DETERMINANTS OF THE PERFORMANCE OF AUTOMOBILE INDUSTRY FROM 1995 TO 2010-A CASE
STUDY OF PAKISTAN
Thesis
By MUHAMMAD AQIL
In Partial Fulfillment of the Requirement for the Degree of Doctor of Philosophy (Ph. D.) in Social Sciences
Under the Supervision of
DR. FAROOQ AZIZ
Presented to Hamdard Institute of Education and Social Sciences HAMDARD UNIVERSITY
July 2014
Dedicated to
My Parents, Wife and Children Who always support me!
i
Abstract
The study was primarily a quantitative and causal research. Its basic objective was to find out the nature and degree of macroeconomic factors that influence the performance of automobile industry in Pakistan from 1995 to 2010. The independent variables included per capita Gross National Income, Inflation Rate, Exchange Rate, Discount Rate, Unemployment Rate, Consumption Rate and Foreign Direct Investment. A sample of 10 firms was selected to explore the influence of macroeconomic variables on the performance of the firms and industry. The performance of the automobile industry was represented by Annual Sales, Annual Profit, Return on Assets, Return on Equity and Net Profit Margin. The impact of selected predictors was explored on five dependent variables of the industry. For this purpose, regression and correlation analysis was conducted to see the impact of macroeconomic variables on the performance of entire automobile industry. The panel data analysis revealed that Foreign Direct Investment was the most influential factor. In addition to this, Consumption rate, Discount rate and Unemployment rate also influenced the performance of automobile industry. Besides the analysis of entire industry, the impact of macroeconomic variables on the performance of individual firms was also studied. The analysis showed that the performance of all of the selected firms was influenced by one or more variables. Foreign Direct Investment appeared to be the most influential factor that helped improve the performance of firms in terms of sales and profitability. Other influential elements were Exchange rate, Consumption rate, Discount rate, Per Capita Gross National Income and Inflation rate. However, Unemployment rate appeared to be insignificant as its effect was almost negligible on the progress of individual firms. Therefore, this was concluded that the selected macroeconomic variables had significant impact on the performance of automobile industry in Pakistan from 1995 to 2010.
ii
Certificate of Approval
This is to certify that MR. MUHAMMD AQIL has completed his research thesis entitled “Determinants of the performance of automobile industry-A case study of
Pakistan from 1995 to 2010” in partial fulfillment of the requirement for the degree of
Doctor of Philosophy under my supervision. I have reviewed the contents and the methodology which is according to the prescribed standard of the Hamdard Institute of
Education and Social Sciences, Hamdard University Karachi.
This Thesis is based on his own personal research work carried under my supervision and is not copied from any thesis, written earlier on the subject.
Signature ______
Dr. Farooq Aziz (Supervisor)
Dated: ______Karachi
iii
Acknowledgment
I am very thankful to Almighty Allah Who is the most beneficent and merciful. He helped me to accomplish this difficult task. I could not jot down even a single word without His support. Allah provided me courage, patience, energy and skills to prepare this research document.
I am also grateful to my research supervisor Dr. Farooq Aziz who always guided and motivated me to cope with various issues pertaining to my research. I am undoubtedly very lucky to have such a cooperative and helpful advisor. I would also extend a vote of thanks to my friend Dr. Saqib Rehman who offered regular assistance in understanding different technical problems of the thesis.
I am also obliged to my parents, my wife and my children whose sacrifice and patience enabled me to work with concentration and zeal. I have the same feelings to my students and colleagues who encouraged me to carry on the research even in difficult times.
Above all I am grateful to my Allah again Who is the final cause of every good work especially in this study. He is the only one entitled to take all the credit of this study.
Muhammad Aqil
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Contents
Abstract ...... i
Certificate of Approval ...... ii
Acknowledgment ...... iii
Contents ...... iv
List of Table ...... xiii
List of Figures ...... xvii
List of Abbreviation ...... xix
Chapter One ...... 1
Introduction ...... 1
1.1 Background of the Study ...... 1
1.1.2. Evolution of automobile industry in the world ...... 3
1.1.3 Growth of global automobile industry...... 15
1.1.4 Economic significance of global automobile industry ...... 17
1.1.5. Factors influencing the auto industry ...... 18
1.2 Purpose of the Study ...... 18
1.2.1. General purpose ...... 18
1.2.2. Specific Purpose ...... 19
1.3 Justification of the Study ...... 19 v
1.4 Scope of the Study ...... 20
1.5 Hypothesis...... 20
1.6 Definition of Key Terms ...... 21
1.7. Basic Assumptions ...... 21
Chapter 2 ...... 22
Review of Literature ...... 22
2.1 Performance Measurement ...... 22
2.1.1. Rationale for performance measurement ...... 23
2.1.2. Approaches to performance measurement ...... 24
2.1.3. Key financial indicators for automobile industry ...... 26
2.2 Determinants of the Performance ...... 34
2.2.1. Past studies in automobile sector ...... 35
2.2.2. Contribution of this research ...... 35
2.2.3. Macroeconomic determinant of the performance of the firms and industry
...... 36
2.2.4. Review of statistical tools...... 44
2.3. Automobile Industry in Pakistan ...... 45
2.3.1. History of automobile industry in Pakistan ...... 45
2.4. Economic Significance Pakistan’s Automobile Industry ...... 55 vi
2.4.1. Contribution of auto parts suppliers and original equipment manufacturers to GDP...... 56
2.4.2 Contribution to employment ...... 56
2.4.3 Contribution to export ...... 57
2.4.4 Development of auto parts industry ...... 58
2.4.5. Automobile sector and investment ...... 60
2.5 Original Equipment Manufacturers ...... 61
2.5.1. Pak Suzuki Motors Company Limited...... 62
2.5.2. Indus Motors Company Ltd ...... 62
2.5.3. Honda Atlas Car Pakistan Ltd ...... 63
2.5.4. Dewan Farooq Motors Ltd ...... 64
2.5.5. Hinopak Motors Limited ...... 64
2.5.6. Ghandhara Nissan Limited ...... 65
2.5.7. Ghandhara Industries Limited ...... 66
2.5.8. Millat Tractors Limited...... 67
2.5.9. Al-Ghazi Tractors Limited ...... 67
2.5.10. Atlas Honda Ltd ...... 68
2.6. Foreign Technology in Automobile Industry ...... 69
2.7 Regulators and Authorities of Automobile Industry ...... 72
2.7.1. Engineering development board (EDB...... 73 vii
2.7.2. Federal board of revenue (FBR) ...... 73
2.7.3. Pakistan Automobile Manufacturing Association (PAMA) ...... 73
2.7.4. Pakistan Association of Automotive Parts and Accessories
Manufacturers (PAAPAM) 74
Chapter Three ...... 75
Research Methodology ...... 75
3.1. Strategy ...... 75
3.1.1. Justification of the Model ...... 78
3.2. Population ...... 78
3.3. Sampling ...... 79
3.4. Hypotheses ...... 81
3.5. Research Instrument...... 82
3.6. Procedure for Data Collection ...... 82
3.7. Data Sources ...... 83
3.7.1. Data Sources for Independent variables ...... 83
3.7.2. Data sources for dependent variables ...... 86
3.7.3. General sources of data ...... 86
3.8. Plan for Data Analysis ...... 87
3.8.1. Organization of data ...... 87
3.8.2. Scatter plots and correlation ...... 88 viii
3.8.3. Regression analysis ...... 88
Chapter Four ...... 91
Data Analysis and Findings ...... 91
4.1 Determinants of the Performance of Pak Suzuki Company Ltd...... 91
4.1.1. Macroeconomic variables and sales of Pak Suzuki Company ...... 94
4.1.2. Macroeconomic factors and profit of Pak Suzuki ...... 97
4.1.3. Macroeconomic variables and ROA of Pak Suzuki ...... 98
4.1.4. Macroeconomic variables and ROE of Pak Suzuki ...... 99
4.1.5. Effect of macroeconomic factors on NPM of Pak Suzuki ...... 101
4.1.6. Conclusions and Decision ...... 104
4.2. Determinants of the Performance of Indus Motors Company Ltd...... 104
4.2.1. Effects of macroeconomic variables on sales of Indus Motors ...... 106
4.2.2. Effects of macroeconomic variables on profit of Indus Motors ...... 108
4.2.3. Effects of variables on ROA of Indus Motors ...... 109
4.2.4. Effect of macroeconomic variables on ROE of Indus Motors ...... 110
4.2.5. Effects of variables on NPM of Indus Motors ...... 111
4.2.6. Conclusion and decision ...... 112
4.3. Determinants of the Performance of Honda Motors Ltd...... 113
4.3.1. Effects of macroeconomic variables on sales Honda Motors ...... 115
4.3.2. Effects of macroeconomic variables on profit of Honda Motors ...... 116 ix
4.3.3. Effects of macroeconomic variables on ROA of Honda Motors ...... 118
4.3.4. Effects of macroeconomic variables on ROE of Honda Motors ...... 119
4.3.5. Effects of macroeconomic variables on NPM of Honda Motors ...... 120
4.3.6. Conclusion and Decision ...... 121
4.4. Determinants of the performance of Dewan Motors Ltd...... 122
4.4.1. Effect of macroeconomic variables on Sales ...... 123
4.4.2 Effect of macroeconomic variables on Profit ...... 124
4.4.4 Effect of macroeconomic variables on ROE ...... 126
4.4.5. Effect of macroeconomic variables on NPM ...... 127
4.4.6. Conclusion and Decision ...... 128
4.5. Determinants of the performance of Hinopak Company ...... 128
4.5.1. Effects of macroeconomic variables on sales of Hino Pak ...... 130
4.5.2. Effects of macroeconomic variables on profit of Hinopak ...... 132
4.5.3. Effect of macroeconomic variables on ROA of Hinopak ...... 133
4.5.4. Effects of macroeconomic variables on ROE of Hinopak ...... 133
4.5.5. Effects of macroeconomic variables on NPM of Hino Pak...... 134
4.5.6. Conclusion and decision ...... 134
4.6. Influence of Factors on the performance of Ghandhara Industries Ltd. ... 135
4.6.1. Effects of macroeconomic variables on sales of Ghandhara Industries 137
4.6.2. Effect of macroeconomic variables on profit of Ghandhara Industries . 138 x
4.6.3. Effect of macroeconomic variables on ROA of Ghandhara Industries . 139
4.6.4. Effect of macroeconomic variables on ROE of Ghandhara Industries .. 140
4.6.5. Effect of macroeconomic variables on NPM of Ghandhara Industries . 140
4.6.6. Conclusion and Decision ...... 141
4.7. Determinants of the performance of Ghandhara Nissan Ltd...... 142
4.7.1. Impact of macroeconomic factors on sales of Ghandhara Nissan ...... 144
4.7.2. Conclusion and decision ...... 145
4.8. Determinants of the Performance of Millat Tractors Ltd...... 146
4.8.1. Impact of macroeconomic factors on sales of Millat Tractors ...... 148
4.8.2. Impact of macroeconomic factors on profit of Millat Tractors ...... 150
4.8.3. Impact of macroeconomic factors on ROA of Millat Tractors ...... 151
4.8.4. Impact of macroeconomic factors on ROE of Millat Tractors ...... 152
4.8.5. Impact of macroeconomic factors on NPM of Millat Tractors ...... 152
4.8.6. Conclusion and decision ...... 153
4.9. Determinants of the Performance of Al-Ghazi Tractors Ltd...... 154
4.9.1. Effects of macroeconomic factors on sales of Al-Ghazi Tractors ...... 156
4.9.2. Impact of macroeconomic factors on profit of Al-Ghazi Tractors ...... 158
4.9.3. Impact of macroeconomic factors on NPM of Al-Ghazi Tractors ...... 159
4.9.4. Conclusion and decision ...... 159
4.10. Determinants of the Performance of Honda Atlas Bikes ...... 160 xi
4.10.1. Impact of macroeconomic factors on sales of Honda Atlas ...... 162
4.10.2. Impact of macroeconomic factors on profit of Honda Atlas ...... 163
4.10.3. Conclusion and decision ...... 164
4.11. Determinants of the Performance of Automobile Industry- Panel Data
Approach ...... 165
4.11.1. Impact of macroeconomic factors on sales of Auto Industry ...... 166
4.11.2. Impact of macroeconomic factors on profit of Auto Industry ...... 167
4.11.3. Impact of macroeconomic factors on ROA of Auto Industry ...... 168
4.11.4. Impact of macroeconomic factors on NPM of Auto Industry ...... 169
4.11.5. Conclusion and decision...... 170
Chapter Five ...... 172
Conclusions and Recommendations ...... 172
5.1 Performance of Industry-Panel Data Approach ...... 172
5.1.1. Annual sales of the industry ...... 172
5.1.2. Annual profit of the industry ...... 173
5.1.3. ROA of the industry ...... 174
5.1.4. NPM of the industry ...... 174
5.2 Influential Macroeconomic Determinants of the Industry ...... 174
5.3. Analysis of Firms’ Performance ...... 176
5.3.1. Annual sales ...... 176 xii
5.3.2. Annual profit ...... 178
5.3.3. Return on assets ...... 179
5.3.4. Return on equity ...... 180
5.3.5. Net profit margin ...... 181
5.4. Recommendations ...... 182
5.5 Future Research Direction ...... 183
References ...... 184
Appendix A- Scatter Plots ...... 198
Appendix – B: Heteroscedasticity Tests ...... 216
Appendix – C: Normality Tests ...... 227
xiii
List of Table
S.No Title of Table Page No
1 Market Share of Ford in 1913 05 2 Import Penetration of U.S. Automobile Market 1960-84 07 3 Worldwide passenger car production (1933 and 1938) 12 4 Production Trend of Vehicles 1997-2011 16 5 Country-wise output of vehicles 1970-2010 (in Units) 17 6 Performance Measurement Models 25 7 Automobiles production 1988-1995 54 8 Automobile production- 1995-2010 55 9 Vehicle industry share to GDP 56 10 Employees in OEMs 57 11 Automobile parts exports 58 12 Vehicles Exports 59 13 Automobile vendors’ contribution to economy 60 14 Domestic investment in auto assemblers 61 15 Progress of Pak Suzuki Co 62 16 Progress of Indus Motors Co 63 17 Progress of Honda Motors Co 64 18 Progress of Dewan Farooq Motors Co 64 19 Progress of Hinopak Motors Co 65 20 Progress of Ghandhara Nissan Ltd 66 21 Progress of Ghandhara Industries Ltd 66 22 Progress of Millat Tractors Ltd 67 23 Progress of Al-Ghazi Tractors Ltd 68 24 Progress of Atlas Honda Ltd. 69 25 Automobile firms and joint ventures 70 26 Japanese and other brands’ market share 71 27 No. of foreign assemblers by vehicle 71 28 Share of Japan in automobile parts 72 29 Sample and market share 81 30 Macroeconomic variables 85 31 Variables for Pak Suzuki Company 92 32 Correlation Analysis for Pak Suzuki 93 33 Model Summary for Sales Pak Suzuki 95 34 Model Summary for Profit of Pak Suzuki 97 35 Model Summary for ROA of Pak Suzuki 99 36 Models Comparison for ROE of Pak Suzuki 100 37 Model Summary for ROE for Pak Suzuki Company 101 38 Model Summary for NPM for Pak Suzuki 102 39 Variables for Indus Motors Company 105 40 Correlation Analyses for Indus Motors 106 xiv
41 Model Summary for Sales of Indus Motors 107 42 Model Summary for Annual Profit of Indus Motors 109 43 Model Summary for ROA of Indus Motors 110 44 Model Summary for ROE of Indus Motors 111 45 Model Summary for NPM of Indus Motors 112 46 Variables for Honda Motors Company 114 47 Correlation Analysis of Honda Motors 115 48 Model Summary for Annual Sales of Honda Motors 116 49 Model Summary for Annual Profit of Honda Car 117 50 Model Summary for ROA of Honda Car 118 51 Model Summary for ROE of Honda Car 119 52 Model Summary for NPM of Honda Motors 121 53 Variables for Dewan Motors Company 122 54 Correlation Analysis of Dewan Motors 123 55 Model Summary for Annual Sales of Dewan Motors 124 56 Model Summary for Annual Profit of Dewan Motors 125 57 Model Summary for ROA of Dewan Motors 126 58 Model Summary for ROE of Dewan Motors 126 59 Model Summary for NPM of Dewan Motors 127 60 Variables for Hino Pak Company 129 61 Correlation Analysis for Hino Pak 130 62 Model Summary for Sales of Hino Pak 131 63 Model Summary for Profit of Hino Pak 132 64 Model Summary for ROA of Hino Pak 133 65 Model Summary for NPM of Hino Pak 134 66 Variables for Ghandhara Industries Ltd. 136 67 Correlation Analysis of Ghandhara Industries 137 68 Model Summary for Sales of Ghandhara Industries 138 69 Model Summary for Profit of Ghandhara Industries 139 70 Model Summary for ROA of Ghandhara Industries 140 71 Model Summary for NMP of Ghandhara Industries 141 72 Variables for Ghandhara Nissan Ltd. 143 73 Correlation Analysis for Ghandhara Nissan 144 74 Model Summary for Annual Sales of Ghandhara Nissan 145 75 Variables for Millat Tractors Ltd. 147 76 Correlation Analysis for Millat Tractors 148 77 Model Summary for Sales of Millat Tractors 149 78 Model Summary for Profit of Millat Tractor 150 79 Model Summary for ROA of Millat Tractor 151 80 Model Summary for ROE of Millat Tractor 152 81 Model Summary for NMP of Millat Tractor 153 82 Variables for Al-Ghazi Tractors Ltd. 155 83 Correlation Analysis Al-Ghazi Tractors 156 84 Model Summary for Sales of Al-Ghazi Tractors 157 85 Model Summary for Profit of Millat Tractor 158 86 Model Summary for NMP of Al-Ghazi Tractors 159 xv
87 Variables for Honda Atlas Bikes 161 88 The Correlation Analysis for Honda Bikes 162 89 Model Summary for Sales of Honda Atlas Bikes 163 90 Model Summary for Profit of Honda Atlas 164 91 Correlation Analysis for Automobile Industry 166 92 Model Summary for Sales of Automobile Industry 167 93 Model Summary for Profit of Automobile Industry 168 94 Model Summary for ROA of Automobile Industry 169 95 Model Summary for NMO of Automobile Industry 170 96 Determinants of Annual Sales 176 97 Determinants of Annual Profit 178 98 Determinants of Return on Assets 180 99 Determinants of Return on Equity 180 100 Determinants of Net Profit Margin 181 101 Tests of Normality-Sales of Pak Suzuki 227 102 Tests of Normality-Profit of Pak Suzuki 227 103 Tests of Normality-ROA of Pak Suzuki 227 104 Tests of Normality-ROE of Pak Suzuki 227 105 Tests of Normality-NPM of Pak Suzuki 227 106 Tests of Normality-Sales of Indus Motors 228 107 Tests of Normality-Profit of Indus Motors 228 108 Tests of Normality-ROA of Indus Motors 228 109 Tests of Normality-ROE of Indus Motors 228 110 Tests of Normality-NPM of Indus Motors 228 111 Tests of Normality- Sales of Honda Car 229 112 Tests of Normality- Profit of Honda Car 229 113 Tests of Normality- ROA of Honda Car 229 114 Tests of Normality- ROE of Honda Car 229 115 Tests of Normality- NPM of Honda Car 229 116 Tests of Normality-Sales of Hino Pak 230 117 Tests of Normality-Profit of Hino Pak 230 118 Tests of Normality-ROA of Hino Pak 230 119 Tests of Normality-NPM of Hino Pak 230 120 Tests of Normality-Sales of Ghandhara Industries 230 121 Tests of Normality-Profit of Ghandhara Industries 230 122 Tests of Normality-ROA of Ghandhara Industries 231 123 Tests of Normality-ROE of Ghandhara Industries 231 124 Tests of Normality-NPM of Ghandhara Industries 231 125 Tests of Normality-Sales of Millat Tractors 231 126 Tests of Normality-Profit of Millat Tractors 231 127 Tests of Normality-ROA of Millat Tractors 232 128 Tests of Normality-ROE of Millat Tractors 232 129 Tests of Normality-NPM of Millat Tractors 232 130 Tests of Normality-Sales of Al-Ghazi Tractors 232 131 Tests of Normality-Profit of Al-Ghazi Tractors 232 132 Tests of Normality-ROA of Al-Ghazi Tractors 233 xvi
133 Tests of Normality-ROE of Al-Ghazi Tractors 233 134 Tests of Normality-Profit of Honda Bikes 233 xvii
List of Figures
