a result of the production process. It is used to assess
the contribution of a sector as a percentage of GDP in an economy.
Value added in the construction sector is the aggregate output of construction
industry minus values of all the material purchases from other sectors of the economy
to produce this output. It was observed that the value added or net output of the
construction sector is only a trivial part of the total construction process given that a
large percentage of total construction output consists of raw material from another
sector of the economy (Ramsaran and Hosein, 2006).
According to Lowe (2003) the value added contribution of construction to GDP
varies between the range of 7 to 10 per cent for developed countries and 3 to 6 per
cent for less develop countries (Lowe, 2003). The estimates of construction value
added in the developing countries could be higher as the figures may not include the
informal sector activities like architectural and technical consultancy, business
services, real estate activities etc. which could generate a significant casual
employment in urban and rural areas (Ganesan, 2000).
2.7.3 Employment Generating Potential
The construction industry is as a prime source of employment generation, offering job
opportunities to millions of unskilled, semi-skilled and skilled people. It is considered
as labour-intensive industry which is greatly reliant on the availability of local
manpower.
The local and federal state authorities and policymaker normally used this sector
of the economy as a tool for generating employment in the economy because a good
development policy is not only focus on an expansion of production capacity but also
emphasize on creation of new job opportunities and increase employment level. The
32
construction work load generally fluctuates in either a cyclical or random manner. As
a result, there is always either a shortage or a surplus of manpower. Manpower
resources are thus crucial to this industry (Ho, 2010). International Labour Office
statistics indicate that construction industry employs between 2 to 15 per cent of a
country’s total labour force. Field and Ofori (1988) stated that the construction
makes a noticeable contribution to the economic output of a country; it generates
employment and incomes for the people and therefore the effects of changes in the
construction industry in the economy occur at all levels and in virtually all aspects of
life (Ofori, 1988).
According to International Labour Office Geneva Report (2001), high-income
countries produce 77 per cent of global construction output with 26 per cent of total
employment. The rest of the world (comprising low- and middle-income countries)
produces only 23 percent of output but has 74 per cent of employment (ILO, 2001) as
shown in Figure 2.4 and Figure 2.5 below. The construction sector in low income
countries absorbs large population of labour as compared to high income countries.
Figure 2.4: Globle Construction Output Distribution
Low Income High Income
23 %
77 %
33
Figure 2.5: Global Construction Employment Distribution
There are no two opinions about the importance of construction sector and its
contribution to developing and developed economies gross domestic product (GDP),
capital formation, and employment (Hillebrandt, 2000); It is also one of the major
contributors in the development of technology in a nation and satisfying a wide range
of physical, economic and social needs, including shelter and employment creation. It
also contributes in improving the growth of other sectors of the economy due to
strong backward and forward linkages. In other words, the construction sector is
crucial to economic development and the nation’s wealth.
2.7.4 Highlights of Key Studies over Construction and Economic Growth
As we have discussed in previous sections that there are number of studies available
which discusses the role, importance and significance of construction sector in socio
economic development. There are many studies available which investigated and
estimated the correlation and causality link between construction and aggregate
economy either by using an input/ output approach or econometric analysis.
Few important studies from the available literature over economic development
and construction sector and their significant findings are reported in Table 2.1 below.
High Income Low Income
26 %
74 %
34
Table 2.1 Important studies over construction and economic development
S.No Study Key Findings
1
Giang and Pheng (2011).
Role of construction in economic
development: Review of key
concepts in the past 40 years.
This study was conducted on the basis of
review of literature. The key finding of the
study demonstrated significant relationship
between the construction industry and
economic growth in developing countries.
It was noted that further expansion of the
construction industry beyond the adaptive
capacity of the economy will only be
waste of national resources.
Remarks: This study is purely a literatures review based theoretical study. It
discusses the role of the construction sector in the economic development of a country
as the title of the study shows.
2 Saka and Lowe (2010).
An assessment of linkages
between the construction sector
and other sectors of the Nigerian
economy
The econometric approach was used to
analyze data of this study. The study
concluded that the Nigerian construction
sector is very important because of its
significant forward and backward linkages
and multipliers on sectors of the economy.
It leads many sectors and virtually all
economic sectors feed back into the
construction sector.
Remarks: This study used Granger causality analysis to investigate the link between
construction and other sectors of the economy. However the study did not present any
model neither to analyze the behavior of construction sector nor forecast for
construction output.
35
3 Khan (2008).
Role of Construction Sector in
Economic Growth: Empirical
Evidence from Pakistan
Economy.
The result shows that there is a strong
causal relationship between the aggregate
economy and the construction sector of
Pakistan. The construction flow precedes
GDP and there is uni-directional causal
relationship.
Remarks: This study only discussed the causal relation between construction sector
and GDP by using bivariate Granger causality analysis.
4 Anaman and Osei- Amponsah
(2007).
Analysis of the causality links
between the growth of the
construction industry and the
growth of the macro economy in
Ghana
An econometric analysis was used to
examine the causal relationship between
economic growth and construction
industry of Ghana. It was noted that the
direction of link is from the construction
industry to GDP of Ghana. This implies
that the construction industry of Ghana
plays important role in GDP of Ghana.
Remarks: This study also conducted the bivariate Granger causality analysis between
the growth of the construction sector and GDP of Ghana same as Khan (2008) study,
however Khan (2008) used output of construction sector as a variable.
5 Rameezdeen et al (2006).
Study of linkages between
construction sector and other
sectors of the Sri-Lankan
economy
The Leontief input, output matrix
approach was used to organize this study.
An aggregated sectoral analysis revealed
high dependence of construction on
manufacturing followed by services and an
increasing dependence of construction on
the services sector.
36
Remarks: This study used the input / output table approach to analyze the causal link
between construction and other sectors of the economy. The input / output analysis
approach is not as sophisticated as econometric analysis.
6 Lopes et al (2006).
Investment in construction and
economic growth
This study stated that in developing
countries, there was a minimum required
level in the ratio of construction value
added to GDP, below which a relative
decrease in the volume of construction
corresponded to decreasing growth in
GDP per capita. It was concluded on the
basis of the ratio analysis.
Remarks: This study measured the ratio between construction value added and
economic growth in developing countries. The causality link and forecasting was not
the scope of the study.
7 Hosein and Lewis (2005).
Quantifying the relationship
between aggregate GDP and
construction value added in a
small petroleum rich economy: a
case study of Trinidad and
Tobago
A major finding of this study was that the
contribution of the construction industry to
the rest of the economy was greater and it
could be further increased if more local
resources were used in the production
process.
Remarks: This study also discussed the bivariate causality between construction and
GDP, in the same way as Khan (2008) and Anaman (2007).
37
8 Dasgupta & Chakraborty (2005).
The structure of the Indian
economy
The study identified the components of
final demand, which act as the driving
force behind the entire analysis of linkages
in the input-output static open framework.
The study concluded with some policy
implications about the major sectors of
Indian economy.
Remarks: This study discussed the sectoral linkages of Indian economy and used
input / output table approach, which is not as efficient as econometric causality
analysis.
9 Fadhlin et al (2004).
An overview on the growth and
development of the Malaysian
construction industry
The key findings of this study were: a
positive correlation existed between
construction output and GDP, and the
industry’s annual growth rates generally
follow the growth trend of the economy.
These conclusions were made on the basis
of descriptive statistical analysis of data.
Remarks: This study estimated the correlation between construction output and
economic growth on the basis of simple regression analysis which may be spurious.
The study does not cover the causality analysis
10 Lean (2001),
Empirical tests to discern
linkages between construction
and other economic sectors in
Singapore.
The study concluded that the inter-sectoral
linkages were complex, the flow of
causality in some cases was bi-directional,
and the variation in construction output
had a multiplier effect on the economy
over the short to medium term. The study
suggested that there was an ‘inter-sectoral
transmission mechanism’ between
construction sector and aggregate output.
38
Remarks: This study used Granger causality econometric analysis to examine the
link between the construction and other sectors of the economy. However, it does not
develop the model for the construction sector to analyze its behavior and forecast.
11 Sing (1999)
An Economic Analysis of
Malaysian Construction Industry
By using An Input-Output
Approach
The study concluded that the construction
industry generated extensive backward
linkages with service sectors and
manufacturing sectors. It was also found
that the construction industry had a
uniform economic impact within the
economy.
Remarks: This study used input, output analysis to determine the linkages of the
construction sector. Input / output approach almost obsolete in causality analysis.
12 Tse and Ganesan (1997)
Causal relationship between
construction flows and GDP:
evidence from Hong Kong
A significant finding of this study on the
basis of econometric analysis was that the
GDP led construction flow and not the
other way round.
Remarks: This study estimated the causal relation between construction and GDP by
using bivariate Granger causality analysis; However, it does not develop a model for
construction sector like VAR or VECM.
13 Polenske & Sivitanides (1989)
Linkages in the construction
sector
An important finding of this study was that
in all countries the backward linkage of
construction was stronger and higher than
the average forward linkage estimated.
Remarks: This study compared the backward and forward linkage of construction
sector in various countries on the basis of simple descriptive statistics. The study used
the available information about the linkage for making comparison only.
39
14 Turin (1969)
Construction Industry, based on
the Proceedings of the
International Symposium on
Industrial Development.
This study concluded that the share of
value added by construction as a
percentage of GDP also increased as per
capita GDP increases. It was found around
3% to 5% in developing countries and 5%
to 8% for more developed countries over
the period of 1955-1965
Remarks: This study compiled the information about the construction sector of
developing and developed countries, compared on the basis of descriptive statistics.
2.7.5 Research gap
The available published literature confirms that there is a strong link between
construction and economic growth and construction sector is a highly important sector
for the socioeconomic development of a country. However, available literature only
discusses the relationship and causal link between the construction industry and the
economic growth. Unfortunately, there is no study available that developed an
econometric model for construction sector to analyze the long run and short run
association, response functions, behavior and linkage of construction sector with other
major sector of the economy, including GDP such as a vector error correction model
(VECM) equation for construction sector. The present study has filled this gap and
developed a VECM equation for MCS to examine short run as well as the long run
relationship between construction and other key sectors of the Malaysian economy
including GDP. Furthermore the estimated equation is used to develop IRFs for MCS
and also used to predict the MCS future output.
2.8 Chapter Summary
Development is a continuous process of improvement in economic growth of a
country together with improvement in standard of living and self-esteem, of the
40
people. There are several theories and models available to examine and understand
the economic development process of a country. The sectoral development and inter-
sectoral linkage theory has achieved great attention after the Second World War and
the independence of a number of countries in Asia and Africa. The appropriate
integration of sectors in an economy is a great source of socio-economic development
of a country. Linkage direction can be measured by various techniques, but the
modern approach is an econometric model approach such as Granger causality, VAR
system and VECM.
The construction sector plays a significant role in socio-economic development of
the country. The large numbers of research studies have investigated the relationship
between construction sector and economic growth and found that there is a strong
correlation between construction sector and economic growth of a country. The
construction sector is a driving force for social economic development of a country; it
provides an infrastructure for economic and social development of the country and
great source of capital formation and employment generation as well. Therefore, it is
necessary to understand the linkages of construction sector in an economy for future
planning of socio-economic development of a country.
41
CHAPTER 3
AN OVERVIEW ON FACTS, PROBLEMS AND PROSPECTS OF MCS
3.1 Introduction
The MCS is one of the major sectors of the Malaysian economy made up of many
components such as a large number of contractors, thousands of workers, developers,
client consultant, engineers, architects, managers, quantity surveyors, supervisors, and
material suppliers. All these construction players have a significant role in the growth
of MCS and in the socioeconomic development of Malaysia. The discussion of this
chapter can be divided into two major parts. The first part discusses the overview of
Malaysian economic development and the second part will discuss the role of MCS in
the economic development of Malaysia. It will cover the challenges, issues and
difficulties of the MCS in local as well as international environment and finally, the
prospects of MCS.
3.2 Overview of Malaysia Economic Development
The Federation of Malaya was granted independence in August 1957 within the
Commonwealth of Nations. In 1963 the Federation of Malaysia was established after
getting independence from British rule. Initially, it consisted of Malaya, Sabah,
Sarawak, and Singapore but because of internal political conflicts Singapore left the
Federation and became an independent state in 1965. Present Malaysia is separated
into two main landmasses known as Peninsular Malaysia in the west and Malaysian
Borneo in the east part (see Figure 3.1). Kuala Lumpur is the capital city and
Putrajaya is the seat of the government.
42
Figure 3.1 Malaysia Map
Source: World atlas (Worldatlas, 2014)
It has a one of the best economic development record in Asia with an average
economic growth rate of 6.5 % from 1957 to 2011. It is regarded as one of the most
successful nations to achieve a very smooth transition to modern economic growth
and development by the end of 20th century. Economic performance was at its peak
from the early 1980s through the mid-1990s as gross domestic product (GDP)
sustained on average of almost 8% per annum (Drabble, 2010). Before 1957,
Malaysia was low income poor agrarian country. The major economic products were
rubber and tin. A few years after its independence, the country classification system
was introduced by the World Bank, in which Malaysia was declared as a middle
income country (Yusof and Bhattasali, 2008).
Malaysia began to diversify its economic activities in 1960s by focusing on the
manufacturing sector as a way to reduce over reliance on agriculture sector. By the
year 1990 Malaysia had achieved the status of Newly Industrialized Country (NIC).
This was an outstanding performance and a remarkable achievement that has virtually
eliminated poverty and obtained NIC status in a short period of span. Malaysia can be
43
considered as the best example of a country in which economic interest and the role of
various racial and ethnic groups have been reasonably and rationally managed since
independence without significant loss of growth and development pace (Drabble,
2010). Malaysia intends to formulate its economic policies in such a way that all
groups or communities in society can benefit in an equitable manner (Yusof and
Bhattasali, 2008). In February 1990, the former Prime Minister Tun Dr. Mahathir
Mohummad introduced the vision of the strong industrialized economy and
modernized Malaysia.
He defined crystal clear path for making Malaysia as developed nation not only in
an economic sense, but also in terms of social justice, political stability, and system of
government, quality of life, social and spiritual values, national pride and confidence.
This vision is known as Malaysia Vision 2020. The main objective of this vision is to
transform Malaysia into a prosperous, competitive, dynamic, robust and resilient
country by the year 2020, (Mohummad, 1990). Now Malaysia is considered as an
upper middle income country and it’s rushing towards achieving Vision 2020 to
become a developed and high-income nation. The government will have to take
strong measures to uplift its economy and maintain GDP at above 6% per annum till
2020 for Malaysia to become a developed nation and high income economy.
3.3 Malaysian Economic growth and development policies
Today Malaysia is a multiethnic upper middle income society, which has heavily
relied on income from natural resources to develop. It has, successfully, moved
toward manufacturing and gradually increased income for all society members
irrespective of caste, language and color. The success of Malaysia’s economic
development is the outcome of its dynamic strategies and policies, which benefitted
all groups of society equally. The general objectives of the country’s economic
development are described in the five year development plan. Malaysia launched ten
Malaysian development plans. The first five year plan 1966-1970 was made in 1965,
and the 10th Malaysian plan 2011-2015 was launched by the present government in
June, 2010. Since 1970 Malaysian economic development strategies has been based
on three long term policies;
44
1) New Economic Policy (NEP) 1970-1990
2) New Development Policy (NDP) 1991-2000
3) National Vision Policy (NVP) 2001-2010
3.3.1 New Economic Policy (NEP) 1970-1990
The Outline Prospective Plan (OPP) 1970-1990 was uncovered in early 1970 for
execution of NEP. It was a twenty years long term policy of stimulating the
Bumiputera as well as non- Bumiputera share of economic resources. The primary
goals of NEP were to unite Malaysia, achieved national unity by eliminating poverty,
increasing income and standard of living and creating employment opportunities for
all Malaysians irrespective of race. Malaysian society was restructured to attain inter-
ethnic economic equality between leading groups Malay Bumiputera and the Chinese,
thus removing the identification of race with economic integrity.
Another important aspect of NEP apart from eradicating poverty and
redistribution of wealth was a high level of economic growth and restructuring of the
economy. Stability in economic growth played a crucial role in the effective
restructuring of the Malaysian economy and the equalizing of ethnic imbalances
(Simpson, 2005). The government adopted the export oriented strategy and introduced
new dimensions namely; The “Look East” policy and “Malaysian Incorporated”
policy.
The NEP achieved outstanding success in twenty years (1970-1990). It helped in
shifting Malaysian society from one categorized by extensive poverty, income
inequality and race (an important element in an individual’s occupation, and earning
prospective), to a rapidly modernizing economy. The comparative picture of poverty,
occupational structure (agriculture and non- agriculture) ownership share (Bumiputera
and Non – Bumiputera), quality of life (literacy rate, life expectancy, infant mortality
rate and calorie intake) and growth rate as per official figure of 1970 and 1990 can be
seen in Table 3.1.
45
Table 3.1 NEP outcome over the period 1970 -1990
INDICATORS 1970 1990
Poverty (%) 50 17
Occupational Structure (%)
Agriculture 53 29.9
Non- agriculture 47 70.1
Ownership Share (%)
Bumiputera (Malay) 2.4 20.3
Non- Bumiputera (Chines and Indian) 28.3 46.2
Foreigners 63.3 25.1
Quality of Life
Literacy rate (%) 60.2 78.5
Life expectancy (years) 63.5 71
Infant mortality (per thousand live birth) 39.4 15
Calories intake per day per person 2170 2775
GDP
GDP growth (in 1978 prices) RM Billion 21.4 79.1
Source: (Drabble, 2000; Ghosh, 1996; GomezJomo, 1999; Snodgrass, 1995)
Table 3.1 shows that the overall poverty level in Malaysia significantly reduced
by 33% within the period of 20 years of NEP, the occupational structure ratio between
agriculture and non-agriculture change from 53: 47 to 30: 70, ownership, share among
the three groups Bumiputera, Non- Bumiputera and foreigners was 2.4, 28.3, and 63.3
percent respectively in 1970 and in 1990 it was 20.3, 46.2 and 25.1 percent
respectively. It clearly indicated that all classes of Malaysian society received tangible
benefit from the execution of NEP. Malaysia also made astonishing progress in
improving human development index (HDI) between 1970 and 1990 and ranked
fourth in the world for improvement in the HDI, which is the most popular index of
social development (Snodgrass, 1995). The four major aspects of quality of life
(literacy rate, life expectancy, infant mortality and calorie intake) mentioned in Table
3.1 above indicate that the overall quality of life was satisfactorily improved in NEP
period . All these objectives were to be organized and achieved under the framework
of prompt and continuous economic growth. During 1970 -1990 the average GDP
growth was 6.7 % and in absolute money term, it increased by 57.7 %. This indicates
the remarkable performance and achievement of NEP (EPU, 2004).
In short the Malaysian government was consistent in its approach and the
execution of long-term economic strategies. The NEP proposed changes were radical
46
and difficult to implement, but government commitment to NEP and stability of the
political system made it possible, even though the country had three prime ministers
during the NEP period, resulting in the NEP achieved extraordinary tremendous
success during 1970 and 1990.The overriding objective of the NEP, maintained in
the NDP and the NVP, was to preserve national unity by eradicating poverty
irrespective of race, and by restructuring Malaysian society to reduce the
identification of race with economic function and geographical location (Yusof,
2008).
3.3.2 New Development Policy (NDP) 1991-2000
The NEP ended in 1990.Thereafter the Malaysian government introduced the NDP in
July, 1991 as a successor of the NEP with continuity of the two main objectives of
NEP, i.e. poverty elimination and restructuring of Malaysian society. It modified the
previous policy by shifting the focus toward elimination of hardcore poverty instead
of general poverty, focusing on minimization of relative poverty in the same period of
time. The NDP focus was to establish an active Bumiputera Commercial Industrial
Community (BCIC) to increase fruitful and effective participation of Bumiputera in
the industrialized economy, instead of increased ownership share.
Apart from the above other objectives of the NDP were;
1) To encourage private sector investment and engage them in restructuring
objectives by producing new opportunities for their development and
advancements; and
2) To focus on the development of human capital, which is the essential element
for obtaining a high level of economic growth and justified distribution (EPU,
2004).
The NDP designed under a long term aspiration plan, the Vision 2020.The Vision
2020 aims to transform Malaysia into prosperous, competitive, dynamic, robust and
resilient country by the year 2020.It envisage Malaysia as a developed nation and
defines a vision for the economy with a view to accelerating industrialization, growth
and modernization of Malaysia.
47
The NDP has a ten year time period in which three, five years plans, Sixth, Seventh and
Eighth were launched and implemented as a first phase of the plan for Vision 2020
(Fadhlin, et al, 2004). The NDP achieved remarkable reductions in general poverty as
well as in hardcore poverty. It also reduced the gap between rich and poor and
narrowed down the income inequality in the society. Poverty reduced generally by
10% between 1990 and 2000, hardcore poverty (which was the main target of this
policy) reduced to 0.5 % from 3.5%. The Bumiputera employment level reached
56.6% of total employment (3. 83 million) at the expiry of NEP. During NDP period,
it further increased and reached 4.78 million. The real GDP was growing by an
average rate of 7% per annum during 1991-2000. By the end of NDP period, real
GDP in monetary value (at1987 prices) was RM 209.3 billion, which was three times
higher than 1990 i.e. RM 79.1 billion. Similarly per capita income was RM 6298 in
1990 and reached RM 13359 in 2000. See Table 3.2 (EPU, 2004).
Table 3.2 NDP Outcome
Indicators 1990 2000
Poverty (%)
General 17.1 7.5
Hardcore 3.5 0.5
Employment (million)
Bumiputera 3.83 4.78
Ownership of corporate equity
Bumiputera (RM billion) 22.3 54.4
Share (%) 20.3 19.1
GDP
Economic growth (GDP in 1978 prices) RM Billion 79.1 209.3
Per capita GDP (at current price) RM 6,298 13,359
Source: EPU, 2004
3.3.3 National Vision Policy (NVP) 2001-2010
OPP-2 (1991-2000) successfully achieved their targets despite the Asian currency
crises1997-98. The Malaysian Government launched OPP-3 (2001-2010) which can
be considered as a second phase effort to achieve Vision 2020 objective, fully
developed nation status. The NVP establishes the ground of the strategies and
programs under the OPP - 3 (Thaib, 2013).
48
The NVP was the extension of NEP 1970-1990 and NDP 1990-2000 with an
objective to steer national development toward making an advanced, enlightened and
scientific as well as an ethical, moral and caring Bangsa Malaysia. The prime
objective of this policy was to transform Malaysia as a developed nation based on its
own culture and frame. The policy focuses was on strategies to build a knowledge
based economy and strengthen human resource development. It is targeted to maintain
sustainable high economic growth by consolidating the source of growth, the financial
institutions, and business corporate units as well as aggregate economy management.
In addition, it aims to promote enthusiasm in the agriculture, manufacturing and
services sector through greater use of modern technology, skill and knowledge. It also
focuses on encouraging an equitable society by eliminating poverty, decreasing the
gap between rich and poor and removing disparities among the various ethnic groups
of the society and enhancing competitiveness to face the modern challenges of
globalization and liberalization (Azatbek, 2012).
The NVP covers two, five years master plans 8th (2001-2005) and 9th (2006-2010)
that were designed in the light of the second decade of the Vision 2020 objective of
uniting Malaysia. The Eighth Development Plan of Malaysia stresses on the
knowledge based economy instead of input driven in order to increase potential output
growth to accelerate structural transformation within the manufacturing and services
sectors and to reinforce socioeconomic stability. While the ninth and tenth
development plans give more attention to investment in human capital in order to
develop an efficient and more productive workforce for high productivity and growth
(Thaib, 2013).
3.3.4 Economic Transformation Program (ETP)
The sixth Prime Minister (PM) of Malaysia, Dato “Seri Mohumad Najib” took charge
of the PM office in 2009. According to him, Malaysia needed models that satisfy the
current requirement of Malaysia as well as the requirement of the modern world. He
believed that the key component of Malaysian economy needed to be modernized and
restructured for obtaining high income growth and to face modern challenges of the
global economy. He introduced the Economic Transformation Program (ETP) to
49
improve Malaysia's economy (Thaib, 2013). Under this program 12 National Key
Economic Areas (NKEAs), are jointly identified by the private and public sectors to
boost the economy on the basis of contribution to high income, sustainability and
inclusiveness. These NKEAs are expected to make significant contributions to
Malaysia’s economic performance that will drive the highest possible income over the
next 10 years till 2020. The 12 NKEAs consist of 11 main industries and one city. The
industries are Oil, Gas and Energy; Financial Services; Tourism; Business Services;
Electronics and Electrical; Wholesale and Retail; Education; Healthcare;
Communications Content and Infrastructure; Agriculture; and the Greater Kuala
Lumpur city (ETP, 2010).
3.3.5 Structural Transformation and Major Sectors of Malaysian Economy
Malaysia is one of the fast-growing economies in the Asian and Asia Pacific region
that has done well despite the setback of the 1985/1986 oil shock, 1997/1998 Asian
financial crisis and 2005/2006 global financial crunch (Mahadevan, 2006). Over the
last four decades (1970-2010) average annual growth rate was around 7%. The major
reasons for this high growth rate of the Malaysian economy were accompanied by a
significant structural transformation of the economy. The economy of Malaysia was
gradually transformed from an agrarian society to an industrial based and service
oriented one. The focal point of the Malaysian economy shifted from agriculture
sector to the manufacturing sector and its economy output base expanded from one
highly dependent on rubber and tin, to that of manufacturing and wide range of
primary products (Fadhlin, 2004). In 1970, the contribution of agriculture to the GDP
was as high as 29.0 percent. This share declined sharply to 18.7 percent in 1990 and
to 8.7 percent in 2000 and in 2010 it was around 7 %. In contrast the manufacturing
sector output significantly increased from 13.9% in 1970 to 33.4 % in 2000 and
around 28 % in 2010. Another important sector of the Malaysian economy is services
sector, which is the largest contributor to GDP raised from 32% in 1970 to 57 % in
2010. The contribution of Mining and Quarrying sector decreased from 11% in 1970
to 7 % 2010, while the contribution of the construction sector fluctuated between 3 %
and 5%, during the period of 40 years. Now the Malaysian economy is driven by
50
Manufacturing and services sectors by contributing approximately 85% to GDP
(EPU, 2004). The changing structure of the economy can also be observed through
the contribution of the major sectors to the GDP between 1970 and 2010 as shown in
Table 3.3.
Table 3.3 Contribution to GDP (%)
Sectors 1970 2010
Agriculture (%) 26.1 7.3
Mining and Quarrying (%) 11 7
Manufacturing (%) 13.9 28
Construction (%) 3.5 4.5
Services (%) 32 57
Source: Economic Planning Unit Malaysia and DOS Malaysia
3.3.6 Inflation and Unemployment
It is incredible that Malaysia has been able to achieve and maintain high economic
growth without inflationary pressure over the last 40 years. Although Malaysia
experienced double digit inflation during the mid-1970s due to “oil shock”, it still
maintains inflation between 2-4%. Even in the high growth period 1985-1996
inflation was not above 4% (Arif, 1998). The rapid expansion in Malaysian
economy’s output increased the employment level by 3%. The average
unemployment during 1970s was around 6.3% and in 2010 it was 3.4% as shown in
Table 3.4
Table 3.4 Inflation and Unemployment rate (%) 1971-2010
Years Indicators
Inflation Unemployment
1971-1975 2.9 6.6
1976-1980 2.4 6.2
1981-1985 3.1 5.9
1986-1990 1.4 7.5
1991-1995 3.5 3.9
1996-2000 3.4 2.9
2001-2005 1.8 3.4
2005-2010 2.6 3.4
Source: Ministry of Finance, Economic Report, various issues (1972 to 2012)
51
3.3.7 Balance of Trade
Malaysia has had a remarkable performance in international trade for the last four
decades ranging from 1970 to 2010, which has played a significant role in the
Malaysian economy. It maintained a positive trade balance over the forty year period,
except 1982 and 1983, in which imports were greater than exports due to weakened
world prices for major exports of Malaysia (crude oil, palm oil, and rubber and tin).