Figure Title Page No. No. 3.1 Research Model 77 A-1 Scatter plot for GNI 198 A-2 Scatter plot for IR 198 A-3 Scatter plot for ER 199 A-4 Scatter plot for DR 199 A-5 Scatter plot for CR 199 A-6 Scatter plot for UR 200 A-7 Scatter plot for FDI 200 A-8 Scatter plot for sale of Pak Suzuki 201 A-9 Scatter plot for profit of Pak Suzuki 201 A-10 Scatter plot for ROA of Pak Suzuki 201 A-11 Scatter plot for ROE of Pak Suzuki 202 A-12 Scatter plot for NPM of Pak Suzuki 202 A-13 Scatter plot for sale of Indus Motors 202 A-14 Scatter plot for profit of Indus Motors 203 A-15 Scatter plot for ROA of Indus Motors 203 A-16 Scatter plot for ROE of Indus Motors 203 A-17 Scatter plot for NPM of Indus Motors 204 A-18 Scatter plot for sale of Honda Motors Car 204 A-19 Scatter plot for profit of Honda Motors Car 204 A-20 Scatter plot for ROA of Honda Motors Car 205 A-21 Scatter plot for ROE of Honda Motors Car 205 A-22 Scatter plot for NPM of Honda Motors Car 205 A-23 Scatter plot for sale of Hino Pak Company 206 A-24 Scatter plot for profit of Hino Pak Company 206 A-25 Scatter plot for ROA of Hino Pak Company 206 A-26 Scatter plot for ROE of Hino Pak Company 207 A-27 Scatter plot for NPM of Hino Pak Company 207 A-28 Scatter plot for sale of Ghandhara Industries 207 A-29 Scatter plot for profit of Ghandhara Industries 208 A-30 Scatter plot for ROA of Ghandhara Industries 208 A-31 Scatter plot for ROE of Ghandhara Industries 208 A-32 Scatter plot for NPM of Ghandhara Industries 209 A-33 Scatter plot for sale of Ghandhara Nissan 209 A-34 Scatter plot for profit of Ghandhara Nissan 209 A-35 Scatter plot for ROA of Ghandhara Nissan 210 A-36 Scatter plot for ROE of Ghandhara Nissan 210 A-37 Scatter plot for NPM of Ghandhara Nissan 210 A-38 Scatter plot for sale of Millat Tractors 211 A-39 Scatter plot for profit of Millat Tractors 211 xviii
A-40 Scatter plot for ROA of Millat Tractors 211 A-41 Scatter plot for ROE of Millat Tractors 212 A-42 Scatter plot for NPM of Millat Tractors 212 A-43 Scatter plot for sale of Al-Ghazi Tractors 212 A-44 Scatter plot for profit of Al-Ghazi Tractors 213 A-45 Scatter plot for ROA of Al-Ghazi Tractors 213 A-46 Scatter plot for ROE of Al-Ghazi Tractors 213 A-47 Scatter plot for NPM of Al-Ghazi Tractors 214 A-48 Scatter plot for Sale of Honda Bikes 214 A-49 Scatter plot for profit of Honda Bikes 214 A-50 Scatter plot for ROA of Honda Bikes 215 A-51 Scatter plot for ROE of Honda Bikes 215 A-52 Scatter plot for NPM of Honda Bikes 215 B-1 Histogram for Residuals of Pak Suzuki Sales 216 B-2 Histogram for Residuals of Pak Suzuki profit 216 B-3 Histogram for Residuals of Pak Suzuki NPM 216 B-4 Histogram for Residuals of Indus Motors Sales 217 B-5 Histogram for Residuals of Indus Motors profit 217 B-6 Histogram for Residuals of Indus Motors ROA 217 B-7 Histogram for Residuals of Indus Motors ROE 218 B-8 Histogram for Residuals of Indus Motors NPM 218 B-9 Histogram for Residuals of Honda Car Ltd. Sales 218 B-10 Histogram for Residuals of Honda Car Ltd. Profit 219 B-11 Histogram for Residuals of Honda Car Ltd. ROA 219 B-12 Histogram for Residuals of Honda Car Ltd. ROE 220 B-13 Histogram for Residuals of Honda Car Ltd. NPM 220 B-14 Histogram for Residuals of Hino Pak Sales 220 B-15 Histogram for Residuals of Hino Pak Profit 221 B-16 Histogram for Residuals of Hino Pak ROA 221 B-17 Histogram for Residuals of Hino Pak ROE 221 B-18 Histogram for Residuals of Ghandhara Industries Sales 222 B-19 Histogram for Residuals of Ghandhara Industries Profit 222 B-20 Histogram for Residuals of Ghandhara Industries ROA 222 B-21 Histogram for Residuals of Ghandhara Industries NPM 223 B-22 Histogram for Residuals of Ghandhara Nissan Sales 223 B-23 Histogram for Residuals of Millat Tractors Sales 223 B-24 Histogram for Residuals of Millat Tractors Profit 224 B-25 Histogram for Residuals of Millat Tractors ROA 224 B-26 Histogram for Residuals of Millat Tractors ROE 224 B-27 Histogram for Residuals of Millat Tractors NPM 225 B-28 Histogram for Residuals of Al-Ghazi Tractors Sales 225 B-29 Histogram for Residuals of Al-Ghazi Tractors Profit 225 B-30 Histogram for Residuals of Al-Ghazi Tractors NPM 226 B-31 Histogram for Residuals of Honda Bikes Sales 226 B-32 Histogram for Residuals of Honda Bikes Profit 226 xix
List of Abbreviation
Auto Automobile
CBR Central Board of Revenue
CBU Completely built up
CKD Completely knocked down
CR Consumption Rate
DR Discount Rate
ER Exchange Rate
EDB Engineering Development Board
FDI Foreign Direct Investment
EPR Earning per share
FBS Federal Bureau of Statistics
GDP Gross Domestic Products
GNI Gross National Product
IR Inflation Rate
IMF International Monetary Fund
KSE Karachi Stock Exchange
Ltd Limited
LCV Light Commercial Vehicle
MOIP Ministry of Industries and Production
NPM Net Profit margin
OEM Original Equipment Manufacturers xx
OICA Organization Internationale des Constructeurs d’Automobiles
PAAPAM Pakistan Association of Automotive Parts and Accessories
Manufacturers
PACO Pakistan Automobile Corporation
PAMA Pakistan Automobile Manufacturing Association
PIDC Pakistan Industrial Development Corporation
Pvt Private
R & D Research and Development
ROE Return on Equity
ROI Return on Investment
ROS Return on Sales
SBP State Bank of Pakistan
SKD Semi knocked down
UR Unemployment Rate
WTO World Trade Organization
Introduction 1
Chapter One
Introduction
1.1 Background of the Study
The human history is full of inventions and discoveries. In ancient times, man invented tools for hunting, movement, sheltering and other purposes so as to fulfill the basic needs of life. The journey of modernization continued with a view to bringing about comforts and luxuries in the life style. However, the most significant phase of human history was Industrial revolution that gave birth to hundreds of new products in the field of engineering, medicines, biology, chemistry, communication and transports. As a result, human civilization experienced a new life style that was full of comforts and luxuries.
Amongst the inventions induced by industrial revolution, development of automobile was a tremendous achievement which affected several spheres of social and economic life. It made the mobility of people quick and easy from one place to another; it made the exchange of goods possible between two poles of the world; it enhanced business and economic transactions; and it gave new styles to human life. Therefore, automobile is considered as one of the most significant inventions in the human history.
1.1.1 History of automobile. The engineers mad several efforts in the past to facilitate quick, comfortable and easy mobility of people and goods, but all of them were in vain until the invention of automobile. A French army Captain, Nicolas-Joseph
Cugnot was the first person who succeeded to build the first auto vehicle. He developed
Fardier à Vapeur, a three-wheeled steam-powered artillery tractor. The vehicle was not Introduction 2
suitable for travelling due to its technical constraints. However, some serious attempts were made in England and America to improve the vehicle by introducing heavy steam- powered engines in the early decade of 19th Century. The addition of internal combustion engine was another advancement which was launched by a Swiss engineer Francis Isaac
De Rivaz in 1807. He used the mixture of Oxygen and Hydrogen to generate energy. It was not a successful design either as the fuel was not safe enough for consumption. So, extensive modifications were required to commence the commercial production of vehicles.
In 1860, Jean Joseph Estienne Lenoir, a French man, succeeded to manufacture a two- stroke gas driven engine. Later on he built another gas-driven car in 1862 which had the enhanced speed of 3 kilo meter per hour. Both of his cars got popularity by 1865 and they were frequently seen on roads. With some minor modifications in the design of
Lenoir, the first gasoline-powered car was produced by Charles and Frank Duryar in
1893. However, It is generally acknowledged that first automobile with gasoline powered internal combustion engine was produced by Karl Benz in 1885 in Germany.
1.1.1.1 Eras of automobile. Automobiles are grouped into different eras on the basis of modifications and innovations. The first era was Vertaran Era which started with the innovation by Karl Benz in Germany in 1888. Most of the vehicles produced in that era were cars and they were not appropriate for domestic consumption and needed to be improved and modified.
The next era was the Brass Era which continued from 1905 to 1914. This is called
“Brass era” because of the mass usage of brass in the production of industrial goods.
Several measures were taken to improve the performance of vehicles. As a result, the cars Introduction 3
were built with front engine rear wheel and sliding gear combustion. Ford Model T,
Mercer Raceabout and Bugatti Type 13 were some of the popular cars of this era. Vintage
Era (1919-1929) was the subsequent period when the front engine cars with closed bodies and standardized control were manufactured. Austin 7, Ford Model A, Bugatti type 35 and Cadillac were some of the exemplary cars of this era.
In Pre-War Era (1930-1948), the vehicles were produced on the basis of mechanical technology in spite of the sufferings of the Great Depression in America.
Ford V-8, Bugatti Type 57, Citreon Traction Avant and Volks Wagon Beetle were some of the popular vehicles of this period. The Post-War Era (1949-1967) was characterized with high speed, well integrated and artfully designed vehicles. Morris Minor, Jaguar E type and Datsun were few exemplary automobiles in this era.
In the Modern Era (1967 to date), much emphasis was given on fuel efficiency and engine output. Light commercial vehicles and heavy commercial vehicles were manufactured during this period in order to facilitate the transportation of goods. Toyota
Corolla, Dodge Aries and Ford Tauru were some popular vehicles of this period.
In 2012, the most popular vehicles of the world included Toyota Corolla,
Volkswagen, Volkswagen Jetta, Hyundai Elantra and Ford Fiesta. With few exceptions, all of the popular vehicles of this period were small, fuel-efficient and compact.
1.1.2. Evolution of automobile industry in the world. The mass level production of vehicles gave birth to the development of automobile industry. The history of automobile industry started in 19th Century when Duryea Motor Wagon Company of
Springfield, Massachusetts was established in 1886 in America. Later on, Daimler
Company in Germany stepped forward and developed its plant in 1886. There were only Introduction 4
300 vehicles produced from 1886 to 1898 and the output was not sufficient to form an industry in its real sense. At that time the automobile sector was characterized by fragmentation, mergers, divestitures and frequent entry and exit of automobile firms. The industry evolved in different parts of the world in different ways and the history of this evolution is given below.
1.1.2.1 Evolution of American Automobile Industry. The United States of America is considered as the pioneer of automobile industry as the first automobile company,
Duryea Motor Wagon, was established in USA in 1886. Later on, many other entrepreneurs took initiatives to produce different vehicles. By the end of 1904, Michigan became the center of car production and it captured 42% market share of the automobile industry. The mass level production could not start until the arrival of Henry Ford who successfully challenged the patent of Seldon and opened the doors of auto sector for other investors. Before the establishment of Ford Company, the manufacturers were engaged in small-scale production of “high-end” cars. Ford Company changed the horizon by emphasizing upon increase in sale with reduced price. In this way it was capable of enjoying the benefits of economies of scale and capturing the market share of around
46.11% in 1913. The detail of market share is depicted in table 1.
In addition to Ford’s efforts, William C. Durant stepped forward to form General
Motors Corporation (GM) in 1908. In 1923, Alfred P. Sloan, a newly appointed president of General Motors, introduced unique marketing techniques to move the company up on the ranking table. Since the market was saturated and it was difficult to find out new buyers, Sloan went for launching new models of cars. His objective was to motivate the existing car holders to replace their old cars with the latest models. By 1926, the new Introduction 5
marketing strategies enabled General Motors to raise the market share to 28% which restricted Ford Company to a market share of 35% only.
Table 1 Market share of Ford in 1913 Producer Assembly Plant Location Sales in Market 1913 Share Luxury(Over$ 2500) 18,500 4.79% Packard Detroit, Michigan 2300 0.6% Piece-Arrow Buffalo, New York 2000 0.52% White Cleveland, Ohio 1500 0.39% Franklin Syracuse, New York 1400 0.36% Winton Cleveland, Ohio 1300 0.34% Locomobile Bridgeport, Connecticut 1100 0.28% Oldsmobile Lansing, Michigan 1000 0.26% Others ------7900 2.05% Medium($1500-$2500) 62,500 16.19% Cadillac Detroit, Michigan 15,000 3.89% Chalmers Detroit, Michigan 8,000 2.07% Hudson Detroit, Michigan 5,000 1.30% Oakland Pontiac, Michigan 4,000 1.04% Mitchell Racine, Wisconsin 3,000 0.78% Cole Indianapolis, Indiana 3,000 0.78% Ramblers Kenosha, Wisconsin 3,000 0.78% Others ------21,500 5.57% Moderate($600-499) 120,000 31.09% Willys-Overland Toledo-Ohio 35,000 9.07% Buick Flint-Michigan 26,000 6.74% Studebaker South Bend-Indiana 25,000 6.48% Hupmobile Detroit- Michigan 12,000 3.11% Reo Lansing- Michigan 9,000 2.33% Maxwell Terrytown, New York 4,000 1.04% Paige-Detroit Detroit- Michigan 3,000 0.78% Others ------6,000 1.55% Inexpensive(Under$600) 185,000 47.93% Ford Detroit- Michigan 178,000 46.11% Others ------7,000 1.81%
Note. From The Changing Auto Industry by James M. Rubenstein, 1992, New Fetter Lane, London, EC4P 4EE. p 43.
Introduction 6
Chrysler Corporation was another important firm that entered into the industry in
1925. The company soon became a recognized producer due to the manufacturing of well-designed cars. The management of company took effective measures to catch the attention of potential customers. As a result, the company emerged as an important part of the Big Three companies of United States of America.
The automobile sector performed well in America as it was supported by good engineers, managers and investors. However, the Great Depression of 1930s appeared to be the first test for American automobile industry. After some hazards, the industry managed to overcome the negative repercussions and got on the right track again. During the first and second world wars, the production and growth of the industry were severely affected for understandable reasons. However, the post war period engender prosperity for the sector in form of better automatic transmissions, air conditioning, V-8 engines, functional power steering and brakes.
The decade of 1950s proved to be very fortunate for the industry and the sale for cars in the United States reached 4.8 million units which increased to 7.2 million by
1955. There were 13.7 million units produced during 1955. America led the industry by contributing 9.2 million vehicles to the production of world. During the decade, many firms such as Kaiser, Studebaker, Packard, Nash and Hudson left the market due to bankruptcy and other internal reasons. Subsequently, there left only three large domestic manufacturers in USA which formed a cartel. However, the subsequent decade changed the scenario for the U.S. auto sector and the production went down to 7.9 million units while there was a rising trend in rest of the automobile world. The primary cause for this downward trend was the invasion of Japanese firms which were capitalizing the Introduction 7
advantages of quality and price over American companies. The invasion of Japanese firms is depicted in table 2 which shows the rising trend in import of vehicles in America.
Table 2 Import penetration of U.S. automobile market 1960-84 (Hundreds of thousands unless otherwise specified) Year Total New Car Registration Imports Import Share(%) 1960 6.58 0.5 7.6 1961 5.85 0.38 6.5 1962 6.94 0.34 4.9 1963 7.56 0.39 5.16 1964 8.07 0.48 5.95 1965 9.31 0.57 6.12 1966 9.01 0.66 7.33 1967 8.36 0.78 9.33 1968 9.40 0.99 10.53 1969 9.45 1.06 11.22 1970 8.39 1.23 14.66 1971 9.83 1.29 13.12 1972 10.49 1.53 14.59 1973 11.35 1.72 15.15 1974 8.70 1.37 15.75 1975 8.26 1.50 18.16 1976 9.75 1.45 14.86 1977 10.39 1.98 18.28 1978 10.95 1.95 17.86 1979 10.36 2.35 22.68 1980 8.76 2.47 28.20 1981 8.44 2.43 28.89 1982 7.75 2.27 29.29 1983 8.92 2.46 57.58 1984 10.12 2.52 24.90 Note: From Japan Automobile Manufacturers Association (JAMA), http://njkk.com/about/industry8.htm)
Hence, the big three companies of America had to struggle for their survival as
Chrysler slid into bankruptcy and Ford petitioned the government for import relief. Later on, the big three companies got rid of the problems during 1970s. Thus America sustained the first position in the production of automobiles by manufacturing 9 million Introduction 8
vehicles with 27% market share. Japan stood second with 6.9 million vehicles and 21% market share. Germany, France and Britain stood 3rd, 4th and 5th in ranking. (History of
American Technology, 1998)
The oil crisis hit the American auto sector adversely and Japan got the opportunity to enter the market with its fuel-efficient vehicles. In the early 1980s, Japan became the world leading producer of vehicles with over 11 million units followed by
USA and Germany. The ranking remained almost same by the end of decade. The decade of 1990 was characterized by recession due to Iraq-Kuwait war along with mergers and joint ventures between foreign and local firms. The first decade of 21st century started with a negative note due to September 11, attack. There was another shock absorbed by the industry when oil prices reached the peak in 2008. However, the most difficult period for the industry was global financial crisis of 2008-09 that affected the entire economy including the auto sector. As a result, a bailout plan for the rescue of industry was approved by the government.
Despite all these issues and problems, the American automobile sector is still one of the leading sectors of the world. In 2010, the American auto industry ranked 2nd in the production of vehicles when it generated the output of 7,761,443 units.
1.1.2.2. Evolution of Japanese automobile industry. Besides the United States and
European countries, Japan also contributed substantially to the development of automobile industry. In 1902, Mr. Komanosuke Uchiyama had the honor to produce the first Japanese vehicle in Ginza. However, the mass level production of vehicles could not start until the arrival of Mitsubishi Zosen Company that manufactured 22 Mitsubishi model in 1914. By 1930, the automobile market of Japan was dominated by American Introduction 9
manufacturers. The American firms were producing around 20,000 units against the domestic Japanese production of 500 units per annum. Realizing the monopoly status of
American producers, the Japanese government approved Automobile Manufacturing
Industries Act in 1936 which brought two Japanese companies, Toyota and Datsun, into the market.
The Automobile Manufacturing Industries Act could not stop the invasion of
American firms and the market was ruled by the big three companies of America. Hence, local Japanese producers were forced to take effective measures to break the oligopoly of foreign firms. After World War II, many firms in Japan redesigned their strategy and went for effective innovations. During 1960s, a number of measures were taken by the enterprises to manufacture fuel efficient and cost effective vehicles. In 1967, the Japanese
Automotive Manufacturers Association (JAMA) was established with a view to helping the local manufacturers to deal with the challenges of General Agreement on Tariff and
Trade (GATT).
During 1970s, the oil crisis created the need for the small and fuel efficient vehicles. Hence, Japanese auto manufacturers got the advantage over American producers as the American producers had been focusing on the vehicles of high power with large engines. The Japanese producers capitalized that opportunity and entered into the American market. In the beginning of 1970s, Japan became the third largest exporter of the world by exporting 1090,000 vehicles. By 1974, Japan took the leadership of world exporter by exporting 2620,000 units to different parts of the world (JAMA, 2001).
Due to the wave of consumerism and high standard of living in Japan, there was a shift in the use of vehicles from commercial purpose to personal consumption. The shift Introduction 10
was very prominent as there were around 14% people who owned their personal cars in
Japan. During 1970s, the proportion reached up to 50.6% (JAMA, 2001).
The second oil crisis in the late 70s influenced the American economy adversely.
The logical consequence was the availability of more room for Japanese fuel efficient small vehicles in the market. However, the invasion of Japanese exporters was not tolerated and various protective measures were taken by American and European regulators. In order to cope with the impact of protective measures, Japanese automobile companies changed their strategy by forming joint ventures and establishing their plants in United States and Europe. Honda established Honda of American Manufacturing plant in Ohio which started its production in 1982. Similarly, Nissan Motors Manufacturing
Corporation USA commenced its operation in Tennessee during 1983; Toyota initiated production at NUMMI plant in 1984. Besides these producers, other manufacturers including Mitsubishi with Chrysler, Mazda with Ford, and Suzuki with GM Canada, Fuji and Isuzu started their operation in America and Canada. In the United Kingdom Nissan,
Honda and Isuzu entered the auto industry and in Austria, Nissan and Mitsubishi were the major Japanese manufacturers.
The period from 1987 and 1989 proved to be very prosperous for the local automobile sector as the industry gave highest output of 13.5 million units and sales volume of 7.78 million units (JAMA, 2001). However, in 1991, the asset-inflated economy collapsed and the outcome was decline in production, sale and profit. Hence the firms were forced to take extra ordinary measures to cope with the challenges of weak economy. Introduction 11
Due to affordability, better reliability and increasing popularity of Japanese vehicles, Japan became the largest car producer in 2000. In 2008, Toyota superseded
General Motors and became the biggest car manufacturer in the world. In 2010, Japan ranked 2nd in the production of vehicles by producing 9,625,940 vehicles (OICA, 2008).
However in 2010, Japan stood 3rd with 9625940 vehicles. In spite of Chinese domination in the automobile industry, Japan enjoyed a very good position in the world and it had the potential to beat China and America to attain the first position again.
1.1.2.1. Evolution of European Automobile Industry. Europe is recognized as the pioneer continent for automobile. Nicholas-Joseph Cugnot built the first steam powered automobile in form of a tractor in France in 1769. In spite of inventing the first vehicle,
Europe could not establish automobile industry until the arrival of Karl Benz in Germany.
Benz established the plant in 1901 and enabled the country to produce around 900 cars per annum. In 1926, Daimler-Motoren-Gesellschaft and Benz & Cie. were merged to form Daimler-Benz AG. Due to the huge investment and mergers, Germany became the
4th largest producer of the world in 1933 with an output of 92,226 passenger cars which was 4.3% of total world output. By the end of 1938, Germany took 3rd position in the manufacturing of cars (Erik Eckermann, 2001).
Besides Germany, serious efforts were also made in Britain to develop the automobile sector. The history of this industry began when Frederick Simms acquired
British rights to Daimler's engine. As a result, he established Daimler Motor Syndicate
Limited for Daimler-related products. Initially, Great Britain manufactured the vehicles for the domestic market only. Therefore, the vehicles were designed for the narrow and winding roads of the England and they had less attraction for foreign consumers. Introduction 12
However, the protective measures and vast colonies of the British Empire enabled the country to retain the 2nd position in the production of cars in 1938.
Table 3 Worldwide passenger car production (1933 and 1938) Country 1933 1933 Market 1938 1938 Market Units Share% Units Share% USA 1573512 72.9 2000985 65.6 ENGLAND 220775 10.2 341082 11.2 GERMANY 92226 4.3 276804 9.1 FRANCE 163770 7.6 199750 6.6 CANADA 53849 2.5 125081 4.1 ITALY 32000 1.5 59000 1.9 USSR 10252 0.5 26800 0.9 Note: From World History of the automobile by Erik Eckermann 2001. Society of automative engineers, Inc. 400 Commonwealth Drive Warrendale. P.A. 15096-0001 USA. p 101.
The automobile sector in France was developed very late in 1887. Various experiments were conducted to enhance the performance of vehicles. By the end of 1933,
France became the 3rd largest producer of passenger cars. However the position was lost within five years due to the financial crisis in the country.
After Second World War, the European infrastructure was destroyed along with the structure of automobile industry. However, after the war, the continent rehabilitated remarkably and industrial development restarted in all spheres with a faster pace. By
2011, there were three countries of this continent which were included in the top ten list of world vehicles production and they included Germany that secured 4th position with
5905985 vehicles, Spain stood at 9th position with 2387900 vehicles and France was 10th in the ranking with 2227742 (OICA, 2008). Introduction 13
1.1.2.4. Development of Pakistan automobile sector. General Motors and Sales Co.
Ltd. was the first automobile company that was established in Pakistan in 1948. The plant was established in 1949 in order to conduct the experiment for establishing automobile plant in Pakistan. Since the experiment of General Motors Company was successful, the base of plant was enhanced and it started to assemble Bedford truck. Due to the successful entry of the company in Pakistan, few more U.S. producers of automobile stepped forward to form joint ventures with Pakistani firms.
The history of automobile sector in Pakistan had four phases i.e. pre- nationalization, nationalization, post-nationalization and privatization periods. By 2010, the industry was producing almost all type of vehicles in the country. The major firms of the industry included Pak Suzuki Company Limited, Indus Motors, Honda, Dewan
Farooq, Hino Pak and Ghandhara Industries, Ghandhara Nissan, Millat Tractors, Al-
Ghazi Tractors and Honda Motorcycle.
The industry had a significant position in the large scale manufacturing sector in
Pakistan. It offered direct and indirect employment to millions of people, it attracted billions of Rupees domestic and foreign investment and it contributed substantially to the
GDP of country. Despite the problems and issues in the industry, it had the potential to grow and contribute to the national economy in a better way.
1.1.2.5. Evolution of automobile industry in other regions. In addition to America,
Europe and Japan, the automobile industry also evolved in other regions of the world.
China is the first country that needs to be focused due to its tremendous growth in last few years. Despite the marvelous present of Chinese automobile sector, the past is not attractive enough. Unlike the early development of industry in Europe and America, the Introduction 14
automobile sector came into being in China in 1949 under the regime of the Communist
Party. Due to the priorities of rulers, there was an alliance with the USSR. The USSR provided assistance in the first automobile project for the country in 1956 by means of
First Automobile Works (FAW) and the first car Jiefang CA-30 was produced. However, the relationship between China and the USSR deteriorated during 1960s and Russia withdrew the assistance. As a result, China had to take measures for self reliance.
The second phase of Chinese auto sector started from 1978 and continued till 1994.
The period was characterized by centralized planning, low output of vehicles, protectionism and establishment of new factories. The concentration phase (1995-2004) brought about growth and prosperity when the government approved Automotive
Industrial Policy. Due to extraordinary efforts by the policy makers and manufacturers the automobile sector performed tremendously and China became number one country in the production of automobiles in 2008. The performance of Chinese automobile industry was remarkable and the industry was on the top of the world automobile producers in
2010 with 18264667 vehicles.
South Korea was another significant Asian country that contributed tremendously in the development of automobile industry. The industry began its journey in 1955 when a Korean businessman Mu-Seong assembled the first car Sibal. The decade of 1960s was characterized by policy making, joint ventures and investment. From 1970 to 1990, there were development of new ventures, transfer of technology, production of spare parts domestically and mass level production of vehicles in the country. By 2010, South Korea became 5th largest producer of vehicles with 4271941 units (OICA, 2008) Introduction 15
Another significant contributor to the world automobile sector is India which has the strength of dense population and abundant resources. Till 1944, there was no production of vehicles in the country and the cars were imported directly from United
Kingdom. Mahindra & Mahindra was the first company that assembled a utility Jeep CJ in 1945. Due to nationalization, the auto industry could not grow rapidly in the region. In
1970, the government imposed restrictions on the import of vehicles and ultimately, the local production of vehicles grew to an extent. Due to the transformation of closed economy into a free trade economy, certain companies including Suzuki and Toyota of
Japan and Hyundai of South Korea were given licenses to make investment in the country. Later on, several other foreign firms also formed joint ventures with the Indian firms. As of 2010, India is the 6th largest manufacturer of vehicles with 3536783 units
(OICA, 2008)
1.1.3 Growth of global automobile industry. The automobile industry started its journey in 1886 when Duryea Motor Wagon Company was established in the
United States of America. At that time, there were few vehicle manufactured by the industry. However, the subsequent period brought about the success and the industry became one of the largest sectors in the manufacturing industry. The story of the success of this industry is depicted in the table 4.
According to the table, there was a rising trend for production of vehicles from 2002 to 2007. The industry went into negative zone in 2008 and 2009 due to global financial crisis. However, after one year, there was a substantial rise 26% that showed the recovery of the sector.
Introduction 16
Table 4 Production trend of vehicles 1997-2010 Production Year (Units) % Change
1997 54,434,000 N.A.
1998 52,987,000 -2.7%
1999 56,258,892 6.2%
2000 58,374,162 3.8%
2001 56,304,925 -3.5%
2002 58,994,318 4.8%
2003 60,663,225 2.8%
2004 64,496,220 6.3%
2005 66,482,439 3.1%
2006 69,222,975 4.1%
2007 73,266,061 5.8%
2008 70,520,493 -3.7%
2009 61,791,868 -12.4%
2010 77,857,705 26.0% Note: Adapted from OICA
Besides the overall performance of the industry, country-wise progress is shown in table 5. The table summarizes the performance of major countries pertaining to the production of vehicles from 1950 to 2010. In 1970, America ranked number 1 in the production of automobiles units. The competition continued between America and the
European countries up to 1980 and then Japan became the largest producer of vehicles. In Introduction 17
the 21st Century, China emerged quickly as an industrial super power and became the
largest automobile producer by 2010.
Table 5 Country-wise output of vehicles 1970-2010 (in Units)
Country 2010 2005 2000 1995 1990 1980 1970 Rank /Region
01 China 18,264,667 5,708,421 2,069,069 1,434,772 509,242 222,288 87,166
02 United States 7,761,443 11,946,653 12,799,857 11,985,457 9,782,997 8,009,841 8,283,949
03 Japan 9,625,940 10,799,659 10,140,796 10,195,536 13,486,796 11,042,884 5,289,157
04 Germany 5,905,985 5,757,710 5,526,615 4,667,364 4,976,552 3,878,553 3,842,247
05 South Korea 4,271,941 3,699,350 3,114,998 2,526,400 1,321,630 123,135 28,819
06 India 3,536,783 1,638,674 801,360 636,000[17] 362,655 113,917 76,409
07 Brazil 3,381,728 2,530,840 1,681,517 1,629,008 914,466 1,165,174 416,089
08 Mexico 2,345,124 1,624,238 1,935,527 935,017 820,558 490,006 192,841
09 Spain 2,387,900 2,752,500 3,032,874 2,333,787 2,053,350 1,181,659 539,132
10 France 2,227,742 3,549,008 3,348,361 3,474,705 3,768,993 3,378,433 2,750,086 Note : From Wikipedia Retrieved January 12, 2013 from http://en.wikipedia.org/wiki/List_of_countries_by_motor_vehicle_production#cite_note-1
1.1.4 Economic significance of global automobile industry. The
world automobile industry has a significant impact on the economic growth of world. The
automobile sector would be the sixth largest economic power of the world if it were a
country (OICA, 2008). The industry manufactures more than 60 million vehicles in a
year which require around 9 million direct employees which is around 5% of the world
total manufacturing employment. In addition to this, each direct employment results in
the creation of 5 indirect jobs in the society. Therefore, the estimated employees in the
world automobile industry are around 50 million. The sector also plays a significant role Introduction 18
in the investment of research and development as the expenditure was over €84 billion
(OICA, 2008). The global automotive industry is also one of the biggest generators of revenue across all economic sectors. The facts and figures reveal that the sector is very prominent in the world economy. Due to the need of mobility, there are bright prospects for the growth of this sector in future.
1.1.5. Factors influencing the auto industry. An industry is influenced by internal and external environment. The external environment contains political, economic, social and technological factors. All of them influence the performance of industry directly or indirectly. However, the most dominant factors are macroeconomic determinants that influence the industry substantially. These determinants include per capita income, inflation, unemployment, interest rate, consumption rate, foreign direct investment and rate of exchange. A high growth rate, for instance, increases the purchasing ability of the consumers which results in rise in demand and production. A decrease in rate of interest results in increase in supply of credit and ultimately, the demand for vehicles goes up. The monopoly or oligopoly market structure leaves few choices for the consumers and enables the firms to book high profit and sales. Therefore, the effect of economic factors in the industry is vital on the progress of an industry.
1.2 Purpose of the Study
The purpose of this study is elaborated as follows:
1.2.1. General purpose. Generally, the study aims at finding out the macroeconomic determinants of the performance of automobile industry of Pakistan over a period of fifteen years from 1995 to 2010. Introduction 19
1.2.2. Specific Purpose. The specific purpose of the study is to take into consideration the effects of Gross National Income, Inflation Rate, Exchange Rate,
Unemployment Rate, Consumption Rate, Discount Rate and Foreign Direct Investment on the Sales, Profit, Return on Assets, Return on Equity and Net Profit Margin of the selected original equipment manufacturers.
1.3 Justification of the Study
There are following arguments for justification of the study:
a. With the help of this research, various governmental bodies will be able to design
concrete and precise policies for the automobile sector.
b. The study will benefit the entrepreneur in two ways. The first aspect is that the
owners will come to know the factors that influence the performance of industry
and firms. Further, they can make the strategies in accordance with the influence
of these factors.
c. The study will serve as a reference point for research scholars. They will use the
data and methodology for further study.
d. The non-government and social organizations will be benefited by making their
strategy pertaining to employment, consumer rights, labor laws etc. according to
the findings of this research.
e. The study is likely to help the economists and industrialists in understanding the
relationship of automobile industry with the macroeconomic factors. Introduction 20
1.4 Scope of the Study
Automobile Industry is categorized into two large segments i.e. Assembling Unit and Auto Parts & Accessories Unit. The contribution of Auto Parts & Accessories Unit does not lie in the area of this study. The scope of this research will be limited to the contribution of Assembling Units which are commonly known as Original Equipment
Manufacturers. The period covered in the study will be from 1995 to 2010. The study takes into account the impact of seven macroeconomic factors which have been mentioned above. Moreover, the scope of performance of the firms is also limited to sales, profit, Return on Assets, Return on Equity and Net Profit Margin of the assembling companies. The study takes into account the data from 1995 to 2010. Further, it is limited to the member companies of Karachi Stock Exchange and PAMA.
1.5 Hypothesis
The topic of research is “Determinants of the Performance of Automobile Industry from 1995 to 2005- A Case Study of Pakistan”. So, the study is based on the following hypothesis:
Ho: The Per Capita Gross National Income, Inflation rate, Exchange rate, Discount rate, Unemployment rate, Consumption rate or Foreign Direct Investment has no impact on the performance of automobile industry in Pakistan.
H1: The Per Capita Gross National Income, Inflation rate, Exchange rate, Discount rate, Unemployment rate, Consumption rate or Foreign Direct Investment has significant impact on the performance of automobile industry in Pakistan. Introduction 21
1.6 Definition of Key Terms
a. Automobile Industry: It means the original equipment manufacturers which are
engaged in the production of vehicles.
b. Determinants: They include the macroeconomic variables which directly or
indirectly influence the sales, profit or profitability ratios of the automobile firms
c. Performance: It means the sales, profit, Return on Assets, Return on Equity and
Net Profit Margin of the firms.
1.7. Basic Assumptions
The study is conducted on the basis of following assumptions:
a. The impact of certain macroeconomic factors on the performance of automobile industry in Pakistan is determinable.
b. Macroeconomic factors have measurable influence on the progress of industry.
c. Performance of the industry is measurable by means of sales, profit, Return on
Assets, Return on Equity and Net Profit Margin of the original equipment manufacturers.
Review of Literature 22
Chapter 2
Review of Literature
This section provides a comprehensive review of literature from three aspects.
The first segment presents literature on the criteria for measuring the performance of firms. The second part describes the factors which are likely to affect the performance of an organization. The final part enunciates the detailed literature review of automobile industry in Pakistan.
2.1 Performance Measurement
Organizations are established to achieve specified goals. The mission may be maximizing profit, providing welfare to the society, increasing market share, securing monopoly in the market etc. So, the organizations create vision, formulate policies, develop strategies and set short term objective in order to achieve the goals and mission.
If a firm fails in the accomplishment of its mission, it may face poor efficiency, waste of resources, high labor turnover, loss of market share, poor quality of production, financial inefficiency or dissolution of the firm. Therefore, this is inevitable for an organization to keep an eye on the accomplishment of mission consistently so as to ensure the continuity, growth and long run survival. The process of evaluating the progress of an organization is called Performance Measurement. Performance can be defined as
“A state of competitiveness of the organization, reached through a level of efficiency and productivity which ensures a sustainable market presence”(Ana-Maria, G, Florica,
B., Lina R., 2010). Review of Literature 23
. Performance is also defined as
“The accomplishment of a given task measured against preset known standards of accuracy, completeness, cost and speed.”
Before discussing the methods of performance measurements, there should be a complete knowledge as why an organization needs this process.
2.1.1. Rationale for performance measurement. Performance
Management is considered as a process that identifies measures and develops progress in an organization by establishing links between individual’s objectives and organization’s overall mission. Therefore, measurement of performance enables an organization to accomplish the mission efficiently. Besides this major benefit, a good monitoring system has number of other advantages such as motivation to management and employees, facilitation of explicit and implicit control, description of job precisely, efficient use of resources and effective decision-making. On the other hand, any shortcoming or failure in this regard may result in a disaster for the firms.
The history of measuring performance is very old and it started in 350 BC (Niven,
2002). Sun Tzu (Niven, 2002) concludes that:
"The General who wins the battle does many calculations in his temple before the battle is fought. The General, who loses, makes but few calculations."
As per the IT Performance Management Group (ITPMG, 2007), there are many benefits which may be derived from measurement. It helps increase opportunities to enhance the knowledge of managers, reduce the unknown elements of future, facilitate accurate decision-making and finally curtail the overall risks. Review of Literature 24
However, performance measurement is not a simple process and it should be handled with proper objectivity. So, it should be objective, clear, well integrated and linked to the targets of the organization (Anon, 2006 a).
2.1.2. Approaches to performance measurement. There a many modern and traditional methods to measure the performance and there is a long list of performance measurement models developed over a period of time. The summary is given in Table 6. However, most relevant technique is Key Performance Indicators.
Pekeliling (Kritzinger, J.A., 2009) looks at Key Performance Indicators as a tool which may be used to measures the performance continuously. He emphasizes that Key
Performance Indicators should not be large in number and preferably should not exceed ten. According to him, it is necessary for a company to use those factors which may increase business with existing and prospective customers.