Table 3.5 shows that the total international trade volume of Malaysia during 1971 to
1975 was RM 68,125 million in which export and import volume amounted to RM
35,962 million and RM 32,163 million respectively. The surplus, trade volume was
only RM 3,799 million. During 2005 to 2010 the total trade volume reached RM
5,391,306 million with export and import amounting RM 3,053,178 million and RM
2,338,128 million respectively. The trade surplus amount was RM 715,050 million.
This was due to rapid industrialization and significant growth in export of primary
and industrial products.
Table 3.5Malaysia Trade Balance 1970-2010
Years Total Trade
Volume RM
Million
Export
Volume RM
Million
Import
Volume RM
Million
Surplus/
Deficit RM
Million
1971-1975 68,125 35,962 32,163 3799
1976-1980 171,082 97,189 73,893 23,296
1981-1985 294.921 153,073 141,848 11,225
1986-1990 506,582 281,192 225,390 55,802
1991-1995 1,256,174 635,910 616,664 22,846
1996-2000 2,517,921 1,386,346 1,131,575 254,771
2001-2005 3,750,846 2,112,122 1,638,724 473,398
2006-2010 5,391,306 3,053,178 2,338,128 715,050
Source: Malaysia Economic Statistics –Time Series 2011
The major reasons of this remarkable economic growth and development,
performance are sound economic policies, political stability and government
commitment towards providing a physical and social infrastructure. Due to high
economic growth and development rate and country aggregate economy management,
the Malaysian economy has great potential to attain the status of industrialized and
developed nation that was defined in Vision 2020 (Arif, 1998).
52
The above discussed economic background which was necessary for a further
discussion of the construction sector. The following section will discuss the role of
MCS in the economic development of Malaysia.
3.4 Malaysian Construction Sector (MCS)
The construction industry is a significant and productive sector of the Malaysian
economy. As a developing nation, Malaysia has realized the pivotal role of the
construction sector not only in economic growth, but also in improving the quality of
life and living standards of the Malaysian people. Malaysia recognized the importance
of the construction sector since its independence in 1957 when the industry was low-
tech, labor intensive crafts-based industry (Kamal et al., 2012). The construction
boom in Malaysia began in the early 1990s, just after the launch of Vision 2020.
Today the MCS is more advanced, modernized and well equipped. It has a
potential to deliver complex heavy infrastructure and skyscraper projects by using
highly sophisticated mechanized techniques. This has resulted in rapid execution of
many projects such as high rise commercial and industrial buildings, highways,
expressways, bridges and tunnels, housing schemes, schools and hospitals and sports
and spa centers, monorail and mass rapid transit rail system, and power plants.
The MCS continues to grow significantly in the domestic as well as international
market. Some of the major projects that were completed by the Malaysian
construction industry during the study period are the world tallest tower of its time,
PETRONAS Twin Towers (1992- 1998); the Kuala Lumpur International Airport
(1993-1998) which has a capacity of handling 35 million passengers per year. It was
voted three (3) times as the world best airport; North South Expressway in 1994;
Maju Express Way; Penang Bridge (1989) with 65,000 vehicles running daily over it;
Strom-water Management and Road Tunnels constructed during 2003-2007;
Commonwealth Games Village (1993); Pavilion and Bangsar Apartments; Price
Court Medical Centre and several other projects. Malaysian contractors have also
completed worldwide projects outside the Malaysia like Burj-al- Khalifa (Dubai),
53
International Circuit Bahrain, New Doha International Airport, Dukhan Highway in
Qatar (Hasan, 2012).
Today, MCS has become one of the major sectors of the Malaysian economy,
although its contribution is relatively small as compared to other sectors of the
economy like manufacturing, mining and quarrying, agriculture and forestry and
service. In spite of that the importance of the sector cannot be ignored, it is one of the
most important sectors of the Malaysian economy. It provides great support to the
aggregate economy by backward and forward linkages with other sectors of the
economy.
However, despite these noticeable progresses, the MCS as a whole is still fill with
challenges of low productivity and quality, unskilled, time and cost overrun,
occupational health and safety issues, environmental issues and over reliance on
foreign workers.
3.4.1 MCS Output and GDP Malaysia
A number of studies have been conducted over GDP and construction sector
association. A study conducted by Turin (1969) over construction sector and GDP,
found the share of value added by construction as a percentage of GDP in developed
countries was around 5 to 8 percent and for developing countries it was 3 to 5 percent
during 1955-1965 (Turin, 1969). A similar study conducted by Lowe (2003),
concluded that the construction value added range of highly developed countries is 7
to 10 percent and for developing economies it is 3 to 6 percent (Lowe, 2003).
Table 3.6 indicates the average output of construction sector in the last two
decades, in which Malaysia had launched four (4), five (5) years plan, such as 6th,
7th, 8th and 9th Malaysia Plan (MP). During the 6th plan the average output of the
construction sector was RM 11,228 million that was increased by 41% in 7th plan and
reaching to RM 15,789 million despite of the Asian crises over 1997 to 1998. There
was a slight decline in the 8th plan which recovered in the 9th plan. The average
construction output during 8th and 9th plan was RM 14,752 and RM 15,897 million,
54
respectively. The construction boom in Malaysia began in the early 1990s, just after
the launch of Vision 2020, when number of heavy infrastructure and skyscraper
projects launched and rapidly executed like high rise commercial and industrial
buildings, highways, expressways, bridges and tunnels, housing schemes, schools and
hospitals and sports and spa centers, monorail and mass rapid transit rail system,
airport and power plants. The average GDP volume of the Malaysian economy during
6th five year plan (1991 to 1995) was RM 236,889 million and it was increased by 38
% in the 7th plan (1996 to 2000). The GDP volume was RM 327,103 million. The
increment value between 7th and 8th plan was 22% and between 8th and 9th plan, it
was only 6%. The major reason for this low increment was due to global financial
crises.
Table 3.6: Average output of construction Sector (6th to 9th plan)
Five Years Plan Average output
(RM) million
Average GDP
(RM) million
MP 6 (1991 to 1995) 11,228 236,889
MP 7 (1996 to 2000) 15,789 327,103
MP 8 (2001 to 2005) 14,752 402,195
MP 9 (2006 to 2010) 15,897 424,861
Source: Department of Statistics Malaysia (indexed 2000 constant price)
Figure 3.2: Construction output and GDP (1990-2010) depicts that construction
output grew sharply between 1991 to 1997 and reaching to RM 19,103 million in
1997 from RM 8,693 million in 1991. Due to Asian economic crises during 1998 to
1999 output of industry rapidly decreased by RM 5,216 million and attained level of
RM 13,887 million. From 1991 to 2010, the Malaysian construction sector suffered
two economic crises. One is the Asian economic crises between the year 1997 to 1998
and the other is the global financial crises over 2007 to 2008. It was noted that the
Malaysian construction sector was not as much influenced by the global financial
crises as by the Asian crises in which construction output decreased by RM 5,216
million from 1998 to 1999. From 2001 to 2007 industrial output fluctuated between
RM 14,427 to RM 14,903 million. Thereafter, industry output gradually increased and
reached RM 17,426 million in 2010. This situation reflects the significance of
construction industry in the Malaysian economy, highlights its role in infrastructure
development, and shows the importance of the sector in industrialization and the
55
urbanization process. During the same period of time GDP increased from RM 196,
506 million to RM 559,554 million.
Figure 3.2: Construction output and GDP (1990-2010)
Source: Department of Statistics Malaysia (indexed 2000 constant price)
3.4.2 MCS Output as a Percentage of GDP Malaysia
Figure 3.3 shows the contribution of MCS output in GDP of the Malaysia. The MCS
has been contributing to an average of 4.09 % of GDP with a minimum 3% in 2008
and a maximum 5.7 % in 1998. The Malaysia GDP growth was above 8% during
1991 to 1996 and highest growth was 10 % in 1996. The GDP growth was dropped by
18% and reaching to -8% in 1998 due to the Asian financial crises. The Malaysian
economy rapidly recovered and bounces back by 14% in 1999 and in 2000 once again
it was above 8%. Over 2002 to 2008, GDP fluctuated between 4.8% and 6.8%. The
Malaysian economy faced another downturn in 2009 and immediately recovered in
2010 and attained the growth level of 7.2 %.
050000
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56
Figure 3.3: Contribution of Construction output and GDP growth (%)
Source: Department of Statistics Malaysia (indexed 2000 constant price)
Table 3.7 indicates that during the first decade of Vision 2020 which was covered
by two master plans MP-6 and MP-7, the average contribution of the construction
industry to GDP was 4.7% and 4.8%, respectively while the average GDP was
declined from 9.4% in MP-6 to 4.9% in MP-7. In the second decade of Vision 2020,
which consists of MP-8 and MP-9, the average contribution of the construction output
decline by 1%, and in both plans the contribution was 3.7%.
Table 3.7: Average construction output percentage of GDP in various MPs
Source: Department of Statistics Malaysia (indexed 2000 constant price)
3.4.3 MCS and GDP Growth
Construction output is the function of level of investment in the sector. It varies with
the construction investment in the economy. Figure 3.4 illustrates the fluctuation in
annual growth in the construction sector with respect to the percentage change in
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08
20
09
20
10
Co
ntr
ibu
tio
n (
%)
Years
cons% GDP %
Master plan (MP) Construction
output as a % of GDP
GDP growth
(%)
MP 6 (1991 - 1995) 4.7 9.4
MP 7 (1996 - 2000) 4.8 4.9
MP 8 (2001 - 2005) 3.7 4.8
MP 9 (2006 - 2010) 3.7 4.5
57
GDP growth of Malaysia for the past two decades (1991 to 2010). It can be observed
from Figure 3.4 that the construction sector growth generally follows the aggregate
economy trend except in 2000 to 2001 and 2008 to 2009. The construction sector
expands with the expansion of economic activities and contract as economy size
reduced, which was the same as the observation given by George Ofori (1990), that
when economic activities expand and the economy flourished, the demand for goods
and services increase as people income level increase and people want to improve
their living standard. Hence, construction activities increase. Construction companies,
developers and institutions invest money in property sector. The government also
allocates resources to upgrade the existing facilities available for public and to
develop social and physical infrastructure for improving the quality of life of the
general public (Ofori, 1990). Figure 3.4 shows that during 1991 to 1997 the
construction sector growth was higher than the GDP growth and the highest level of
construction sector growth was 21% in 1995. The highest negative growth in the
construction sector was in 1998 i.e. -23% due to the Asian financial crises.
Furthermore, it shows that the construction sector grows at a higher rate than the GDP
growth when the aggregate economy expands, and during the period of recession the
construction sector declines more rapidly and remain in recession longer than the
aggregate economy, same as the outcome of the World Bank study conducted on
issues and strategies of construction industry in developing countries concluded that
fluctuation of construction output is higher than manufacturing and the aggregate
economy (Henriod, 1984) and (Fadhlin et al, 2004).
Figure 3.4: Yearly construction sector and GDP growth
Source: Department of Statistics Malaysia (indexed 2000 constant price)
-30
-20
-10
0
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30
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58
Table 3.8 indicates that the most favorable plan for construction sector was MP 6
in which the average growth of the construction sector was even higher than the GDP
growth, i.e. 14.7 % per year while the GDP growth was 9.4 % per year. The growth of
the construction sector was drastically affected in MP 7 and MP 8 due to the Asian
financial crises in 1997 to 1998 and most of the infrastructure work and heavy
projects were completed during this period. In MP 7 and MP 8, the average growth
was -0.24 and 0.96, respectively. Overall average growth of the construction sector
during the last two decades of Vision 2020 was 4.8% against the average GDP growth
of 5.9%.
Table 3.8: Average growth in various MP Malaysia
Master Plan Average
construction sector
growth (%)
Average
GDP growth (%)
MP 6 (1991 - 1995) 14.7 9.4
MP 7 (1996 - 2000) -0.24 4.9
MP 8 (2001 - 2005) 0.96 4.8
MP 9 (2006 - 2010) 3.6 4.4
Average growth (1991-2010) 4.8 5.9
Source: Department of Statistics Malaysia (indexed 2000 constant price)
3.4.4 MCS and Major Sectors of Malaysian Economy (Comparison)
Malaysia is categorized as a newly industrialized developing country with multi-
sectoral economy. Recently the World Economic Forum ranked Malaysia 24th most
competitive nation out of 148 countries, higher than China and South Korea due to
her impressive and extraordinary economic performance (Vijian, 2013). Malaysia
intends to formulate its economic policies in such a way that all groups or
communities in society benefit in an equitable manner (Yusof and Bhattasali, 2008).
Malaysia’s rapid economic growth is the result of prompt structural changes in the
economy. Its economy has undergone rapid transformation since independence. The
major shift is from natural resource based economy to large scale industrialized
economy. This transformation changed the structure of the Malaysian economy. The
changing composition of the country’s GDP and contribution of employment is
59
mirrored in the changing structure of the Malaysian economy and can be seen in the
Table 3.9 and Table 3.10.
Table 3.9: Sectoral share to GDP
Sectors 1970 1980 1990 2000 2010
Agriculture 29 22.9 18.7 8.5 7.3
Manufacturing 13.9 19.6 27 30.9 27.3
Mining &Quarry 13.7 10.1 9.7 10.6 7.0
Construction 3.8 4.6 3.5 3.9 3.1
Services 36.2 40 42.3 49.3 57.7
Source Department of Statistics Malaysia (various years)
Table 3.10: Employment contribution
Sectors 1970 1980 1990 2000 2010
Agriculture 50.0 36.0 25.0 16.0 13.0
Manufacturing 11.0 16.0 19.0 23.0 18.0
Mining & Quarry 0.5 0.5 0.5 0.2 0.5
Construction 6.5 7.0 7.0 8.0 9.0
Services 31.0 39.0 45.0 50.0 56.0
Source Malaysia economic planning unit five year plan (Various years)
Malaysian economic structure can be categorized as Primary, Secondary and
Tertiary. Agriculture and forestry, and mining and quarrying are considered as
primary sectors of the Malaysian economy. Manufacturing and construction sectors
are included in the secondary sector category, while services sector is included as
tertiary. These five major sectors of Malaysian economy are considered as the
backbone of the economy. The services and manufacturing sectors are the two top
contributors to Malaysia’s GDP, contributing to 58% and 27%, respectively.
Furthermore, these two sectors provide 74% employment to the economy as per the
2010 labor force employment data.
The study period of this research presented here is 1991 to 2010; therefore focus
on last two decades is made. Figure 3.5 and Figure 3.6 show that the contribution of
agriculture and mining and quarrying sector to GDP gradually decreased from 12.5,
and 11.7 percent to 7.6, 7.9 percent, respectively, in shifting from MP 6 to MP 9. The
two sector services and manufacturing increase their contribution in the same period
of time from 42.9 and 25.3 percent to 55.4 and 28.8 percent respectively.
60
Figure 3.5 and Figure 3.6 also show that the construction sector is the smallest
sector as compared to other sectors of the economy in terms of output contribution to
GDP. Actually the significance of the construction sector is not much connected to its
size, but its pivotal role is in the economic growth as well as the socioeconomic
development ( Bon, 1988). The entire physical infrastructure, urban space, community
utilities and shelter, and natural environment are built and developed by the
construction sector. Actually the creation and designed of the built environment is
highly depended on the construction process. It supports other sectors of economy by
consuming their output as intermediate goods in its production process. Most of the
economic sectors are influenced by the construction sector such as agriculture, mining
and quarrying, manufacturing, and tourism. It develops infrastructure for other
sectors of the economy that is an essential contribution to the process of development
by providing the physical foundation upon which development efforts and good
quality of life are based (World Bank, 1984), and ( Agung, 2009). Now it is globally
accepted that the construction sector plays an important role in the socioeconomic
development of a country as well as in the region. The construction sector is not only
important for the economic health of the country, but also important for the social
health of the country by improving the quality of life.
Figure 3.5: Key sectors average contribution (%) to GDP in various plans
Source: Department of Statistics Malaysia (indexed 2000 constant price)
61
Figure 3.6: Key sectors average contribution (%) to GDP in various plans
Source: Department of Statistics Malaysia (indexed 2000 constant price)
3.4.5 The Performance of Subsectors of MCS
The Malaysian construction activities can be divided into two major areas, General
construction which can be further categorized into residential construction, non-
residential, and civil engineering work. The second major area is in the area of special
trade, which covers all activities related to sewerage and sanitary work, electrical,
plumbing, air conditioning and telecommunication work activities of metal work,
carpentry and glass work, marble, tiling and flooring work (CIDB, 2007). All
commercial buildings such as markets, factories, production units , offices,
educational institutes, health centers and buildings and other similar structures are
included in non-residential construction, whereby civil engineering work comprises
on all infrastructures such as roads, highways, corridors, bridges, railway structures,
airports, dams, irrigation structures , power plant construction, landscaping and other
related development projects. In Figure 3.7 all sub-sectors output decreased during
1985 to 1988 due to World economic downturn (Oil Shock), but thereafter they have
a positive trend throughout 1988 to 2010. The civil engineering was the major
contributor throughout the period from 1985 to 2009 as a result of heavy investment
in infrastructure and enormous projects, which were executed such as Kuala Lumpur
International Airport, which has a capacity of handling 35 million passengers per
62
year. North South Expressway, Maju Express Way, Penang Bridge with 65,000
vehicles running daily over it, Strom-water Management and Road Tunnels. The
Federal Government expenditure increase from RM 3,635 million in 1985 to RM
24,852 million in 2010. The growth of non- residential sector also sharply increased
during 2005 to 2010 due to the rapid increase in demand for office building, retail
building and rapid construction of industrialized buildings. In the year 2010, it was
the leading sector of the construction industry by contributing 30 % in the total gross
output of the construction sector. The contribution of this segment was RM 27,046
million in 2010 as compared to RM 9,248 million in 2005. The activities of residential
sector were also positive during 1985 to 2010 as a result of relaxed Government
policies regarding property sector, such as an exemption in Property Gains Tax,
relaxation in borrowing loan for obtaining residential property, supported residential
property transaction with foreigners even allowing loan for this purpose. The volume
of residential output in 2010 was ten (10) times higher than 1985 i.e. RM 20,362
million (CIDB, 2007). The output growth of various segments of construction sector
actually follows the trend of overall growth configuration of the sector.
Figure 3.7: Subsectors output level,
(Source: Department of Statistics Malaysia)
The Government policies and economic conditions play a crucial role to change
the proportion of sub-sectors output. Figure 3.8 shows the proportion of each sub-
sector of construction sector between 1985 and 2010. The civil engineering output is
0
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n)
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Residential Nonresidential Civil Engg Special Trade
63
dominated throughout the period, except in 2010, in which non-residential sector led
the construction industry output by 30 % and civil engineering followed by 27%.
Residential sector has stability in its contribution during the same period of time and
study.
Figure 3.8: Subsectors contribution,
(Source: Department of Statistics Malaysia)
Table 3.11 depicts that the highest average contribution in the gross output of the
construction sector was 36.65% with a standard deviation of 3.67 % from civil
engineering projects. The contribution of civil engineering project was fluctuating
between 27.21% and 42.26 %. It was continuously increasing from 1988 to 1993,
which reflect the heavy infrastructure project, which were launched and executed
during this period. This was the time when Tun Dr. Mahathir Mohammad introduced
the Vision 2020 mission. The average proportion of residential and non-residential
sectors were very close to each other i.e. 21.14 and 22.75 percent, respectively, with a
minimum contribution of 15.96 and 18.58 percent respectively, while the maximum
was 27.12 and 29.61 percent, respectively. The lower proportions of residential and
nonresidential sectors reflect the low standard of living and slow growth of industrial
development of the country. The residential segment was the second highest
contributor with an average contribution of 25.05 % over a period of 2002 to 2007.
This was the period in which Government of Malaysia emphasized on home
ownership and adopted a soft policy for property sector especially to foreigners. The
0
5
10
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Residential Nonresdntial Civil Engg Special Trade
64
highest share of residential sector was 27.12 % in 2005. Similarly, non-residential
sector increases when Government adopted industrialized friendly policy resulting in
the share of non- residential sector had reached at 29.61 % in 2010. The growth of the
special trade segment also had increased stimulated by the increase in residential and
non-residential construction activities. The average contribution of special trade was
18.39 percent with a minimum of 15.65 % and maximum of 20.89 % from 1985 to
2010.
Table 3.11: Contribution (%) of construction subsectors in gross out of Construction
Indicators Residential Non-Residential Civil
engineering
Special trade
Avg. Proportion 21.14 22.75 36.65 18.39
Std. Deviation 2.67 2.81 3.76 1.45
Range 11.16 11.03 15.05 5.24
Minimum 15.96 18.58 27.21 15.65
Maximum 27.12 29.61 42.26 20.89
Data source: Department of Statistics Malaysia (1985-2010)
Figure 3.9 shows the average proportion of four segments of the construction
sector (residential, non-residential, civil engineering and special trade) during 1985 to
2010.
Figure 3.9: Average contribution (1985-2010) %
Source: Department of Statistics Malaysia (1985-2010)
22.14
22.7536.65
18.39
Residential Nonresdntial Civil Engg Special Trade
65
3.4.6 Employment Contribution of MCS
The construction sector is supposed to be a labour intensive sector that has a
mechanism of generating employment and offering job opportunities for millions of
unskilled, semi-skilled and skilled people (Khan, 2008). It plays a significant role in
reducing unemployment and ultimately minimizing poverty. The Malaysian
construction industry plays an important role in generating wealth for the country,
developing of socioeconomic infrastructures and buildings. The industry is providing
job opportunities to more than one million people. In 2010, 1.02 million people were
engaged in the construction sector constituting, 9.2 % of the total available workforce.
Figure 3.10 depicts the employment contribution of the construction sector over the
period 1985 to 2010. From 1985 to 1988 employment contribution reduced by 2% and
reached to 5 % of the total available labour force due to the downfall in the aggregate
economy of Malaysia but thereafter construction sector has a positive trend till 2010,
where by its proportion was 9.2% of total labour force, which is a remarkable
contribution to the aggregate economy. In the first decade (1991 to 2000) of Vision
2020 the average annual employment rate was 8.07% of the available workforce in
the construction sector of Malaysia, while in the second decade (2001 to 2010) it
further increased and reached to 9.15 % per year, despite of the fact that the youth of
Malaysia prefers to be unemployed than getting a job in the construction industry due
to several reasons such as low wage, uncomfortable working environments , difficult ,
dangerous and dirty scope of work and inappropriate safety measure at a construction
site (Yusof, 2011).
Figure 3.10: Employment contribution
Source: Department of Statistics Malaysia (1985-2010)
3
4
5
6
7
8
9
10
19
85
19
86
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87
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88
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89
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90
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07
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09
20
10
Co
ntr
ibu
tio
n %
Years
66
This performance reflects that the construction sector playing an effective role in
socioeconomic development of society by providing job opportunities, increasing
income sources and reducing unemployment from the society.
3.4.7 Employment by Category (Full Time Paid Employees)
Malaysian construction personnel can be divided into four major groups, construction/
operative staff, technical and supervisory staff, managers and professional staff and
office clerk and general staff. According to the Economic Census of the construction
sector, 2011 full time paid employees in the construction sector were 946, 108, in
which 85.8 % were categorized as construction staff . The proportion of managerial
and professional staff was 3.8 %, technical and supervisory staff 4.1% and clerical
and general workers proportion was 6.3 % as shown in Figure 3.11 (Department of
Statistics Government, 2011)
Figure 3.11: Construction personnel by trade category % (2010)
Source: Economic Census 2011: Construction
3.8 4.16.3
85.8
Managerial & Professional Technical & Supervisory
Clerkal & General Construction/ operative
67
3.4.8 Registered Contractor, Sub-Contractor, and Suppliers
The importance of contractors cannot be ignored in the construction sector.
Contractors have significant value in the construction sector and the output of
construction industry largely depends on the strength of contractor, sub-contractor and
the material supplier. At the moment there are two registration centers for
construction contractors in Malaysia, CIDB and Pusat Khidmat Kontraktor (PKK).
The CIDB classified contractors for registration into seven categories, grade 1-7 on
the basis of tendering capacity, financial strength and manpower strength. It deals
with foreign contractors separately (CIDB, 2007). Figure 3.12 shows that the major
chunk of contractors is registered in grade G1 category i.e. 51% of total registered
contractors at CIDB. They are allowed to bid for tenders up to the value RM 200,000.
13% are registered in the G2 category in which they can bid for the job value of not
exceeding RM 500,000. G1 and G2 categories are considered micro enterprise as
defined by Standard Industrial Classification (SIC). 20% of the contractors are
registered under the category G3 and G4. They are considered as small sized
enterprises and their tender limits allowed are 1 and 3 million, respectively. Grade G5
and G6 contractors are included into the medium sized enterprise. Their proportions
are 8% of the total contractors and have a tender limit of up to 5 million and 10
million, respectively. G7 has no limit for bidding tender. It is considered as large
enterprises. According to CIDB data, 7% of the contractors are registered under the
category of G7.
Figure 3.12: Composition of grade 1-7 and foreign contractors (%),
(Source: CIDB 2012)
51
13
16
46
2 7 0.3 G1
G2
G3
G4
G5
G6
G7
Foreign
68
According to Standard Industrial Classification (SIC) they can be grouped as micro,
small, medium and large enterprises. Table 3.12 shows the number of registered
contractors in each classified group by CIDB and number of active contractors in each
category. It was observed from data that more than 80 % contractors are active in each
category from G1to G7 such as in G1 category 83%, G2, category 78%, G3 category
73%, G4 category 79%, G5 category 78%, G6 category 84% and G7 category 86%
are active. Overall, 81%, i.e. 51743 out of 64,066 registered contractors are active and
playing effective role in the growth and production of construction sector. The highest
number of active contractors is in grade G7, which has no limit for tender and
considered as large companies as per SIC definition.
Table 3.12: Categories of Contractors by CIDB
Grade Tender Limit (RM) Registered
Contractor
Active
Contractor
SIC
G1 Not exceeding 200
thousand
32,752 27,317 Micro sized
enterprise
G2 Not exceeding 500
thousand
8,187 6,404
G3 Not exceeding 1
million
10,437 7,631 Small sized
enterprise
G4 Not exceeding 3
million
2,686 2,113
G5 Not exceeding 5
million
3,817 2,968 Medium sized
enterprise
G6 Not exceeding 10
million
1,398 1,169
G7 No limit 4,573 3,925 Large sized
Foreign No limit 216 -
Total 64,066 51,743
Data source: CIDB 2012 Report
PKK criteria of contractor categorization for registration is different from CIDB,
PKK classified and registered contractors on the basis of paid up capital. PKK
categorized the contractor in 6 classes from A – F. PKK award Bumiputera status to
qualified contractors. Bumiputera status is given to contractors who are eligible to
tender all government projects (CIDB, 2007). Table 3.13 shows the categorization of
contractor for registration by the PKK, in accordance with the paid-up capital.
69
Table 3.13: Contractor categorization for registration at PKK
Source: http://pkk.kkr.gov.my/pendaftaran
These two different standards and requirements for registration create unnecessary
duplication and complication in the measurement of human capital and financial
resource data of construction firms working in Malaysia.
3.4.9 Registered Consultants
The consultant is also an important segment of the construction business. There are
three major types of consultant in the Malaysian construction industry. They are the
engineers, architects and quantity surveyors. Each category has its own registration
body / board such as engineers registered with the Board of Engineers Malaysia
(BEM); Board of Quantity Surveyor Malaysia (BQSM) registered quantity surveyors
and architects registered with Board of Architect Malaysia (BAM). The total strength
of consultant in the Malaysian construction industry in 2010 was 68,280. Figure 3.13
shows that 92% of the consultants are registered in the engineering category with
BEM, 3% are quantity surveyors registered with the BQSM and 5% consultants are
architect registered with BAM (CIDB, 2012). These consultants are well scattered in
Malaysia. The major drawback of industry is disintegration between project execution
process and plan and design process. There is a great discrepancy arises between the
consultants who plan, design and documented the project and the team of consultant
who execute the project (Ibrahim et al., 2010). The team of consultants and main
contractors for the project are appointed by the client on the basis of lowest tender
price criteria. PWD is the largest public sector project’s client in Malaysia that sets
the standards, established rules and procedure for tendering and award of contracts
(Kamal et al, 2012).