These measures are mainly grouped in financial and non-financial measures. The financial measures include sales volume, profitability, cost of production, operating expenses, market share etc. On the other hand, non-financial measures are quality control, labour satisfaction, social responsibility, dealing with competition, management performance etc.
Review of Literature 25
Table: 6 Performance measurement models Year of Title of model/framework Authors Introduction Before 1980s ROCE, ROE, ROI and derivates Simons (2000) 1980 EVA- Economic Value Added Model Stewart (2007) SMART- Strategic Measurement Analysis and Cross and Lynch 1988 Reporting Technique (1988) ABC- Activity Based Costing – Cooper and Kaplan 1988 ABM- Activity Based Management (1988) 1989 SPA- Supportive Performance Measures Keegan et al. (1989) 1990 PMQ- Performance Measurement Questionnaire Dixon et al. (1990) Customer Value Inc. 1990 CVA- Customer Value Analysis (2007) 1991 RDF- Results and Determinants Framework Fitzgerald et al. (1991) Kaplan and Norton 1992 BSC- Balanced Scorecard (1992) 1994 SPC- Service-Profit Chain Heskett et al. (1994) 1995 ROQ- Return on Quality Approach Rust et al. (1995) CPMS- Consistent Performance Measurement 1996 Flapper et al. (1996) System CPMF- Cambridge Performance 1996 Neely et al. (1996) Measurement Framework IPMS- Integrated Performance Measurement 1997 Bititci et al. (1997) System IPMF-Integrated Performance Medori and Steeple 1998 Measurement Framework (2000) 1998 CBS-Comparative Business Scorecard Kanji (1998) 1999 BEM-Business Excellence Model EFQM (2007) DPMS-The Dynamic Performance Measurement 2000 Bititci et al. (2000) System 2001 PP-Performance Prism Neely et al. (2001) MSDD-Manufacturing System Design 2001 Cochran et al. (2001) Decomposition Epstein and Westbrook 2001 APL-Action-Profit Linkage Model (2001) CEVITA-Capability Economic Value of Intangible Ratnatunga et al. 2004 and Tangible Assets Model (2004) 2004 PPVC-Performance Planning Value Chain Neely and Jarrar (2004) PDGBS-Performance, Development, St-Pierre and Delisle 2006 Growth Benchmarking System (2006) Balachandran et al. UCDF-Unused Capacity Decomposition 2007 (2007) Framework
2010 EFQM Excellence Model EFQM 2010 Note: Adapted from Performance Measurement Models-Comparative Review by Lisiecka, K.; CzyżGwiazda, E. 2013. http://ebookbrowsee.net/lisiecka-pdf-d527842407
Review of Literature 26
Key performance indicators are not merely an analysis of rows of data on spreadsheets or reports. They address the key issues by making the results visible (Anon,
2009). When results and indicators are properly monitored and trended, the outcome elaborates a true picture of the process along with the effectiveness. Subsequently, Key performance indicators generate the intended action and improve performance. Parmenter
(2007) concludes that when Key performance indicators applied on the organization make it possible for the members to move together in the right direction. These Indicators serve as monitor that keeps on checking critical aspects of organizational performance
(Parmenter, 2007). Hough (2007) also endorses the idea. He states that the profile of key performance indicators clearly specifies the desired level of performance from an individual. Due to this significance, these indicators are critical factors to alert the managers immediately if something goes wrong.
Pekeliling (Kritzinger, J.A., 2009) enunciates that Key Performance Indicators should be able to measure the performance continuously. According to him, a firm must know and understand the important factors which are necessary to increase the business in right direction. This is also necessary that key performance indicators should be stated in measurable and quantitative terms and they should be able to encompass the results of entire progress. A company needs to have a clear cut understanding about its critical success factors in order to have a good performance measurement system (Parmenter,
David, 2002).
2.1.3. Key financial indicators for automobile industry. The application of Key Financial Indicators varies from one type of business to another. In a manufacturing concern, production is undoubtedly a complex process. The nature and Review of Literature 27
flow of information are also complex and the application of key performance indicators becomes difficult. Therefore, there must be an alignment in the application of KPIs and objectives of a manufacturing concern (Anon, 2006 b). As we can see that there is a shift in performance measurement approaches from financial to non-financial aspects.
Since all of the aspects cannot be taken into account, we consider financial standards that are used to evaluate the performance of automobile industry. There are many reasons to choose financial indicators as a criterion for performance measurement. First of all, the financial standards are easy tools to evaluate the progress of an organization as they are specific, measurable, relevant, achievable and timely in their nature. Another edge of financial standards over other measures is that they extensively offer testable and concrete information (Ijiri, 1975). Further, the financial ratios are comparatively precise performance standards as compared to other standards. Financial measures allow a high level stability criterion. For example, profitability is a stable criterion to measure the performance of any profit-making organization (Steers, 1975). The performance measures based on accounting provides a wide range of historical analyses that covers short-term, medium-term and long-term evaluation of progress (Price et al., 1986, 132). Price and Mueller (1986), advocate that the financial measures are easy to generalize on various forms of organizations.
They further argue that such standards facilitate a historical comparison between the competitors over a period of time.
Despite these benefits, the financial measures are exposed to many drawbacks.
For instance, they generally focus on short term benefits; they lack strategic focus; they Review of Literature 28
encourage local optimization; and they are focused internally (Bourne, M., Mills et al,
2000). However, due to their benefits over the shortcomings, they are appropriate measures of the performance as they fulfill the requirements of this study.
As mentioned above that there are several aspects from which the performance may be measured. These include high sales volume, profitability, customer satisfaction, employees’ advancement, quality of product, innovation and inventions. It is also pertinent to note that the financial stability of firms is very significant for staff, investors, bankers, government and regulatory authorities alike (Lin & Piesse 2004:73). Therefore, the study is restricted to financial measure and the most significant measures are sales volume, Annual Profit and financial ratios pertaining to profitability.
2.1.3.1. Annual Profit as a measure of performance. As mentioned above, the primary task of a business organization is to maximize the profit or minimize loss. Every policy of a business aims at earning more and more profit. Therefore, the success or failure of an organization depends upon the consistent achievement of this profit. Merely profit is not sufficient to reflect the performance of an organization and this is necessary to combine other aspects along with profitability. However, profit is one of the significant factors that depict the progress of a company.
Growth along with the dimension of profitability is generally interpreted with respect to return on assets or net profit margins. If the entrepreneurship is defined as the creation of rents by means of innovation (Stewart, 1991), assuming that rents are the above average earnings with respect to competitors (Norton, 2002), then measures of profitability seem to be appealing. Delmar et al (2003), in contrast, identified that profit is undoubtedly an important indicator of success, however, the relationship between profits Review of Literature 29
and size of the firm is visible only in the aggregates of firms or over long periods for individual firms.
The profit is computed by deducting cost of goods sold and operating expenses from Sales Revenue. In this way, it not only displays the revenue side, but also the expense side. Therefore, the firms achieving high profits may be called good performers.
2.1.3.2. Annual Sales as a measure of performance. Another important element to measure the performance is Annual Sales in monetary terms. Although this measure has limited scope as compared to profit, it reveals the progress of a company pertaining to demand of its goods and marketing strategies. Therefore, high sales volume reflects that the organization is successful in raising demand by means of marketing, better quality, innovation or other means.
Delmar et al (2003) enunciated different standards of performance and he declared sales as the best indicator for the purpose of performance evaluation. The rationale is that the data for sales can easily be collected and this data depicts short and long-term variations in an organization. Further, Barkham et. al (1996) also states that a sale is chosen as one of the favorite indicators by stakeholders. There are many other reasons for taking sales as a measure of performance. The most significant reason is the growth in sales volume also affects number of employees and worth of assets. So, this measure serves as a basis of overall expansion of the firm. However, Delmar et al (2003) pointed out that sales is not always the only good measure of performance and it should be supported by the level of employment and assets.
2.1.3.3. Financial ratios as a measure of performance. Ratio is a relationship that is expressed in numeric terms between variables which are related with each other. Review of Literature 30
The analysis of financial ratio is one of the most important techniques for measuring the progress in various areas. These ratios are used as yardsticks to measure the progress, efficiency and development of an organization. They are also used to evaluate the current financial status and historical performance of a firm over a period of time.
The history of financial statement analysis started in the last century (Horrigan,
1978). Initially, these ratios were used to analyze insolvency and bankruptcy of business
(Altman, 1968). Later on, their emphasis was shifted towards the evaluation of business performance from various angles (Chen & Shimerada, 1981; Brand, Danos, & Brasseaux,
1989; Poston, Harmon, & Gramlich, 1994; Lawder, 1989; Kimmell, 1994; Gardiner,
1995; Kane, 1995).
Ward (2003), mentions that business managers often depend upon analysis of ratio because it gives an opportunity to monitor the business activities. They also do so for comparing their business with other enterprises. On the other hand, the other stakeholders are also able to review the progress of business and make decisions regarding their affiliation (Hingorani N.L., Ramanathan, A.R. 1986).
In addition to the measurement of performance, such analysis helps to diagnose potential problems with a historical background (Spathis, K., and Doumpos M., 2002).
According to Howard Finch (2005), financial ratios play a vital role in managerial decision making. These ratios provide a comparison of various figures from the financial statement. However, they are not merely mathematical calculations; they serve as a tool for interpretation and prediction. As a result, they give a concrete framework for the future steps which need to be taken by the management (Chien, T., Danw, S. Z. 2004). Review of Literature 31
Financial ratios are categorized on functional basis or on the basis of financial statement.
2.1.3.3.1. Functional classification and application of ratios. The functional classification of ratios is based on the functions the ratios perform. This classification is further divided into Profitability, Liquidity, Activity and Solvency ratios.
The first and most important type of ratio is profitability ratio. Profit is the primary objective of every business organization. All the management decisions aim at ensuring the maximization of profit. A strong profitability position safeguards the interests of all stakeholders. The significant profitability ratios include Net profit ratio, Expense ratio,
Price earnings ratio (P/E ratio), Gross profit ratio (GP ratio), Return on capital employed ratio, Earnings per share (EPS) ratio, Return on shareholder’s investment/Return on equity, Dividend payout ratio, Operating ratio, Dividend yield ratio and Return on common stockholders’ equity ratio.
Another important type of ratio is Liquidity ratio. These ratios are employed to measure the capability of a firm to settle short term debt. This category is significant for the lenders such as commercial banks and suppliers. Current ratio, quick ratio and liquidity ratio are some of the major ratios in this group.
Activity ratios are employed to measure the ability of a firm in creating revenues by means of transforming its output into cash or sales. The significant activity ratios include
Receivables turnover, Average collection period, Inventory turnover ratio, Average payment period, Asset turnover ratio, Working capital turnover ratio, Accounts payable turnover and fixed assets turnover ratio. Review of Literature 32
Solvency ratios are used to evaluate the capability of a firm to gain survival in long run period. The ratios which are frequently used to identify the long-term solvency include Debt to equity ratio, Times interest earned ratio, Proprietary ratio, Fixed assets to equity ratio, Current assets to equity ratio and Capital gearing ratio.
2.1.3.3.2 Classification of ratios on the basis of financial statements. Another approach is used to classify the ratios according to the financial statements. The ratios which are calculated from the data of Income Statement called Income Statement ratios such as net profit ratio, time interest earned ratio, operating ratio, gross profit ratio etc.
On the other hand, the second type is balance sheet ratios which are debt to equity ratio, liquid ratio and current ratio. The third group is of composite ratio which takes data from both the statements and they include receivables turnover, accounts payable turnover, inventory turnover and working capital turnover.
2.1.3.3.3. Selection of appropriate ratio for measuring the performance. As we have discussed that every ratio has a specific function and objective. Solvency and Liquidity ratios, for instance, are important for the lenders and suppliers. They evaluate the ability of an organization in a particular domain. Similarly, activity ratios are good measure as to how well a company performs in its operating cycle. However, profitability ratios are significant for almost all the stakeholders. Managerial performance is measured by profitability. Similarly, lender, suppliers, shareholders, investors and competitors are interested in the profitability. As a result, the progress of a firm should be measured by means of profit ratios to a great extent. In this research, the profitability ratios have to evaluate the performance of automobile industry and its firms. Hence the dependent Review of Literature 33
variables are profitability ratios of the automobile industry. More specifically, they are
Return on Investment, Return on Sales and Return of Equity.
2.1.3.3.4. Return on Assets (ROA). Return on Assets is one of the most important profitability ratios. It reflects the percentage earned on each unit of money invested in the assets of a firm. It is calculated with the help of following formula (Beaver, 1966):
ROA = (Net Profit/ Total Assets) * 100
Many researchers have established that Return on Assets is an effective measure of performance of organization. Rasiah (2010) stated that higher the ROA, better the firm’s profit. Berger and Humphrey (1997) also measured the profitability by using this ratio considering it to be a good standard. Rushdi and Tennant (2003) also established that ROA is helpful in measuring the performance and profitability.
Return on Asset is one of the most significant financial ratios. Its popularity can be highlighted by the fact that it is 3rd most frequently ratios in the field of business
(Mankin & Jewell, 2010). In addition to this, some aspects of Return on Assets are used in predicting the failure of business. The original Z-Score of Altman (1968) contained
Return on Assets along with five factors which were used to predict the failure of business. This ratio was also used by Beaver (1966) to predict the business failure along with five other factors. Hossari and Rahman (2005) conducted a research in order to rank the popularity of different financial ratios for predicting business failures. The study took into consideration 53 relevant studies from 1966 to 2002 and listed 48 separate ratios in a sequence. In this list, the ROA was found to be the only common and frequently used ratio in all of the failure prediction studies. Review of Literature 34
2.1.3.3.5. Return on Equity (ROE). This is the second most significant ratio to measure the profitability and performance of the firm. It is computed by dividing net profit by stockholders’ equity with the help of following formula:
ROE = (Net Profit/ Stockholders’ Equity) * 100
It measures the profitability from shareholders point of view. It states the return generated by a firm on investment made by shareholders. Therefore, ROE measures how efficiently a firm is using shareholders capital to generate profit (Garg Ax et al, 2005).
ROE is deemed to be the most important ratios of profitability along with ROA to measure the corporate financial performance (Rappaport 1986:31). Monteiro (2006:3) presents this ratio as the most important one from investors’ view point. Ugur Z. (2006) considers this ratio as an inevitable measure for profitability.
2.1.3.3.6. Net Profit Margin (NPM). This is another vital ratio that depicts how much profit is generated on each unit of money invested in sales. It is computed with the help of following formula:
Net Profit Margin Ratio = (Net Profit/ Sales) * 100
Where Sales is the sales revenue and Net Profit is the income derived after all the costs and expenses from sales revenue. This is also an important measure to evaluate the performance of a firm pertaining to sales and profits.
2.2 Determinants of the Performance
The purpose of this research is to explore those factors which significantly affect the performance of automobile industry. The precise aim is to find out the determinants of profitability and sales of automobile industry. Review of Literature 35
2.2.1. Past studies in automobile sector. There were many studies conducted to evaluate the performance of automobile industry. The past researches, however, emphasized upon a particular segment of the automobile industry. For example,
Smusin and Makaya (2009) conducted a research to find out the impact of short run macroeconomic variables on the sales of cars only. Similarly, Baber et al. (1999) attempted to explore the impact of exchange rate variations on car sale of American and
Japanese Manufacturers. Another research was conducted to observe the impact of growth pattern, ownership structure and pattern in trade and governmental role in automobile industry of specified Asian Countries (Nag, Benarjee and Chatterjee, 2007).
However, it did not address the core issues responsible for the growth and development of an automobile industry. (Narayanan and Vashisht, 2008). Therefore, there was a real need to identify the macroeconomic determinants that generally influence the performance of automobile industry over a period of time. So, this study aims at filling in this gap by means of empirical evidence.
2.2.2. Contribution of this research. As discussed earlier, there are many factors which may influence the progress of a firm. These factors can be viewed and grouped in different ways. For instance, PEST analysis can be used to identify the influential elements which are Political, Economic, Social and Technological factors.
Each of the variables has a great impact on the progress. For example, political uncertainty may decrease sales and profit; economic depression reduces production and sales; social elements such as taste of consumers may also affect the profitability and technological advancement calls for huge investment for survival. Review of Literature 36
Besides PEST Analysis, there are many other approaches to find out the determinants of the growth of an industry. Some experts go for the analysis of industry by means of Porter’s Model. Some experts observe the impact of micro economic variables on the performance. However, one of the important methods is to use macro economic variables as determinants for the measurement of firms’ performance.
2.2.3. Macroeconomic determinant of the performance of the firms and industry. Macro economic variables influence the profitability and sales of firms to a great extent. For instance, growth in Gross National Products may result in rise in sale volume of a firm, inflation causes rise or fall in profits, unemployment generates variations in sales volume, and Foreign Direct Investment may improve the returns on investments and so on.
Kangari (1988) developed a model to predict the success and failure of business and concluded that the macroeconomic variables had significant role in determining the performance of firms. Further, Russell and Zhai (1996) also stated that the macroeconomic variables were very vital in determining the failure or profitability.
Oxelheim and Wihlborg (1997) built a model called Macro Economic Uncertainty
Strategy to reveal the impact and interdependence of these factors.
Although there are many macroeconomic indicators, the most influential factors which may be responsible for the performance of a firm include Per Capita Gross
National Products, Discount Rate, Inflation Rate, Exchange Rate, Unemployment Rate,
Consumption Rate and Foreign Direct Investment. These variables have a vital role and significant impact in the determination of industrial growth.
Review of Literature 37
2.2.3.1. Per Capita Gross National Income (GNI). The World Bank (2014) defines
Gross National Income (GNI) per capita (formerly GNP per capita) as follows:
“GNI per capita is calculated as the gross national income divided by the midyear population, converted to U.S. dollars using the World Bank Atlas method. GNI is the sum of value added by all resident producers plus any product taxes (less subsidies) not included in the valuation of output plus net receipts of primary income (compensation of employees and property income) from abroad.”
GNI growth increases the national income of a country and the rise in per capita income may raise the purchasing power of consumers. As a result, the consumers are able to buy more goods and services. Hence, a rising trend in GNP per capita stimulates demand for commodities. Ultimately, the rise in demand enables the industries to make more production and profits.
This analysis is also applicable on automobile industry. When National Income rises, the profitability of automobile sector also increases as people go for buying more vehicles to improve their standard of living. Subsequently, GNI growth affects the sale and profitability of automobile firms indirectly. Rise in GNI gives birth to hike in income. High income level increases the demand for vehicles as the demand for automobile is a more elastic to income. Finally, rise in demand increases the sales and profit.
J. M. Dargay (2001) explored the impact of wages on the ownership of car. He concluded that increasing level of income led towards higher car ownership as their ability to purchase the vehicle improved. Similarly, J. Dargay (2007) moved a step forward when he examined the impact of income and prices on transport travelling in the Review of Literature 38
United Kingdom. The analysis revealed that there was a correlation between households’ income and travelling. If the income decreased, the households went for travelling by public transport and avoid buying the vehicles. Shahabudin (2009) established that income level, interest rate and unemployment rate had significant influence on car sales as well as on profit.
2.2.3.2. Inflation rate. Inflation is defined as a persistent rise in price level of goods and services. The World Bank (2014) defines Inflation as follows:
“Inflation as measured by the consumer price index reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly or monthly. The
Laspeyres formula is generally used.”
Inflation has a strong correlation with sales and profitability of a firm. It has been observed that rising trend in inflation increases industrial profit. When price level rises, the entrepreneurs have more opportunities to book high returns in short run.