Category Paid up capital (RM)
A 600,001
B 400,001
C 100,001
D 35,001
E 17,501
F 10,000
70
Figure 3.13: Composition of consultant
Source: CIDB 2010
3.4.10 MCS workforce
The manpower is an essential factor of construction industry because the positive
growth, productivity and competitiveness of the industry are dependent on skilled,
trained and educated workforce. Unfortunately Malaysian construction sector has to
rely on foreign workers due to failure of domestic manpower to meet the demand for
the industry. One of the major reasons for the disinterest of local manpower in the
construction industry is its image like 3Ds (dangerous, dirty, and difficult) and low
job status etc. (Jamil and Yusof, 2011). The local youth of Malaysia feel that the
construction industry environment is unsafe, unhealthy, insecure job and low pay
structure (Malaysia Central Bank, 2009). Malaysian construction industry import
foreign labor, mostly from Indonesia, Bangladesh and Nepal. Most of the foreign
workers are in general trade and unskilled labor category. The four major reasons
highlighted by (Abdul-Aziz and Abdul-Rashid, 2001), for preferring foreign labor by
construction employers of Malaysia are: foreign labor is willing to work for overtime,
willing to accept low wage, submissive and docile, flexible and mobile. The
construction wage statistics 2010 shows that the foreign skilled worker wage per day
was RM16 to RM 23 and for semiskilled worker RM 11 to RM 13 (CIDB, 2012).
Figure 3.14 shows that in 2010, the number of foreign construction workers was
320,000 out of 941608 constructions personnel, which is 34 % of total construction
92%
3% 5%
Engineers Quantity surveyors Architects
71
manpower. Beside that there are high numbers of illegal workers, who are not
included in these statistics due to unavailability of registration or documentation.
Figure 3.14: Foreign and local worker
Source: Department of Statistics Malaysia (2010)
3.4.11 Productivity of MCS Worker
As shown in Figure 3.15 the average gross output of construction personnel in 1985
was RM 23,000. It was decreased to RM 17,588 in 1987 due to slow growth in the
construction sector. From 1988 to 1997 the average gross productivity of construction
worker continuously rises and reached RM 62,307 because the Malaysian government
launched many heavy projects and made a heavy expenditure on infrastructure
development. From 1998 to 2003 the construction productivity of per worker
decreased due to negative growth in the construction sector from 1998 to 1999 and
slow growth from 2000 to 2003, thereafter the average productivity curve has a
positive slope from 2004 to 2010. The Malaysian Construction Sector, had
experienced the strong productivity growth of 1.5% since 2004, because of the
execution of projects under the Ninth Malaysia Plan (9MP) and in relation of ‘value
added per employee’, the Construction Sector recorded a growth of 2.3% in 2007,
while its total output per employee productivity is 3.8%. It was the outcome of the
construction of major infrastructure projects that supported to the growth in
productivity (CIDB, 2008). Each Malaysian construction worker was able to produce
an output value of RM 89,637 (USD 29500) in 2010, that was one of the best
320000
626108
Foreign worker Local worker
72
Malaysian construction productivity among the Asian and ASEAN countries (MPC,
2011).
Figure 3.15: Construction output per employee,
Source: Department of Statistics Malaysia (1985-2010)
Figure 3.16 shows the average value added of construction sector contributed by a
construction worker in a year. The GDP of construction per employee was RM 17,596
in 1991 after that, gradually increased and reached to RM 24,322 in 1995 and then
remained stable till 1997 at above RM 24,000. From 1998 to 2003 the value added
output declined due to the Asian financial crises, the contribution to GDP reached the
level of RM 15,248 per person per year. Thereafter, it was fluctuating between the
amounting of RM 15,090 to RM 17,101 till 2010.
Figure 3.16: Construction value added per employee
Source: Department of Statistics Malaysia (1991-2010)
Years
Years
73
3.4.12 Role of Public and Private Sector in Malaysian Construction Industry
Public and private sectors are significant elements of the Malaysian construction
industry, behaving as the clients in the industry. Both sectors are playing a
fundamental role in the development of the construction sector and finally motivate
the Malaysian economy. Figure 3.17 shows the distribution of construction projects
between public and private sector from 1999 to 2011. The private sector was
dominated throughout the period, except the year 2001, 2002, and 2007, in which
high demand for construction activities of public sector, which contributed to 51% of
the total investment of awarded projects (CIDB, 2012). The Government of Malaysia
pumped huge amount of money to construct roads, highways, railways, public
buildings and utilities for stimulating the national economy. The higher contribution
of the private sector was due to the Government privatization program and expansion
of private sector investment policy. The private sector provides the efficient source of
output, income and employment opportunities in the economy.
Figure 3.17: Investment trend (public and private sector)
Data source: CIDB 2012 Report
3.5 MCS and Global Market
Globalization has provided many opportunities to Malaysian construction firms and
contractors to enhance their business in the international market. The most important
0
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00
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Pulic Sector Private Sector
74
reasons for Malaysian construction firms to enter into global market were to expand
and find new opportunities for business growth (Ragayah, 1999), including the
stationary condition of the local market, efficient allocation of resources and getting
the full benefit from the global openings for generating revenue (Maznah et al., 2006)
The Malaysian construction companies first entered in the global market in the
late sixties. During 1970 Malaysian construction sector completed a number of
projects in Sri Lanka and Mauritius such as bridges, wheat silos, training colleges
(Aziz, 1994). The Malaysian construction firms have been more active in the global
market in the 1990s by following the world economic trend, to globalize their
business internationally (Nations, 2006). The Malaysian government provides support
to local construction firms through export-led growth policy and through CIDB that
help local contractor to establish venture into international markets. Malaysian firms
enter into global market either as a main contractor, sub-contractor or joint venture
with the host country firm or other foreign firm (Fadhlin et al, 2004). At the moment
Malaysian contractors have been supplying their services to 49 countries to meet the
global construction market demand as shown in Table 3.14. From 1986 to 2010,
Malaysian construction firms have secured 511 projects all over the world. Few
International achievements of Malaysian construction companies are; Steel structure
work for Burj al Arab Hotel Dubai; International Circuit Bahrain, the remarkable
thing about this project is that it was completed at recorded schedule time of 16
months and handed over the project two days before the schedule; designing and
construction of New Doha International Airport; and the four lane dual carriage
Dokhan Highway in Qatar (Hasan, 2012). The largest market for Malaysian
construction firms is Middle East countries such as Dubai, Qatar, Saudi Arabia,
Yemen, Jordan, and Iran. They have 98 projects (19% of total projects) value at RM
44,987 million in the Middle East region; Most of the projects have been completed
and some projects are still under construction. The second largest market in term of
value for Malaysian construction firms is China, where Malaysian contractors have
secured a total of 43 projects value at RM 18,062 million. India is also a big market
for Malaysian construction firms where they have secured a total of 77 projects (15%
of total projects) of amounting RM 14,230 million (CIDB, 2007).
75
Table 3.14: List of Overseas Countries/Projects That Malaysian Contractors Venture
as of 2010
Country No. of Projects Value (RM Million)
Algeria 1 854
Argentina 2 214
Australia 3 18
Azerbaijan 2 2
Bahrain 11 3115
Bangladesh 6 164
Bosnia 5 1141
Brunei 8 1662
Cambodia 76 1281
Cameroon 2 37
China 43 18062
Dubai 55 11844
Ghana 1 133
Hong Kong 5 107
India 77 14230
Indonesia 15 856
Iran 2 2002
Ireland 1 6
Japan 1 250
Jordan 1 450
Laos 1 2090
Libya 4 1672
Maldives 7 97
Mauritius 1 86
Mongolia 1 76
Morocco 1 800
Myanmar 6 75
Nepal 1 39
Pakistan 3 849
P.N Guinea 5 705
Philippines 16 334
Qatar 11 4050
S. Arabia 12 21371
Seychelles 2 60
Singapore 21 250
S. Africa 3 1891
Sri Lanka 6 1045
Sudan 19 2804
Syria 2 483
Taiwan 7 894
Thailand 41 2685
T.& Tobago 1 230
Turkmenistan 1 52
UK 1 2
USA 1 103
Uzbekistan 1 7
Vietnam 17 2563
Yemen 1 581
Zimbabwe 1 8
Total 511 102,330
Source: CIDB Malaysia various reports
76
The numbers of projects that have been awarded to the Malaysian companies in
international markets during the last decade (2001 to 2010) were 449 amounting to
RM 81,298 million, which is a highly remarkable performance of the MCS. Figure
3.18 shows that the highest value of projects was obtained by the Malaysian
construction firms in overseas countries in the year 2007 i.e. RM 19,551 million,
which was the approximately double as compared to 2006 awarded projects value i.e.
RM10, 190 million. However, there was a sharp decline in 2010, when the Malaysian
contractors secured only RM 1,491 million of projects as compared to 2009 in which
they have secured RM 14,011 million projects.
Figure 3.18: Value of overseas projects (2001-2010)
Data source: CIDB various reports
Figure 3.19 more or less follows the same trend as in Figure 3.18. It shows that
during the second decade of Vision, 2020 (2001 to 2010) the highest number of
projects in overseas market awarded to Malaysian construction firms was in the year
of 2007, where 69 projects were secured by Malaysian contractors during this year
and most of them were from the Middle East region, and it is likely to be related to oil
and gas sector and also residential, health, education and commercial construction
(Mustaffa et al., 2012). The second highest number of projects awarded to Malaysian
construction companies in 2002 was 60. While the lowest number of projects secured
0
2000
4000
6000
8000
10000
12000
14000
16000
18000
20000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
2407
5656 5764
3207
955410190
19551
9467
14011
1491
Val
ue
(R
M M
illio
n)
Years
77
by the Malaysian construction firms in the year 2010 was only 11, which was 31%
below as compared to 2009 of 36 projects.
Figure 3.19: Number of overseas projects (2001-2010)
Source: CIDB various reports
3.6 Problems, Issues and Challenges of MCS
The MCS is a significant sector of the Malaysian economy and plays an essential and
effective role in generating wealth, capital formation, strengthening other sectors of
the economy and in the development of social and economic infrastructures. The
Malaysian government has made rigorous efforts and developed various policies with
the Construction Industry Development Board (CIDB) for construction players to
improve their knowledge, skill and advancement. However, the sector is under a
constant pressure, and has no strong evidence of success. Numerous reports and
studies like; (Alfan, 2013; CIDB, 2007; Ibrahim et al, 2010; Kamal et al, 2012;
Kamar et al., 2011), showed that the Malaysian construction industry is facing sizable
challenges and issues regarding productivity performance, quality of output,
sustainability, environment and shortage of resources in term of manpower and capital
both (Alfan, 2013; Kamar et al, 2011). The following subsections will discuss and
0
10
20
30
40
50
60
70
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
36
60
46
32
5658
69
55
36
11Nu
mb
er
of
Pro
ject
s
Years
78
highlight the underpinning problems and challenges identified by the literature which
is holding the MCS from modernization and advancement
3.6.1 Construction Approach
Most of the construction firms of Malaysia, especially G1 – G6 grade firms (small
and medium enterprises) preferred to follow the traditional method of construction in
order to save cost and to make high profits. One of the significant reasons for using
conventional approaches in the construction process is the availability of low cost
foreign manpower in the market that encourages the construction industry to promote
and support labour intensive way of construction instead of using modern technology,
and thereby resulting in slow and inefficient productivity and comparatively low
quality of output. Switching from conventional method to advance technology is a
serious issue for the Malaysian construction sector. The Malaysian Construction
Industry needs to respond quickly without any further delay and move towards to
modernization within the capacity and knowledge acquired. In this regard industry
should examine the steps and strategies adopted by the UK, Hong Kong, Singapore
and the other countries to modernize and developed their construction sector and
taken lessons from their experience (Hamid and Kamar, 2010). The government has
been trying to implement different kinds of policies for adopting modern technology
in the industry but still not widespread in the sector.
3.6.2 Comprehensive Integrated Solution Provider
MCS have no ability to provide a complete integrated solution package including
heavy equipment’s, materials and financing package. The MCS needs to produce
ability for providing a complete integrated solution in order to have a competitive
advantage in local and international market. In order to compete in overseas market
the MCS will have to prepare for paradigm shifts to improve its competitiveness, and
enhance value added in the allied industries like material supplier, heavy machinery
and equipment supplier, and funding agencies. The major advantage of offering a total
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integrated solution with a financial package in overseas markets is that the
construction companies have better bargaining power to use Malaysian products and
services in overseas construction projects (CIDB, 2007). The MCS will have to
address these serious issues and challenges, and overcome the problems of the
industry on urgent basis to achieve its Vision 2015, to be among the best in the world.
There is a need to take a holistic approach to review the factors influencing the
industry, develop improvement strategies and implement these strategies to the
overall industry and the industry players.
3.6.3 Error Free Construction
By nature construction industry is an industry where defective work is part of normal
service. Removal of defects is the liability of construction firm. Reconstruction
against the defects or faulty work is an additional burden to the firm which reduces
the profitability of the firm. Therefore the objective of the construction firm should be
performing right and an accurate job in its first attempt. Free from defects culture in
the construction industry can be promoted through experienced, qualified, trained
technical staff.
Sustainability and green construction is the fresh challenge for Malaysian
construction industry. The importance of sustainable development has been identified
and secures popularity since the United Nation Conference on Environment and
development in Rio de Janeiro in 1992, in which 27 standards were established for
world sustainable development. The Malaysian government is also committed to
taking drastic action against pollution, global warming and ozone depletion for
addressing sustainability issues. In this regard, they introduced Green Building
Indexed (GBI) to stimulate sustainability in built environment and raise awareness
among the stakeholder of industry about environmental issues. The GBI rating score
is based on six key criteria. These criteria include energy efficiency, indoor
environment quality, sustainable site planning, material and resources, water
efficiency and innovation. The most serious challenge for the Malaysian construction
industry is to practice sustainable development and at the same time provide
affordable building without compromising on it quality (Kamar et al, 2011).
80
3.6.4 Timely Adequate Financing
Availability of adequate financial resources is the fundamental requirement for the
success of any project. Malaysian contractors’ especially small and medium sized
enterprises have been facing difficulty in securing timely, adequate funding from
banks and other financial institution for their projects due to incomplete
documentation and poor credit rating. The major hindrances in this regard are
complex legal requirements and lengthy documentation procedures. Furthermore
Malaysian banks and financial institutions are more traditionalists in issuing loans for
overseas projects (CIDB, 2007).
3.6.5 Long Chain and Large Number of Contractors
The Malaysian construction sector has high numbers of contractor and sub-
contractors, subcontracting the job are common practice in the construction sector,
which make cause of delays and cost overrun.
This long chain of subcontracting and outsourcing the job increase the cost of the
project and create different kind of payment disputes and claim issues. There is a need
to register only capable and committed contractors for enhancing productivity and
quality of work in the industry. The number of contractors can be controlled by
raising the standards of prequalification.
3.6.6 Fragmentation in the Industry
The construction industry is in its nature a fragmented industry. Generally it has a
negative effect on the construction industry because the industry has a long structure
in which the number of construction activities, performs with different groups and
most of the time they remain in isolation thus inefficiency and delays take place
(Chan and Leung, 2004).
The fragmented nature of the construction industry develops a sharing knowledge
gap between the designer and constructors of the project. The lack of coordination
81
between the two important stakeholders of the industry creates, time and cost overrun
problems and quality issues on the project. On the average, 75% of the problems on
the construction site are generated in the design phase (Madelsohn, 1997). The
deficiencies of designed and the problem of time and cost overrun can be overcome
through information exchange and sharing of knowledge between the concerned
groups because the knowledge is one of the most important elements for obtaining
competitive advantage and optimizing benefit from the project (Taha and Hatta,
2010). The success of a construction project is highly dependent on strong
coordination between the concerned parties of the project as coordination is
considered as a prerequisite to success of a construction project (Hai et al, 2012). The
information technology (IT) can play effective role in improving the level for sharing
information among consultant, client and contractor and reducing the communication
gap between the concerned players of the industry.
3.6.7 Fair and Transparent Bidding Process
Competitive tendering is the widely used famous tendering system in the conventional
contractual arrangement or design-bid-build. However, it should be transparent and
fair, especially in high quality public procurement. Transparency supports the wise
use of allocated scarce development funds and it is considered as one of the most
effective ways to restraint corruption. Transparency has the ability to serve several
purposes for reforms like to discourage corruption; encouraging the integrity and
efficiency of the public service; increase competition; good governance; sound
administration; and non-discrimination (Parliament of Malaysia research Unit, 2013).
The Malaysian Government has already taken steps in this regards like the
announcement of the open tender system for bidding public projects that will generate
more value for money and more competent contractor will emerge in the industry,
however, there is a need for more transparency in the award of projects with
application of open tender systems for bidding of Government projects (Aziz, 2011).
82
3.6.8 Construction Sector Payment System and Adjudication Act
The success of a construction project is highly dependent on effective, efficient timely
payments practice. A smooth cash flow in construction projects saves the project from
time and cost overrun so that it can be completed within the planned time and
allocated budget without compromising on quality (Munaaim et al., 2013). The MCS
has also been facing late or non-payment problems in public and private projects both
which affect many construction industry players. The existing mechanism to resolve
the payment disputes and issue like Arbitration and Litigation are expensive, time
consuming and not delivering the desired results. The Malaysian Government has
approved and gazettes the Construction Industry Payment and Adjudication Act
(CIPAA) with a hope that it would provide the contractors an alternative way to
resolve the disputes quickly and at affordable costs (Aziz, 2011). The Master
Builders Association Malaysia (MBAM) proposed to government for establishing the
specialist construction court with CIPAA because for successful execution and
obtaining the desired result from CIPAA, healthy, effective and expert court support
is necessary (Tee, 2007).
3.6.9 Public Private Partnership Projects and Land Issues
Malaysian construction industry heavily relies on public sector project investment.
However the construction industry has started to launch Public Private Partnership
projects (PPP). The Malaysian Government is giving a lot of attention on PPP. Under
10th MP fifty two (52) projects worth RM 63 billion were identified to be executed
under the PPP scheme to promote and reinforce the private sector investment in the
sector (Kamal et al, 2012). However, there are some important points that need to be
addressed. In PPP project scheme major role supposed to be played by the private
sector in execution of projects, in other words the major responsibility is transferred
from the public sector to the private sector. The big players of the construction
industry who have strong financial strength and sound technical capability will take
interest and ready to invest money in PPP projects scheme only when the government
will give some kind of assurance, share risk, provide incentive and create more
attraction for them in PPP projects. Furthermore the Government should resolve the
83
issues related to the land instead of leaving on contractor to settle the matter when it
works on the PPP project (Aziz, 2011).
3.6.10 Educated, Trained and Skilled Manpower
Trained, educated and skilled manpower is the core challenge for a MCS that has the
significant value of the industry in meeting the demand for modern construction. The
MCS heavily relies on foreign labor due to easy availability of low wage. According
to official statistics of 2010, 34% of the total construction workforce is foreigner.
Foreign workers are usually untrained and unskilled when they started work, which
affect the productivity and quality of work as well. There is an urgent need of MCS to
educate and train to its existing local workforce and professional to meet the
construction industry manpower demand, replace foreign labor to reducing the
dependence on foreign worker. The government should continue offering construction
related courses in the educational and vocational institutes to provide latest and update
knowledge and training to construction personnel so as to make them more
competitive, proficient and enhance their operational skills for domestic and global
market. Beside this develop the interest in local youth in making a career in the
construction industry by improving its image (Aziz and Rashid, 2001; Ibrahim et al,
2010).
3.6.11 Research and Development (R&D)
The research and development (R&D) is the fundamental tool for business
development. Unfortunately, construction companies are not using this important tool.
In Malaysia, most of the research and development work in the construction sector is
carried out by the educational institutes but their work is neither enough nor
accordance with the construction industry requirements (Ibrahim et al, 2010). The
relationship between construction companies and educational institutes should be
improved and strong. They should come closer and develop a strong working tie to
overcome the shortcoming of R&D. There is a strong need to design a
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comprehensive plan for R&D that cover all important areas of industry and market
requirement and can lead to the enhancement and benefit for the industry (Naidu,
1998).
3.7 Summary
The construction sector is a significant and productive sector of the Malaysian
economy. It plays a crucial role in socio- economic and infrastructure development of
the country. It provides great support in the development of other sectors like
agriculture, manufacturing, transport, tourism, water and power, mineral, mining and
services (education, health, communication). As a developing nation Malaysia has
realized the pivotal role of the construction sector not only economic growth, but also
in improving the quality of life and living standards of Malaysian people. The
construction boom in Malaysia began in the early 1990s, just after the launch of
Vision 2020. The MCS continues to grow significantly in the domestic as well as
international market.
The Malaysian construction industry as a whole is still have challenges of low
productivity and quality, unskilled, time and cost overrun, occupational health and
safety issues, environmental issues and heavy reliance on foreign workers. It is a high
time to address these challenges and needs to be a more rigorous effort to modernize
the local construction industry. There is a need to make rapid changes and reforms, as
made by other countries like UK, Australia, Hong Kong, Singapore, Korea made, so
that the MCS achieved its vision to be a world class, innovative and knowledgeable
solution provider by 2015 (CIDB, 2007). There is a dire need of a comprehensive
research study over the MCS, its relation to other sectors of the economy and GDP as
well. There is a need to analyze its behavior against the investment in other sectors of
the economy and response of other sectors when investments are made in construction
sector. It will be helpful in making future policies for the MCS and aggregate
economy as well.
85
CHAPTER 4
RESEARCH METHODOLOGY
4.1 Introduction
The chapter will discuss the empirical analysis of the model and data used to examine
inter-sectoral linkages between MCS and other major sectors of the Malaysian
economy. The topics that will be discussed in this chapter can be divided into four
sections. The first section will define the nature of the research and the variables
included in the study and nature of data and their sources. The second section will
describe the procedure of construction of the empirical model equation. The third
section will describe the techniques, which are used to validate the model equation.
The last section will describe the development of impulse response functions,
forecasting and its accuracy measurement procedures.
Figure 4.1 defines the stepwise process of analysis, from acquiring data to
conclusion. This study is based on purely quantitative analysis and both branches of
statistic descriptive and inferential are used in the analysis. In inferential statistic, the
time series technique is used for various analysis, such as unit root test, optimal lag
length, bivariate Granger causality analysis, Johansen Cointegration test, VECM
(residual analysis, structural stability tests), IRFs and forecasting for MCS output.
86
Figure 4.1 Analysis flow chart
4.2 Paradigm of Research
A quantitative paradigm of research is used for conducting this study. It is necessary
to use quantitative methods of research to know the direction of causal link, long run
and the short run relationship and to estimate an error correction model equation
Quantitative Analysis
Discussion Analysis
Forecasting for MCS
VECM for MCS
Cointegration Test
Causality Analysis
Optimal lag length
Unit Root Test
Inferential Statistics Descriptive statistics
Time Series Analysis
IRFs for MCS
Finalizing the data
Secondary Data Acquisition
87
between construction sector, and other key sectors of the Malaysian economy. It is
also necessary to estimate the contribution and impact of construction sector in
aggregate economy and to forecast the future output value series from 2014 to 2020,
for construction sector of Malaysia.
4.3 Variables of Interest
The output money value of major key sectors of the Malaysian economy is included
as a studied variable to satisfy the objectives of the study such as construction output
(CONS), manufacturing output (MANF), mining and quarrying output (MINQ),
agriculture and forestry output (AGRF), services value (SERV), and gross domestic
product (GDP),
4.4 Data Size and Sources
This study is conducted on the basis of the last two decades time series quarterly data
from 1991-Q1 to 2010-Q4. The reason for selecting this period is the government’s
vision of a developed Malaysia by the year 2020. The quarterly time series data (1991
to 2010) is obtained from published documents such as economic census Malaysia
construction - 2010 report and Malaysia economic statistic time series - 2011, by the
Department of Statistics (DOS), Economic and financial data for Malaysia - 2011 by
Bank Negara, CIDB reports 2010 and 2012, and Malaysia productivity corporation
(MPC) report 2011. The major issue in data management was that the available
quarterly time series data was on a different base price index like price index 1988
and price index 2000. This form of data cannot be used directly for analysis.
Therefore, we converted all data at same base price index i.e. 2000 with the help of
index formula Equation 4.1 (Koop, 2005).
.
𝑁𝑒𝑤 𝑏𝑎𝑠𝑒 𝑖𝑛𝑑𝑒𝑥 =𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑜𝑙𝑑 𝑏𝑎𝑠𝑒 𝑖𝑛𝑑𝑒𝑥 ×𝑜𝑣𝑒𝑟𝑙𝑎𝑝 𝑣𝑎𝑙𝑢𝑒 𝑜𝑛 𝑛𝑒𝑤 𝑖𝑛𝑑𝑒𝑥
𝑜𝑣𝑒𝑟𝑙𝑎𝑝 𝑣𝑎𝑙𝑢𝑒 𝑖𝑛 𝑜𝑙𝑑 𝑏𝑎𝑠𝑒 𝑖𝑛𝑑𝑒𝑥 (4.1)
88
4.5 Empirical Analysis
A descriptive statistical analysis is conducted to know the basic characteristics of the
data and for preliminary information about the Malaysian economy, major sectors of
the economy and especially for Malaysian construction sector. Furthermore this
analysis is used to measure correlation of coefficient that describes the strength of the
association between the two concerned variables. The Pearson correlation formula is
used here to measure correlation between MCS and the other major sectors of
Malaysian economy and is defined as:
𝑟 =𝑛 (∑ 𝑋𝑌)−(∑ 𝑋)(∑ 𝑌)
√[𝑛(∑ 𝑋2)−(∑ 𝑋)2
][𝑛(∑ 𝑌2)−(∑ 𝑌)2]
(4.2)
where r is the coefficient of correlation, n is the number of paired observation, X and
Y are concerned variables.
The econometric Granger causality approach is used here to identify the direction
of the causal relationship between the construction sector and the major sectors of the
Malaysian economy. The vector error correction model equation is estimated to
measure the long run and short association between MCS and the concerned sectors
of the Malaysian economy. The following assumptions are satisfied before estimating
the model equation and examining the bivariate Granger causality:
i) All the data variable series should be non- stationary (having a unit root
problem) at a level and convert into stationary series after differencing the
series.
ii) There must be co-integration between the data variable series.
iii) Optimal lags length should be used for developing the model.
The Dicky-Fuller (DF), Augmented Dicky-Fuller (ADF) and the Phillips Perron
(PP) tests are conducted to identify the unit root and stationarity problem in the data
series. The Johansen co-integrating rank tests are conducted to identify the number of
co-integration equations in the data set and the optimal lag length is estimated through
standard criteria such as Likely Ratio (LR), Akaike Information Criteria (AIC),
Schwartz Bayesian Information Criteria (BIC), and Hannan Quinn (HQ). After
89
satisfying all fundamental requirements, model equation is developed for MCS. The
strength, correctness and effectiveness of the model equation is tested through
residual and structural stability tests.
The impulse response functions (IRF) series is developed to measure the response
of MCS against the shock received from other sectors of the Malaysian economy and
also measure how the other sector respond against the shock of MCS. Finally,
estimated model equation is used to forecast the MCS output series from 2014-Q1 to
2020-Q4. An Econometric software E-Views-6, statistical software SPSS 16 and MS
office 2010 are used to carry out the complete data analysis.
4.5.1 Unit Root Test
The unit root test is conducted to check the stationarity problem with data series. It
was observed that mostly time series data has unit root problem such as trends over
time, seasonality or some other kind of non-stationarity that produces unreliable
results and forms spurious regression. The identification and removal of unit root
problem is very important because if non-stationary variables are not identified and
used in the Granger causality and VECM, it will lead to a problem of spurious
regression (Granger, 1974), whereby the results suggest that there are statistically
significant relationships between the variables in the regression model when, in fact,
that is evidence of contemporaneous correlation rather than meaningful causal
relations (Granger, 1974; Harris, 1992). The regular trends and seasonal effects are
normally handled by introducing an appropriate explanatory variable. The usual
solution for unit root or stationary problem suggested by the Dickey Fuller is
differencing of series from one period to the next period and sometimes data series
needs to be differenced more than time for converting it into stationary series.
This study is using augmented Dickey Fuller (ADF) test to identify the unit root
and stationary problem in each series. The ADF test is the most popular and most
commonly used in time series analysis to examine the stationarity and the unit root
problem in the series because of its simplicity and good performance. Furthermore the
size and power properties of the ADF test can be enhanced if an appropriate lag order
90
is employed (Harris, 1992). The ADF model is typically developed on the bases of the
DF model which contains following regression model equations (4.3 to 4.5).
∆𝑌𝑡 = 𝜏𝑌𝑡−1 + 𝜀𝑡 (4.3)
∆𝑌𝑡 = 𝛼1 + 𝜏𝑌𝑡−1 + 𝜀𝑡 (4.4)
∆𝑌𝑡 = 𝛼1 + 𝛼2𝑇 + 𝜏𝑌𝑡−1 + 𝜀𝑡 (4.5)
These model equations 4.3 to 4.5 can be modified in equation 4.6 (ADF model
equation) by introducing autoregressive progresses when error term 𝜀𝑡 is not white
noise. The important point is that the null hypothesis, Ho: 𝜏 = 0, that is, Yt has a unit
root problem and alternate hypothesis H1: 𝜏 ≠ 0 that is, Yt does not have a unit root
problem, and will not be changed. When the DF test is applied to equation 4.5 is
known as ADF test.
∆𝑌𝑡 = 𝛼1 + 𝛼2 𝑇 + 𝜏 𝑌𝑡−1 + 𝛾𝑖 ∑ ∆ 𝑌𝑡−1𝑛𝑡=1 + µ𝑡 (4.6)
Where (Yt : time series variable)∆𝑌𝑡 = 𝑌𝑡 − 𝑌1 , ∝2 is a drift term and T is the time
trend. The null hypothesis still remains the same that, 𝐻0 : 𝜏 = 0 and its alternative
hypothesis 𝐻1 : 𝜏 ≠ 0, n is the number of lags necessary to obtain white noise or
serially independent error term and µt is the error term (Gujarati, 1995). The simpler
Dickey Fuller (DF) test removes the summation term. However, the implied t-statistic
is not the Student t distribution, but instead is generated from Monte Carlo
simulations (Granger, 1981). Note that failing to reject H0 implies the time series is
non-stationary and has a unit root problem.
4.5.2 Identification of Order of Integration
When the non-stationary series can be changed in stationary series through
differencing, the series is said to be integrated series. The number of times, series to
be differenced to make it stationary series that number will be considered as the order
of integration. A time series Yt is said to be integrated of order one or I(1), if the
series convert into stationary series after differencing one i.e. ∆𝑌𝑡 . If a time series is
91
stationary series, it is known as integrated of order zero or I(0). It is considered as a
special case of order one I(1) because of random walk and having a white noise error
term. If the series needs to be differenced two times to make a stationary series, then it
said to be integrated order two or I(2) series. Similarly, if any time series needs to be
differenced “k” times to make it stationary then the series is known to be integrated
order “k” or I(k). When the two time series have the same order of integration then
the series are said to be co-integrated and it makes some sense because the series do
not move away from each other over time. Thus, there is a possibility of the long run
equilibrium relationship between them. If the two series have a different order of
integration, this implies that there is no long run equilibrium relationship between
them and obtained relationship through regression will be spurious. However, when
some variables in a system have one order of integration such as I(1) and some have
other order of integration such as I(2), it is important to find out whether the variables
are multi co-integrated.
4.5.3 Optimal Lag Order
One of the most widely used models in empirical economics and management
analysis is vector autoregressive (VAR) model. The most significant and critical
element in the specification of VAR system is selection of optimal lag length of the
auto regressive lag polynomial. The very small number of lags means the residual
behavior is not like white noise, and model parameters and their standard error will
not be defined well and accurately estimated. Alternatively, too many lags reduce the
explanatory power of the model due to loss of degree of freedom. Therefore,
determination of appropriate lag length is an important issue in VAR model.
Generally, the same lag length is used for all variables and equations included in the
VAR system for a better outcome.
There are several approaches in classical procedure for selecting the optimal lag
order for a VAR model such as Akaike Information Criteria (AIK), Schwarz
Information Criteria (SC), Hannan-Quinn (HQ) approach, Financial Prediction Error
(FPE), and. All approaches provide equally good suggestion for lag selection. This
study uses the HQ information criteria that mathematically is define as:
92
𝐻𝑄(𝑝) = 𝑛𝑙𝑜𝑔(�̂�2) + 2𝑙𝑜𝑔𝑝 log(𝑛) (4.7)
where n is the sample size and p is number of parameters estimated.
If the residual sum of square is ∑ 𝜀̂2 then: �̂�2 =∑ �̂�2
(𝑛−𝑝) .
4.5.4 Co-integration and Rank Tests
Co-integration method has been a very popular tool in applied economic
(econometrics) work since their introduction about twenty years ago. However, the
strict unit-root assumption that these methods typically rely upon is often not easy to
justify on economic or theoretical grounds (Hjalmarsoon and Osterholm, 2007). Co-
integration is a statistical property of time series variables. Two or more time series
are co-integrated if they share a common stochastic drift. Before the 1980s, many
economists used linear regressions on non-stationary time series data, which Nobel
laureate Clive Granger and others showed to be a dangerous approach that could
produce spurious correlation (Fuller, 1979). In 1987 laureate Clive Granger and
laureate Robert Engle formalized the co-integrating vector approach, and coined the
term (Granger, 1987).
When data were non-stationary purely due to unit roots, they could be brought
back to stationarity by the linear transformation of differencing, as in xt – xt-1 = ∆xt
(Granger, 1987). The stationary linear combination in the data series is called the co-
integrating equation. In simple words, if the series Y and X have unit root or
stationarity problem, but some linear combination of the series are stationary then we
can conclude that Y and X are co-integrated and may be interpreted as a long run
equilibrium relationship between variables.
Several co-integration techniques are available for the time series analysis. These
tests include the Stock & Watson (1988) procedure, the Engle Granger (1987) test and
Johansen’s (1988) Co-integration test. Their common objective is to determine the
most stationary linear combination of the time series variables under consideration.
This study employs the Johansen approach, which was developed by Johansen
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Juselius (1992). This approach is based on canonical correlation that is used for
determining the linear relationship between two or more multi-directional variables.
The major advantage of Johansen co-integration approach is that it allows us to
estimate and examine more than one co-integrating vector at the same time in a
multivariate system.
The Johansen co-integrating approach defines two statistical rank tests for
determining the co-integration between the variables, namely trace rank test and
maximum eigenvalue rank test. These co-integrating rank tests are conducted to
determine the number of linearly independent columns of long run relationship in a
system. The maximum eigenvalue test, tests the hypothesis that there are r+1 co-
integrating vectors versus the hypothesis that there are “r” co-integrating vectors
(Maddala and Kim, 1998).
4.5.4.1 Trace Test
The trace rank test is conducted to test the null hypothesis that there are at most r co-
integrating vectors in a system on the basis of equation 4.8
𝜆𝑡𝑟𝑎𝑐𝑒 (𝑟) = −N ∑ ln(1 − �̂�𝑖𝑛𝑖=𝑟+1 ) (4.8)
Where �̂�𝑖 is the eigenvalue (estimated characteristic roots) obtained from the
estimated long run relationship matrix. N is the number of observations that can be
used.
4.5.4.2 Maximum Eigenvalue Test
Maximum eigenvalue test is used to test the null hypothesis of “r” co-integrating
vectors against the alternative hypothesis of r+1 co-integrating vectors the likelihood
ratio statistic is shown in equation 4.9.
𝜆𝑚𝑎𝑥(𝑟, 𝑟 + 1) = −N ∑ ln(1 − �̂�𝑟+1𝑛𝑖=𝑟+1 ) (4.9)
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The correct co-integrating order is determined by using trace rank test equation
4.8 and maximum eigenvalue test equation 4.9. The important point in this regard is
that if the number of co-integrating vector is equal to number of endogenous
variables, then VAR will be the appropriate approach.
4.5.4.3 Johansen’s Co-integration Methodology
Johansen’s methodology takes its starting point in the vector auto-regression (VAR)
of order p given by equation 4.10.
𝑌𝑡 = ∝1 𝑌𝑡−1 + ⋯ + ∝𝑝 𝑌𝑡−𝑝 + 𝛽𝑋𝑡 + 𝜀𝑡 (4.10)
Where Yt is a K- vector of non-stationary I(1) variables, Xt is a d-vector of
deterministic variables and 𝜀𝑡 is a vector of innovations.
4.5.5 Bivariate Granger Causality
Granger causality is a mathematical concept of causality that is based on prediction.
This is a statistical hypothesis test for determining whether one time series is useful in
forecasting another (Granger, 1969).
According to Granger a time series X is said to Granger-cause Y if it can be
shown, usually through a series of t-tests and F-tests on lagged values of X (and with
lagged values of Y also included), that those X values provide statistically significant
information about future values of Y (Granger, 1969). In simple words a variable X
Granger causes Y if past values of X can help calculate current value of Y. if past
values of X have explanatory power for calculating current value of Y it suggests that
X might be causing Y (Koop, 2005). Granger causality is thus a pretty powerful tool,
in that it allows us to test for things that we might otherwise assume or take for
granted.
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It can be assessed in a direct way by regressing each variable on its lagged values
and the exogenous variable, the mathematical formulation of Granger causality
models are as given below:
𝑌𝑡 = 𝛽0 + ∑ 𝛽𝑗 𝐽𝑗=1 𝑌𝑡−𝑗 + ∑ 𝛾𝑘
𝐾𝑘=1 𝑋𝑡−𝑘 + 𝜇𝑡 (4.11)
𝑋𝑡 = 𝛽0 + ∑ 𝛽𝑗 𝐽𝑗=1 𝑋𝑡−𝑗 + ∑ 𝛾𝑘
𝐾𝑘=1 𝑌𝑡−𝑘 + 𝜇𝑡 (4.12)
Where, 𝜇𝑡 is uncorrelated and the white noise error term series, and causality may be
determined by estimating equations and testing the null hypothesis that H0:
∑ 𝛾𝑘 𝐾𝑘=1 = 0 that is 𝑋𝑡−𝑘 does not Granger cause 𝑌𝑡 (𝑌𝑡−𝑘 does not Granger
cause 𝑋𝑡 ) against the alternative H1: ∑ 𝛾𝑘 𝐾𝑘=1 ≠ 0 that is 𝑋𝑡−𝑘 Granger cause 𝑌𝑡
(𝑌𝑡−𝑘 Granger cause 𝑋𝑡 ) for equation 4.11 and equation 4.12 respectively. The
coefficient 𝛾𝑘 is a measure of the influence of 𝑋𝑡−𝑘 on 𝑌𝑡 in equation 4.11 and
measure of influence of 𝑌𝑡−𝑘 on 𝑋𝑡 in equation 4.12. If the coefficients of 𝛾𝑘 are
statistically significant (𝛾𝑘 = 0) for equation 4.11, but are not statistically significant
for equation 4.12, then Yt is said to have been caused by Xt . An alternative way of
explaining this concept is that the past value of Xt, have explanatory power for Yt, but
Yt , has no effect on Xt .Thus, there is a unidirectional causal link. The reverse
causality holds if coefficients of 𝛾𝑘 are statistically significant for equation 4.12, but
not significant for equation 4.11. If 𝛾𝑘 is statistically significant for both equations
4.11 and 4.12, then causality runs both ways (Bi-directional).
The next issue in Granger causality is causality between co-integrated variables.
The well-known theorem in this regard is the Granger Representation Theorem. This
theorem says that if the two variables are co-integrated, then association can be
expressed in an error correction model [ECM] (Granger, 1983). The next section will
discuss the mathematical formulation of vector error correction model (VECM).
4.5.6 Vector Error Correction Model (VECM)
Under the investigation of causality link, if the variables have a unit root (non-
stationarity) and are co-integrated then econometricians suggest that the work with
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ECM. The ECM estimates the long run equilibrium relationship implied is co-
integration and short run adjustment mechanism among the concerned variables when
they deviate from equilibrium. The ECM is transformed into VECM, when more than
one or a set of causality equations are involved in a system. The fundamental process
of VECM is incorporating the co-integration information on the ECM. Thus the
model is able to provide long run equilibrium association and short run adjustment to
changes in independent variables through the estimated parameters. The mathematical
formulation of general VECM can be described as under equation 4.13 (Granger,
1983; Gujarati, 1995).
∆𝑌𝑡 = Π𝑌𝑡−1 + ∑ Φ𝑖Δ𝑌𝑡−𝑖𝑚−1𝑖=1 + Γ𝐷𝑖 + 𝜇𝑡 (4.13)
Where 𝑌𝑡 is a column vector of k observable endogenous variables, which are
construction, manufacturing, mining and quarrying, agriculture and forestry, service
sector output and gross domestic product (GDP), Π is the matrix of co-integrating
vectors (long run parameter matrix). The matrix may be factored as 𝛼 𝑎𝑛𝑑 𝛽, where 𝛼
represents speed of adjustment toward long run equilibrium for specific variables,
when they move out of long run equilibrium. Φ represents a matrix of coefficients of
the endogenous variables. The first term in equation captures the long run impact over
dependent variables and second term captures short run adjustments. The “m” is the
optimal number of lag length included in the model, which is determined by using the
likelihood Ratio (LHR) and Hunnan Quinn (HQ) criteria. 𝐷𝑖 is a matrix of a
deterministic trend such as intercept, time trend in the series and Γ is a coefficient of
the matrix of deterministic trend and 𝜇𝑡 is the vector error (Gujarati, 1995).
After developing the VECM equation the next important task is testing of the
explanatory power, significance and effectiveness of the model equation. In this
regard coefficient of determination (R2), Durbin Watson (D.W) value and F- statistics
are important parameters that will help to examine the strength, accuracy and
significance of the estimated model equation.
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4.5.7 The Coefficient of Determination (R2)
The R2 is a summary measure that informs how well the sample regression line fits
the data. It measures the total variation in the dependent variable that is explained by
the variation in the independent variable. In other words R2 is the percentage of the
variance of the dependent variable explained by the independent variable. In
mathematical terms, it can be defined as:
𝑅2 = 𝐸𝑥𝑝𝑙𝑎𝑖𝑛𝑒𝑑 𝑆𝑢𝑚 𝑜𝑓 𝑆𝑞𝑢𝑎𝑟𝑒 (𝐸𝑆𝑆)
𝑇𝑜𝑡𝑎𝑙 𝑆𝑢𝑚 𝑜𝑓 𝑆𝑞𝑢𝑎𝑟𝑒 (𝑇𝑆𝑆)=
∑(�̂�−�̅�)2
∑(𝑌𝑖−�̅�)2 (4.14)
Or alternatively as
𝑅2 = 1 −∑ 𝜇𝑖
2
∑(𝑌𝑖−�̅�)2 (4.15)
With 0 ≤ 𝑅2 ≤ 1, where Yi = (Y1….Yt), �̅� = ∑𝑌𝑡
𝑇
𝑇𝑖=1 and 𝜇𝑖
2 = (𝜇12 … . . 𝜇𝑇
2)
The large value of 𝑅2 may be concluded that the model equation has good
explanatory power and good fit for estimating the growth curve. However, the large
𝑅2 does not directly imply that the estimated model equation is a powerful equation.
There may be chances of an autocorrelation problem with the error terms of the
estimated equation (Agung, 2009). The Durbin Watson statistic is important to
examine autocorrelation in the model equation.
4.5.8 The Durbin Watson (D.W)
The most widely used test for detecting autocorrelation in the estimated model is
Durbin-Watson statistic that will be denoted by “d”. Mathematically, it can be defined
as:
d =∑ �̂�𝑡
2+∑ �̂�𝑡−12 −2 ∑ �̂�𝑡�̂�𝑡−1
∑ �̂�𝑡2 (4.16)
Since ∑ �̂�𝑡2 𝑎𝑛𝑑 ∑ �̂�𝑡−1
2 are approximately same, there is a difference of only one
observation, therefore equation 4.16 may be the written as if the sample size is large:
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d = 2 (1 −∑ �̂�𝑡�̂�𝑡−1
∑ �̂�𝑡2 ) , 0 ≤ 𝑑 ≤ 4 (4.17)
The estimated “d” must lie within these limits (0 to 4). If there is no
autocorrelation in the model, then d should be equal to two (d=2). As a rule of thumb
if “d” is found to be 2 we can assume that there is no first order autocorrelation in the
model that suggest the model equation is well estimated (Agung, 2009; Gujarati,
1995).
4.5.9 The F- Statistics
F test in regression analysis is used to measure the overall significance of the
estimated regression equation. In case of multiple regression models with intercept,
we use F-test to test the null hypothesis H0: 𝛽1 = 𝛽2 = ⋯ = 𝛽𝑘 = 0 against the
alternate hypothesis H1: 𝛽𝑗 ≠ 0, for at least one value of j.
If the test result fails to reject the null hypothesis, then it suggests that the
residuals are white noise, homoscedastic, independent and normal. Mathematically F-
statistic can be calculated as:
F =𝐸𝑆𝑆 (𝑘−1)⁄
𝑅𝑆𝑆 (𝑛−𝑘)⁄ (4.18)
Where ESS is explained sum of squares, RSS is restricted sum of square, k is the
number of parameters to be estimated and n is the total number of observations
(Gujarati, 1995).
4.5.10 Model Structure Stability Test
Malaysia experienced two major shocks (Asian currency crises 1997-98 and Global
financial crunch 2008) during the studied period. These shocks might have influenced
the estimated model equation, therefore it is important to examine the structural
stability of the estimated coefficients. The cumulative sum (CUSUM) test is most
widely used to test the structural stability of any model. The CUSUM test was
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developed by Brown et al (1975), based on recursive residuals. The main purpose of
this test is to examine structural break in the model if any, because the one major
drawback of unit root test is that if there is a structural break in the deterministic
trend, then unit root tests will lead to the deceptive conclusion about the unit root
problem in the data series (Perron, 1989). The CUSUM plot shows the variation in a
random pattern centered about zero and discovers systematic movements of
coefficient.
𝑊𝑚 =1
�̂�∑ 𝜔𝑡
𝑚𝑡=𝑘+1 , 𝑚 = 𝑘 + 1 … . , 𝑇 (4.19)
Where 𝜔𝑡the recursive residual, m is sample number.
The test is conducted with null hypothesis H0: There is no structural break. The
null hypothesis will be rejected if 𝑊𝑚 crosses the defined boundary for some “m” by
the significance level of the test i.e. 5%. The Brown et al (1975) is also proposed
CUSUM square test. The major advantage of this test is it does not require any
information about structural breaks in the data series for the analysis. It uses the
squared recursive residuals:
𝑆𝑚 =∑ 𝜔𝑡
2𝑚𝑡=𝑖
𝑆2 , 𝑆2 = ∑ 𝜔𝑡2𝑇
𝑡=𝑘+1 , 𝑚 = 𝑘 + 1 … , 𝑇 (4.20)
The null hypothesis is rejected if “Sm” crosses the suggested boundary by the test
level of confidence i.e. 95%.
4.5.11 Impulse Response Functions (IRFs)
IRFs identify the responsiveness of the dependent variable in the vector
autoregressive (VAR) system, when one standard deviation positive shock is put in
the error term. It is a unit shock that is applied to each variable and examines its effect
on the other variable of VAR system. In other words a shock in one variable will be
shifted to another variable in the system through the dynamic structure model. There
are several techniques to develop the IRFs such as Residual One Unit, Residual One
Standard Deviation, Cholesky –degree of freedom adjusted (Cao, 2010).
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This study employs the Cholesky –dof adjusted procedure which is most widely
used in inter-sectoral linkage studies. The study focuses on the MCS; therefore it
develops IRFs for the only construction sector of Malaysia as an endogenous and
exogenous variable as well. This study establishes IRFs to determine how a positive
one standard deviation shock to the MCS will be transferred in other concerned
sectors and how the MCS behave against the shock produced in the other sectors.
4.5.12 Forecasting
Forecasting is a management tool that helps management to survive with the
uncertainty of the future. It generally depends on data from the past and present and
analysis of trends. It is the science of predicting future outcomes. It is an integral part
of decision making activities of management. The need for forecasting is increasing
day by day as management using more scientific, mathematical techniques to deal
with the competitive environment.
There are several traditional approaches for forecasting such as exponential
smoothing, regression, time series, and explanatory model forecasts. This study uses
the estimated VECM equation for the construction sector to forecast the MCS output
for the period 1991Q1 to 2013 Q4, which is X-post forecast because the data is known
and then future forecast from 2014Q1 to 2020 Q4.
To examine the accuracy and predictive power of the estimated model equation,
the mean absolute percentage error (MAPE) and the Theil inequality coefficient
forecasted approaches are used. The accuracy or goodness of fit refers to how well the
estimated model is able to produce the data that are already known (X- post forecast)
[Makridakis et al. 1998]
4.5.12.1 Forecasting Error
Forecast error is the gap between forecasted value and the actual value in a particular
time. If the forecast error term is small then we can say the forecasting is good and
reliable. The small forecast error implies that the predictive power of estimated
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equation is satisfactory. If Yf, forecast observation and Yt, is actual observation for
time period “t” then forecast error is:
𝑒𝑡 = 𝑌𝑓 − 𝑌𝑡 (4.21)
This is a one-step forecast because it is forecasting one period ahead of the last
observation in the data series. If there are “n” observations and forecast for n time
period, then statistical measures are mean error (ME), mean absolute error (MAE) and
mean square error (MSE). This study employs the MAE and in relative term MAPE.
𝑀𝐴𝐸 =1
𝑛∑ |𝑒𝑡|𝑛
𝑖=1 (4.22 a)
𝑀𝐴𝑃𝐸 =1
𝑛∑ |𝑃𝐸𝑡|𝑛
𝑖=1 (4.22 b)
4.5.13 Theil’s Inequality Coefficient
Theil approach is actually based on U- statistic developed by Theil (1966)
(Makridakis et al., 1998). Mathematically, it can be defined as:
𝑈 = √∑ (𝐹𝑃𝐸𝑡+1−𝐴𝑃𝐸𝑡+1)2𝑛−1
𝑖=1
∑ (𝐴𝑃𝐸𝑡+1)2𝑛−1𝑖=1
, 𝐹𝑃𝐸𝑡+1 = 𝐹𝑡+1− 𝑌𝑡
𝑌𝑡 , 𝐴𝑃𝐸𝑡+1 =
𝑌𝑡+1− 𝑌𝑡
𝑌𝑡 (4.23)
where,
𝐹𝑃𝐸𝑡+1 is forecast percentage error and 𝐴𝑃𝐸𝑡+1 actual percentage error.
The value of Theil U- statistics can be zero if forecast values are exactly the same
as actual values of FPEt+1 = APEt+1. Alternatively, the value of U- statistic will be 1,
when FPEt+1 and APEt+1 moves in different directions or exactly opposite. Thus the
small value (close to zero) of U-statistics suggests that the estimated model is good or
has better predictive strength. The three important components of Theil statistics are
biased proportion, variance proportion and covariance proportion (Makridakis et al.
1998).
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4.6 Summary
This chapter 4 described the complete methodology that was adopted to investigate
the inter-sectoral causality link between MCS and other major sectors of the
Malaysian economy. The investigation ranged from data acquisition to validation of
results such as, development of a VECM model equation for construction sector,
development of impulse response functions (IRFs) for MCS and forecasting ability of
estimated model equation and validation process of the estimated model. The main
variables of the study are major sectors of the Malaysian economy, such as the
construction sector (CONS), manufacturing sector (MANF), mining and quarrying
(MINQ), agriculture and forest (AGRF), services sector (SERV) and overall gross
domestic product (GDP). The quarterly output data of these sectors in money term
(Ringgit thousand million) from 1991Q1 to 2010Q4 were obtained from the Statistics
Department Government of Malaysia for this study. The Granger Causality approach
is discussed to examine the bivariate causality between the construction sector and the
other concerned sectors. The restricted VAR procedure such as identification of the
unit root problem, determination of the optimal lag order, identification of integration
order, co-integration rank test and finally general form of VECM is discussed to
estimate the VECM equation for construction sector of Malaysia. The coefficient of
determination (R2) is defined to measure the explanatory power of the estimated
equation. The DW test is discussed to test the autocorrelation in the estimated model.
The CUSUM square test is described for testing the structural stability of the
estimated model equation. The IRFs are discussed to observe the reaction of
construction sector against the shock produced in other sectors and the other sectors
behavior against the shock in the construction sector. Finally, it elaborates the
Forecasting procedure and its accuracy measures such as MAPE and Theil U-
statistics.
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CHAPTER 5
INTER-SECTORIAL LINKAGES OF MCS
5.1 Introduction
Malaysia is a newly industrialized country that is trying to shift toward developed
nation status for which Malaysia need a strong economy. The inter-sectorial linkages
play significant role in the economic development of a country. It is therefore
necessary to study and understand the inter-sectorial linkage theory, so that the
association and linkage direction can be identified. Strategies can be developed for
positive growth among the various sectors of the economy that maintains the
momentum of economic growth and development of the country. In this chapter, we
will investigate the linkages between construction and other key sectors of the
Malaysian economy: namely agriculture, manufacturing, mining and quarrying and
services and finally linkage between construction and overall GDP of Malaysia. The
stationarity / unit root test, optimal lag order and Granger causality test results
reported in this chapter are the core element of error correction model that will be
discussed in the next chapter.
5.2 Relationship between Construction and Other Sectors of Malaysian Economy
The Malaysian construction sector (MCS) has a significant role in the Malaysian
economy. The activities of the MCS have great significance to the achievement of
social economic development goals in providing infrastructure, income generation
and employment creation. It has not only affected the GDP of Malaysia but also affect
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the output of other key sectors such as manufacturing (MANF), mining and quarrying
(MINQ), agriculture and forestry (AGRF) and services (SERV) sector of Malaysia.
In Table 5.1 sectoral correlation depicts that the construction sector of Malaysia
has strong correlation with other sectors of the economy and with overall GDP of
Malaysia on the basis of the quarterly data of the last two decades (1991Q1 -2010Q4).
Table 5.1 Sectoral correlation
Sectors CONS MANF AGRF MINQ SERV GDP
CONS 1
MANF 0.746 1
AGRF 0.557 0.819 1
MINQ 0.748 0.821 0.512 1
SERV 0.737 0.960 0.850 0.738 1
GDP 0.774 0.981 0.654 0.788 0.992 1
Source: Authors Calculation
The Pearson correlation coefficient between construction (CONS) sector and all
other major sectors including GDP is greater than 0.5. This implies that the MCS
output data is strongly correlated with other sectors of the economy and has a
significant position in the Malaysian economy.
The correlation between construction (CONS) and manufacturing (MANF) sector
is 74.6%, which indicates that the two sectors are strongly interrelated. The
manufacturing sector is a significant supplier of intermediate goods to the
construction sector, such as fabricated metal products, basic metal products, timber,
wood, plastic products, machinery and equipment. The engineering, construction and
non-dwelling construction industries highly depend on inputs from the non-metallic
mineral products and fabricated metal products industries of manufacturing sector. In
contrast, the dwelling construction industry is relatively more reliant on cement and
concrete product manufacturing, ceramic product manufacturing and timber product
manufacturing (Mccrary, 2006).
The correlation between CONS and agriculture and forestry (AGRF) is 55.7 %
shows that the CONS and AGRF are also strongly correlated as construction sector
provides a significant support to the agriculture sector by constructing dams,
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reservoirs, canals for irrigation purpose, construct roadways, railway lines etc. For
transporting the agricultural products from fields/farms to market and provide
buildings for storing the output, while the agricultural sector is significant for the
construction industry as it provides greater employment opportunities in the
construction of these projects, and other such activities. Furthermore AGRF provides
wood/timber for construction work.
The MCS also has strong correlation with mining and quarrying (MINQ) and
service (SERV) sector, i.e. 74.8 % and 73.7 %, respectively. Most popular products of
the quarry are used in the construction sector without any manufacturing process e.g.-
as stone ballast in road and railway tracks and as quartz sands in high quality material
industrial production such as glass. It is also used in concrete work. In concrete
product approximately 80% are aggregate similarly asphalt is made of 95 % aggregate
(Ginevra and Furcas, 2011). The mining and quarrying sector’s continuous supply of
raw material to other sectors of economy specially manufacturing and construction, is
the assurance for the socio-economic development of country (Wan, 2007). The
construction sector also plays a significant role in stimulating the services sector by
providing infrastructure such as educational institutes for education services, hospitals
for health services, office buildings for banking and other financial institutes, roads
and highways for transport services. The construction sector produces one of the
highest multiplier effects through its strong association with other sectors of the
economy (Park, 1989).