The effect of inflation on firms’ profit is viewed in three ways. The first area is supply side or production, the second angle is demand side i.e. consumer behavior and the third one is the financing cost of both demand and supply. The effect of inflation on the profit of firms varies due to difference in nature of industries, markets, financial conditions and pricing dynamics. However, some common conclusions can be derived on the basis of different researches.
Sheshinski and Weiss (1977) established that a monopolistic firm optimally follows ‘S’, ‘s’ pricing rule during inflation. The firm generally maintains a fixed nominal price until the real price below ‘s’ is eroded by inflation. As a result, it goes for Review of Literature 39
increasing nominal price to ‘S’. During inflation, ‘S’ increases and ‘s’ falls which results in the rise in price. Benabou (1988, 1992) studied the behavior of monopolistic market structure. He established the concept that during inflation, the price adjustment of ‘S’,’s’ pricing with high frequency and wide gap provided a scope for search to consumers.
High costs for search caused higher equilibrium prices and greater price dispersion.
Almost the same theory was elaborated by Diamond (1993) but he developed his model with “sticker” prices. Lucas (1973) studied the impact of inflation uncertainty on price markups. He elaborated that rising trend in inflation increased the profit margin for the firms. Benabou and Gertner (1993) also stated that inflation raises the profit margin because a rise in the search cost of buyer implies greater profit margins of the firm.
The effect of inflation on gross margin or profit levels is scarcely highlighted both in theoretical and empirical literature. Benabou (1992) conducted a research on inflation and markup and concluded that both anticipated and abrupt inflations have a meager but important and inverse impact on markup. Kaskarelis (1993) also conducted a research in the UK manufacturing industry and reached the same conclusions.
2.2.3.3. Discount rate. Discount rate is defined as the rate of interest which is charged by the central bank on first class securities. It is one of the most important elements that shape the environment for business enterprises. The change in rate of interest transforms the supply of credit and direction. As a result, the demand and supply of products are influenced. For instance, when there is a rising trend in the discount rates, the cost of borrowing increases. Hence the consumers are discouraged to buy goods on expensive credit and the demand decreases which results in the reduction of sales and Review of Literature 40
profit of a firm. The same phenomenon takes place on supply side. The rise in cost of borrowing increases the cost of production and the profits of firms shrink.
Ganley and Salmon (1997) conducted a research in the UK in order to find out the effects of monetary policy on the production of industries. They concluded that output level of manufacturing concerns declined sharply and quickly due to contracting monetary policy. Hayo and Uhlenbrock (1999) found out that rise in interest rate had a negative correlation with output of nonferrous metal, iron, chemical, steel and office machinery related industries. However, the effects of monetary policy vary from one industry to another due to size of firms, nature of industry, import and export based firms etc. (Ganley and Salmon 1997).
Ludvigson (1998) examined the effect of interest rate on sales of car. He concluded that the increase of basic interest rate and car sales were significantly and negatively correlated. The negative relationship was because of the fact that commercial banks were less capable of granting loans to the potential car buyers for buying the vehicles. Victor and Natalya (2009) also concluded that car sales were quite sensitive to credit interest rate fluctuations.
2.2.3.4. Exchange rate. Changes in rate of exchange of foreign currency in terms of domestic currency also have substantial influence on the sales and profitability of firms especially when the industry is import-oriented. For instance, if the spare parts and raw material are imported from abroad, rise in the rate of exchange of foreign currency increases the cost of production. As a result, the profit margin tends to decline if the selling price remains constant. On the other hand, if selling price increases, fall in demand and sales volume may reduce profitability. If the currency is appreciated, the Review of Literature 41
exports decline due to the price elasticity of demand. However, depreciation in domestic currency reduces the cost of import which may result in the rise in demand. Therefore, it is pertinent to note that the fluctuation in rate of exchange have great impact on the performance of an industry.
The theoretical and empirical literature is available in ample amount on the relationship between exchange rate variations and the prices of goods sold in international markets. This is known as exchange rate pass-through. Krugman (1986) was the first person who established that the depreciation of importer’s currency may increase the exports if the goods have greater elasticity of demand. However, if the degree of elasticity is low, the deprecation will have no significant impact on export sales. Obadan
(2006) also states that fluctuation in exchange rate has a remarkable effect on export and import by means of the effects of relative prices of goods.
Despite this simple analysis, there are many theories that make the fluctuation of exchange rate a complex process. Charles (2006) explained that exchange rate is one of the most difficult and controversial economic policy tools. He is of the opinion that depreciation in the domestic currency just offers protection to domestic industry when the local cost of production rises much less than the rate of depreciation and prices of imported equivalent increases by the full amount of the depreciation.
Many other studies have been conducted on exchange rate exposure which means the sensitivity with the value of an asset or liability in order to a cause fluctuation in real exchange rates. Adler and Dumas (1984), Bodnar et al. (1998) put forward an argument that the exchange rate exposure will at best be perfectly proportional to profits in case of pure exporters. Review of Literature 42
2.2.3.5. Unemployment rate. Unemployment is another significant macroeconomic variable that may affect the sales and profitability. According to the World Bank (2014)
“Unemployment refers to the share of the labor force that is without work but available for and seeking employment.”
The theoretical model states that a rise in unemployment rate has dual effects on sales and profit of the firms. A rise in the rate of unemployment reduces the bargaining power of labors and they may be forced to work more on lower wages. It may result in the rise in profit of firms. On the other hand, a rise in this rate may reduce the level of per capita national income. As a result, the buying power of consumers decreases and the sale of luxurious commodities may also go down. Shahabudin (2009) conducted a research upon the impact of macroeconomic factors on sales of vehicles. He concluded that there was a significant influence of unemployment rate on car sales. The theory of Marx (1863) also discussed the impact of unemployment on profitability of the firms. According to this theory, the general labor productivity and profitability may be promoted by unemployment by means of higher exploiting ability of the employers.
2.2.3.6. Consumption expenditure. Final consumption expenditure is the sum of private and public expenditures. In other words, the consumption expenditures include households’ final consumption expenditure and government final consumption expenditure. This is a natural phenomenon that a rise in consumption expenditures reflects growth in aggregate demand for goods and services. As a result, the sales revenue increases due to a rise in quantity sold and upsurge in price level. Consumption expenditures affect the sales of a firm including automobile firms. Further, an increase in Review of Literature 43
consumption gives birth to rising price level due to high demand. So, it also influences the profits and profitability of firms in general.
2.2.3.7. Foreign direct investment. Foreign Direct Investment is another significant macroeconomic variable that influences the profitability and growth of a firm. A foreign direct investment is different from ordinary foreign investment in a sense that in FDI, the investor transfers money along with technology and management to the other country.
Hayter Roger G. (1997) explains that FDI includes those transactions that are monitored and organized by firms or industry outside of the nation. The International Monetary
Fund defines FDI as follows:
“Foreign Direct Investment is the Investment that is made to
acquire a lasting interest in an enterprise operating in an economy other
than that of the investor, the investor’s purpose being to have an
effective voice in the management of the enterprise.”
Foreign Direct Investment has a direct impact on the profitability of firms for many reasons. Foreign Direct Investment facilitates transfer of technology and management control. As a result, a firm is in a better condition to increase the sales volume and profits. When a firm receives Foreign Direct
Investment, the new technology enables it to improve the quality and innovation of goods. Further, the trained labors also facilitate production of better quality with lower cost. Hence the sales and profitability of firms increase.
Many researchers have attempted to explore the effects of foreign direct investment on performance of industries. Blomström and Kokko (1997) proved empirically that Foreign Direct Investment increases productivity and Review of Literature 44
development of the firms in the host countries. Alfaro (2003) analyzed the cross- country data for the period 1981-1999 and drew mixed response. According to his research, Foreign Direct Investment has an inverse effect on the growth of primary sector. Whereas it affects the manufacturing sector positively due to the facilitation of transfer of technology and training of labors. Lensink and Hermes
(2003) conducted a cross-country study of 88 countries to find out the effect of variation of foreign direct investment on the development of firms. He also concluded that FDI had a positive relationship with the growth. Djankov and
Hoekman (2000) concluded from the analysis of firm-level data for the period of
1994-1998 that there was an inverse correlation between FDI and turnover of the firms.
2.2.4. Review of statistical tools. There were several approaches used by the researchers to evaluate the performance of the automobile industry in different countries.
The Structure-Conduct-Performance paradigm was used to evaluate the progress of automobile industry in South Asian Countries (Nag, Benerjee, Chatterjee, 2007). Another method was used by Smusin and Makayeva (2009). In this method, they selected 10 macroeconomic variables so as to see their impact on car sales of the selected countries.
It used both theoretical and practical approach to test the hypotheses. In India, a research was conducted to analyze the determinants of competitiveness of Indian Auto Industry by
Narayanan G. and Pankaj Vashisht (2008). They applied econometrics tools to derive the results that included Stochastic Frontier Analysis and Panel Data Analysis with heteroskedastic panels. A similar research was conducted in Malaysia in order to forecast the demand of Malaysian Automative Industry and Transport. The research used Multiple Review of Literature 45
Regression Analysis and used macroeconomic variables to see the impact of some independent macroeconomic variables on Sales and Demand of Automobiles (Hamid et al, 2008).
In Pakistan, there was couple of researches conducted to evaluate the performance of automobile industry. However, all of them used the performance of the industry as independent variable. For instance, a working paper released by FBR (2011) discussed the role of automobile industry on the economic growth of Pakistan. It used simple descriptive analysis to find out the impact of Automobile Industry on economic growth of
Pakistan over a period of time. However, no direct attempt was made to find out the impact of macroeconomic variables on the performance of automobile sector in Pakistan.
So, there is no direct literature review available on this topic.
2.3. Automobile Industry in Pakistan
In this segment, the history, evolution and structure of automobile industry in
Pakistan is described in detail. The first segment explains the historical background of the industry in detail. The second part deals with the structure of Industry pertaining to
Original Equipment Manufacturers and auto part suppliers. The last part deals with regulatory authorities and policy makers who formulate the rules, regulations and laws for the industry.
2.3.1. History of automobile industry in Pakistan. In 1947, Pakistan got separated from United India. Both the newly born countries were agrarian economies.
There were already very few industries in the sub-continent and 95% of them were occupied by India. Due to this low industrial base, the share of manufacturing sector to
GDP was only 7% in Pakistan during 1947-48. Review of Literature 46
During 1950s, the country started taking measures to improve the industrial base in the economy. In the First Development Plan, more emphasis was laid down on the improvement of manufacturing sector and subsequently, Pakistan Industrial Development
Corporation was established. Due to the efforts of government authorities, the growth of manufacturing was accelerated by 57% at the end of 1955.
Since automobile sector was very vital in the large scale manufacturing sector, serious efforts were made by the private sector in order to establish the first automobile company. General Motors and Sales Co. Ltd. also built its plant in Pakistan in May 1949.
The company was established on experimental basis and very soon the foundry was converted into assembly plant which started assembling Bedford trucks.
After this take off, many new firms joined the automobile industry over a period of time. The history of automobile industry in Pakistan can be divided into three periods i.e. before nationalization, during nationalization and after nationalization.
2.3.1.1. History of automobile industry before nationalization. The duration of this period is from 1949 to 1971. As mentioned above, the auto sector started its journey in
1949 when General Motors Company established its plant in the country. It was followed by two more American assemblers. There were three plants working in the country having the capacity of producing 2000 cars per year in 1950 (First Development Plan,
1950). In 1955-56, the vehicles of Ford Company were started to assemble in the country by Ali Automobile Company Limited. The popular vehicles included Ford Angela Car,
Truck and Ford Combi.
The second five year plan allocated six million rupees for vendors of automobile and five million rupees for the installation of plants for assembling vehicles (Second Review of Literature 47
Five-year plan, 1960, p.263). In this plan, an initiative was taken for the progressive production of four thousand engines and chassis for heavy vehicles. In 1962, Kandavala
Industries commenced to assemble American Motors and Jeeps. Due to the super normal profit, many new firms entered the market to avail this opportunity. The new firms were
Wazir Ali Industries, Hye Sons, Rana Tractors, Raja Autos, Atlas Automobile and
Ghandhara Industries (History of Pakistan's auto industry, 2003).
During 1965 to 1970, a special emphasis was given on Automobile Industry by promoting progressive manufacturing of vehicles in the country (Third five year plan,
1965). The third five year plan provided a detailed analysis and future strategy for the automobile industry. The analysis revealed the current status that there were 31,140 vehicles registered during 1959-60. Further, the passenger cars in the country were around 50,000. It was also estimated that the absorption capacity for cars would be around 4,000 units per annum. The plan projected 73 per cent upsurge in the demand of commercial vehicles and 10% rise in passenger cars by the end of 1965. Progressive manufacturing of vehicles was the distinguished feature of the policy made during that period. Concrete measures were already taken by the policy makers to bring about progressive manufacturing of jeeps, motorcycles, scooters and tri-wheelers (Third five year plan, 1965).
The measures taken in third five year plan produced positive results and many new entrepreneurs entered the market to enjoy the benefits of monopolistic competition. For automobile assemblers, Jaffer Industries and Monnoo Motors joined the industry. In addition to original equipment manufacturers, the allied sector was also strengthened Review of Literature 48
when General Tyre and Rubber Company, Allvin Engineering and Exide Battery started their operation (History of Pakistan's auto industry, 2003).
The period from 1971 to 1978 is called no plan period as there was political unrest in the country due to the separation of the East Pakistan. Despite that turmoil, the policy makers of automobile industry paid attention to the industry and they approved 6 assembly plants for four wheelers and three plants for two wheelers. Before
Nationalization, the commercial vehicles had 4,500 units plant capacity on annual basis, trucks had the plant capacity of 9,000 units, car plants had capacity of 8,000 units, jeeps had capacity of 6000 units and scooters and motor cycles had the plant capacity of 14,000 units (No Plan Period, 1971).
The pre-nationalization period (1949-1971) proved to be a very significant period for the industry regarding the structure of automobile industry. Number of new entrepreneurs entered the market; couple of joint ventures was undertaken by domestic and foreign entrepreneurs and some concrete measures were taken by the regulators to bring improvements. However, there were some impediments faced by the industry due to which the industry could not accomplish the objectives of self-sufficiency and desired level of indigenization (Ahmad, 1991). Before nationalization the industry achieved some deletion levels in vehicles. For instance, the car industry achieved 5.8% deletion level,
Truck industry got 24.1% level and Jeeps had 17.8% level of deletion (Ahmad, 1991).
2.3.1.2. Automobile Industry during nationalization. After the separation of East
Pakistan, Mr. Zulfiqar Ali Bhutto took over the charge and introduced a comprehensive program for nationalization of private enterprises including automobile industry. The government was inspired by Socialism and it wanted to bring the economic revolution by Review of Literature 49
strengthening the public sector economy. Therefore, the government nationalized 32 industries by releasing Economic Reform Order in 1972. The federal government stepped forward in August, 1973 by acquiring most of the shares of private industries. After nationalization, the firms in automobile industry were given new names. For example
Ghandhara Motors became National Motors, Ali Autos got the name of Awami Autos,
Rana Tractor became Millat Tractor and Jaffer Industries became Trailor and
Development Corporation (Ahmad, 1991).
In 1973, the federal ministry formed Pakistan Automobile Corporation with a view to managing the automobile units and assisting the government in policy making
(Khan, 1990). The PACO group succeeded to combine the scattered firms under the umbrella of an organized body and many firms were assigned to this group. (Aqil, 2009).
The formation of PACO brought about improvement in automobile sector with respect to investment and employment. Naya Daur Motors worked under the supervision of PACO and got the ability to produce customized dies for complex and large components. In addition to this, a semi-mechanized foundry was set up for the production of sand and moulds, resin sand core making system and CO2 core sheeters (Khan, 1990).
By the end of nationalization period, it was identified that most of the automobile firms were concentrated in Karachi and there was a need for diversification of the industry. As a result, a substantial amount of Rs. 220 million for assembly program was allocated in the fifth five year plan (1977-83). The private sector was allocated Rs. 100 million and remaining amount of Rs. 120 million was channelized towards public sector
(Fifth Five year Plan, 1977). Review of Literature 50
The fifth plan laid down emphasis upon the heavy commercial vehicles. The installed capacity in National and Republic Motors was 13,500 for the production of trucks and buses per annum. It was planned to raise the annual capacity to 19000 units by allocating Rs.55 million in the plan. In addition to this, National Motors also implemented a program for the production of buses and trucks and the target was to achieve the deletion level from 37% to 89% up to the end of five year plan (Fifth Five year Plan, 1977).
The analysis of plan of 1977 also revealed that the market for light and mini commercial vehicles would likely to grow with 7.5% annual rate. It was expected that demand for these vehicles would reach up to 15,600 units in 1983. The single shift per annum installed capacity for these vehicles was around 5,000 units. The 5th Plan provided a substantial amount of Rs. 45 million for this purpose (Fifth Five year Plan,
1977).
The fifth five year plan depicted a good picture of the production capacity of Jeeps as there was an annual capacity for around 4,000 units. The plant estimated a 6% annual growth in demand for army and civilian jeeps. The level of indigenization in CJ-5 Jeeps was expected to rise from 32% to 80% by the end of 1983 (Fifth Five year Plan, 1977).
The fifth five-year plan (1977) did not ignore two wheelers industry. The plan predicted a 15% annual rise in demand for two wheelers by 1983 and the expected demand was around 100,000 units. The plan proposed a provision for investment of
Rs.100 million for this industry. The plan also projected 8% rise in car demand during
1977-83. The expected enhanced capacity was around 12,000 units per year on double shift basis by 1986-87. Review of Literature 51
The scrutiny of nationalization period (1972 to 1978) exposed many positive and negative facts about the performance of automobile sector. In this period, the assembly of
Suzuki cars, vans and pick up was commenced; the manufacturing of Isuzu trucks and buses was started and Baluchistan Wheel and Bolan Casting were established by PACO.
The allied sector was also established in this period.
However, the nationalization of automobile industry increased the volume of public sector to an un-manageable extent. The public sector seemed to be ineffective as there was lack of profit motive and absence of the element of competition. Due to the inefficient performance of public sector, there was a real need for denationalization and privatization.
2.3.1.3. Automobile industry after nationalization. On 5th July 1977, the government was removed by the Martial Law order. As a result, many new policy measures were taken and denationalization was one of them. In September 1978, the government released an order called “Transfer of Managed Establishment Order” with a view to empowering the Federal Government to transfer the ownership of public sector units to their former owners. However, the ownership might have been transferred to any other interested party in case there was no positive response received from the previous owners.
The impact of these reforms on the automobile industry was positive. The private sector entrepreneurs capitalized the opportunity to meet the increasing demand of vehicles in the country. Many private sector firms formed collaboration with the state- owned enterprises. For instance, Suzuki and Avami Autos began to produce pick-ups and cars with a new title of Pak Suzuki Motor Company Ltd. By means of a joint venture, Review of Literature 52
Republic Motors Co. started the progressive manufacturing of Fiat tractors. Ghandhara
Nissan was also allowed to produce trucks in the private sector. The Toyota Motor
Corporation and House of Habib were given permit to set up plant for Corolla and other cars by April 1989 (Automobile industry in Pakistan: history and prospects, 1991).
The post nationalization phase began from 1978 and continued to 1990. The period had many distinguished features such as disinvestment, deregulation and decentralization. This phase proved to be a turning point for the automobile industry and many new automobile firms entered the market. The most significant aspect of this period was the outstanding performance PACO that served as a sound basis for investors. Many joint ventures were also made during the period. The dark side of this period was that the target of 100% deletion level could not be achieved.
The PACO group achieved deletion level for various vehicles. For example, the group achieved 56% deletion level in trucks, 77% in tractors, 37% in cars, 47% in two wheelers, 25% in jeeps, 39% in pickups and 35% in vans (Khan 1990). The group acquired the market share in different types of vehicles. The group claimed 73% market share in passenger cars, 91% in tractors, 94% in vans and pickups, 85% in jeeps and 65% in trucks and buses (Khan 1990).