In short, there is a strong correlation between MCS and the aggregate economy of
the Malaysia and construction sector plays a pivotal role in the economic development
of a country. However the outcome of Pearson correlation does not say anything
about the direction and causality of link between the sectors. Causality analysis will
have to be conducted for exploring the direction of linkages. There is a need to
understand the nature and the direction of the linkage between the two sectors for
maintaining the momentum of economic growth and development in the country.
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5.3 Measurement of MCS Inter-sectorial Linkages
There are three popular methods for measuring inter-sectorial linkages as discussed in
Section 2.4 of Chapter 2. Granger causality econometric time series technique is used
here to investigate the MCS linkages with other sectors of the economy and a GDP of
Malaysia. The basic condition for calling Granger causality is that each variable series
should be stationary and free from the unit root problem. To resolve this problem
Augmented Dickey Fuller unit root test and Phillip Peron test is organized for each
variable series before causality test. Granger causality is very sensitive about the lag
length; hence the optimal lag length is determined by standardized popular tests.
5.3.1 Unit Root (Stationarity) Test
The most common problem in time series data is stationary and unit root which lead
to spurious regression results. The solution of this problem suggested by Dickey
Fuller is differencing of data series. The stationary and unit root problem in each
variable data series is tested on the bases of following equations (Equations 5.1 to
5.6). These equations are developed in the light of generalized equations formulated
by Dickey Fuller (1979) as discussed and mentioned in Section 4.5.1 of chapter 4,
research methodology.
Equation 5.1 is developed to examine the unit root problem in the construction
sector (CONS) data series. Similarly Equation 5.2 is developed for the manufacturing
sector (MANF), Equation 5.3 for agriculture and forestry (AGRF), Equation 5.4 for
mining and quarrying (MINQ), Equation 5.5 for the service sector (SERV) and
Equation 5.6 for GDP.
Construction Sector
∆𝐶𝑂𝑁𝑆𝑡 =∝0+ 𝛼1𝑇 + 𝛽1𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾𝑖𝑛𝑖=1 ∆𝐶𝑂𝑁𝑆𝑡−1 + 𝜇𝑡 (5.1)
Manufacturing Sector
∆𝑀𝐴𝑁𝐹𝑡 =∝0+ 𝛼1𝑇 + 𝛽1𝑀𝐴𝑁𝐹𝑡−1 + ∑ 𝛾𝑖𝑛𝑖=1 ∆𝑀𝐴𝑁𝐹𝑡−1 + 𝜇𝑡 (5.2)
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Agriculture Sector
∆𝐴𝐺𝑅𝐹𝑡 =∝0+ 𝛼1𝑇 + 𝛽1𝐴𝐺𝑅𝐹𝑡−1 + ∑ 𝛾𝑖∆𝐴𝐺𝑅𝐹𝑡−1 + 𝜇𝑡𝑛𝑖=1 (5.3)
Mining and Quarrying Sector
∆𝑀𝐼𝑁𝑄𝑡 =∝0+ 𝛼1𝑇 + 𝛽1𝑀𝐼𝑁𝑄𝑡−1 + ∑ 𝛾𝑖𝑛𝑖=1 ∆𝑀𝐼𝑁𝑄𝑡−1 + 𝜇𝑡 (5.4)
Services Sector
∆𝑆𝐸𝑅𝑉 =∝0+ 𝛼1𝑇 + 𝛽1𝑆𝐸𝑅𝑉𝑡−1 + ∑ 𝛾𝑖∆𝑛𝑖=1 𝑆𝐸𝑅𝑉𝑡−1 + 𝜇𝑡 (5.5)
GDP
∆𝐺𝐷𝑃𝑡 =∝0+ 𝛼1𝑇 + 𝛽1𝐺𝐷𝑃𝑡−1 + ∑ 𝛾𝑖𝑛𝑖=1 ∆𝐺𝐷𝑃𝑡−1 + 𝜇𝑡 (5.6)
where
∆𝐶𝑂𝑁𝑆𝑡 = First difference of construction sector series (𝐶𝑂𝑁𝑆𝑡 − 𝐶𝑂𝑁𝑆𝑡−1)
∆𝑀𝐴𝑁𝐹𝑡 = First difference of manufacturing sector series (𝑀𝐴𝑁𝐹𝑡 − 𝑀𝐴𝑁𝐹𝑡−1)
∆𝐴𝐺𝑅𝐹𝑡 = First difference of agriculture sector series (𝐴𝐺𝑅𝐹𝑡 − 𝐴𝐺𝑅𝐹𝑡−1)
∆𝑀𝐼𝑁𝑄𝑡 = First difference of mining and quarrying sector (𝑀𝐼𝑁𝑄𝑡 − 𝑀𝐼𝑁𝑄𝑡−1)
∆𝑆𝐸𝑅𝑉𝑡 = First difference of services sector series (𝑆𝐸𝑅𝑉𝑡 − 𝑆𝐸𝑅𝑉𝑡−1)
∆𝐺𝐷𝑃𝑡 = First difference of GDP series (𝐺𝐷𝑃𝑡 − 𝐺𝐷𝑃𝑡−1)
∝0 is constant, 𝛼1drift term and T is time trend and 𝜇𝑡 is the error term for the
series.
5.3.2 Hypothesis for Unit Root Test
The following hypothesis is tested for identifying the unit root problem in each
concerned series.
Null hypothesis: H0: Series has a unit root problem, statically 𝛽1 = 0
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Alternate hypothesis: H1: No unit root problem in the series, statistically 𝛽1 ≠ 0
The criteria for rejecting the null hypothesis ( 𝛽1 = 0 ) is, the estimated value of
𝛽1 should be greater than the MacKinnon critical value in absolute term at
conventional significant level (1%, 5%, and 10%). In this regard estimated value of 𝛽1
compared with the value suggested by MacKinnon for rejecting the null hypothesis.
5.3.3 Unit Root Test Results
In Table 5.2 Unit Root Test Results depicts that all variables series have a unit root
problem at the level (original series). As per MacKinnon critical values criteria, the
estimated values through the DF test at the level, all variables fall in the critical region
at all conventional significant levels. Hence the null hypothesis statistically cannot be
rejected, which implies that all data series have a unit root problem at the level.
However, both ADF and PP tests at first difference rejected the null hypothesis at 1%
and 5% conventional levels of significance and suggested that there is no unit root
problem in the variables data series and variables are stationary. In other words, each
variable data series has constant mean and variance of the first difference of the
series. This implies that each observation of variable data series is an independent
observation and does not carry the impact of previous observation. Hence the variable
data series can be used for further unbiased regression analysis, such as causality
analysis, VECM, forecasting.
Table 5.2 Unit Root Test Results
Variables Lag
order
DF test at
level with
intercept
ADF test (first
difference)
with intercept
PP test (first
difference)
with intercept
Order of
integration
CONS 4 -2.5901 -3.0777** 9.8436** I(1)
MANF 4 -0.9901 -5.3748** 12.6824** I(1)
AGRF 4 -2.2333 -3.5938** 9.6871** I(1)
MINQ 4 -0.1020 -4.9525** 13.8359** I(1)
SERV 4 -1.0530 20.9246** 73.2907** I(1)
GDP 4 -0.1769 -5.1636** 12.6795** I(1)
Mackinnon critical value for the rejection null hypothesis of 5%, level of significance
with, intercept is -2.932. (** denote rejection of the null hypothesis at 5% significance
level)
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Another important outcome of this test is that all the variables are integrated order
one I(1), which implies that there is a possibility of a stable, long run relationship
between the variables that can be measured by co-integrating equation.
5.3.4 Optimal Lag Length
Selection of lag order is a very important issue in the Granger causality analysis,
because the causality model is very sensitive to the lag length. Table 5.3 indicates the
lag order suggested by the different selection criteria for the model. The optimal lag
length suggested by the SC criteria is 1, LR and HQ, 5, and FPE and AIC criteria
suggested 7. We can choose any one optimal lag length of our model because all
criteria are equally good. For this study, we are considering the optimal lag length
suggested by LR and HQ criteria i.e. 5 lags
Table 5.3 Lag Length Selection Table
Lag
order
SC LR HQ FPE AIC
0 103.45 NA 103.33 2.83 103.26
1 94.78* 711.40 93.99 1.59 93.47
2 94.91 119.09 93.44 5.96 92.47
3 95.33 91.79 93.18 3.07 91.75
4 95.91 73.63 93.08 1.96 91.21
5 96.54 62.26* 93.03* 1.42 90.71
6 97.60 38.31 93.41 1.73 90.63
7 98.01 50.94 93.15 1.30* 89.92*
5.3.5 Pair wise Granger Causality Analysis
The basic idea of causality is the occurrence of one event by another. It is the
relationship between cause and effect with a view that cause cannot occur after the
effect. If a variable “A” affects a variable “B”, then “A” should help in the improving
the prediction of “B”. There are several ways to measure causality. However, the most
popular powerful test is a Granger causality test. The Granger causality was
developed in 1960s by Prof Clive W.J Granger and has been extensively used for
measuring causality link between the variables from that time. The mathematical and
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statistical structure of model is developed on the basis of the linear regression
stochastic procedure.
Following equations (5.7 to 5.16) are pair wise constructed on the bases of
Journalized Granger causality model equation as defined and discussed in Section
4.5.5 of Chapter 4 (Research Methodology) for testing linkage direction between
MCS and defined key sectors of the Malaysian economy.
The pair of equations 5.7 and 5.8 is developed to examine the causal link between
construction (CONS) and manufacturing (MANF) sector of the Malaysia. Equation
5.7 investigates that whether MANF affect the CONS or not, while Equation 5.8
examines that the CONS has impact on MANF or not. Similarly the pair of equations
5.9 and 5.10 is developed to examine the causality link between CONS and AGRF.
Equation 5.9 determines that whether AGRF has an impact on CONS or not, while
Equation 5.10 investigates that CONS affects the AGRF or not. In the same manner
Equation 5.11 find out whether the MINQ has an impact on CONS or not, while the
Equation 5.12 measures the CONS affect the MINQ or not. Equation 5.13 and
Equation 5.14 are formulated to investigate the causal link between CONS and SERV.
finally Equation 5.15 and Equation 5.16 are set for investigating the causal link
between CONS and GDP.
Causality between Construction and Manufacturing
𝐶𝑂𝑁𝑆𝑡 =∝1+ ∑ 𝛽1𝑖𝑗𝑗=1 𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾1𝑖
𝑘𝑘=1 𝑀𝐴𝑁𝐹𝑡−1 + 𝜇1𝑡 (5.7)
𝑀𝐴𝑁𝐹𝑡 =∝2+ ∑ 𝛽2𝑖𝑗𝑗=1 𝑀𝐴𝑁𝐹𝑡−1 + ∑ 𝛾2𝑖
𝑘𝑘=1 𝐶𝑂𝑁𝑆𝑡−1 + 𝜇2𝑡 (5.8)
Causality between Construction and Agriculture
𝐶𝑂𝑁𝑆𝑡 =∝3+ ∑ 𝛽3𝑖𝑗𝑗=1 𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾3𝑖
𝑘𝑘=1 𝐴𝐺𝑅𝐹𝑡−1 + 𝜇3𝑡 (5.9)
𝐴𝐺𝑅𝐹𝑡 =∝4+ ∑ 𝛽4𝑖𝑗𝑗=1 𝐴𝐺𝑅𝐹𝑡−1 + ∑ 𝛾4𝑖
𝑘𝑘=1 𝐶𝑂𝑁𝑆𝑡−1 + 𝜇4𝑡 (5.10)
Causality between Construction and Mining and Quarrying
𝐶𝑂𝑁𝑆𝑡 = 𝛼5 + ∑ 𝛽5𝑖𝑗𝑗=1 𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾5𝑖
𝑘𝑘=1 𝑀𝐼𝑁𝑄𝑡−1 + 𝜇5𝑡 (5.11)
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𝑀𝐼𝑁𝑄𝑡 = 𝛼6 + ∑ 𝛽6𝑖𝑗𝑗=1 𝑀𝐼𝑁𝑄𝑡−1 + ∑ 𝛾6𝑖
𝑘𝑘=1 𝐶𝑂𝑁𝑆𝑡−1 + 𝜇6𝑡 (5.12)
Causality between Construction and Services
𝐶𝑂𝑁𝑆𝑡 = 𝛼7 + ∑ 𝛽7𝑖𝑗𝑗=1 𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾7𝑖
𝑘𝑘=1 𝑆𝐸𝑅𝑉𝑡−1 + 𝜇7𝑖 (5.13)
𝑆𝐸𝑅𝑉𝑡 = 𝛼8 + ∑ 𝛽8𝑖𝑆𝐸𝑅𝑉𝑡−1𝑗𝑗=1 + ∑ 𝛾8𝑖
𝑘𝑘=1 𝐶𝑂𝑁𝑆𝑡−1 + 𝜇8𝑡 (5.14)
Causality between Construction and over all GDP
𝐶𝑂𝑁𝑆𝑡 = 𝛼9 + ∑ 𝛽9𝑖𝑗𝑗=1 𝐶𝑂𝑁𝑆𝑡−1 + ∑ 𝛾9𝑖
𝑘𝑘=1 𝐺𝐷𝑃𝑡−1 + 𝜇9𝑡 (5.15)
𝐺𝐷𝑃𝑡 = 𝛼10 + ∑ 𝛽10𝑖𝑗𝑗=1 𝐺𝐷𝑃𝑡−1 + ∑ 𝛾10𝑖
𝑘𝑘=1 𝐶𝑂𝑁𝑆𝑡−1 + 𝜇10𝑡 (5.16)
Where,
𝛼𝑖 is constant term
𝛽1…10,𝑖 and 𝛾1…10,𝑖 are the coefficient of independent variable
𝜇1…10𝑡 error term
These equations (5.7 to 5.16) are used for testing causality hypothesis. The causality
between the concerned variables tested in a bivariate analysis as defined by the
Granger causality theory.
5.3.6 Hypothesis Testing
The Ordinary least square (OLS) regression technique applied for estimating equation
5.7, equation 5.8 to equation 5.16. In this regard 10 separate nulls and alternate
hypotheses are developed and tested, which are reported in the Table 5.4.
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Table 5.4 Null and Alternate Hypothesis
S.No Null Hypothesis Alternate Hypothesis
1 CONS does not cause MANF
𝛾11 = 𝛾12 = ⋯ . = 𝛾1𝑘 = 0
CONS Granger cause MANF
At least one 𝛾1𝑖 ≠ 0
2 MANF does not cause CONS
𝛾21 = 𝛾22 = ⋯ = 𝛾2𝑘 = 0
MANF Granger cause CONS
At least one 𝛾2𝑖 ≠ 0
3 CONS does not cause AGRF
𝛾31 = 𝛾32 = ⋯ = 𝛾3𝑘 = 0
CONS Granger cause AGRF
At least one 𝛾3𝑖 ≠ 0
4 AGRF does not cause CONS
𝛾41 = 𝛾42 = ⋯ = 𝛾4𝑘 = 0
AGRF Granger cause CONS
At least one 𝛾4𝑖 ≠ 0
5 CONS does not cause MINQ
𝛾51 = 𝛾52 = ⋯ = 𝛾5𝑘 = 0
CONS Granger cause MINQ
At least one 𝛾5𝑖 ≠ 0
6 MINQ does not cause CONS
𝛾61 = 𝛾62 = ⋯ = 𝛾6𝑘 = 0
MINQ Granger cause CONS
At least one 𝛾6𝑖 ≠ 0
7 CONS does not cause SERV
𝛾71 = 𝛾72 = ⋯ = 𝛾7𝑘 = 0
CONS Granger cause SERV
At least one 𝛾7𝑖 ≠ 0
8 SERV does not cause CONS
𝛾81 = 𝛾82 = ⋯ = 𝛾8𝑘 = 0
SERV Granger cause CONS
At least one 𝛾8𝑖 ≠ 0
9 CONS does not cause GDP
𝛾91 = 𝛾92 = ⋯ = 𝛾9𝑘 = 0
CONS Granger cause GDP
At least one 𝛾9𝑖 ≠ 0
10 GDP does not cause CONS
𝛾10 1 = 𝛾10 2 = ⋯ = 𝛾10 𝑘 = 0
GDP Granger cause CONS
At least one 𝛾10 𝑖 ≠ 0
5.3.7 Pair wise Causality Hypothesis Test Result
10 pair wised inter sectorial hypotheses were established for Granger causality test.
Seven out of ten hypotheses rejected on the basis of F-statistics and probability value
of 5% and 10% level of significance. Table 5.5 depicts 6 out of 10 hypotheses
rejected at the 5 % level of significance and 1 hypothesis rejected at the 10 % level of
significance. So the 7 out of 10 hypotheses suggested that there is a statistically strong
evidence of linkage between MCS and other identified key sectors of the Malaysian
economy. Three hypotheses, construction does not cause to manufacturing,
construction does not cause to agriculture and forestry and service sector does not
cause to construction sector could not be statistically rejected. This implies that the
construction sector does not affect the manufacturing and agriculture and forestry
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sector, while service has not impacted on the construction sector. The uni- directional
causality exists between these sectors.
Table 5.5 Empirical Results of Granger Causality
Sectors Null Hypothesis Optimal
Lag
F-
Statistics
P- value Result
Construction
and
Manfacturing
CONS does not cause
MANF
5 0.6644 0.6517 Accept
MANF does not cause
CONS
5 3.2138 0.0119** Reject
Construction
and
Agriculture
CONS does not cause
AGRF
5 0.1729 0.9717 Accept
AGRF does not cause
CONS
5 4.3975 0.0017** Reject
Construction
and Mining
and Quarry
CONS does not cause
MINQ
5 2.9998 0.0170** Reject
MINQ does not cause
CONS
5 5.1676 0.0005** Reject
Construction
and Services
CONS does not cause
SERV
5 10.5327 2.E-07** Reject
SERV does not cause
CONS
5 1.3606 0.2510 Accept
Construction
and GDP
CONS does not cause
GDP
5 2.0722 0.0804* Reject
GDP does not cause
CONS
5 4.9385 0.0007** Reject
**, * denotes rejection of null hypothesis at 5% and 10% significance level
respectively (Source: Author’s calculation)
5.3.8 Linkage Direction
The direction of linkage is identified on the basis of empirical results given in Table
5.5 and reported in Table 5.6. The Table 5.6 depicts that the construction sector has
bi-directional or two way causality with mining and quarrying and overall GDP of
Malaysia. The change in mining and quarrying output and GDP affect the
construction output, similarly change in construction sector output has a significant
effect over the mining and quarrying sector output as well as the GDP of Malaysia.
There is a uni-directional or one way link between construction and manufacturing,
construction and agriculture and forestry, construction and service sector. In case of
construction and manufacturing the direction of causality is from manufacturing to
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construction sector, which implies that the change in manufacturing output has
significant impact over construction sector while construction has not significant
effect over manufacturing. Similarly, agriculture and forestry sector affect the
construction sector, but construction has not significant effect over agriculture and
forestry sector. However, the causality link between construction and service sector is
the other way around. The construction has a significant effect on service sector,
while the service sector does not affect the construction sector significantly.
Table 5.6 Direction of linkage
Description Direction of Linkage
Construction and Manufacturing Unidirectional (manufacturing to
construction)
Construction and Agriculture &
Forestry
Unidirectional (agriculture and forestry to
construction)
Construction and Mining &
Quarrying
Bi-directional
Construction and Services Unidirectional (construction to services)
Construction and overall GDP Bi-directional
5.4 Graphical Models for Pair Wise Granger Causality
Figure 5.1 shows the linkage model at 5 % level of significance and in Figure 5.2
shows the direction of linkage between MCS, and major selected sectors of the
Malaysian economy and the GDP of Malaysia at the conventional level of
significance of 5% and 10% respectively. Figure 5.1 depicts at the 5% level of
significance there is a uni-directional relationship between the construction and all
concerned sectors including GDP except the mining and quarrying. There is a
bidirectional causality link between construction and the mining and quarrying sector.
The direction of causality suggests that the linkage is backward, forward or both
backward and forward. Such as the causality direction between manufacturing and
construction is from manufacturing to construction. This implies that the construction
sector has backward linkage with the manufacturing sector of Malaysia, meaning that
the construction sector consumed output of the manufacturing sector as its input like
cement, steel, glass, paint etc. The same situation with agriculture and forestry sector,
the Malaysian construction sector has backward linkage with agriculture and forestry
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sector of Malaysia. However in case of service sector the construction has forward
linkage because the direction of link is from construction to service sector. The output
of construction sector becomes input for service sector. In case of mining and
quarrying and the construction bi-directional causality exists, meaning that both
backward and forward linkages exist between the sectors at the same time.
Figure 5.2 depicts when the levels of significance relaxed from 5% to 10 % than
the direction of linkage between the GDP and construction sector improve and
convert from uni-directional to bi-directional, which implies that at the 90% level of
confidence there is both backward and forward type of linkages exist between the
MCS and GDP of Malaysia. This implies that the GDP of Malaysia support of
construction sector and the construction sector support to GDP. The permanent
growth in the construction sector can potentially stimulate economic growth and
development in Malaysia. The high economic growth motivates the construction
sector by improving infrastructure and filling the gap, hence the linkage is bi-
directional between MCS and GDP of Malaysia.
Figure 5.1 Linkage Model at 5 % level of significance
Manufacturing
GDP
Construction
Agriculture &
Forest
Services
Mining &
quarrying
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Figure 5.2 Linkage Model at 10 % level of significance
The bivariate Granger causality analysis identified existing linkage and their
direction between MCS and other major sectors of the Malaysian economy including
GDP. However it does not say anything about the magnitude of impact or the rate of
influence of one sector to the other. It is therefore required to develop a vector error
correction model (VECM) to understand short term and long run causality link on
individual basis and to measure the rate of influence of one sector to another in the
Malaysian economy system.
The time series econometric VECM uses under vector autoregressive (VAR)
system. VECM has nice statistical properties such as;
a) No spurious (misleading/ biased) regression problem
b) Variables have unit root , hence difference of variable stationary
Manufacturing
GDP
Construction
Agriculture &
Forest
Services
Mining &
quarrying
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c) Variables are co-integrated, hence equilibrium error is stationary and long run
association exist between the variables
d) VECM has excellent forecasting performance
Therefore model is not only use for estimating short and long run influencing
factor but also use to analyze the impulse response function and making long run
forecasting. The next chapter will discuss the development of VECM for the MCS
which is the main objective of this research study.
5.5 Summary
The Pearson correlation test suggests that there is a strong correlation between MCS
and the other sectors of the Malaysian economy including GDP. It implies that the
MCS has pivotal role in the Malaysian economy. The unit root test shows that the all
data variables series have a unit root problem at level however stationarity in all data
series can be achieved by the first difference of the series and all variables have same
order of integration i.e. integrated order one I(1). This is indication of long run
association between the variables.
The empirical findings from the Granger causality analysis of optimal lag length
five suggest that MCS has bi-directional causality links with mining and quarrying,
and with overall GDP of the country. It has unidirectional causality links with
manufacturing, agriculture and forestry and services sector of Malaysia.
The backward linkages of MCS are stronger than forward linkages because of its
higher dependence on output of other sectors that MCS uses as an input like the
output of mining and quarrying sectors (marbles, stones, and other materials), and
output of the manufacturing sectors (cement, steel, paint and the number of plumbing
and electrical items). Furthermore it confirms that the MCS activities play a
significant role in promoting growth of the key sectors of Malaysian economy as well
as the GDP of Malaysia.
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The results of this study are comparable with the study conducted by Lean (2001)
under the title “Empirical Tests to Discern Linkages between Construction and Other
Economic Sectors in Singapore” which concluded that there is the bi-directional
causal relationship between construction and other producing sector of the Singapore
economy (Lean, 2001).
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CHAPTER 6
VECM, IRF AND FORECASTING FOR MCS
6.1 Introduction
This chapter presented herein discusses on the estimation of the vector error
correction model (VECM) for the construction sector of Malaysia. All the basic
assumptions for estimation of the model have been discussed and satisfied in the
previous chapter such as non-stationarity of variables at level, integration of variable
after first differencing, optimal lag length and more than one equation involved in
causality. This chapter will focus on the estimation of a long run and short run
association of MCS with other major sectors of the Malaysian economy through
VECM. The VECM will determine the rate of change in MCS output due to a one
unit change in the output of other sectors of the economy. The chapter will also cover
impulse response functions and forecasting of MCS output.
6.2 Vector Error Correction Model (VECM)
The error correction model (ECM) converts into vector error correction model
(VECM) when more than one or set of equations is involved in causality model. The
VECM is a multivariate error correction model, which handles sets of the equation at
the same time. The model will help to understand and examine empirical long run and
short run association between the concerned variables in optimal lag order.
The VECM provides information about short run (SR) and long run (LR)
adjustment (causality) to changes in independent variables through the estimated
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parameters. It has one equation for each variable. Therefore we have following six
VECMs:
𝐶𝑂𝑁𝑆 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.1)
𝑀𝐴𝑁𝐹 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.2)
𝑀𝐼𝑁𝑄 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.3)
𝐴𝐺𝑅𝐹 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.4)
𝑆𝐸𝑅𝑉 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.5)
𝐺𝐷𝑃 = 𝑓(𝐶𝑂𝑁𝑆, 𝑀𝐴𝑁𝐹, 𝑀𝐼𝑁𝑄, 𝐴𝐺𝑅𝐹, 𝑆𝐸𝑅𝑉, 𝐺𝐷𝑃) (6.6)
However, under this study our target is MCS, therefore we shall limit our
concentration and discussion over model Equation 6.1 in which construction sector of
Malaysia is a dependent variable and other major sectors of the Malaysian economy
are used as an independent variables including GDP. In other model equations (6.2 to
6.6), we will observe and note only the behavior of construction sector (CONS) as an
independent variable.
6.3 Co-integration Examination
The existence of co-integration between the variables indicates that the variables
have long-run association and long run causality exist between the variables
(Johansen, 1988). According to Engel Granger if the variables are found to be co-
integrated then they can be specified by error correction mechanism. In other words,
if there is a solid evidence of co-integration between the variables then a valid error
correction model must exist there (Granger, 1981).
121
The Johansen Jusilus (JJ) co-integration test is used in order to determine the long
run causal relationship among the concerned variables. The two tests Rank Traces and
Rank Maximum Eigen Value is conducted to examine the co-integration among study
variables. The empirical test results are presented in Table 6.1 and Table 6.2 (see
Appendix – A for complete results). Both the trace statistic and maximum eigenvalue
confirms the existence of co-integration among the major sectors of the Malaysian
economy.
Table 6.1 Co-integration Examination Results Based on JJ Rank (Trace) Statistics
Hypothesized number of co-
integrating equations
Eigen
value
Trace
Statistics
5% critical
value
Probability
None** 0.5892 171.6133 95.7536 0.0000
At most one** 0.4116 105.7637 69.8188 0.0000
At most two** 0.3433 66.5150 47.8561 0.0004
At most three** 0.2355 33.3914 29.7970 0.0102
At most four** 0.1726 15.5117 15.4941 0.0496
At most five 0.0200 1.4972 3.8414 0.2211
Author’s calculations, (** denotes rejection of null hypothesis at 5% significance
level)
Table 6.2 Co-integration Examination Results Based on JJ Rank (Eigenvalue)
Statistics
Hypothesized number of co-
integrating equations
Eigen
value
Max.
Eigenvalue
5% critical
value
Probability
None** 0.5892 65.8496 40.0775 0.0000
At most one** 0.4116 39.2485 33.8768 0.0104
At most two** 0.3433 31.1236 27.5843 0.0168
At most three 0.2355 19.8735 21.1316 0.0742
At most four 0.1726 14.0206 14.2646 0.0546
At most five 0.0200 1.4972 3.8414 0.2211
Author’s calculations, (**denotes rejection of null hypothesis at 5% significance
level)
Johansen co-integration rank trace test suggested that there are five co-integrating
equations at the 5% significance level. The estimated value of Trace statistics from
none to at most four co-integration equations, are greater than the critical value at 5 %
level and probability is less than the significance level. Hence, it means rejection of
the null hypothesis from none to at most four co-integrating equation. However the
hypothesis of at most five co-integrating equations cannot be rejected. This is because
estimated trace statistics is the less than the critical value at 5% level of significance
and probability is also greater than 0.05 or 5%.
122
The maximum Eigenvalue test approved three co-integrating equations because
the maximum eigenvalue criterion is more conservative than trace statistics. The
estimated values of maximum eigenvalue from none to at the most two co-integrating
equations are greater than the 5% critical value. Therefore, we must reject the null
hypothesis of none, at most one and at most two co-integrating equations. But the
three to five co-integrating equations hypothesis cannot be rejected on the basis of
probability, which is greater than 5% level of significance and critical value which is
greater than estimated maximum eigenvalue.
The matrix of five co-integrating equations on the basis of trace statistic is
presented in Table 6.3 below while complete set of all co-integrating equation is
available in Appendix - B
Table 6.3, 5 Co-integrating Equations (Normalized Coefficient)
Cointegrating
equations
Coint-1 Coint-2 Coint-3 Coint4 Coint-5
CONS 1..0000 0.0000 0.0000 0.0000 0.0000
MANF 0.0000 1.0000 0.0000 0.0000 0.0000
MINQ 0.0000 0.0000 1.0000 0.0000 0.0000
AGRF 0.0000 0.0000 0.0000 1.0000 0.0000
SERV 0.0000 0.0000 0.0000 0.0000 1.0000
GDP -0.0129 -0.3500 -0.205 -0.0175 -0.7921
Constant -2371.0238 6284.2173 -7582.5862 -6676.3750 27380.8894
Source: see Appendix - B
6.4 VECM for Construction Sector
As we have mentioned in Section 6.2 that under this study our target is VECM
Equation 6.1 in which construction sector is the function of other major sector of the
Malaysian economy. The VECM for construction sector provides information about
the short run (SR) and long run (LR) adjustment (causality) of MCS against one unit
change in the independent variables, through the estimated parameters (coefficient of
endogenous variables). The estimated coefficient shows the rate of change in MCS
due to one unit increase or decrease in an independent variable, when all other factors
remain constant. The direction of change will follow the sign of the coefficient.
Furthermore this model suggests the speed of adjustment toward LR equilibrium.
123
The estimated equation for model 6.1 over optimal lag order 5 is expressed in
Equation 6.7. The equations for other models (6.2 to 6.6) are available in Appendix –
C.
D(CONS) = C(1)*[CONS(-1) – 0.0129*GDP(-1) – 2371.0238] + C(2)*[MANF(-1) –
0.3500*GDP(-1) +6284.2173] + C(3)*[MINQ(-1) – 0.0205*GDP(-1) – 7582.5862] +
C(4)*[AGRF(-1) – 0.0175*GDP(-1) – 6676.3570] + C(5)*[SERV(-1) –
0.7921*GDP(-1) + 27380.8894] + C(6)*D[CONS(-1)] + C(7)*D[CONS(-2)] +
C(8)*D[CONS(-3)] + C(9)*D[CONS(-4)] + C(10)*D[CONS(-5)] +
C(11)*D[MANF(-1)] + C(12)*D[MANF(-2)] + C(13)*D[MANF(-3)] +
C(14)*D[MANF(-4)] + C(15)*D[MANF(-5)] +C(16)*D[MINQ(-1)] +
C(17)*D[MINQ(-2)] + C(18)*D[MINQ(-3)] + C(19)*D[MINQ(-4)] +
C(20)*D[MINQ(-5)] + C(21)*D[AGRF(-1)] + C(22)*D[AGRF(-2)] +
C(23)*D[AGRF(-3)] + C(24)*D[AGRF(-4)] C(25)*D[AGRF(-5)] +
C(26)*D[SERV(-1)] + C(27)*D[SERV(-2)] + C(28)*D[SERV(-3)] +
C(29)*D[SERV(-4)] + C(30)*D[SERV(-5)] + C(31)*D[GDP(-1)] + C(32)*D[GDP(-
2)] + C(33)*D[GDP(-3)] + C(34)*D[GDP(-4)] + C(35)*D[GDP(-5)] + C(36)
(6.7)
This model equation 6.7 is comprised of two major factors, co-integration and
error correction mechanism.
For our convenience and understanding we can break this long complex equation
6.7 into two parts. First, 6.8 - a, which consists of co-integrating equations and
represents the speed of adjustment toward long run and long run association
(causality) between the concerned variables; and secondly, 6.8 – b, which captures the
error correction mechanism and represent short run causality between the variables.
Long-run causality
D(CONS)L = C(1)*[CONS(-1) – 0.0129*GDP(-1) – 2371.0238] + C(2)*[MANF(-1) –
0.3500*GDP(-1) +6284.2173] + C(3)*[MINQ(-1) – 0.0205*GDP(-1) – 7582.5862] +
C(4)*[AGRF(-1) – 0.0175*GDP(-1) – 6676.3570] + C(5)*[SERV(-1) –
0.7921*GDP(-1) + 27380.8894] (6.8 - a)
124
Short run causality
D(CONS)S = C(6)*D[CONS(-1)] + C(7)*D[CONS(-2)] + C(8)*D[CONS(-3)] +
C(9)*D[CONS(-4)] + C(10)*D[CONS(-5)] + C(11)*D[MANF(-1)] +
C(12)*D[MANF(-2)] + C(13)*D[MANF(-3)] + C(14)*D[MANF(-4)] +
C(15)*D[MANF(-5)] +C(16)*D[MINQ(-1)] + C(17)*D[MINQ(-2)] +
C(18)*D[MINQ(-3)] + C(19)*D[MINQ(-4)] + C(20)*D[MINQ(-5)] +
C(21)*D[AGRF(-1)] + C(22)*D[AGRF(-2)] + C(23)*D[AGRF(-3)] +
C(24)*D[AGRF(-4)] C(25)*D[AGRF(-5)] + C(26)*D[SERV(-1)] + C(27)*D[SERV(-
2)] + C(28)*D[SERV(-3)] + C(29)*D[SERV(-4)] + C(30)*D[SERV(-5)] +
C(31)*D[GDP(-1)] + C(32)*D[GDP(-2)] + C(33)*D[GDP(-3)] + C(34)*D[GDP(-4)]
+ C(35)*D[GDP(-5)] + C(36) (6.8 - b)
6.5 Long Run and Short run Causality Coefficients
The model equation 6.7 has overall 36 coefficients out of which first five coefficients
C (1) to C (5) are co-integrating equation coefficients and representative to long term
association between the construction sector and the major sectors of the Malaysian
economy. The remaining 31 coefficients from C (6) to C (35) are short run causality
coefficients. These coefficients capture the rate of change in the construction sector in
the short run due to variation in the independent variable. C (36) is a constant term.
The sign, value, t-statistics and the probability of these coefficients are displayed in
Table 6.4 below (all models [6.1 to 6.6] coefficients are available in Appendix – D to
I). The significance of each coefficient can be measured from its corresponding
probability value in the Table 6.4.
The foremost important thing is the sign and significance of C (1). It must be
negative and significant for correctness of the model and the existence of long run
causality between the dependent and independent variables. The negative value (-
0.4899) of C (1) and its corresponding probability (0.0010) ensure that model 6.1 for
MCS is desirable and has not any fundamental problem. There is a long run
relationship exists between the construction sector and other key sectors of the
Malaysian economy. Furthermore, this coefficient informs that the speed of
125
adjustment (correction) of the disequilibrium of previous period is 49% toward long
run equilibrium.
Table 6.4, Coefficients Value and Probabilities of Model Equation 6.7
Coefficient Value Standard Error t-statistics Probability
C(1) -0.4899 0.1371 -3.5719 0.0010**
C(2) -0.0740 0.0273 -2.7049 0.0102**
C(3) 0.1861 0.1015 1.8327 0.0747*
C(4) -0.1950 0.1458 -1.3370 0.1892
C(5) 0.0061 0.0311 0.1981 0.8440
C(6) 0.3705 0.1565 2.3671 0.0231**
C(7) 0.6717 0.1756 3.8247 0.0005**
C(8) 0.0247 0.1978 0.1251 0.9011
C(9) 0.4909 0.2251 2.1980 0.0341**
C(10) 0.2996 0.2356 1.1585 0.2539
C(11) 0.0583 0.0499 1.1675 0.2503
C(12) 0.0037 0.0454 0.0825 0.9346
C(13) -0.0088 0.0533 -0.1655 0.8694
C(14) 0.0053 0.0427 0.1246 0.9014
C(15) -0.0185 0.0401 -0.4622 0.6465
C(16) -0.2292 0.0924 -2.4795 0.0177**
C(17) -0.0212 0.0845 -0.2519 0.8024
C(18) -0.0880 0.0875 -1.0059 0.3208
C(19) -0.1748 0.0869 -2.0118 0.0514*
C(20) -0.1043 0.0879 -1.1858 0.2430
C(21) 0.1032 0.1206 0.8556 0.3975
C(22) 0.3435 0.1260 2.7256 0.0097**
C(23) 0.1247 0.1003 1.2435 0.2213
C(24) 0.1394 0.0784 1.7779 0.0834*
C(25) 0.0053 0.0718 0.0740 0.9414
C(26) -0.0636 0.0396 -1.6050 0.1168
C(27) 0.0038 0.0029 1.2975 0.2023
C(28) 0.0030 0.0022 1.3661 0.1799
C(29) 0.0013 0.0016 0.8243 0.4149
C(30) 9.56E-06 0.0009 0.0097 0.9923
C(31) -0.0017 0.0447 -0.0384 0.9695
C(32) -0.0209 0.0330 -0.6326 0.5307
C(33) 0.0019 0.0350 0.0563 0.9553
C(34) 0.0133 0.0293 0.4568 0.6504
C(35) 0.0239 0.0234 1.0234 0.3126
C(36) -13.0725 77.6753 -0.1682 0.8672
Source: Author’s calculations, (**’* denotes significant coefficient at 5% and 10 %
significance level, respectively)
126
The estimated VECM for construction sector after incorporating the long run and
short run coefficients value of each independent variable is expressed in Equation 6.9
given below.
D(CONS) = - 0.4899*[CONS(-1) – 0.0129*GDP(-1) – 2371.0238] -
0.0740*[MANF(-1) – 0.3500*GDP(-1) + 6284.2173] + 0.1861*[MINQ(-1) –
0.0205*GDP(-1) – 7582.5862] – 0.1950*[AGRF(-1) – 0.0175*GDP(-1) –
6676.3570)] + 0.0061* [SERV(-1) – 0.7921*GDP(-1) + 27380.8894] +
0.3705*D[CONS(-1)] + 0.6717*D[CONS(-2)] + 0.0247*D[CONS(-3)] + 0.4909*
D[CONS(-4)] + 0.2996*D[CONS(-5)] + 0.0583*D[MANF(-1)] +
0.0037*D[MANF(-2)] – 0.0088*D[MANF(-3)] + 0.0053* D[MANF(-4)] -
0.0185*D[MANF(-5)] - 0.2292*D[MINQ(-1)] – 0.0212*D[MINQ(-2)] –
0.0880*D[MINQ(-3)] – 0.1748* D[MINQ(-4)] - 0.1043)*D[MINQ(-5)] +
0.1032*D[AGRF(-1)] + 0.3435*D[AGRF(-2)] + 0.1247*D[AGRF(-3)] + 0.1394*
D[AGRF(-4)] + 0.0053*D[AGRF(-5)] - 0.0636*D[SERV(-1)] + 0.0038*D[SERV(-
2)] + 0.0030*D[SERV(-3)] + 0.0013* D[SERV(-4)] - 0. 0000*D[SERV(-5)] -
0.0017*D[GDP(-1)] – 0.0209*D[GDP(-2)] + 0.0019*D[GDP(-3)] + 0.0133*
D[GDP(-4)] + 0.0239*D[GDP(-5)] - 13.0725 (6.9)
6.6 Explanatory Power and Efficiency of Equation 6.9 for MCS Model 6.1
There are 3 important parameters (R2, D.W statistic and F-statistic) that is normally
used to measure the strength and efficiency of the model.
The coefficient of determination, (R2), shows the explanatory power of
independent variables to dependent variable and if the large value of R2 (60% or
more) occurs, it may be concluded that the estimated model is good and fit for
estimating and forecasting. But the large value of R2 does not directly imply that the
model is accurate and efficient for estimation. The value of Durbin Watson (D.W)
should also be in an appropriate range. The thumb of rule is around 2 neither too little
nor too high from 2. Both situations indicate that there is an auto-correlation in the
model. Third important factor is an F- statistic which should be significant. It
indicates that all explanatory variables jointly influence the dependent variable
127
(Agung, 2009). The estimated values of important parameters of model Equation 6.9
for MCS are mentioned in Table 6.5 below (see Appendix – J for all models 6.1 to
6.6). The coefficient of determination (R2) is 82.45% which suggest that the estimated
model has strong explanatory power for MCS. The D.W value (1.953) is much closed
to 2 that indicate the model has no auto correlation problem and the probability of the
F-statistic is significant. This implies that all included independent variables have
combined effect over MCS. The estimated VECM for construction satisfied the all
necessary and sufficient conditions so the estimated Equation 6.9 for MCS model 6.1
is an efficient model equation that can be used for forecasting without any harm.
Table 6.5, Results of Equation 6.9 for MCS model 6.1
Parameters Value
Coefficient of determination (R2) 0.8245
Adjusted R2 0.6629
F -statistics 5.1006
Probability of F-statistic 0.0000
Durbin Watson statistic 1.9532
Source: Author’s calculations
6.7 Validation of the Estimated Equation 6.9 for MCS Model 6.1
The most important thing in the time series regression model is that the model should
be non-spurious (unbiased). The following two fundamental conditions should be
satisfied for accurate and non-spurious econometric model:
1) R2 should be less than a Durbin Watson statistic
2) Residual should be stationary and white noise.
The first condition is satisfied, as can be seen in Table 6.5 the value of R2 is less
than D.W statistic i.e. 0.8245 < 1.9532, which means estimated Equation 6.9 for MCS
model 6.1 is not a spurious model equation. It is perfectly correct on the basis of R2
and D.W value.
128
The second condition is residual should be stationary and white noise that means,
residual should be free from serial correlation, auto-correlation and hetroskedacity. To
examine these factors in the residual following tests are conducted.
a) Residual serial correlation test
b) Residual hetroskedacity test
c) Residual corrologram
6.8 Residual Graph
Before going to residual test discussion is made on residual plot. Figure 6.1 depicts
the residual plot of a VECM Equation 6.9 for MCS model 6.1. There are three
outliers’ which are outside the dotted lines. Two points are negative (1998 and 2008)
and one is a positive peak (1995). If we recall the history of the particular period we
can easily understand the reason.
In 1995 the construction sector of Malaysia was at the peak with a 23% growth
rate. The Government of Malaysia had announced the vision 2020 of developed
nation status and started heavy infrastructure projects such as Kuala Lumpur
International Airport, PETRONAS Twin Tower, Penang Bridge, North South
Expressway and also developing the Kuala Lumpur city.
In1998 there was negative shock in the Malaysian economy due to Asian currency
crises, resulting in construction sector growth sharply declined by 33%. It shrunk
from 10 % to -22% during 1997 to 1998. Similarly, during 2008 the construction
activities were low due to global financial crises and completion of most of the
running projects. However, in 2009 the construction sector was the only sector that
recorded a positive growth during every quarter of 2009 and registered 5.8% annual
growth because of stability of building material prices and stimulus package of
amounting to RM 67 billion for the construction sector.
129
Figure 6.1 Residual Graph for VECM Equation 6.9
6.9 Serial Correlation Test for Residual
The residual of the model should be free from serial correlation for good, unbiased
and non-spurious model. In this regard the Breusch-Godfrey serial correlation LM test
is conducted with a null hypothesis that residual is not serially correlated versus the
alternate hypothesis that residual is serially correlated.
H0: No serial correlation in the residual,
H1: Residual is serially correlated
The test results for VECM Equation 6.9 are reported in Table 6.6. The value of
observed R-square and probability of chi-square suggests that the null hypothesis
cannot be rejected. The probability value of Chi-square (X2) is greater than the 5%
significance level i.e. 30.36 %. Therefore, statistically we are unable to reject the
null hypothesis of no serial correlation in the residual. This means that the residual is
free from serial correlation which is desirable and indicates the VECM equation 6.9
for MCS model 6.1 is good for estimation without any problem
-400
-300
-200
-100
0
100
200
300
94 96 98 00 02 04 06 08 10
D(CONS) Residuals Years
130
Table 6.6 Breusch-Godfrey Serial Correlation LM Test
Observed R2 2.3842 Probability X2 0.3036
F-Statistic 0.5993 Probability F-statistic 0.5546
Source: Author’s calculations
6.10 Residual Hetroskedasticity Test
One of the properties for good model is that its residual should be homoscedastic
(mean and variance is not volatile). To examine this property autoregressive
conditional hetroskedasticity (ARCH) test is conducted with the null hypothesis that
there is no hetroskedasticity in the residual of model equation 6.9 against the alternate
hypothesis that, residual is homoscedastic. Test results are displayed in is
homoscedastic
Table 6.7, which suggest on the basis of probability of chi-square that the null
hypothesis of no hetroskedasticity in the residual cannot be rejected or in other words
residual is homoscedastic. The absence of hetroskedasticity ensures that the estimated
model equation is efficient.
H0: No hetrosckedasticity in the residual,
H1: Residual is homoscedastic
Table 6.7, Results of Hetroskedasticity
Observed R2 1.1286 Probability X2 0.9515
F-Statistic 0.2095 Probability F-statistic 0.9515
Source: Author’s calculations
6.11 Residual Correlogram
The correlogram of residual is developed to examine autocorrelation and partial auto
correlation in the residual series. Figure 6.2 shows that all spikes of autocorrelation
and partial correlation are under the 2 standard deviation dotted line. The probability
value throughout is greater than 5% level of significance, which accept that the null
131
hypothesis of no autocorrelation in the series. It further validates and confirms the
result of Breusch-Godfrey serial correlation LM test. We have strong evidence for no
auto correlation in the estimated model Equation 6.9.
Figure 6.2 Residual correlogram model M1
The residual of estimated model Equation 6.9 qualifies all tests for stationary and
white noise. Residual is free from serial correlation, there is no hetroskedasticity, no
auto correlation, which implies that the residual is stationary and white noise. Hence
the VECM for construction (Equation 6.9) is statistically sound and perfect. There is
no technical problem with the model equation. It can be used for estimation and
forecasting.
6.12 Residual Normality Distribution Test
This test is conducted to examine that the residual data series is normally distributed.
The Jarque – Bera test and its probability is used to examine the null hypothesis, the
residual of model Equation 6.9 is normally distributed. The test results are available in
132
Figure 6.3. The estimated Jarque-Bera value and its probability reject the null
hypothesis. Residual of model Equation 6.9 is not normally distributed. As the bar
chart in Figure 6.3 shows there are three outliers, two negative sides and one positive
side, therefore distribution is negatively skewed.
Figure 6.3 Residual Distribution Graph
However, this result does not affect our model performance because this test is
presented for specific discussion only and not for use in any model selection (Agung,
2009). In order to confirm this statement cumulative sum (CUSUM) and cumulative
sum square (CUSUM square) structure stability tests are conducted.
6.13 Structure Stability Test for Model Equation 6.9
The structure stability test is conducted to identify the structural breaks in the model.
The model free from structural breaks is considered as a stable model and can be used
for measuring impulse response and forecasting the future values. In order to examine
the stability of the model CUSUM and CUSUM square tests are conducted with the
0
2
4
6
8
10
12
-400 -300 -200 -100 0 100 200
Series: Residuals
Sample 1992Q3 2010Q4
Observations 74
Mean 1.71e-13
Median -2.948769
Maximum 222.3865
Minimum -380.9265
Std. Dev. 97.93111
Skewness -0.715096
Kurtosis 5.050378
Jarque-Bera 19.26929
Probability 0.000065
133
null hypothesis that there is no structural break exists in the model. The criteria for
rejecting the null hypothesis is the estimated residual value cross the upper or lower
bound, which are set at 5% significance level. The result of CUSUM test is shown in
Figure 6.4, suggest that the null hypothesis (H0) cannot be rejected; hence the model
residual is not only within the upper and lower bounds but also tapping the zero. This
implies that there is no structural break in the estimated model Equation 6.9 and the
model can surely be used for forecasting and measuring the impulse response of the
dependent variable i.e. MCS.
Figure 6.4 CUSUM Test for Structural Stability
In order to ensure the result of CUSUM test one more test CUSUM square is
conducted. The result of CUSUM square is also provided the same result as CUSUM
test. Figure 6.5 for CUSUM square test depicts that the residual of model Equation
6.9 is within the upper and lower bonds of 5 % level of significance. This implies that
there is no structural break in the estimated model equation and model is perfectly OK
for further actions such as IRFs calculation and forecasting.
-20
-15
-10
-5
0
5
10
15
20
02 03 04 05 06 07 08 09 10
CUSUM 5% Significance
134
Figure 6.5 CUSUM Square Test
6.14 Impulse Response Functions (IRFs)
IRF is used to measure the direct and indirect impact of change in one variable to the
other variables of model equations. It is used to determine how model variables react
to a shock in one of the dependent variables in a model (Hamilton and James, 1994).
It stimulates the effect of a shock to one endogenous variable on the other concerned
variables in the model. For instance if the reaction of “Y” after a positive shock in
“X” is negative , then presumably “Y” will respond negatively to innovation in “X”.
This theory is applied here to see how construction sector of Malaysia impact on
the other major sectors of Malaysian economy when one standard deviation positive
shock produced in the construction sector, and how the other sectors respond and
absorbed this shock. Similarly how does the construction sector respond against the
positive shock of the other sectors? What time it takes to absorb the shock?
A series of IRFs is developed to measure the impact of one standard deviation
positive shock in the construction sector on the other sectors of the model such as
manufacturing, mining and quarrying, agriculture and forest, service sector and finally
overall GDP of the Malaysia and how the one standard deviation shock in these
-0.4
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
02 03 04 05 06 07 08 09 10
CUSUM of Squares 5% Significance
135
sectors affect the Malaysian construction industry (see Appendix M). To satisfy this
objective, the estimated model Equation 6.9 is used and noted the recursive response
according to the VECM for construction results. This allows us to isolate the impact
of one standard deviation positive shock in one variable on all other included variable
in the model.
Figure 6.6 reveals the behavior of manufacturing against the construction shock. It
shows the response of the manufacturing sector is initially positive, reaches a
maximum and then sharply decline, and entered into the negative region in the next
five periods (Five quarter). This implies that the expansion in construction sector
encourage the output growth of manufacturing sector. Because the construction sector
has strong backward linkages with manufacturing sector and heavily used
manufacturing sector output as an input for productions such as cement, steel, glass,
paints.
Figure 6.6 Response of MANF to CONS
Figure 6.7 depicts the response of construction against the shock of manufacturing
is more sensitive. It made “w” shape, initially negative reaches a minimum and then
positive, negative and positive. Construction sector takes 20 quater to absorb the
impact of shock from manufacturing.
Quarter (Period)
136
Figure 6.7 Response of CONS to MANF
Figure 6.8 captures the response of mining and quarrying and MCS against the
shock. It shows the response of mining and quarrying is initially positive same as
manufacturing but it will take long period of time to regain its original path as
compare to other sectors of the Malaysian economy. The shock will keep positive
impact in first 15 quarter and then remain negative for next 10 quarter. So it makes
“S” shape and takes over all 25 quarter to absorb the shock completely. This implies
that the mining and quarrying sector is more sensitive to construction shock than other
sectors of the Malaysian economy.
Figure 6.8 Response of MINQ to CONS
Figure 6.9 reveals the behavior of construction against the mining and quarrying
sector shock. The response of MCS more or less same as mining and quarrying
response initially positive and makes “M” shape in the positive region. After 10
Quarter (Period)
Quarter ( Period)
137
quarter effect of shock enter into the negative region where it develops “W” shape in
the next 10 quarter thereafter it is normalized.
Figure 6.9 Response of CONS to MINQ
Figure 6.10 shows the responses of agriculture and forestry against the positive
shock of MCS. It depicts the agriculture and forestry does not give a significant
response of construction shock or in other words construction shock has no significant
effect over agriculture and forestry. Because the agriculture and forest sector of
Malaysia has already abundant resources and temporary positive shock in
construction does not bring significant change in its output.
Figure 6.10 Response of AGRF to CONS
In contrast Figure 6.11 shows that the construction sector has a significant effect
of the agriculture and forestry shock. Actually the construction sector has strong
backward linkage with agriculture sector and uses its output as an input for its
Quarter (Period)
Quarter (Period)
138
production. The main item of agriculture and forestry sector which heavily consumed
by the construction sector is wood and timber.
Figure 6.11 Response of CONS to AGRF
Another important sector of the Malaysian economy is service sector. Figure 6.12
shows the responses of service against the construction sector shock. MCS shock has
a significant effect over service sector. The response of service sector initially positive
reaches a maximum than negative, positive, negative and finally attain its natural path
in negative region. Service sector will adjust this shock in 9 quarter.
Figure 6.12 Response of SERV to CONS
Figure 6.13 shows the reaction of construction sector if one standard shock
produced in service sector. Initially behavior is negative and from quarter two it is
positive and remains in positive region from quarter 4 to quarter 10.
Quarter (Period)
Quarter (Period)
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Figure 6.13 Response of CONS to SERV
Figure 6.14 captures the behavior of the aggregate economy of Malaysia, if one
standard deviation positive shock is given to MCS. It shows that the positive shock in
the construction sector has a significant effect over the GDP of Malaysia. It sharply
shoots up the economy. The aggregate economy will absorb this shock and normalize
the situation in approximately next 8 quarter.
Figure 6.14 Response of GDP to CONS
Figure 6.15 shows that the GDP shock will produce significant changes in MCS
output and the response of construction sector will remain be positive for next 15
quarter. This implies that the construction sector is more sensitive to GDP shock.
Quarter (Period)
Quarter (Period)
140
Figure 6.15 Response of CONS to GDP
Figure 6.16 depicts the response of MCS, if one standard deviation positive shock
produced in the construction sector. The shock has a positive impact on MCS and the
MCS will absorb it in next10 quarter. This implies that the output growth of the MCS
suddenly increase due to shock and then gradually down. The effect of temporary
shock remains in construction sector for 10 quarter. Thereafter the construction sector
comes back on its original position.
Figure 6.16 Response of MCS to its own shock
Quarter (Period)
Quarter (Period)
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6.15 Forecasting and Validation of Forecasted Values
Another important task of this study is the forecasting of MCS output and validation
of forecasted values. The estimated VECM Equation 6.9 for construction is used for
forecasting the MCS output. The forecasted values are compared with the original
data set from 1991Q1 to 2013Q3 for validation of the estimated model equation. The
model was constructed on the bases of 1991Q1-2010Q4 data set. However for
validation, the sample size of the data increased. In this regard the latest data from
2011Q1 to 2013Q3 is added in the data set. Table 6.8 depicts the comparison of the
actual and forecasted output value of MCS from 1992Q3 to 2013Q3 as well as the
absolute percentage error (ABPE). The mean absolute error is only 5.34 %, which is
quite satisfactory and confirms that the estimated model has a strong predicted power.