2.3.1.4. Privatization phase. Couple of measures was taken by the new government to bring about trade liberalization, deregulation and privatization. A privatization commission was formed to work on 100 state-owned units. The speedy process of privatization influenced many sectors of the economy positively including financial sector, cement industry, power industry and vehicle industry. The privatization process Review of Literature 53
added Rs. 60 billion to the account of government from the sale of the nationalized firms
(Bukhari , 1998, p. 6).
There were five automobile units out of 15 units which were handed over to their previous owners. The outcome was the generation of Rs. 583 million for the government
(Bukhari, 1998). The economic reforms of the new government caused several effects on the vehicle industry. The positive side was the increase in degree of competition and the market concentration was reduced due to the entry of new firms. The dark side was duty free import of cars under Yellow Cab Scheme. The outcome of this policy was terrible for the domestic industry as the output went down sharply from 65000 units per annum to
45000 units. Due to this policy, the industry suffered massive losses and became unable to achieve the desired level of indigenization.
2.3.1.5. Automobile industry in 1995. Since the period of research is from 1995 to
2010, this is inevitable to describe the condition of vehicle industry in 1995 so that the developments after this period could easily be analyzed.
2.3.1.5.1 Level of Production from 1988 to 1995. The level of output of various types of vehicles from 1988-89 up to 1994-95 is depicted in the table 7 that shows that the output level had declining trend except for Suzuki Cars.
Review of Literature 54
Table 7 Automobiles production 1988-1995 Period 4x4 Suzuki Motor Trucks Buses L.C.Vs Total Jeeps car cycles 1988-89 3,340 1,857 1,996 777 72,804 11,899 1,857 3,340 777 1,996 11,899 19,869 92,673
1989-90 1,581 25,747 92,783 1,715 626 11,609 134,061
1990-91 2,805 25,166 98,647 2,029 805 11,882 141,334
1991-92 1,774 28,911 97,162 1,629 1,114 11,641 142,231
1992-93 1,227 26,992 95,793 2,247 1,177 10,641 138,077
1993-94 816 19,514 63,958 1,394 427 5,128 91,237
1994-95 1,310 6,096 60,960 703 312 5,154 74,535
Note. Adapted From Economic Review Journal April issue, 1998, Automobile industry in Pakistan: history and prospects. .
2.3.1.5.2. Level of indigenization. In 1995, the desired level of indigenization was not up to the mark. The existence of local parts in the automobiles industry was from
46% to 80%. However, the level of indigenization was different in different types of vehicles. The industry could achieve indigenization level of around 45% in heavy vehicles, 47% in passenger cars, 73% in two wheelers and 80% in tractors.
2.3.1.5.3. Level of Production from 1996 to 2010. In 1995, the industry was capable of producing vehicles in wide range such as passenger cars, pick-ups, trucks, buses, jeeps, motorcycles and rickshaws. This was the condition of industry in 1994-95 and it was equipped to give the entry to new age of extensive growth for next fifteen years.
The level of production from 1996 to 2010 is depicted in the following table 8:
Review of Literature 55
Table 8 Automobile production- 1995-2010 Period Suzuki L.C.Vs 4x4 Motor Trucks Buses Farm Total car vehicle cycles tractors 1995-96 33,419 2,682 2,274 N.A. 2,994 474 16,093 57,936 1996-97 37,032 4,553 792 106,797 2,917 456 10,417 162,964 1997-98 38,676 4,843 657 92,978 1,683 681 14,144 153,662 1998-99 42,927 3,834 622 87,504 1,083 1,124 26,644 163,738 1999-00 35,332 3,785 380 86,959 913 1,480 24,559 153,408 2000-01 41,556 4,982 459 108,850 912 1,328 31,635 189,722 2001-02 42,679 5,900 564 120,627 1,134 1,088 23,801 195,793 2002-03 66,432 7,815 374 175,169 1,929 1,288 26,240 279,247 2003-04 103,662 8,888 807 303,383 2,022 1,380 35,770 455,912 2004-05 133,722 16,294 1,564 416,189 3,204 1,782 43,200 615,955 2005-06 170,487 19,152 2,472 520,124 4,518 825 48,887 766,465 2006-07 176,016 19,672 3,298 467,267 4,410 993 54,098 725,754 2007-08 164,710 21,354 1,590 660,593 4,993 1,148 53,256 907,644 2008-09 84,308 16,160 932 509,054 3,135 662 59,968 674,219 2009-10 121,647 15,768 1,172 736,861 3,425 628 71,607 951,108 Note: Adapted from Role of Automobile industry in economic development of Pakistan by Aqil, 2009, HIESS and PAMA http://pama.org.pk/. 2.4. Economic Significance Pakistan’s Automobile Industry
Despite many weaknesses, automobile industry played a vital role to the economy of
Pakistan over a period of time. It contributed substantial amount to GDP, it added millions of rupees to national exchequer, it offered jobs to thousands of people directly or indirectly, and it attracted large amount of foreign direct investment. A brief description of the economic contribution of automobile industry is given below. Review of Literature 56
2.4.1. Contribution of auto parts suppliers and original equipment manufacturers to GDP. There was around Rs. 48 billion share of automobile industry to GDP in 1998-99. The assemblers had the contribution of around and Rs. 25 billion and rest of the amount was contributed by Vendors (Aqil, 2009). The share of industry to GDP rose up to Rs. 153.89 billion in 2003-04 (Expert Advisory Cell, 2004. a, p. 47). The auto parts supplier had a share of Rs. 24.81 billion and original equipment manufacturers added 129.08 billion. In 2004-05, the original equipment manufacturers added Rs. 159.89 to GDP and Vendors contributed Rs. 38.37 billion to the funds of the country. Hence, the share of overall industry to the GDP went up to Rs. 198.26 billion.
The summary is depicted in table 9:
Table 9 Vehicle industry share to GDP (In Rs. Millions) Year Parts supplier OEMs Total % Share in share Share share total GDP 1998-99 23 25 48 7.17% 2003-04 24.81 129.08 153.89 3.39% 2004-05 38.37 159.89 198.26 4.26% 2009-10 2.8%
Note. Adapted From Role of Automobile industry in economic development of Pakistan by Aqil, 2009, HIESS and from Ministry of Industries and Production and from PAMA http://pama.org.pk/ and from Digest of industrial sector-2004.
2.4.2 Contribution to employment. Automobile industry creates employment opportunities that support national income and economic growth of the country. This sector creates employment in the country at various levels directly or indirectly. The summary of employment is shown as under: Review of Literature 57
Table 10 Employees in OEMs OEM 2001 2002 2003 2004 2005 2010 Indus Motor Ltd. 628 697 1021 1226 1429 2225 Honda Atlas Cars Ltd. 431 470 477 625 1032 1003 Ghandhara Industries Ltd. 81 68 90 94 79 83 Pak Suzuki Motor Ltd. 1144 1130 1109 1378 1723 2499 Hinopak Motors Ltd. N.A. 223 84 541 101 1000 Ghandhara Nissan Ltd. 127 88 72 240 255 681 Atlas Honda Ltd. 767 814 961 1582 2300 3679 Total 4834 5132 5593 7780 9314 11170 Note. Adapted From website of PAMA. PAMA Member, http://pama.org.pk/home/members
Table 10 depicts the status of employment in automobile companies. It shows that
there was a positive trend in employment level from 2001 to 2010. The original
equipment manufacturers provided jobs to more than five thousand individuals in 2001
and the figure rose to 11,170 persons in 2010. The rise in level of employment during ten
years was about 131%.
According to the Expert Advisory Cell (2004. a), there were 116,000 employees
in the vendor industry in 2001-02. However, the number of employees in this sector rose
to 140,000 (Indus Motors Company, 2005). In 2010, the number of people employed in
the industry was around 200,000 (PAPAAM, 2010).
2.4.3 Contribution to export. The vehicle industry was an import based sector
that called for imported components from abroad. However, some positive signs could be
found in the export of motorcycles, rickshaws and tractors along with the parts of
automobile. Review of Literature 58
As depicted in table 11, the export of auto parts was $ 7 million in 1999 which rose to $ 90 in 2010. The vehicles and auto parts were mostly exported to Africa, Sri Lanka and Afghanistan. The figures of export were not significant, yet they showed an increasing trend. The export of parts was better than the export of vehicles from 1995 to
2010.
Table 11 Automobile parts exports (In U.S.Dollar, million) 1999 2000 2001 2002 2003 2010 7 12 23 27 31 90
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and from Digest of industrial sector-2004. p. 57. Ministry of Industries and Production and from PAAPAM Directory 2005.
In addition to the export of automobile parts, motor vehicles were also exported and rapid growth was observed. The exports of vehicles worth Rs. 77.994 million during
1995-96 but the export figures increased to Rs.1347.318 million by the end of 2005.
The share of automobile vehicles in the total exports of the country was very low.
Though a positive trend was depicted in the export from 1995 to 2010, the share in total exports of the country was not impressive at all. The detail is depicted by table 12.
2.4.4 Development of auto parts industry. The original equipment manufacturers require support from many sectors for raw material, spare parts and other components. On the other hand, the assemblers help a number of auxiliary industries. As a result, several of backward and forward sectors grow with the growth of automobile industry. The most important sector in this regard is spare parts sector that includes numerable components such as electronic, plastic, glass, metal, rubber etc. Therefore, the development of automobile industry gives birth to accessories and vendor industry. Review of Literature 59
Table 12 Vehicles Exports (In Rs. millions) Period Export of automobiles Share in total export
1996 77.994 0.015% 1997 80.319 0.016% 1998 169.754 0.045% 1999 214.998 0.055% 2000 212.322 0.047% 2001 433.436 0.080% 2002 479.556 0.086% 2003 522.448 0.080% 2004 1178.298 0.170% 2005 1347.318 0.160% 2006 1694.930 0.17% 2007 2093.080 0.20% 2008 2348.125 0.19% 2009 2902.937 0.21% 2010 2378.742 0.14%
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and from Pakistan Statistical Year Book 2011. pp 205-216 and from Pakistan Statistical Year Book 2005. pp 207-208, Islamabad. Statistics Division, PBS, Government of Pakistan
According to Pakistan Investment Guide and Expert Advisory Cell (2004. b) the number of vendors in the country were around 2000 which were grouped into tier one and tier two and unorganized vendors. In tier one, there were around 780 vendors, in tier
2 there were 380 vendors and 840 suppliers were there in unorganized and after market sector. Review of Literature 60
The contribution of automobile parts was quite substantial to the economy. It generated direct employment for around 160,000 persons, injected investment for Rs. 72 billion, added Rs. 24.81 billion to GDP, contributed Rs. 8 billion to national exchequer, brought about import substitution of Rs. $ 371 million and saved foreign exchange for $
195 million (Expert Advisory Cell, 2004 a). The snap shot of the progress of auto parts sector is shown in table 13.
Table 13 Automobile vendors’ contribution to economy Period No. of Share in Foreign Import Revenue Investment Employee GDP exchange substitution to GoP saving 1999 116200 Rs. 23 b $ 84 m $ 210 m Rs. 5 b Rs.28.6 b
2005 187500 Rs. 38.37 b $ 639 m $ 338 m Rs.2.27b Rs. 82.0 b
Percentage +61.35% +66.82% +661% +61% +145.4% +186.71% Change
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995- 2005) by Aqil, 2009, HIESS, Hamdard University and From PAAPAM Directory 2005.
2.4.5. Automobile sector and investment. Due to the efforts of regulators, the sector was successful in attracting local and foreign investments. The investment in car industry was Rs. 30 billion in 2004 which was expected to rise to Rs.70 billion in 2010. Similarly, the investment in heavy vehicle industry was expected to go up to Rs. 7 billion from Rs. 2.5 billion.
The performance of original equipment manufacturers was also good with respect to attracting domestic investment as shown in table 14: Review of Literature 61
Table 14 Domestic investment in auto assemblers (In Rs. millions) Company 2001 2002 2003 2004 2005 Honda Atlas Cars Ltd. 1094 1128 1188 1417 1619 Pak Suzuki Company 3000 3094 3105 3874 5144 Indus Motor Company Ltd. 2154 2357 2442 2579 N.A. Atlas Honda Ltd. 42.15 181.38 132.06 1056.59 2000 Dawood Yamaha Ltd. 10.37 16.00 139.65 24.58 13.33 Ghandhara Nissan 710 708 709 1093 1119 Suzuki Motorcycles 208 260 270 358 430 Hinopak Motors 20.8 42 44 139.6 133.4 Master Motor Ltd. N.A. N.A. 161 208 155 Sind Engineering Ltd. 193.607 221.692 254.801 197.153 143.261 Ghandhara Industries Ltd. N.A. N.A. 0.042 5.485 N.A. Pakistan Cycle Co-op. 33.15 33.57 35.64 38.73 40.63 Plum Qingqi Motors Ltd. 485.9 505.3 471.5 555.2 513.5 Sigma Motors(Pvt) Ltd. 3 13 34 115 160 Total 7955 8560 8987 11661 11471
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and From PAMA Directory 2005 2.5 Original Equipment Manufacturers
The automobile industry in Pakistan has two segments. One segment is engaged in the assembling process called Original Equipment Manufacturers and the other one is involved in the supply of automobile parts and accessories which is known as auto parts vendors. Original Equipment Manufacturers are engaged in the assembling, production and distribution of vehicles. Following is the brief summary of major automobile assemblers: Review of Literature 62
2.5.1. Pak Suzuki Motors Company Limited. This is the most prominent automobile company in Pakistan. It started its operation in 1982 and was incorporated in
1983 as a public limited company. In 1986, the company was listed at Karachi Stock
Exchange. The registered capital was Rs.500 million which was divided into ordinary shares of Rs.10/each. The company was also listed in Lahore Stock Exchange.
As on 2010, the company had a paid up capital of Rs. 823 million, assets of Rs.
19250 million, Sales of Rs. 34738.46 (KSE, 2009) and had 1959 employees. By 2010, the company had been producing vehicles of different variety. The most popular cars were
Mehran in 800 cc, Alto in 1000 cc, Cultus for 1000 cc and Liana for 1300 cc. In addition to the production of cars, light commercial vehicles were also produced by the company such as Ravi Pickup, Bolan Van and Potohar Jeep (Pak Suzuki Company Ltd., 2010).
The growth of company within 15 years is depicted in the table 15:
Table 15 Progress of Pak Suzuki Co. (In Rs. Millions) Title Paid-up Equity Assets Sales No. of Profit capital employee after tax 1995-96 491.31 541.20 4663.15 7904.30 540 -5.92 2009-10 823.00 14497.92 19250.36 42642.76 2499 211.14 Change 331.69 13956.72 14587.21 34738.5 1959 217.06 % Change 40.30% 96.27% 75.78% 81.46% 78.39% 102.80% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report and annual reports of Pak Suzuki Company
2.5.2. Indus Motors Company Ltd. A joint venture between House of Habib and Toyota Motors took place and the outcome was the formation of Indus Motors
Company in 1989. It started its commercial production in 1993 and the plant was capable of producing of 38,000 units for passenger cars and 8,000 units for LCVs annually. Review of Literature 63
As on 2010, the company had a paid up capital of Rs. 786 million divided into ordinary shares of Rs. 10 each. It had the equity of Rs. 12587.62 million, assets Rs.
27138.28 million, sales Rs. 60093.14 million and profit after tax Rs. 3443.40 million
(KSE, 2009). Toyota Corolla, Coure and Hilux were some most popular vehicles produced by the company (Indus Motors, 2010).
Table 16 shows the performance of the company at a glance from 1995 to 2010.
Table 16 Progress of Indus Motors Co. (In Rs. Millions) Title Paid-up Equity Assets Sales No. of Profit Capital Employee After Tax 1995-96 786 1,340.40 2,213.49 4,136.10 546 187.235 2009-10 786 12587.62 27138.28 60093.14 2225 3443.40 Change 0 11247.215 24924.793 55957.04 1679 3256.168 % Change 0.00% 89.35% 91.84% 93.12% 75.46% 94.56% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report and annual reports of Indus Motors Company
2.5.3. Honda Atlas Car Pakistan Ltd. Atlas Group of Companies formed this firm on 5th August, 1992. The objective was to manufacture and assemble Japanese brand vehicles in Pakistan. As of 2010, the company had a paid up capital of Rs. 1428 million. It had equity of Rs. 1975.65, assets of Rs. 8961.27 million, sales of Rs. 15854.14 million and loss of Rs.852.20 million (KSE, 2009). Honda Civic Car was the most popular vehicle of this company.
Review of Literature 64
Table 17 Progress of Honda Motors Co. (In Rs. Millions) Title Paid-up Equity Assets Sales No. of Profit capital employee after tax 1995-96 400 745 1,779 2,783.23 418 201.17 2009-10 1,428 1975.65 8961.27 15854.14 1003 -852.20 Change 1028 1230.645 7182.267 13070.91 585 -1053.37 % Change 7198.88% 62.29% 80.15% 82.44% 58.33% -123.61% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report and annual reports of Honda Motors Company
2.5.4. Dewan Farooq Motors Ltd. The company signed an agreement with
Hyundai Motor Company and Kia Motors Corporation in December 1998. The objective was to conduct progressive manufacturing and distribution of Hyundai and Kia vehicles in Pakistan. The paid up capital of the company was Rs 889.773 million. (Dewan Farooq
Motors Ltd, 2010).
Table 18 Progress of Dewan Farooq Motors Co. (In Rs. Millions) Title Paid-up capital Equity Assets Sales Profit after tax 2000-01 766.438 797.59 2726.18 1001.29 63.56 2009-10 889.733 -471.36 4496.92 1557.02 -852.2 Change -123.295 1268.95 -1770.74 -555.73 915.76 % Change -1608.68% 15909.80% -6495.3% -5550.1% 144078.04% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report and annual reports of Dewan Motors Company
2.5.5. Hinopak Motors Limited. Toyota Tsusho Corporation and Hinopak
Company formed a joint venture in 1985. The primary product line of the company was Review of Literature 65
to produce trucks and buses. By 2010, the company had paid up capital of Rs. 124 million divided into ordinary shares of Rs. 10 each. The amount of equity was Rs.
1503.98 million, assets Rs. 5743.37 million, Sales Rs. 11127.55 million and profit after tax Rs. -148.07 (KSE, 2009). The performance of the company from 1995 to 2010 is depicted in the table 19:
Table 19 Progress of Hinopak Motors Co. (In Rs. Millions) Title Paid-up capital Equity Assets Sales Profit after tax 1995-96 103.338 391.978 1,484.41 2,359.88 176.109 2009-10 124.006 1503.98 5743.37 11127.55 -148.07 Change 1028 1230.645 7182.267 13070.91 -1053.37 % Change 7198.88% 62.29% 80.15% 82.44% -123.61% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report
2.5.6. Ghandhara Nissan Limited. In 1981, the company was registered as a private limited company and then it was transformed into a public limited company and listed in the Karachi Stock Exchange in 1992. It worked as unique original equipment manufacturers as it manufactured wide range of automobiles that included cars, light and heavy commercial vehicles. As of 2010, the company had paid up capital of Rs. 450.025 million having par value of ordinary shares of Rs. 10 each. It had equity of Rs. 633.20 million, assets for Rs. 3360.48 million, sales Rs. 2402.62 million and loss Rs. -88.89 million (KSE, 2010).