Table 6.8 Comparison between Original and Forecasted Value
QTR CONS
RM (Million)
CONSF
RM (Million)
ABPE (%)
1991Q1 1877 NA NA
1991Q2 1871 NA NA
1991Q3 2151 NA NA
1991Q4 2179 NA NA
1992Q1 2065 NA NA
1992Q2 2200 NA NA
1992Q3 2328 2319.91 0.35
1992Q4 2352 2336.88 0.64
1993Q1 2296 2275.83 0.88
1993Q2 2451 2429.82 0.86
1993Q3 2623 2545.11 2.97
1993Q4 2541 2654.72 4.48
1994Q1 2634 2632.55 0.05
1994Q2 2841 2898.86 2.04
1994Q3 3007 2978.33 0.95
1994Q4 2928 3109.02 6.18
1995Q1 3217 3246.59 0.92
1995Q2 3439 3587.85 4.33
1995Q3 3590 3615.11 0.7
1995Q4 3568 3594.8 0.75
1996Q1 3607 3347.3 7.20
1996Q2 3993 3790.48 5.07
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1996Q3 4241 3901.05 8.02
1996Q4 4209 4025.11 4.37
1997Q1 4237 3930.64 7.23
1997Q2 4425 4158.16 6.03
1997Q3 4550 4208.3 7.51
1997Q4 4539 4218.29 7.07
1998Q1 3622 3773.65 4.19
1998Q2 3551 3911.6 10.16
1998Q3 3275 3923.2 19.79
1998Q4 3221 3840.93 19.25
1999Q1 3022 3499.49 15.80
1999Q2 3269 3672.74 12.35
1999Q3 3305 3385.66 2.44
1999Q4 3307 3257.53 1.50
2000Q1 3269 3276.96 0.24
2000Q2 3550 3314.54 6.63
2000Q3 3560 3319.81 6.75
2000Q4 3591 3084.87 14.10
2001Q1 3324 3157.35 5.01
2001Q2 3703 3476.51 6.12
2001Q3 3691 3730.78 1.08
2001Q4 3710 3630.45 2.14
2002Q1 3432 3429.52 0.07
2002Q2 3815 3702.75 2.94
2002Q3 3781 3985.52 5.41
2002Q4 3734 4058.06 8.68
2003Q1 3466 3948.83 13.93
2003Q2 3869 4366.93 12.87
2003Q3 3873 4223.43 9.05
2003Q4 3823 4135.85 8.18
2004Q1 3533 3503.55 0.83
2004Q2 3839 3503.31 8.74
2004Q3 3792 3468.06 8.54
2004Q4 3739 3325.96 11.05
2005Q1 3445 2972.98 13.7
2005Q2 3764 3239.82 13.92
2005Q3 3752 3490.58 6.97
2005Q4 3724 3500.71 5.10
2006Q1 3386 3488.74 3.03
2006Q2 3744 3853.77 2.93
2006Q3 3734 4051.71 8.51
2006Q4 3741 4004.74 7.05
2007Q1 3524 3800.74 7.85
2007Q2 3926 4005.04 2.01
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2007Q3 3911 3896.84 0.36
2007Q4 3918 3785.5 3.38
2008Q1 3711 3589.82 3.27
2008Q2 4078 3872.37 5.04
2008Q3 3960 4068.96 2.75
2008Q4 3856 4005.29 3.87
2009Q1 3854 3941.81 2.28
2009Q2 4308 4400.33 2.14
2009Q3 4681 4613.54 1.44
2009Q4 4485 4741.92 5.73
2010Q1 4187 4357.94 4.08
2010Q2 4483 4668.05 4.13
2010Q3 4814 4663.26 3.13
2010Q4 4736 4583.65 3.22
2011Q1 3491 3760.18 7.71
2011Q2 3775 4037.99 6.97
2011Q3 4180 4180.72 0.02
2011Q4 4610 4528.16 1.78
2012Q1 4885 4491.1 8.06
2012Q2 5162 5064.47 1.89
2012Q3 5061 5270.69 4.14
2012Q4 5760 5488.12 4.72
2013Q1 5413 5357.18 1.03
2013Q2 6484 6198.82 4.40
2013Q3 6253 6213.85 0.63
Mean Absolute Percentage Error (%) 5.34
Source: Author’s calculation
The Model simulation results and the predicted line graph are shown in Figure
6.17. The predicted line is situated between the (+, -) two standard error lines. It is
neither crossing upper bound nor lower bound of two standard errors, which implies
that the model and its forecasted values are part of the 95% level of confidence
region. Quantitative result part of the Figure 6.17 further confirms the value of the
mean absolute error is 5.34%. The bias and variance proportion is negligible.
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Figure 6.17 Forecasted line
Figure 6.18 is divided into two portions, left hand side portion: the left hand side
portion from 1992Q3 to 2013Q3 show how forecasted values of construction output
coincide with original data. The gap between the forecasted values and the original
data shows errors between the two series. The right hand side portion from 2013Q4 to
2020Q4 (29 quarter) is future forecasting output values of MCS, when all other
factors remain constant. The predicted output values of MCS are presented in Table
6.9.
Figure 6.18 Comparison between Original and Forecasted values
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
94 96 98 00 02 04 06 08 10 12
CONSF ± 2 S.E.
Forecast: CONSFActual: CONSForecast sample: 1991Q1 2013Q3Adjusted sample: 1992Q3 2013Q3Included observations: 85
Root Mean Squared Error 254.4131Mean Absolute Error 201.8168Mean Abs. Percent Error 5.348041Theil Inequality Coefficient 0.032730 Bias Proportion 0.000066 Variance Proportion 0.008594 Covariance Proportion 0.991340
0
1000
2000
3000
4000
5000
6000
7000
8000
19
92
Q3
19
94
Q1
19
95
Q3
19
97
Q1
19
98
Q3
20
00
Q1
20
01
Q3
20
03
Q1
20
04
Q3
20
06
Q1
20
07
Q3
20
09
Q1
20
10
Q3
20
12
Q1
20
13
Q3
20
15
Q1
20
16
Q3
20
18
Q1
20
19
Q3
Ou
tpu
t (R
M M
illio
n)
Quarter
CONS
CONSF
145
Table 6.9 Future Forecast of Construction Output
Period Predicted output (RM million)
2013Q4 6117.79
2014Q1 5519.96
2014Q2 5652.70
2014Q3 6082.51
2014Q4 5425.96
2015Q1 5389.08
2015Q2 5116.57
2015Q3 6069.44
2015Q4 5581.40
2016Q1 5899.13
2016Q2 5736.25
2016Q3 6616.34
2016Q4 6188.06
2017Q1 6350.61
2017Q2 6254.20
2017Q3 6840.14
2017Q4 6381.91
2018Q1 6258.62
2018Q2 6279.41
2018Q3 6748.47
2018Q4 6471.98
2019Q1 6219.23
2019Q2 6402.66
2019Q3 6825.59
2019Q4 6732.73
2020Q1 6419.70
2020Q2 6667.86
2020Q3 7007.93
2020Q4 6942.45
Source: Author’s Calculation
6.16 Summary
The VECM suggests that the construction sector of Malaysia plays a very important
role in the Malaysian economy. It supports all major sectors of Malaysian economy
including GDP through its backward and forward linkages. It has long run as well as
short run causality with all major sectors and GDP of the economy. The VECM for
construction is developed over optimal lag order 5 suggested by LR and HQ criteria.
The correctness and efficiency of the estimated model tested through various
146
standardized tests and criteria such as R2 criteria, autocorrelation test, serial
correlation, hetroskedacity tests. These tests suggest that the estimated model has not
any technical problem. The results of CUSUMS and CUSUM square tests suggest
that there is no structural break in the model; therefore the model is perfectly well and
directly can be used for forecasting. The behavior of construction sector observed as a
dependent as well as an independent variable.
The IRFs analysis suggests that all major sectors of the Malaysian economy
positively respond to construction shock. This means the investment in the
construction sector has significant positive impact on the growth of other sectors as
well as overall GDP of the Malaysia. However positive shocks in manufacturing and
service sector do not bring significant positive change in construction output.
The forecasted output values of the estimated model compared with the original
data series and found that the mean absolute percentage error is only 5.34 % with
negligible bias and variance proportion.
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CHAPTER 7
CONCLUSIONS AND RECOMMENDATIONS
7.1 Introduction
This chapter consists of three important parts. Initially, it discusses the conclusions
extracted from the results of this study which have already been demonstrated.
Secondly, it discusses the policy implication of the study and finally
recommendations and future directions have been provided for extension and
continuation of research in the area of construction economics and management
beyond this study.
7.2 Conclusions
The idea of this study was derived from the real social economic development
experiences of developing and developed countries with special reference to the
construction sector and its linkages with economic growth and development.
This study mainly focuses on the role of the construction sector in economic
development of Malaysia and empirically examines the linkages of the construction
sector with other major sectors of the Malaysian economy. It determines how the
MCS is linked to the other major sectors of the Malaysian economy and how the
expansion in investment and output of other sectors affects the MCS and vice versa.
The study follows an endogenous growth model to recognize the configuration of
inter-sectorial linkages between the construction and other sectors of the Malaysian
economy. Long run and short run causality between MCS and other sectors of the
economy is measured through the estimated VECM equation for the construction
148
sector. The estimated model is further used to analyze the impulse response functions
and forecast the MCS output.
All set of objectives of the study are successfully achieved, which are briefly
described below:
The study suggest that there is a strong correlation between MCS and
other sectors of the Malaysian economy and the MCS is linked with all
major sectors of the economy either one way or another. The MCS has
bidirectional causality with mining and quarrying sector and overall GDP
and unidirectional causality with manufacturing, agriculture and forestry
and service sectors. The backward linkages of MCS are stronger than
forward linkages, which show that the MCS plays an effective role in
generating the demand for the output of other sectors.
The VECM equation for MCS was successfully developed. The strength,
efficiency and correctness of the model was examined by the various
standardized tests and found satisfactory. It was also found that there is no
structural break in the model. Therefore the model results are reliable and
unbiased.
The IRFs successfully developed by the estimated model and analyzed the
response of MCS against the one standard deviation, positive shock in
other sectors and the reaction of other sectors against the positive shock in
MCS.
Finally, forecasts for the MCS output data series from 2014 Q1 to 2020Q4
were made and compared with available original data series from 1992Q1
to 2013Q3. It was found that the predicted values were 95 % correct.
7.2.1 Summary of Empirical Finding of the Model
The descriptive statistical analysis was carried out to know the basic facts
about the MCS, such as average output per period, average growth of the
industry, and the average contribution to GDP of Malaysia. The descriptive
analysis suggests that over the past two decades (1991-2010) the average
149
contribution of MCS to GDP was 4 % and the employment contribution about
9 % of total labor force. The average contribution of MCS to GDP is relatively
small as compared to other key sectors of the Malaysian economy. However, it
has a strong correlation with GDP as well as other sectors of the economy
since the Pearson correlation coefficient is greater than 0.5 (50 %) for all
major sectors including GDP .
The unit root test suggests that all the concerned variable data series have unit
root and stationarity problem at level, however first difference of each data
series converted it into the stationary series. The unit root test also suggests
that all the variables are integrated order one, which indicates that the long run
relationships exist among the variables.
The causality and VECM equation are highly sensitive about the lag order of
the concerned variables. Hence, the optimal lag length (Five) was selected on
the base of likely hood (LR) and Hannan Quinn (HQ) criteria for the causality
analysis and VECM equation.
The Johansen co-integration test was conducted to examine the long run
causality association between the MCS and other sectors. The Rank Trace test
and maximum Eigenvalue test for co-integration suggested five and three co-
integrating equations, respectively in the data set. This evidence of co-
integration confirms the existence of an error correction system between the
variables. The first coefficient (C1) of co-integrating part of estimated model
equation for MCS suggested that the speed of adjustment towards long run
equilibrium of the construction sector is 49 %. The coefficient sign of
independent variables in co-integration part of the model suggested that the
nature of long run association with dependent variables of manufacturing and
agriculture and forestry sector has a negative long run association with
construction sector while the mining and quarrying and services sectors has
positive. This implies that the expansion of manufacturing and agriculture
sectors has negative impact on construction sector output in the long run while
increasing the growth of mining and quarrying and service sectors has a
positive impact on MCS output.
The short run causality linkage analysis suggests that the MCS has bi-
directional causality with GDP and mining and quarrying sector of Malaysia.
150
This implies that the change in construction sector investment and output level
affects the GDP and mining and quarrying output and vice versa. The MCS
also has unidirectional causality with services, manufacturing, and agriculture
and forestry sectors. In case of construction and service sector, the direction of
causality is from construction to the service sector. The change in construction
output affects the service sector because of its strong forward linkage with the
service sector. While in case of construction and manufacturing, the direction
of causality is from manufacturing to construction sector. Similarly, in case of
construction and agriculture and forestry, the direction of causality is from
agriculture and forestry to construction sector. Here, backward linkages of the
construction sector are stronger than forward linkages.
A series of impulse response functions were constructed to estimate the
response of MCS against the positive shocks in other sectors of the economy
and vice versa. The IRFs determined how the one standard deviation positive
shock in MCS affects the other sectors and how the positive shock in other
sectors would bring the change in MCS output. A positive shock in the
construction sector has an initially positive impact on other sectors of the
Malaysian economy as well as overall GDP of Malaysia. However, it was
noted that the mining and quarrying is more sensitive to construction shock
and the impact of shock was long lasting in this sector as compared to other
sectors. The construction sector is more sensitive to GDP and manufacturing
sector shock.
Finally, estimated model was used to forecast the quarterly MCS output level
during 2014 to 2020. The comparison between the predicted and original data
series shows that the mean absolute error percentage is only 5.34 % from 1992
to 2013 data, which is quite satisfactory and suggests that the developed model
is an efficient model.
This study also identified and highlighted the challenges and issues of MCS
that are required to be addressed for making MCS more efficient and
competitive in future such as construction techniques, error free construction
in one time, comprehensive integrated solution provider, sustainable and green
construction, fragmentation in the industry, timely adequate financing, fair and
151
transparent bidding process, payments issue, PPP projects and loan issue,
skilled local manpower problem and scarcity of research.
7.3 Policy Implications
The results of this research study, if implemented, may have some policy
implications for the Malaysian construction sector growth as well as social economic
development of Malaysian.
The study suggests that the MCS has two types of linkages with the other sectors
of the Malaysian economy, long run linkages and short run linkages. Sometimes a
sector has negative/positive sign of linkages in short run; however it is not necessary
that the sector holds same sign in the long run linkage as well. Such as construction
and mining and quarrying sector has a negative sign in short run linkage but in the
long run linkage they have a positive sign. Similarly the agriculture and forestry
sector has positive sign in the short run but it has a negative sign in the long run.
Therefore the policy maker should be much more careful about the time horizon when
designing the policy for the sector.
Another important aspect of knowing inter-sectorial linkage from the policy
implication point of view is that the policy for one sector has impact on other sector as
well when sectors are linked positively. For example, the construction and
manufacturing sector have positive links in the long run. If the government has to
impose any kind of tax on manufacturing output, it will adversely affect the
manufacturing sector output. Since the construction and manufacturing sectors are
linked positively in the long run, the tax on manufacturing sector will affect the
construction output negatively as well. Therefore the policy maker should also keep
in mind long run linkages of the sector with a sign of the coefficient before designing
the policy for the sector. .
152
7.4 Contribution and significance of Study
The size of MCS in term of output contribution to GDP is relatively small but its
linkages (backward and forward) and presence in every development activity make it
more attractive and significant sector. The size of a sector is depending upon the level
of investment in the sector and investment in other sectors as well.
This study developed a model for MCS that can be used to analyze how the
change in investment level in major sectors of the economy affects the MCS and how
the change in MCS output affect the growth of other major sectors and GDP of
Malaysia. It measures the rate of change in MCS output level due to one unit of
investment change in other sectors of the economy and vice versa. The model can also
be used to analyze the sensitivity and behavior of the MCS that how MCS response,
what amount of time it takes to regain equilibrium against the positive investment
shock from the other sectors of the economy including GDP and vice versa. The
linkage and relationship between MCS and other major sectors can also be examined
and identified by the estimated model. Furthermore, model can be used to predict the
future output level for MCS. So the government of Malaysia, policy maker and
planner can use this model to develop investment and expenditure plan for MCS.
They can estimate the behavior and effects of this investment on aggregate economy
and other sector as well.
7.5 Novelty of Study
The general objective of this study was to investigate the role of MCS in the
economic development of Malaysia and developed VECM for MCS to measure the
magnitude of relationship and direction of linkage between construction sector and
the other major sectors of the Malaysian economy. This is the first study for
construction sectors in the world, which is conducted over MCS to develop VECM
equation that examine the long and short run linkage between MCS and other major
sectors including overall GDP of the Malaysian economy. There are few studies
available over the linkage between construction sector and GDP and bivariate
Granger causality such as Tse and Ganesan’s (1997), Green (1997), Chan (2001),
153
Rameezdeen and Ramchandra (2008), Khan, (2008), Ryan Bynoe (2009), Saka and
Lowe, (2010). However, there is no study available over VECM for the construction
sector. The valuable contributions of this study are as below:
1. The study developed an econometric VECM for MCS that incorporates the
long run and the short run relationship and causal link between the MCS
and other major sectors including GDP of Malaysia using time series
analysis in an error correction model approach. Furthermore estimated
model is used to analyze IRFs for MCS and forecast of construction output
till 2020. This empirical investigation and development of VECM for
MCS is the main contribution of this study.
2. The organization and co-ordination of quarterly data at 2000 index price
for construction and all other major sectors is another relevant and
valuable contribution of this study. It allows comparative analysis for
major sectors of the Malaysian economy at the macro level and at the
micro level as well.
3. The estimated results from the model provide a detailed framework that
can be used for future planning and research about the MCS.
7.6 General Recommendation
The MCS is an important sector of the Malaysian economy. It plays significant role in
the social and economic development of Malaysia. It makes a substantial contribution
to improve the quality of life in the country. It also plays a pivotal role to achieve
national development goals such as providing shelter at low cost, infrastructure
facilities and generates employment for skilled, semi-skilled and unskilled people.
However it is constantly under pressure after Asian currency crises (1998), and has
not strong evidence of success. It has been facing sizeable challenges and critical
issues regarding, productivity efficiency, quality of output, scarcity of resources in
term of capital as well as skilled manpower, environment and sustainability.
154
The MCS should be capable of addressing above cited issues on urgent basis to
achieve its vision to be a world class innovative and knowledgeable solution provider
by 2015. The following measures can play an effective role to make it strong and
competitive in local and international market as well.
The MCS has to develop as a complete solution provider in a built environment. It
will be a complete paradigm shift from existing methods of operation to modern
approach that will improve competitiveness and efficiency of the sector. The use of
modern technology will reduce the cost, improve quality of work, avoid delays,
minimize errors, reduce design and engineering conflicts and fix the problem
efficiently and accurately without wasting resources.
The fundamental requirement for offering complete solution provider services is
educated, trained and skilled manpower. The MCS should develop strong link with
educational and vocational institutes, so the institutes could design and develop
curriculum, and offer courses according to the current demand and requirement of
the sector. Furthermore the linkage between MCS and educational and vocational
institutes will also support Research and Development (R&D) in this area which is the
key of success for any business sector.
7.7 Recommendations for Future Studies
This study used cumulative data for major sectors of the Malaysian economy.
Malaysia is a federation of different states. Each state might have different geographic
regional properties and the inter-sectorial linkage may be varying state to state.
Therefore it would be much better and more focused to conduct a state wise study
within same political and geographical environment that may provide more fruitful
and useful information for policy makers.
A future study that can be conducted from the facts and discussions presented in
this study is to examine the role of MCS during the recession period of Malaysian
economy such as 1997-98 and 2007-08 crises and measures the demand and supply
sides of the MCS during this period to support the Malaysian economy during crises.
155
Another future study that can be organized is how the construction sector linkages
are established through the isolated (segregated) data of sub sectors of Construction
industry such as housing industry.
156
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PUBLICATIONS & ACHIEVEMENTS
CONFERENCES
1. Raza Ali khan, Noor Amila Zawawi. M.Faris Khamidi, (2012). An Empirical
Assessment of Linkage between the Construction Sector and Economic
Growth (GDP) of Malaysia (2000-2010). International Conference on Civil,
Offshore and Environmental Engineering (ICCOEE 2012) Kuala Lumpur.
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Malaysia (2000-2010).IEEE Colloquium on Humanities, Science and
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Quantification of Causality between Construction and Agriculture Sector of
Malaysia (2000-2010). Annual International Conference on Architecture and
Civil Engineering (ACE 2013), Singapore.
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(2014). Vector Error Correction Model for Causality Link among the
Construction, Manufacturing and Mining & Quarrying Sector in Malaysia
(1991-2010), International Conference on Economics and Statistics (ICES-
2014),Italy (Indexed by ISI, SCOPUS, EBSCO. Full length paper accepted)
5. Raza Ali Khan, M.Shahir Liew, Zulkipli Bin Ghazali, Noor Amila Zawawi,
(2014). Inter-Sectoral Linkages among Key Sectors of Malaysian Economy
(1991-2010), International Conference on Trends in Multidisciplinary
Business and Economics Research (TMBER, 2014), Bangkok. (Indexed by
ISI, SCOPUS, EBSCO. Full length paper accepted)
JOURNAL
1. Raza Ali Khan, M.Shahir Liew, Zulkipli Bin Ghazali. (2013). A Comparison
and Causality Analysis between Construction and Agriculture Sector of
Malaysia (2000-2010); Jet Vol. 2 No 1 GSTF (Indexed by, Ulrichsweb,
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Construction Sector and Malaysia Vision 2020: Developed Nation
Status.Procedia Social and Behavioral Sciences(Indexed by Elsiver: Science
Direct)
3. Raza Ali Khan, M.Shahir Liew, Zulkipli Bin Ghazali. (2013). Growth Linkage
between Oil and Gas and Construction Industry of Malaysia (1991-2010);
journal of Energy Technologies and Policy, Vol.3 No. 11 Special Issue
(Indexed by Universe Digtial Library (Malaysia), EBSCO (US) Ulrichsweb,
EBSCO, CrossRef, and Proquest) (IC Impact Factor 5.54)
4. Raza Ali Khan, M.Shahir Liew, Zulkipli B Ghazali, Noor Amila Zawawi.
(2013). Construction and Mining Including Quarrying Sector of Malaysia: A
Comparative and Causality Analysis During (2001 -2010); Journal of Emerging
Trend in Engineering and Applied Science (JETAS), Vol. 4 No. 5 (Indexed by
DOAJ, WorldCat, CAS, Academic ASAP. Impact Factor 1.157, year 2012.)
5. Raza Ali Khan, M.Shahir Liew, Zulkipli Bin Ghazali, (2014). Service and
Construction Sector of Malaysia: Causality Link (1991-2010), Applied
Mechanics and Materials Vol. 567 (2014) pp 619-624, Switzerland (Indexed
by SCOPUS.)