The progress of the firm in last fifteen years is depicted in table 20:
Review of Literature 66
Table 20 Progress of Ghandhara Nissan Ltd. (In Rs. Millions) Title Paid-up Equity Assets Sales Profit after capital tax 1995-96 100 12.294 653.75 159.988 7.735 2009-10 450.025 633.20 3360.48 2402.62 -88.89 Change 350.025 620.907 2,706.732 2,242.629 (96.628) % Change 77.78% 98.06% 80.55% 93.34% -108.70% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report 2.5.7. Ghandhara Industries Limited. The company was established initially under Companies Act 1913. Later on, it was transformed into National Motors
Limited after nationalization. However, in 1992, it was purchased by Bibojee Limited when the government allowed privatization of state-owned enterprises. Its name was changed once again and it became Ghandhara Industries Limited. As of 2010, the company was engaged in the assembling and distribution of ISUZU trucks and buses.
The paid up capital of the company was Rs. 213.044 million and shares had the par value of Rs. 10 each. The equity was amounted to Rs. 222.64 million, assets worth Rs.2832.00 million, sales Rs. 2086.52 million and profit after tax was Rs. 135.56 million (KSE,
2009). The fifteen years progress of the company is depicted in the table 21:
Table 21 Progress of Ghandhara Industries Ltd. (In Rs. Millions) Title Paid-up Equity Assets Sales Profit after capital tax 1995-96 65.553 -719.341 961.372 222.417 -116.1 2009-10 213.044 222.64 2832.00 2086.52 135.56 Change 147.491 941.983 1,870.625 1,864.103 251.663 % Change 69.23% 423.09% 66.05% 89.34% 185.64% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report.php Review of Literature 67
2.5.8. Millat Tractors Limited. It was established in 1964 in order to conduct marketing of tractors of Ferguson in the country. A plant was established in 1967 with a view to assembling semi-knocked down tractors. The company worked for Pakistan
Tractor Corporation (PTC) after it was nationalized. However, after privatization in 1992, it commenced its operation under private ownership (Millat Tractors, 2010).
As of 2010, the company was engaged in the production of tractors and enjoyed around 49% market share. It had a paid up capital of Rs. 292.843 million. The equity was
Rs. 4192.41 million, assets Rs.11765.89 million, sales Rs.22199.91 million and profit after tax was Rs.2284.50 million (KSE, 2009). The progress of the company is depicted as follows:
Table 22 Progress of Millat Tractors Ltd. (In Rs. Millions) Title Paid-up Equity Assets Sales Profit after capital tax 1995-96 80.094 456.313 680.437 3330.324 96.813 2009-10 292.843 4192.41 11765.89 22199.91 2284.50 Change 212.749 3,736.094 11,085.457 18,869.585 2,187.685 % Change 72.65% 89.12% 94.22% 85.00% 95.76% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report.php
2.5.9. Al-Ghazi Tractors Limited. The company was incorporated on June
26, 1983 and started its commercial operation on September 1, 1983. It had the production capacity of 30,000 tractors per annum. It was engaged in the production of farm tractors and it enjoyed around 51% of market share. As of 2010, it had the paid up capital worth Rs. 214.682 million divided into ordinary shares of Rs. 5 each. Its assets Review of Literature 68
amounted to Rs. 76661.540 million and equity worth Rs. 63627.210 million, Sales Rs
14936.0340 million and profit after tax worth Rs.1908.8720 million. (KSE, 2009). The progress of the company from 1995 t 2010 is depicted as follows:
Table 23 Progress of Al-Ghazi Tractors Ltd. (In Rs. Millions) Title Paid-up Equity Assets Sales Profit after capital tax 1995-96 111.525 384.9520 1015.2090 2264.7480 107.6010 2009-10 214.682 6362.7210 7666.1540 14936.0340 1908.8720 Change 103.157 5,977.769 6,650.945 12,671.286 1,801.271 % Change 48.05% 93.95% 86.76% 84.84% 94.36% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report.php
2.5.10. Atlas Honda Ltd. The Atlas Group and Honda Motor Co., formed a joint venture in order to establish Atlas Honda Limited. The objective of the firm was to manufacture and market motorcycles of different categories (Honda Atlas, 2010).
As of 2010, the company had paid up capital of Rs. 543.932 million. The worth or equity was Rs. 3891.82 million, assets Rs.8522.28 million, sales Rs.25554.77 million and profit after tax worth Rs.712.46 million (KSE, 2009). The progress of the company over a period of fifteen year is displayed as follows:
Review of Literature 69
Table 24 Progress of Atlas Honda Ltd. (In Rs. Millions) Title Paid-up Equity Assets Sales Profit after capital tax 1995-96 132.707 287.531 1039.668 3092.451 101.459 2009-10 543.932 3891.82 8522.28 25554.77 712.46 Change 411.225 3,604.293 7,482.608 22,462.321 610.999 % Change 75.60% 92.61% 87.80% 87.90% 85.76% Note. Adapted from KSE, 2009. http://www.kse.com.pk/downloads/analysis-report.php
2.6. Foreign Technology in Automobile Industry
Automobile industry in Pakistan was dominated by foreign technology. The industry had technical collaboration with foreign firms to a great extent. The most important joint ventures were formed with Japanese, Korean and European firms. As a result, various quality standards were adopted by the industry such as Japan Industrial Standards (JIS),
Society of Automotive Engineers, USA, (SAE) and International Standards Organization
(ISO).
Review of Literature 70
Table 25 Automobile firms and joint ventures OEM Country Company Vehicle
Indus Motor Japan Toyota and Cars Company Daihatsu Atlas Honda Ltd. Japan Honda Cars and Motorcycles Pak Suzuki. Japan Suzuki Cars
Suzuki Motorcycle Japan Suzuki Motorcycles Pakistan Ltd. Ghandara Nissan Japan Nissan Trucks and Cars
Dewan Farooq Korea Kia and Hyundai Cars and L.C.V.s Ltd. Raja Motor Co Italy . Fiat Cars
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and From the Industrial Sector Digest, 2004. p. 56. Ministry of Industries, Production and Special Initiatives
Japanese brand vehicles had domination over other countries automobile and they had from 51% to 99% market share in different categories. The joint venture detail is summarized in table 25.
Besides the vehicles, Japan also had monopoly in spare parts as depicted in table 26 and table 27:
Review of Literature 71
Table 26 Japanese and other brands’ market share Market Car LCV Motorcycle Truck/Bus Tractor Share Japan 90% 50% 90% 100% 0% Non-Japan 10% 50% 10% 0% 100%
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and From the Industrial Sector Digest, 2004. P . 53. Ministry of Industries, Production and Special Initiatives
Table 27 No. of foreign assemblers by vehicle Country Car LCV Jeep HCV Tractor 2/3 Total Wheelers Japan 4 2 1 3 3 13 Korea 2 1 3 Italy 1 1 3 5 UK 1 1 Sweden 1 1 China 3 3 Romania 1 1
Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and From the Industrial Sector Digest, 2004. p. 57. Ministry of Industries, Production and Special Initiatives
Review of Literature 72
Table 28 Share of Japan in automobile parts Name of content Pakistani Vendor Country Foreign Company Car Air Conditioners Thal Engineering Japan Denso
Glass EGS Pakistan Japan NGS
Radiators AlwinEngg. Industries Japan UE Radiators Car Air Conditioners Sanpak Japan Sanden(Hoda Atlas Cars) Shock Absorbers Agriauto Industries Japan Kayaba
Case Set Steering Polymer & Precision Japan I.S. Seiseki
Shock Absorbers Honda Atlas Services Japan Showa Spark Plugs Shaigan Elect & Engg Japan NGK NGS, Japan Techno Pack Japan Koito, (Indus Motor Co.) Air Conditioners Thal Engineering Japan Denso
Shock Absorbers Agriauto Industries Japan Kayaba
Radio Cassette Players Automate Industries Thailand Panasonic Glass NGS Pakistan Japan NGS
Brake Drum Assy. Alson Autos Ltd Japan Nissin Kogyo, (Pak Suzuki Motor) Radiators Loads (Pvt) Ltd Japan Toyo Radiator Wiring Harness Delta Innovations, Korea/ i)Yujin Electric Thal Engineering Japan System, ii) Prime T&T, iii) Furukawa, Note. Adapted From Role of automobile industry in economic development of Pakistan (1995-2005) by Aqil, 2009, HIESS, Hamdard University and From the Industrial Sector Digest, 2004. p. 57. Ministry of Industries, Production and Special Initiatives
2.7 Regulators and Authorities of Automobile Industry
Regulatory authorities play an important part in the development of an industry.
These authorities set different rules of game and ask the firms for strict compliance. Review of Literature 73
Therefore, every firm has to follow the policies and regulations. Pakistan Automobile
Sector is regulated and monitored by many authorities and associations. The detail of each of them is given below:
2.7.1. Engineering development board (EDB). It is a government body which works under the supervisions and control of Ministry of Industries and Production.
It was established to strengthen the engineering base and develop engineering goods and services on modern lines in the country. Pertaining to the automobile industry, the board has the responsibility of inspecting the manufacturing and assembling facilities in the factory. It also has a close connection with FBR pertaining to various tax related issues.
2.7.2. Federal board of revenue (FBR). This works under finance ministry and its objective is to collect tax revenue for the country. It has the responsibility of formulating fiscal policy, levying and collecting federal taxes and hearing appeals for tax related matters. All of these functions are directly related to automobile industry as well.
The policy of FBR affects import, production and sale of vehicles to a great extent.
2.7.3. Pakistan Automobile Manufacturing Association (PAMA). In
1984, PAMA was established in order to protect the interest of original equipment manufacturers. This body had the task to collect, process, analyze and disseminate information about the market share, tax, production and sales of automobile industry. The members of the association as on 2010 included Indus Motor Company Limited.,
Ghandhara Nissan Limited., Ghandhara Industries Limited, Pak Suzuki Company
Limited, Honda Atlas Car Limited, Hinopak Motors Limited, Sind Engineering Ltd,
Master Motor Corporation Limited., Millat Tractors Limited, Dewan Farooq Motors
Limited., Pakistan Cycle Industries Cooperative Society Limited, Sigma Motors (Pvt) Review of Literature 74
Limited, Al-Ghazi Tractors Limited, Atlas Honda Limited., Dawood Yamaha Limited,
Suzuki Motorcycles Limited, Fateh Motors Limited and Plum Qinggi Motors Limited.
2.7.4. Pakistan Association of Automotive Parts and Accessories
Manufacturers (PAAPAM). The association was established in 1988 in order to develop appropriate linkage among the regulators, original equipment manufacturers and auto parts suppliers. Its main purpose was to safeguard the interest of auto parts producers and to facilitate the members with technical expertise and management cooperation. The government of Pakistan recognized the association in
1999.
Research Methodology 75
Chapter Three
Research Methodology
The research is basically a quantitative and causal in nature. Its primary objective is to find out the factors that influence the performance of automobile industry in
Pakistan from 1995 to 2010. The macroeconomic factors are selected after a detailed review of literature and their influence is examined on the performance of the selected firms of automobile industry. The independent variables include GNI per capita, inflation rate, unemployment rate, interest rate, consumption rate, foreign direct investment and rate of exchange. The performance of the automobile firms is represented by Annual
Sales, Annual Profit, Return on Assets, Return on Equity and Net Profit Margin. So, the dependent variables are Annual Sales, Annual Profit, Return on Assets, Return on Equity and Net Profit Margin of each of the nine firms.
3.1. Strategy
After a careful consideration of the hypothesis, objectives of this study and a detailed review of relevant literature in the previous chapter, the theoretical frame work and research model is developed. Since this research is an empirical and causal study, it contains the model that described the independent variables which are likely to affect the dependent variables as shown in figure 1. There are seven independent variables which have been selected from macroeconomic environment and they are GNI per capita, inflation rate, unemployment rate, interest rate, consumption rate, foreign direct investment and rate of exchange. The performance of the automobile firms is dependent Research Methodology 76
variable and it is represented by Annual Sales, Annual Profit, Return on Assets, Return on Equity and Net Profit Margin. The combine effect of independent variables is observed on each of the dependent variables.
The strategy consists of multiple steps. The first step is to establish a theoretical framework for the model. The second step is to establish hypothesis. The next steps are data collection, data analysis and conclusions. Research Methodology 77
Independent Dependent Variables Variables
Macroecono Performance mic of the Determinants` Firms
GNI Per Capita Annual Sales
Inflation Rate Annual Profit Unemploymnt Rate Return on Assets
Exchange Rate Return on Equity Interest Rate
Net Profit Margin Consumption Rate
Foreign Direct Investment
Figure 1: Research Model
Research Methodology 78
3.1.1. Justification of the Model. There are two applications of regression model. One approach is used to make predictions and other is to find out the causes.
There are two ways to establish the cause and effect relationships between the variables.
One way is the theoretical framework and other is the regression techniques. However, theoretical framework serves as the primary method and regression techniques support this model.
In this model, both the methods are used. The theoretical cause-effect relationship is established in the review of literature between the variables. In regression analysis,
Variance Inflation Factor is considered in each equation so as to ensure that the independent variables are not dependent on each other. After this careful analysis, the model is justified on both theoretical and mathematical basis.
3.2. Population
The original equipment manufacturers are the population of this research. These firms manufacture or assemble various forms of automobiles such as trucks, Jeeps, pick- ups, cars, buses, tractors, motorcycles and rickshaws. The population includes the firms which were the members of PAMA as on December 31, 2010. The population consists of the following firms:
1. Pak Suzuki Motor Co. Ltd.
2. Indus Motor Co. Ltd.
3. Honda Atlas Cars (Pakistan) Ltd.
4. Dewan Farooque Motors Ltd.
5. Sigma Motors Ltd.
6. Hinopak Motors Ltd. Research Methodology 79
7. Ghandhara Industries Ltd.
8 . Ghandhara Nissan Ltd.
9. Master Motor Corporation Ltd.
10 . Millat Tractors Ltd.
11. Atlas Honda Ltd.
12. DYL Motorcycles Ltd.
13. Plum Qingqi Motors Ltd.
14. Pakistan Cycle Industrial Cooperative Society Ltd. (Sohrab)
15. Fateh Motors Ltd.
16. Ravi Automobile Pvt. Ltd.
17 . Sazgar Engineering Works Ltd.
18. Habib Motorcycles Pvt. Ltd. Karachi
19. Al-Ghazi Tractor
3.3. Sampling
The population consists of heterogeneous firms producing different types of vehicles. Further, each of the 19 firms in the population has diversified size and market share. Therefore, none of the probability sampling methods is appropriate for the study.
In order to make the sample a true representative of population, a non-probability sampling techniques is useful that is judgmental or purposive sampling method.
Therefore, 52.6 % firms of the total population are selected for the study and ten out of nineteen firms are taken into consideration. The criteria for including the firms into sample are as follows: Research Methodology 80
1. The firm should be an Automobile Assembler i.e. Original Equipment
Manufacturers as the study is limited to Assembling companies only.
2. The company should be public limited company and it should be listed in the
Karachi Stock Exchange as an Automobile Assembler. The rationale behind that is that the non-listed automobile firms are small in terms of size, capital, production and employment. So, their contribution to the automobile industry is not substantial.
According the above criteria 47% of the firms is not included in the sample. Master
Motor Corporation Ltd., Fateh Motors Ltd. and Ravi Automobile Pvt. Ltd. were also established after 1996. In addition to this, Sigma Motors Ltd., DYL Motorcycles Ltd.,
Plum Qingqi Motors Ltd., Pakistan Cycle Industrial Cooperative Society Ltd. and Habib
Motorcycles Pvt. Ltd. were not included as they were either not public limited companies or they were not listed at Karachi Stock Exchange.
According to these standards, nine companies are selected in the sample and they are
Pak Suzuki Motor Co. Ltd., Indus Motor Co. Ltd., Honda Atlas Cars (Pakistan) Ltd.,
Dewan Farooq Ltd., Hinopak Motors Ltd., Ghandhara Industries Ltd., Ghandhara Nissan
Ltd., Millat Tractors Ltd., Al-Ghazi Tractor Ltd. and Atlas Honda Ltd.. As depicted by table 29, the sample includes almost all types of vehicles. Although the sample represents
53% firms of the total population in number, the representation is far more than this percentage in terms of market share, size and output of the firms. Hence, the sample is a true representative of the entire population.
Research Methodology 81
Table 29 Sample and market share S.No Company Product Average Total market market share in 15 share by years sample 1 Pak Suzuki Motor Co. Ltd. Car (800-1000cc) 88% 2 Dewan Farooq Motors Car (800-1000cc) 8% Market Share of Sample Car (800-1000cc) 96% 3 Indus Motors Co. Ltd Car (Above 1000cc) 54.71% 4 Honda Atlas Car Ltd Car (Above 1000cc) 35.41% Market Share of Sample Car (Above 1000cc) 90.12% 5 Hino Pak Ltd. Truck and Buses 46.1% 6 Ghandhara Industries Ltd Truck and Buses 7.44% 7 Ghandhara Nissan Ltd Truck and Buses 20.00% Market Share of Sample Truck and Buses 73.45% 8 Millat Tractors Ltd. Tractors 45.53% 9 Al-Ghazi Tractors Ltd. Tractors 50.27% Market Share of Sample Tractors 95.8% 10 Atlas Honda Ltd. Motorcycles 63.96% Market Share of Sample Motorcycles 63.96% Note: Adapted from PAMA http://pama.org.pk/
3.4. Hypotheses
The following hypotheses were established to explore the impact of macroeconomic factors on the performance of automobile firms:
Ho: Per Capita Gross National Income, Interest rate, Inflation rate, Exchange rate,
Unemployment rate, Consumption rate or Foreign Direct Investment have no effect on the performance of automobile industry in Pakistan.
H1: Per Capita Gross National Income, Interest rate, Inflation rate, Exchange rate,
Unemployment rate, Consumption rate or Foreign Direct Investment have a significant impact on the performance of automobile industry. Research Methodology 82
The methodology for hypothesis is that the multiple hypotheses are designed to determine the impact of macroeconomic variables on the five elements of performance of each firm. For instance, a hypothesis will explain the impact if there is any impact of the macroeconomic factor on a firm’s Sales, Profit, Return on Assets, Return on Equity and
Net Profit Margin. In this way, there will be five hypotheses for each firm and 50 hypotheses for nine firms and an industry in all.
3.5. Research Instrument
The objective of research instrument is to facilitate data collection procedure. The study aims at finding out the macroeconomic variables on the performance of the automobile firms and it requires no primary data. Hence there is no need to use primary data research instruments such as questionnaire, interview and observations. The study is completely based on secondary data for both dependent and independent variables. The independent variables are based on economic data which is available in government publications and relevant websites. Further, the dependent variables are related to the performance of the companies and this data is also available in the annual reports of the companies. Hence, the research instrument for the study is content analysis of companies’ annual reports, government publications and websites of concerned authorities.
3.6. Procedure for Data Collection
The first step is to collect the data for independent variables. The data related to
GNI per capita, Exchange rate, Discount rate, Inflation rate, Unemployment rate,
Consumption rate and Foreign Direct Investment is obtained from 1995 to 2010. The data is presented in columns for each independent variable for fifteen years. In the next step, Research Methodology 83
the data related to dependent variables is collected so as to set the standards for the performance of the automobile firms. The figures of annual sales and annual profit for each company for fifteen years are obtained from the annual reports of the companies and the official website of Karachi Stock Exchange. The data is taken in rupees million.
Further, the rest of three variables are calculated from data obtained from the companies’ reports by means of a formula. For instance Return on Assets (ROA) is obtained if profit is divided by the total assets of the company. Similarly, Return on Equity (ROE) is derived when profit is divided by the total equity of the firm. Finally, Net Profit Margin
(NPM) is obtained when profit is divided by net sales. All of these three dependent variables are shown in percentage from 1995 to 2010 for each firm.