167
APPENDIX A
JOHANSEN CO-INTEGRATION TEST RESULTS
Johansen Co-integration Test
Date: 10/31/13 Time: 12:03
Sample (adjusted): 1992Q3 2010Q4
Included observations: 74 after adjustments
Trend assumption: Linear deterministic trend Series: CONS MANF MINQ AGRF SERV GDP
Lags interval (in first differences): 1 to 5
Unrestricted Cointegration Rank Test (Trace) Hypothesized Trace 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.589287 171.6133 95.75366 0.0000
At most 1 * 0.411622 105.7637 69.81889 0.0000
At most 2 * 0.343340 66.51509 47.85613 0.0004
At most 3 * 0.235522 35.39148 29.79707 0.0102
At most 4 * 0.172601 15.51790 15.49471 0.0496
At most 5 0.020030 1.497269 3.841466 0.2211 Trace test indicates 5 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegration Rank Test (Maximum Eigenvalue) Hypothesized Max-Eigen 0.05
No. of CE(s) Eigenvalue Statistic Critical Value Prob.** None * 0.589287 65.84964 40.07757 0.0000
At most 1 * 0.411622 39.24858 33.87687 0.0104
At most 2 * 0.343340 31.12361 27.58434 0.0168
At most 3 0.235522 19.87358 21.13162 0.0742
At most 4 0.172601 14.02063 14.26460 0.0546
At most 5 0.020030 1.497269 3.841466 0.2211 Max-eigenvalue test indicates 3 cointegrating eqn(s) at the 0.05 level
* denotes rejection of the hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I): CONS MANF MINQ AGRF SERV GDP
-0.006623 -0.001210 0.003450 -0.003683 -0.000178 0.000645
0.004878 0.000856 0.004869 0.007760 0.001842 -0.002058
0.000963 0.000584 -0.000766 0.000854 -0.000519 0.000195
-0.001624 0.000412 -0.000541 0.003311 -1.32E-05 -0.000160
0.002083 -0.000548 -0.002219 -3.70E-05 -0.000462 0.000577
0.001828 0.000333 -0.000926 -0.001621 0.000740 -0.000518
168
APPENDIX B
CO-INTEGRATING EQUATIONS 1 Cointegrating Equation(s): Log likelihood -3183.685 Normalized cointegrating coefficients (standard error in parentheses)
CONS MANF MINQ AGRF SERV GDP
1.000000 0.182730 -0.520958 0.556016 0.026943 -0.097372
(0.02325) (0.12593) (0.13787) (0.03461) (0.03351)
2 Cointegrating Equation(s): Log likelihood -3164.060 Normalized cointegrating coefficients (standard error in parentheses)
CONS MANF MINQ AGRF SERV GDP
1.000000 0.000000 38.04736 26.82689 8.928417 -8.336311
(6.87567) (6.46386) (1.85077) (1.61770)
0.000000 1.000000 -211.0673 -143.7688 -48.71381 45.08806
(37.9351) (35.6630) (10.2112) (8.92533)
3 Cointegrating Equation(s): Log likelihood -3148.498 Normalized cointegrating coefficients (standard error in parentheses)
CONS MANF MINQ AGRF SERV GDP
1.000000 0.000000 0.000000 0.690146 0.361454 -0.311436
(0.45358) (0.08936) (0.05955)
0.000000 1.000000 0.000000 1.224448 -1.188690 0.570159
(2.44854) (0.48237) (0.32146)
0.000000 0.000000 1.000000 0.686953 0.225166 -0.210918
(0.13138) (0.02588) (0.01725)
4 Cointegrating Equation(s): Log likelihood -3138.562 Normalized cointegrating coefficients (standard error in parentheses)
CONS MANF MINQ AGRF SERV GDP
1.000000 0.000000 0.000000 0.000000 0.171139 -0.148571
(0.05270) (0.03599)
0.000000 1.000000 0.000000 0.000000 -1.526344 0.859112
(0.56927) (0.38880)
0.000000 0.000000 1.000000 0.000000 0.035731 -0.048806
(0.07273) (0.04967)
0.000000 0.000000 0.000000 1.000000 0.275761 -0.235987
(0.09649) (0.06590)
5 Cointegrating Equation(s): Log likelihood -3131.551 Normalized cointegrating coefficients (standard error in parentheses)
CONS MANF MINQ AGRF SERV GDP
1.000000 0.000000 0.000000 0.000000 0.000000 -0.012996
(0.00519)
0.000000 1.000000 0.000000 0.000000 0.000000 -0.350044
(0.02668)
0.000000 0.000000 1.000000 0.000000 0.000000 -0.020500
(0.00714)
0.000000 0.000000 0.000000 1.000000 0.000000 -0.017532
(0.00646)
0.000000 0.000000 0.000000 0.000000 1.000000 -0.792191
(0.03235)
169
APPENDIX C
VECM SYSTEM EQUATIONS (M1-M6)
SYSTEM EQUATIONS MODEL M-1 D(CONS) = C(1)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(2)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(3)*( MINQ(-1) - 0.020500388447*GDP(-1) - 7582.58623273 )
+ C(4)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(5)*( SERV(-1) - 0.792191080426*GDP(-
1) + 27380.8894622 ) + C(6)*D(CONS(-1)) + C(7)*D(CONS(-2)) + C(8)*D(CONS(-3)) + C(9)*D(CONS(-4)) +
C(10)*D(CONS(-5)) + C(11)*D(MANF(-1)) + C(12)*D(MANF(-2)) + C(13)*D(MANF(-3)) + C(14)*D(MANF(-
4)) + C(15)*D(MANF(-5)) + C(16)*D(MINQ(-1)) + C(17)*D(MINQ(-2)) + C(18)*D(MINQ(-3)) +
C(19)*D(MINQ(-4)) + C(20)*D(MINQ(-5)) + C(21)*D(AGRF(-1)) + C(22)*D(AGRF(-2)) + C(23)*D(AGRF(-
3)) + C(24)*D(AGRF(-4)) + C(25)*D(AGRF(-5)) + C(26)*D(SERV(-1)) + C(27)*D(SERV(-2)) +
C(28)*D(SERV(-3)) + C(29)*D(SERV(-4)) + C(30)*D(SERV(-5)) + C(31)*D(GDP(-1)) + C(32)*D(GDP(-2)) +
C(33)*D(GDP(-3)) + C(34)*D(GDP(-4)) + C(35)*D(GDP(-5)) + C(36)
SYSTEM EQUATIONS MODEL M-2
D(MANF) = C(37)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(38)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(39)*( MINQ(-1) - 0.020500388447*GDP(-1) - 7582.58623273
) + C(40)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(41)*( SERV(-1) -
0.792191080426*GDP(-1) + 27380.8894622 ) + C(42)*D(CONS(-1)) + C(43)*D(CONS(-2)) + C(44)*D(CONS(-
3)) + C(45)*D(CONS(-4)) + C(46)*D(CONS(-5)) + C(47)*D(MANF(-1)) + C(48)*D(MANF(-2)) +
C(49)*D(MANF(-3)) + C(50)*D(MANF(-4)) + C(51)*D(MANF(-5)) + C(52)*D(MINQ(-1)) + C(53)*D(MINQ(-
2)) + C(54)*D(MINQ(-3)) + C(55)*D(MINQ(-4)) + C(56)*D(MINQ(-5)) + C(57)*D(AGRF(-1)) +
C(58)*D(AGRF(-2)) + C(59)*D(AGRF(-3)) + C(60)*D(AGRF(-4)) + C(61)*D(AGRF(-5)) + C(62)*D(SERV(-1))
+ C(63)*D(SERV(-2)) + C(64)*D(SERV(-3)) + C(65)*D(SERV(-4)) + C(66)*D(SERV(-5)) + C(67)*D(GDP(-1))
+ C(68)*D(GDP(-2)) + C(69)*D(GDP(-3)) + C(70)*D(GDP(-4)) + C(71)*D(GDP(-5)) + C(72)
SYSTEM EQUATIONS MODEL M-3
D(MINQ) = C(73)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(74)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(75)*( MINQ(-1) - 0.020500388447*GDP(-1) - 7582.58623273
) + C(76)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(77)*( SERV(-1) -
0.792191080426*GDP(-1) + 27380.8894622 ) + C(78)*D(CONS(-1)) + C(79)*D(CONS(-2)) + C(80)*D(CONS(-
3)) + C(81)*D(CONS(-4)) + C(82)*D(CONS(-5)) + C(83)*D(MANF(-1)) + C(84)*D(MANF(-2)) +
C(85)*D(MANF(-3)) + C(86)*D(MANF(-4)) + C(87)*D(MANF(-5)) + C(88)*D(MINQ(-1)) + C(89)*D(MINQ(-
2)) + C(90)*D(MINQ(-3)) + C(91)*D(MINQ(-4)) + C(92)*D(MINQ(-5)) + C(93)*D(AGRF(-1)) +
C(94)*D(AGRF(-2)) + C(95)*D(AGRF(-3)) + C(96)*D(AGRF(-4)) + C(97)*D(AGRF(-5)) + C(98)*D(SERV(-1))
+ C(99)*D(SERV(-2)) + C(100)*D(SERV(-3)) + C(101)*D(SERV(-4)) + C(102)*D(SERV(-5)) +
C(103)*D(GDP(-1)) + C(104)*D(GDP(-2)) + C(105)*D(GDP(-3)) + C(106)*D(GDP(-4)) + C(107)*D(GDP(-5))
+ C(108)
SYSTEM EQUATIONS MODEL M-4
D(AGRF) = C(109)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(110)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(111)*( MINQ(-1) - 0.020500388447*GDP(-1) -
7582.58623273 ) + C(112)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(113)*( SERV(-1) -
0.792191080426*GDP(-1) + 27380.8894622 ) + C(114)*D(CONS(-1)) + C(115)*D(CONS(-2)) +
C(116)*D(CONS(-3)) + C(117)*D(CONS(-4)) + C(118)*D(CONS(-5)) + C(119)*D(MANF(-1)) +
C(120)*D(MANF(-2)) + C(121)*D(MANF(-3)) + C(122)*D(MANF(-4)) + C(123)*D(MANF(-5)) +
C(124)*D(MINQ(-1)) + C(125)*D(MINQ(-2)) + C(126)*D(MINQ(-3)) + C(127)*D(MINQ(-4)) +
C(128)*D(MINQ(-5)) + C(129)*D(AGRF(-1)) + C(130)*D(AGRF(-2)) + C(131)*D(AGRF(-3)) +
C(132)*D(AGRF(-4)) + C(133)*D(AGRF(-5)) + C(134)*D(SERV(-1)) + C(135)*D(SERV(-2)) +
C(136)*D(SERV(-3)) + C(137)*D(SERV(-4)) + C(138)*D(SERV(-5)) + C(139)*D(GDP(-1)) + C(140)*D(GDP(-
2)) + C(141)*D(GDP(-3)) + C(142)*D(GDP(-4)) + C(143)*D(GDP(-5)) + C(144
170
SYSTEM EQUATIONS MODEL M-5
D(SERV) = C(145)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(146)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(147)*( MINQ(-1) - 0.020500388447*GDP(-1) -
7582.58623273 ) + C(148)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(149)*( SERV(-1) -
0.792191080426*GDP(-1) + 27380.8894622 ) + C(150)*D(CONS(-1)) + C(151)*D(CONS(-2)) +
C(152)*D(CONS(-3)) + C(153)*D(CONS(-4)) + C(154)*D(CONS(-5)) + C(155)*D(MANF(-1)) +
C(156)*D(MANF(-2)) + C(157)*D(MANF(-3)) + C(158)*D(MANF(-4)) + C(159)*D(MANF(-5)) +
C(160)*D(MINQ(-1)) + C(161)*D(MINQ(-2)) + C(162)*D(MINQ(-3)) + C(163)*D(MINQ(-4)) +
C(164)*D(MINQ(-5)) + C(165)*D(AGRF(-1)) + C(166)*D(AGRF(-2)) + C(167)*D(AGRF(-3)) +
C(168)*D(AGRF(-4)) + C(169)*D(AGRF(-5)) + C(170)*D(SERV(-1)) + C(171)*D(SERV(-2)) +
C(172)*D(SERV(-3)) + C(173)*D(SERV(-4)) + C(174)*D(SERV(-5)) + C(175)*D(GDP(-1)) + C(176)*D(GDP(-
2)) + C(177)*D(GDP(-3)) + C(178)*D(GDP(-4)) + C(179)*D(GDP(-5)) + C(180)
SYSTEM EQUATIONS MODEL M-6
D(GDP) = C(181)*( CONS(-1) - 0.0129958071078*GDP(-1) - 2371.03841019 ) + C(182)*( MANF(-1) -
0.350044284876*GDP(-1) + 6284.21731242 ) + C(183)*( MINQ(-1) - 0.020500388447*GDP(-1) -
7582.58623273 ) + C(184)*( AGRF(-1) - 0.0175315616691*GDP(-1) - 6676.3570607 ) + C(185)*( SERV(-1) -
0.792191080426*GDP(-1) + 27380.8894622 ) + C(186)*D(CONS(-1)) + C(187)*D(CONS(-2)) +
C(188)*D(CONS(-3)) + C(189)*D(CONS(-4)) + C(190)*D(CONS(-5)) + C(191)*D(MANF(-1)) +
C(192)*D(MANF(-2)) + C(193)*D(MANF(-3)) + C(194)*D(MANF(-4)) + C(195)*D(MANF(-5)) +
C(196)*D(MINQ(-1)) + C(197)*D(MINQ(-2)) + C(198)*D(MINQ(-3)) + C(199)*D(MINQ(-4)) +
C(200)*D(MINQ(-5)) + C(201)*D(AGRF(-1)) + C(202)*D(AGRF(-2)) + C(203)*D(AGRF(-3)) +
C(204)*D(AGRF(-4)) + C(205)*D(AGRF(-5)) + C(206)*D(SERV(-1)) + C(207)*D(SERV(-2)) +
C(208)*D(SERV(-3)) + C(209)*D(SERV(-4)) + C(210)*D(SERV(-5)) + C(211)*D(GDP(-1)) + C(212)*D(GDP(-
2)) + C(213)*D(GDP(-3)) + C(214)*D(GDP(-4)) + C(215)*D(GDP(-5)) + C(216)
171
APPENDIX D
COEFFICIENTS FOR VECM - 1
Model M-1
Coefficient Std. Error t-Statistic Prob.
C(1) -0.489930 0.137162 -3.571908 0.0010
C(2) -0.074038 0.027371 -2.704927 0.0102
C(3) 0.186100 0.101543 1.832723 0.0747
C(4) -0.195039 0.145872 -1.337053 0.1892
C(5) 0.006179 0.031186 0.198128 0.8440
C(6) 0.370570 0.156546 2.367161 0.0231
C(7) 0.671755 0.175636 3.824705 0.0005
C(8) 0.024757 0.197894 0.125101 0.9011
C(9) 0.494809 0.225117 2.198005 0.0341
C(10) 0.272996 0.235627 1.158594 0.2539
C(11) 0.058353 0.049981 1.167506 0.2503
C(12) 0.003755 0.045478 0.082568 0.9346
C(13) -0.008825 0.053319 -0.165517 0.8694
C(14) 0.005335 0.042785 0.124694 0.9014
C(15) -0.018582 0.040195 -0.462292 0.6465
C(16) -0.229249 0.092456 -2.479555 0.0177
C(17) -0.021292 0.084508 -0.251948 0.8024
C(18) -0.088061 0.087538 -1.005980 0.3208
C(19) -0.174867 0.086918 -2.011852 0.0514
C(20) -0.104313 0.087962 -1.185893 0.2430
C(21) 0.103218 0.120628 0.855676 0.3975
C(22) 0.343517 0.126033 2.725612 0.0097
C(23) 0.124788 0.100348 1.243552 0.2213
C(24) 0.139476 0.078447 1.777979 0.0834
C(25) 0.005324 0.071894 0.074053 0.9414
C(26) -0.063638 0.039649 -1.605023 0.1168
C(27) 0.003825 0.002948 1.297558 0.2023
C(28) 0.003096 0.002266 1.366193 0.1799
C(29) 0.001322 0.001604 0.824343 0.4149
C(30) 9.56E-06 0.000983 0.009725 0.9923
C(31) -0.001720 0.044712 -0.038467 0.9695
C(32) -0.020913 0.033056 -0.632659 0.5307
C(33) 0.001976 0.035049 0.056370 0.9553
C(34) 0.013385 0.029302 0.456800 0.6504
C(35) 0.023984 0.023436 1.023403 0.3126
C(36) -13.07252 77.67539 -0.168297 0.8672
172
APPENDIX E
COEFFICIENTS FOR VECM - 2
Model M-2
Coefficient Std. Error t-Statistic Prob.
C(37) -1.874752 0.891814 -2.102177 0.0422
C(38) -0.141680 0.177966 -0.796108 0.4309
C(39) 1.311235 0.660223 1.986050 0.0543
C(40) -0.490260 0.948446 -0.516909 0.6082
C(41) 0.057645 0.202769 0.284291 0.7777
C(42) 3.557342 1.017850 3.494956 0.0012
C(43) 2.326008 1.141967 2.036843 0.0487
C(44) -0.496117 1.286687 -0.385577 0.7020
C(45) 0.981353 1.463693 0.670464 0.5066
C(46) 4.455282 1.532026 2.908099 0.0060
C(47) 0.496209 0.324971 1.526933 0.1351
C(48) 0.099614 0.295691 0.336887 0.7381
C(49) 0.144670 0.346673 0.417309 0.6788
C(50) 0.156493 0.278184 0.562551 0.5770
C(51) -0.169578 0.261343 -0.648870 0.5203
C(52) -0.887396 0.601140 -1.476189 0.1481
C(53) 0.647181 0.549464 1.177842 0.2462
C(54) -0.528755 0.569163 -0.929004 0.3588
C(55) -1.602342 0.565136 -2.835322 0.0073
C(56) -0.445372 0.571918 -0.778733 0.4410
C(57) -0.328973 0.784309 -0.419443 0.6773
C(58) 0.276918 0.819455 0.337929 0.7373
C(59) 0.110931 0.652454 0.170021 0.8659
C(60) 0.456133 0.510052 0.894288 0.3768
C(61) 0.333825 0.467446 0.714147 0.4795
C(62) -0.285616 0.257796 -1.107912 0.2749
C(63) -0.002017 0.019167 -0.105239 0.9167
C(64) 0.006684 0.014734 0.453654 0.6527
C(65) 0.003638 0.010431 0.348754 0.7292
C(66) -0.003192 0.006391 -0.499486 0.6203
C(67) -0.037547 0.290713 -0.129156 0.8979
C(68) -0.080362 0.214928 -0.373901 0.7106
C(69) -0.190997 0.227886 -0.838124 0.4072
C(70) -0.177185 0.190520 -0.930007 0.3582
C(71) -0.173775 0.152376 -1.140432 0.2612
C(72) 795.8697 505.0382 1.575860 0.1233
173
APPENDIX F
COEEFICIENTS FOR VECM -3
Model M-3
Coefficient Std. Error t-Statistic Prob.
C(73) 0.123040 0.292286 0.420958 0.6762
C(74) 0.037143 0.058327 0.636809 0.5281
C(75) -0.827571 0.216384 -3.824555 0.0005
C(76) -0.492405 0.310847 -1.584078 0.1215
C(77) -0.147376 0.066456 -2.217644 0.0326
C(78) -0.042766 0.333594 -0.128198 0.8987
C(79) 0.012562 0.374272 0.033565 0.9734
C(80) 0.016763 0.421703 0.039751 0.9685
C(81) -0.231397 0.479715 -0.482364 0.6323
C(82) 0.011139 0.502111 0.022184 0.9824
C(83) 0.163497 0.106507 1.535082 0.1330
C(84) -0.157770 0.096911 -1.627992 0.1118
C(85) 0.052696 0.113620 0.463795 0.6454
C(86) -0.050314 0.091173 -0.551850 0.5843
C(87) -0.123856 0.085654 -1.446006 0.1564
C(88) 0.338154 0.197019 1.716349 0.0942
C(89) 0.278962 0.180083 1.549075 0.1297
C(90) 0.438318 0.186539 2.349732 0.0241
C(91) 0.467350 0.185219 2.523222 0.0159
C(92) 0.254669 0.187442 1.358653 0.1823
C(93) 0.604871 0.257052 2.353106 0.0239
C(94) 0.439401 0.268571 1.636070 0.1101
C(95) 0.384484 0.213837 1.798021 0.0801
C(96) 0.270572 0.167166 1.618581 0.1138
C(97) 0.247276 0.153202 1.614047 0.1148
C(98) 0.222073 0.084491 2.628363 0.0123
C(99) 0.001321 0.006282 0.210236 0.8346
C(100) 0.000905 0.004829 0.187440 0.8523
C(101) -0.000228 0.003419 -0.066703 0.9472
C(102) 0.000744 0.002095 0.354958 0.7246
C(103) -0.201735 0.095279 -2.117308 0.0408
C(104) -0.011083 0.070441 -0.157337 0.8758
C(105) -0.042853 0.074688 -0.573755 0.5695
C(106) -0.031312 0.062442 -0.501465 0.6189
C(107) 0.013904 0.049940 0.278412 0.7822
C(108) 95.62662 165.5229 0.577724 0.5669
174
APPENDIX G
COEFFICIENTS FOR VECM - 4
Model M-4
Coefficient Std. Error t-Statistic Prob.
C(109) -0.630822 0.265726 -2.373957 0.0228
C(110) -0.041692 0.053027 -0.786238 0.4366
C(111) 0.147930 0.196721 0.751982 0.4567
C(112) -0.492873 0.282600 -1.744063 0.0892
C(113) -0.091492 0.060417 -1.514334 0.1382
C(114) 0.831122 0.303280 2.740446 0.0093
C(115) 0.531555 0.340262 1.562192 0.1265
C(116) 0.599992 0.383383 1.564995 0.1259
C(117) 0.532737 0.436124 1.221527 0.2294
C(118) 0.517648 0.456484 1.133990 0.2639
C(119) 0.039763 0.096829 0.410650 0.6836
C(120) 0.102370 0.088104 1.161911 0.2525
C(121) 0.056759 0.103295 0.549484 0.5859
C(122) -0.080786 0.082888 -0.974645 0.3359
C(123) -0.118071 0.077870 -1.516258 0.1377
C(124) -0.221768 0.179116 -1.238124 0.2233
C(125) 0.014157 0.163719 0.086472 0.9315
C(126) 0.063794 0.169589 0.376169 0.7089
C(127) -0.277912 0.168389 -1.650423 0.1071
C(128) -0.302021 0.170409 -1.772323 0.0844
C(129) -0.130372 0.233694 -0.557878 0.5802
C(130) -0.163345 0.244166 -0.668992 0.5075
C(131) -0.228530 0.194406 -1.175531 0.2471
C(132) 0.103986 0.151976 0.684229 0.4980
C(133) -0.069941 0.139281 -0.502160 0.6185
C(134) -0.091693 0.076813 -1.193711 0.2400
C(135) -0.007695 0.005711 -1.347333 0.1859
C(136) -0.003842 0.004390 -0.875034 0.3871
C(137) -0.002735 0.003108 -0.880079 0.3843
C(138) -0.000384 0.001904 -0.201461 0.8414
C(139) -0.032549 0.086621 -0.375759 0.7092
C(140) -0.078503 0.064040 -1.225841 0.2278
C(141) -0.095264 0.067901 -1.402974 0.1687
C(142) 0.036779 0.056768 0.647895 0.5209
C(143) 0.007827 0.045402 0.172391 0.8640
C(144) 218.5082 150.4818 1.452057 0.1547
175
APPENDIX H
COEFFICIENTS FOR VECM -5
Model M-5
Coefficient Std. Error t-Statistic Prob.
C(145) -2.739560 0.933568 -2.934504 0.0056
C(146) -0.449488 0.186298 -2.412731 0.0208
C(147) -0.171801 0.691134 -0.248579 0.8050
C(148) -1.890130 0.992852 -1.903739 0.0645
C(149) -0.474288 0.212263 -2.234438 0.0314
C(150) 2.786313 1.065505 2.615015 0.0127
C(151) 4.206258 1.195433 3.518605 0.0011
C(152) 0.569052 1.346929 0.422481 0.6751
C(153) 0.718279 1.532222 0.468782 0.6419
C(154) 3.796953 1.603754 2.367541 0.0231
C(155) 0.854406 0.340186 2.511584 0.0164
C(156) 0.188195 0.309535 0.607992 0.5468
C(157) 0.345724 0.362904 0.952660 0.3468
C(158) 0.097938 0.291208 0.336317 0.7385
C(159) 0.247921 0.273579 0.906213 0.3705
C(160) 0.418407 0.629284 0.664894 0.5101
C(161) -0.013765 0.575189 -0.023931 0.9810
C(162) -0.971633 0.595811 -1.630773 0.1112
C(163) -1.299605 0.591595 -2.196781 0.0342
C(164) 0.030253 0.598695 0.050531 0.9600
C(165) 0.990698 0.821030 1.206653 0.2350
C(166) 1.206351 0.857821 1.406296 0.1678
C(167) 0.957729 0.683001 1.402236 0.1690
C(168) 0.900743 0.533932 1.686999 0.0998
C(169) 0.582362 0.489332 1.190116 0.2414
C(170) -0.586695 0.269866 -2.174020 0.0360
C(171) -0.028672 0.020064 -1.428987 0.1612
C(172) -0.015784 0.015424 -1.023343 0.3126
C(173) -0.014735 0.010919 -1.349447 0.1852
C(174) -0.012903 0.006691 -1.928487 0.0613
C(175) -0.299084 0.304324 -0.982783 0.3319
C(176) -0.333965 0.224991 -1.484346 0.1460
C(177) -0.391756 0.238556 -1.642199 0.1088
C(178) -0.055383 0.199440 -0.277692 0.7828
C(179) -0.117633 0.159510 -0.737464 0.4654
C(180) 1592.976 528.6838 3.013098 0.0046
176
APPENDIX I
COEFFICIENTS FOR VECM -6
Model M-6
Coefficient Std. Error t-Statistic Prob.
C(181) -4.253521 1.778827 -2.391195 0.0219
C(182) -0.204102 0.354974 -0.574978 0.5687
C(183) 0.453038 1.316890 0.344021 0.7327
C(184) -1.447994 1.891785 -0.765412 0.4488
C(185) -0.477137 0.404447 -1.179728 0.2454
C(186) 6.747699 2.030220 3.323630 0.0020
C(187) 9.151403 2.277786 4.017675 0.0003
C(188) 1.688539 2.566445 0.657929 0.5145
C(189) 2.207686 2.919504 0.756185 0.4542
C(190) 8.548617 3.055802 2.797504 0.0080
C(191) 1.745150 0.648192 2.692334 0.0105
C(192) 1.046584 0.589790 1.774502 0.0840
C(193) 0.645735 0.691479 0.933847 0.3563
C(194) 0.136749 0.554870 0.246453 0.8067
C(195) -0.332347 0.521280 -0.637560 0.5276
C(196) 0.045263 1.199042 0.037749 0.9701
C(197) 2.036946 1.095969 1.858581 0.0708
C(198) -0.277074 1.135262 -0.244062 0.8085
C(199) -2.521365 1.127228 -2.236782 0.0312
C(200) -0.509675 1.140757 -0.446786 0.6576
C(201) 0.258825 1.564395 0.165447 0.8695
C(202) 1.114940 1.634498 0.682130 0.4993
C(203) 0.425174 1.301394 0.326707 0.7457
C(204) 1.817145 1.017358 1.786142 0.0821
C(205) 0.928787 0.932376 0.996151 0.3255
C(206) -0.632090 0.514205 -1.229258 0.2265
C(207) -0.019548 0.038231 -0.511312 0.6121
C(208) -0.001733 0.029389 -0.058977 0.9533
C(209) -0.005058 0.020806 -0.243093 0.8092
C(210) -0.010181 0.012748 -0.798642 0.4295
C(211) -0.993853 0.579860 -1.713955 0.0947
C(212) -0.964209 0.428700 -2.249148 0.0304
C(213) -0.888041 0.454545 -1.953693 0.0581
C(214) -0.283851 0.380014 -0.746950 0.4597
C(215) -0.285064 0.303932 -0.937919 0.3542
C(216) 3538.268 1007.357 3.512427 0.0012
177
APPENDIX J
VECM SYSTEM EQUATIONS RESULTS (M1-M6)
Model M1: Dependent Variable D(CONS) R-squared 0.824497 Mean dependent var 34.27027
Adjusted R-squared 0.662850 S.D. dependent var 233.7647
S.E. of regression 135.7345 Akaike info criterion 12.96577
Sum squared resid 700106.7 Schwarz criterion 14.08667
Log likelihood -443.7336 Hannan-Quinn criter. 13.41291
F-statistic 5.100603 Durbin-Watson stat 1.953193
Prob(F-statistic) 0.000001
Model M2: Dependent Variable D(MANF)
R-squared 0.806158 Mean dependent var 372.7703
Adjusted R-squared 0.627619 S.D. dependent var 1446.229
S.E. of regression 882.5334 Akaike info criterion 16.70996
Sum squared resid 29596876 Schwarz criterion 17.83086
Log likelihood -582.2687 Hannan-Quinn criter. 17.15710
F-statistic 4.515301 Durbin-Watson stat 1.961068
Prob(F-statistic) 0.000006
Model M3: Dependent Variable D(MINQ) R-squared 0.744169 Mean dependent var 32.52703
Adjusted R-squared 0.508535 S.D. dependent var 412.5901
S.E. of regression 289.2444 Akaike info criterion 14.47891
Sum squared resid 3179168. Schwarz criterion 15.59981
Log likelihood -499.7199 Hannan-Quinn criter. 14.92605
F-statistic 3.158160 Durbin-Watson stat 2.033377
Prob(F-statistic) 0.000351
Model M4: Dependent Variable D(AGRF)
R-squared 0.928772 Mean dependent var 30.32432
Adjusted R-squared 0.863167 S.D. dependent var 710.8790
S.E. of regression 262.9607 Akaike info criterion 14.28838
Sum squared resid 2627637. Schwarz criterion 15.40928
Log likelihood -492.6701 Hannan-Quinn criter. 14.73552
F-statistic 14.15707 Durbin-Watson stat 1.927903
Prob(F-statistic) 0.000000
178
Model M5: Dependent Variable D(SERV)
R-squared 0.839455 Mean dependent var 859.7162
Adjusted R-squared 0.691585 S.D. dependent var 1663.548
S.E. of regression 923.8530 Akaike info criterion 16.80148
Sum squared resid 32433166 Schwarz criterion 17.92237
Log likelihood -585.6547 Hannan-Quinn criter. 17.24862
F-statistic 5.676981 Durbin-Watson stat 2.314175
Prob(F-statistic) 0.000000
Model M6: Dependent Variable D(GDP)
R-squared 0.876254 Mean dependent var 1267.581
Adjusted R-squared 0.762278 S.D. dependent var 3610.405
S.E. of regression 1760.315 Akaike info criterion 18.09087
Sum squared resid 1.18E+08 Schwarz criterion 19.21176
Log likelihood -633.3621 Hannan-Quinn criter. 18.53801
F-statistic 7.688053 Durbin-Watson stat 2.062050
Prob(F-statistic) 0.000000
179
APPENDIX K
SHORT RUN COEFFICIENT SIGNIFICANCE TEST (M 1)
Wald Test: Coefficient of C(5)---C(10)
Test Statistic Value df Probability
F-statistic 4.247188 (6, 38) 0.0023
Chi-square 25.48313 6 0.0003
Wald Test: Coefficient C(11)---C(15)
Test Statistic Value df Probability
F-statistic 0.361593 (5, 38) 0.8715
Chi-square 1.807966 5 0.8750
Wald Test: Coefficient C(16)---C(20)
Test Statistic Value df Probability
F-statistic 2.117760 (5, 38) 0.0843
Chi-square 10.58880 5 0.0602
Wald Test: Coefficient C(21)---C(25)
Test Statistic Value df Probability
F-statistic 2.072001 (5, 38) 0.0904
Chi-square 10.36001 5 0.0657
180
Wald Test: Coefficient C(26)---C(30)
Test Statistic Value df Probability
F-statistic 0.949685 (5, 38) 0.4605
Chi-square 4.748423 5 0.4473
Wald Test: Coefficient C(31)---C(35)
Test Statistic Value df Probability
F-statistic 0.525961 (5, 38) 0.7551
Chi-square 2.629806 5 0.7568
181
APPENDIX L
SHORT RUN MCS COEFFICIENTS SIGNIFICANCE TEST
Wald Test: Coefficient C(42)---C(46)
Test Statistic Value df Probability
F-statistic 4.529957 (5, 38) 0.0025
Chi-square 22.64978 5 0.0004
Wald Test: Coefficient C(114) -C(118)
Test Statistic Value df Probability
F-statistic 2.276979 (5, 38) 0.0662
Chi-square 11.38490 5 0.0443
Wald Test: Coefficient C(150)—C(154)
Test Statistic Value df Probability
F-statistic 3.821424 (5, 38) 0.0067
Chi-square 19.10712 5 0.0018
Wald Test: Coefficient C(78)—C(82)
Test Statistic Value df Probability
F-statistic 0.073950 (5, 38) 0.9958
Chi-square 0.369748 5 0.9961
182
Wald Test: Coefficient C(186)—C(190)
Test Statistic Value df Probability
F-statistic 5.360169 (5, 38) 0.0008
Chi-square 26.80085 5 0.0001
183
APPENDIX M
IRF FOR ALL STUDY VARIABLES
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to CONS
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to MANF
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to MINQ
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to AGRF
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to SERV
-100
0
100
5 10 15 20 25 30 35 40
Response of CONS to GDP
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to CONS
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to MANF
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to MINQ
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to AGRF
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to SERV
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of MANF to GDP
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to CONS
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to MANF
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to MINQ
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to AGRF
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to SERV
-200
-100
0
100
200
300
5 10 15 20 25 30 35 40
Response of MINQ to GDP
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to CONS
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to MANF
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to MINQ
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to AGRF
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to SERV
-400
-200
0
200
400
5 10 15 20 25 30 35 40
Response of AGRF to GDP
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to CONS
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to MANF
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to MINQ
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to AGRF
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to SERV
-2,000
-1,000
0
1,000
2,000
5 10 15 20 25 30 35 40
Response of SERV to GDP
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to CONS
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to MANF
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to MINQ
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to AGRF
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to SERV
-4,000
-2,000
0
2,000
4,000
5 10 15 20 25 30 35 40
Response of GDP to GDP
Response to Cholesky One S.D. Innov ations