3.7. Data Sources
Since a diversified data is used, various sources are identified for research. The summary of the data sources is given below:
3.7.1. Data Sources for Independent variables. The data sources of each independent variable are shown as follows:
3.7.1.1. GNI Per Capita. Gross National Income Per capita is an important and common macro economic indicator. The 15-years data of Gross National Income per capita is obtained from national accounts of Pakistan Bureau of Statistics (PBS, 2014).
3.7.1.2. Inflation rate. Inflation is measured by the consumer price index. It reflects the annual percentage change in the cost to the average consumer of acquiring a basket of goods and services that may be fixed or changed at specified intervals, such as yearly. The Laspeyres formula is used and 1960 is taken as base year. The data pertaining Research Methodology 84
to inflation is obtained from International Monetary Fund, International Financial
Statistics and data files (IMF, 2014.a).
3.7.1.3. Exchange rate. It is the exchange rate of US Dollar in terms of Pakistani
Rupee. The official exchange rate may change many times in a year. So, the 15 years weighted average exchange rate has been calculated. The data is retrieved from the State
Bank of Pakistan website (SBP, 2012).
3.7.1.4. Discount rate. This is the rate at which the central bank discounts the first class securities of commercial banks and financial institutions. The discount rate is announced by the State Bank of Pakistan several times in a year as per the need and requirement of monetary policy. The data for Discount Rate from 1995 to 2010 is obtained from the annual reports of State Bank of Pakistan. The weighted average method is used to calculate the annual average Discount Rate.
3.7.1.5. Unemployment rate. The Unemployment Rate in Pakistan is derived from
IMF World Economic Outlook (WEO, 2014). The data is shown in percentage that shows the percentage of those willing and able people who are unable to get the desired job.
3.7.1.6. Consumption rate. Final consumption expenditure is the sum of household final consumption expenditure (private consumption) and general government final consumption expenditure (general government consumption). This is calculated as a percentage of GDP. The data for consumption rate is obtained from The World Bank
National Accounts Data Files (World Bank, 2014).
3.7.1.7. Foreign direct investment. Foreign direct investment is the net inflow of investment to acquire a lasting management interest in an enterprise operating in an economy other than that of the investor. It is the sum of equity capital, reinvestment of Research Methodology 85
earnings, other long-term capital, and short-term capital as shown in the balance of payments. The data for Foreign Direct Investment as a percentage of GDP is obtained from International Monetary Fund, International Financial Statistics and Balance of
Payments databases; World Bank, International Debt Statistics, and World Bank and
OECD GDP estimates.
3.7.1.8. Data for independent variables. The table reflects the data obtained for the macroeconomic variables:
Table: 30 Macroeconomic variables Year GNI IR ER DR UR FDI CR Rs. Mill % Rs % % % % 1995-96 17059.00 10.37 33.56778 16.63 5.1 1.46 85.53 1996-97 18983.00 11.38 38.99364 19.16 5.36 1.15 86.77 1997-98 20415.00 6.23 43.1958 17.25 5.76 0.81 83.33 1998-99 21899.00 4.14 46.7904 15.59 6.01 0.84 86.05 1999-00 27471.00 4.37 50.05458 11.55 5.91 0.42 84.02 2000-01 29605.00 3.15 51.7709 12.35 6.87 0.53 84.06 2001-02 31266.00 3.29 58.43781 8.89 7.82 1.14 83.51 2002-03 34260.00 2.91 61.42578 7.5 8.27 0.64 82.65 2003-04 38524.00 7.44 58.49953 9 8.27 1.14 82.39 2004-05 43595.00 9.06 57.57446 9 7.69 2.01 84.79 2005-06 50030.00 7.92 59.35757 9.46 7.69 3.35 85.85 2006-07 55830.00 7.60 59.85658 9.98 6.2 3.90 84.59 2007-08 64930.00 20.29 60.63417 13.33 5.33 3.32 88.98 2008-09 77716.00 13.65 62.54646 13.26 5.2 1.44 89.34 2009-10 89500.00 13.88 78.49828 12.88 5.46 1.14 90.30 Note: Adapted from the relevant sources mentioned above Research Methodology 86
3.7.2. Data sources for dependent variables. The performance of each automobile firm is reflected by five variables i.e. Annual Profit, Annual Sales, Return on
Assets, Return on Investment and Net Profit Margin. All of the data is obtained from the annual reports of the concerned company and from the Karachi Stock Exchange Website.
The data of annual sales and annual profit is obtained as mentioned in the sources.
However, desired computation is conducted for Return on Assets, Return on Equity and
Net Profit Margin. The data for computation is also derived from the same sources.
3.7.3. General sources of data. The other sources of data are as follows:
3.7.3.1. Ministry of Industries and Production. This is one of the most important sources of data for this research. Various publications issued by the ministry over a period of time are used to obtain the relevant data. The data used in this study is generally released by expert advisory cell or Engineering Development Board of the ministry.
3.7.3.2. Pakistan Bureau of Statistics. This was formerly known as Federal Bureau of Statistics. Pakistan Statistical Year Books of different years is the most significant publication from this source of data.
3.7.3.3. Planning Commission of Pakistan. All five-year plans are released by the
Planning Commission of Pakistan. The data related to history of the industry is obtained from this source.
3.7.3.4. Federal Board of Revenue. It was formerly called Central Board of
Revenue. The data for sales tax, import duty, tariffs and import barriers was obtained from this source. Research Methodology 87
3.7.3.5. Karachi Stock Exchange. This is an important source of data that provides information about profit, capital, sales, assets, equities and liabilities of automobile firms.
For this purpose, the official website of the Karachi Stock Exchange is used.
3.7.3.6. State Bank of Pakistan. Another important source is the State Bank of
Pakistan that provides data by means of its website and annual reports.
3.7.3.7. Pakistan Economic Survey. Various issues of Pakistan Economic Survey provide the relevant data regarding the past and present condition of Pakistan Automobile
Industry.
3.7.3.8. OEMs’ Annual Reports. The websites and annual reports of automobile companies also serve as an important source to provide quantitative and qualitative data.
3.7.3.9. PAMA. PAMA is associations whose objective is to protect the interests of original equipment manufacturers. It functions for providing data regarding the automobile sector. The data pertaining to sales, production, plat capacity and employees of member companies is presented on its website.
3.7.3.10. PAAPAM. The association disseminates information regarding automobile parts and vendors by means of its directories, website and other publications.
3.7.3.11. Other sources. Besides these sources, there are many other sources of data have been used in this research such as the Journal of Economic Review and Pakistan and
Gulf Economist, IMF, the World Bank etc.
3.8. Plan for Data Analysis
The plan for analyzing data consists of a number of steps which are as follows:
3.8.1. Organization of data. The first step is to organize the data for analysis.
There are two sets of data; one is related to seven macroeconomic independent variables Research Methodology 88
and other consists of five dependent variables for measuring the performance of each automobile firm. The data for each firm is organized to carry on further analysis.
3.8.2. Scatter plots and correlation. The scatter plots for dependent and independent variables with respect to time have been plotted in order to conduct and trend analysis and find out correlation between the variables. The graphs depict trends of variables over a period of 15 years. The diagram for scatter plots is shown in Appendix-
A.
3.8.3. Regression analysis. The research aims at finding out the factors which influence the performance of automobile firms. Thus, the study calls for a detailed statistical analysis. Regression technique is the most suitable way to explore the cause and effect relationships between the variables. The multiple or single variable linear regression models are used to carry on the study. In order to run a regression analysis, the software SPSS 17 is used. Regression analysis calls for many assumptions to be fulfilled which are as follows.
3.8.3.1. Assumptions for regression analysis. There are many assumptions that need to be fulfilled for regression analysis. Before running the regression analysis, all the required assumptions are verified. The first assumption is that there should be linearity in the relationships between independent and dependent variables. In order to ensure the linearity, scatter plots have been drawn and correlation analysis is conducted in this study and only significantly correlated variables are chosen for the model.
Another assumption is that there should not be multicollinearity between the independent variables. In order to check the multicollinearity between the independent variables, there are many ways (Berry & Feldman, 1985) and the Variance Inflation Research Methodology 89
Factor is very common method (Freund, Littell & Creighton, 2003). Various recommendations for acceptable levels of VIF have been published in the literature. The most common value is 10 which have been recommended as the maximum level of VIF
(Hair, Anderson, Tatham, & Black, 1995; Kennedy, 1992; Marquardt, 1970; Neter,
Wasserman, & Kutner, 1989). However, maximum value for VIF is assumed to be 5 (
Rogerson, 2001) and even 4 (Pan & Jackson, 2008) in the literature. In order to reduce the probability of multicollinearity between the independent variables, the cut-off value of VIF is set to 4 in this study and no variable having VIF > = 4 is included in the model.
. The assumption of normality is also verified. If the error distribution is significantly non-normal, confidence intervals may be too wide or too narrow. The best test for normally distributed errors is a normal probability plot of the residuals. Another test to check the normality is to use Shapiro-wilk test (Oztuna D, Elhan AH, Tuccar E.,
2006). The test verifies that the disturbances are normally distributed if the level of significance is greater than 5%. (Peat J, Barton B., 2005). Another test is to plot histogram and visually observe and verify normality. These tests are conducted and shown in Appendix C.
Another assumption is Homoscedasticity which means a sequence or a vector of random variables is homoscedastic if all random variables in the sequence or vector have the same finite variance. This is also known as homogeneity of variance. Histograms of residuals shown in Appendix B reflect that the assumption is fulfilled.
3.8.3.2. Selection of best regression model. Having verified all the assumptions, the next step is to set the standard for a best fit regression model. The criterion is based upon various standards. The first condition is that the model should fulfill all the above- Research Methodology 90
mentioned assumptions. The second step is to select only those independent variables whose t value is significant i.e. p <.05. Further, the variables having VIF>4 are not included in the model. Having identified the appropriate variables, various regression models are formed. The best fit model is the one whose R2 value is largest. Further, the F value is also considered in this regard.
3.8.3.3. Hypothesis testing. The hypothesis for each company is tested by means of regression models. Every firm has a null hypothesis which states that there is no significant impact of macroeconomic variables on the performance of the firm. All the hypotheses are tested at 5% level of significance.
Data Analysis and Findings 91
Chapter Four
Data Analysis and Findings
This section elaborates the impact of macroeconomic variables on the performance of automobile industry by means of regression technique. The independent variables include Per Capita Gross National Income, Interest Rate, Inflation Rate,
Unemployment Rate, Exchange Rate, Consumption Rate and Foreign Direct Investment.
The impact of these factors is observed on five dependent variables i.e. Annual Profit,
Sales Volume, Return on Asset, Return on Equity and Net Profit Margin on the selected firms of Pakistan Automobile Industry. There are ten firms which are taken as sample.
They include Pak Suzuki Motor Co. Ltd., Indus Motor Co. Ltd., Honda Atlas Cars
(Pakistan) Ltd., Dewan Farooque Motors Ltd., Hinopak Motors Ltd., Ghandhara
Industries Ltd., Ghandhara Nissan Ltd., Millat Tractors Ltd, Al-Ghazi Tractors Ltd.. and
Atlas Honda Ltd.
4.1 Determinants of the Performance of Pak Suzuki Company
Ltd.
The company is one of the most important original equipment manufacturers in
Pakistan. It is the largest automobile assembler which is engaged in the production of vehicles in a wide range. Due to this significance, the company is given priority in the analysis. In order to conduct a detailed analysis, the following hypothesis is established: Data Analysis and Findings 92
Ho: There is no significant impact of macroeconomic factors on the performance of
Pak Suzuki Company Ltd.
H1: There is significant impact of macroeconomic factors on the performance of Pak
Suzuki Company Ltd.
In order to test the hypothesis, the following data is retrieved as shown in table 30.
Table 31 Variables for Pak Suzuki Company Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 7904.301 -5.92 -0.13 -1.09 -0.1 1996-97 7710.453 391.4 9.19 28.48 5.08 1997-98 8680.93 357.7 9.21 21.89 4.12 1998-99 8914.017 263.35 4.77 14.74 2.95 1999-00 6889.145 -26.6 -0.58 -1.51 -0.39 2000-01 3773.569 207.59 4.89 11.05 5.5 2001-02 10994.07 850.1 10.42 32.1 7.73 2002-03 18484.22 1570.19 16.23 38.57 8.49 2003-04 24461.97 1403.57 10.44 25.63 5.74 2004-05 35374.56 2236.88 11.93 28.58 6.32 2005-06 47187.95 3350.44 15.19 30.68 7.1 2006-07 50844.63 2774.53 13.09 19.85 5.46 2007-08 39669.73 624.79 3.68 4.41 1.57 2008-09 26234.06 255.22 1.45 1.78 0.97 2009-10 42642.76 211.14 1.1 1.46 0.5 Note: Adapted from Annual Reports of Pak Suzuki Company, Karachi Stock Exchange and Economic Survey of Pakistan
In this table, the dependent variables of Pak Suzuki Company include Annual
Sales (AS), Annual Profit (AP), Return on Assets (ROA), Return on Equity (ROE) and Data Analysis and Findings 93
Net Profit Margin (NPM). The correlation between macroeconomic variables and performance of the company was determined and it is shown in Table 31.
Table 32 Correlation analysis for Pak Suzuki GNI IR ER DR UR CR FDI
Sales Pearson .773** .495 .694** -.441 -.302 .398 .822** Correlation
Sig. (2-tailed) .001 .061 .004 .100 .274 .142 .000
Profit Pearson .183 -.111 .319 -.632* .173 -.255 .690** Correlation
Sig. (2-tailed) .515 .694 .247 .012 .537 .358 .004
ROA Pearson -.145 -.362 .136 -.508 .441 -.522* .321 Correlation
Sig. (2-tailed) .605 .186 .630 .053 .100 .046 .244
ROE Pearson -.306 -.445 .023 -.407 .564* -.564* .072 Correlation
Sig. (2-tailed) .268 .096 .934 .132 .029 .029 .800
NPM Pearson -.241 -.499 .110 -.530* .647** -.589* .115 Correlation
Sig. (2-tailed) .388 .058 .695 .042 .009 .021 .683
As shown in the table 32, GNI per capita had a strong correlation with annual sales of the company (r=.773). Inflation Rate was not significantly correlated with any of the dependent variables. Exchange Rate was also strongly related with sales (r=.694).
Discount Rate was correlated with Annual Profit (r=-.632) and Net Profit Margin (r=-
.53); Unemployment Rate was correlated with Net profit Margin (r=.647); Consumption
Rate with ROA (r=-.522), ROE (r=-.564) and Net profit Margin (r=-.589) and Foreign Data Analysis and Findings 94
Direct investment was strongly correlated with Annual Sales (r=.822) and Profit of the company (r=.69).
In order to analyze the data and derive the desired results, the following regression analysis was conducted:
4.1.1. Macroeconomic variables and sales of Pak Suzuki Company.
The first variable selected for analysis was the annual sales of Pak Suzuki
Company. The impact of macroeconomic variables on the annual sales was found out by means of scatter plots, correlation and regression analysis.
In order to verify the trend of independent and dependent variables, scatter plots were drawn. As shown in Appendix A, the scatter plots of Per Capita GNI, Exchange
Rate, and Foreign Direct Investment had increasing trends in last fifteen years. Similarly, the scatter plot of Pak Suzuki sales also had an increasing trend. After drawing scatter plots, a correlation analysis was conducted and which revealed that three macroeconomic factors were strongly correlated with annual sales and they were Gross National Income
Per Capita (r=.773), Exchange Rate (r=.694) and Foreign Direct Investment (r=.822). In
Appendix-A the positive correlation of sales (Figure A-8) with GNI (Figure A-1), ER
(Figure A-3) and FDI (Figure A-7) was also justified and the findings revealed that the sales and three independent variables were mutually dependent.
Having conducted the correlation and trend analysis, regression model was established to determine the type and degree of influential factors. The following data is obtained from the multiple regression analysis.
According to Table 33, two models were identified in order to find out the influence of macroeconomic variables on annual sales of Pak Suzuki Company Limited. Data Analysis and Findings 95
The first model included three variables i.e. GNI per capita, Exchange Rate and FDI. This model was not acceptable because the coefficient GNI (β1= .78, t = 0.718, p= .488) had the p value > .05. It means that there was greater than 5% probability of committing type
I error i.e. to reject the null hypothesis wrongly. Further, the F value (3, 11) = 49.769 was lower than the F Value of other model. In addition to this, the Variance Inflation
Factor (VIF) for GNI (VIF= 4.789) and ER (VIF= 4.151) were much closer to 5 and both the independent variables seemed to be inter-correlated that might cause multicollinearity problem.
Table: 33 Model Summaries for sales Pak Suzuki S.No Model R R² Adj. F βₒ β1 β2 β3 Decision R (Const.) (GNI) (ER) (FDI) 1 GNI, ER, .965 .931 .913 49.769 -31125 .093 629.597 9960.415 Not Fit FDI t-statistics -3.241 .718 2.610 7.280 Sig .000 .008 .488 .024 .000
St.Error 4898 9603 .129 241 1368
VIF 4.789 4.151 1.325
2 ER, FDI .963 .928 .916 77.524 -36129. 778.906 10393.391 Fit
t-statistics -5.581 6.498 8.638 Sig .000 .000 .000 .000
St.Error 4798 6473 120 1203
VIF 1.068 1.068
Hence the best fit model was the second one. On the basis of this analysis, the following equation was established: Data Analysis and Findings 96
AS = - 36129.072 + 778.906 ER + 10393.391 FDI Where AS = Annual Sales; ER= Exchange Rate and FDI = Foreign Direct
Investment.
In this model, the coefficient Exchange Rate (β1= 778.9, t=6.498, p=.000) and
FDI (β2=10393.39, t=8.638, p=.000) were significant. There was no problem of multicollinearity as VIF (1.068) < 5 for both the variables. The R² (0.928) was also significant at F (2, 12) =77.524, p < .001. The high F value described that at least one of the independent variables was significant predictor of sales. The R² (0.928) showed that high degree of around 92.8% variations in the Annual Sales of Pak Suzuki Company was caused by ER and FDI. As per Appendix C, the Shapiro-wilk test (15, p=0.135) verifies that the disturbances were normally distributed as p>0.05. The histogram in Fig B-1 showed that the assumption of homoscedasticity was also fulfilled. The above model also elaborated that Exchange Rate and Foreign Direct Investment had positive impact on
Annual Sales. It means that larger the value of ER and FDI, greater the annual sales of the company. Further, keeping other factors constant, if there were Re 1 rise in exchange rate, the sales would rise by Rs. 779 million. Similarly, a 1% rise in FDI would raise the annual sale by Rs. 10393 million if other factors were kept constant.
The model also describes the theoretical background between the variables. The positive relationship between Exchange Rate and Sales revealed the fact that if US Dollar got expensive, sales of the company would increase. It means that the firm was successful in maintaining reasonable price of vehicles by absorbing the shock of increasing cost of production due to weakness of domestic currency. It further means that the demand for firms’ vehicles was not much influenced by high exchange rate and it was governed by some other factors. The positive effect of Foreign Direct Investment on sales was well Data Analysis and Findings 97
justified as the firm was benefited in terms of sales volume by the inflow of investment with technology transfer from abroad.
4.1.2. Macroeconomic factors and profit of Pak Suzuki. According to
Table 34, just two variables were strongly correlated with annual profit i.e. Discount Rate
(r=-.632) and Foreign Direct Investment(r=.69). The trend analysis in Appendix A, the scatter plots for Profit (Figure A-9) and FDI (Figure A-7) had increasing trend while
Discount Rate (Figure A-4) had declining trend. The multiple regression technique produced the following results:
Table 34 Model summary for profit of Pak Suzuki
Model R R² Adj. F βₒ β1 β2 R (Const.) (FDI) (DR) FDI, DR .848 .720 .673 15.412 1991.157 563.627 -153.527 t-statistics 2.849 3.706 -3.229 Sig .000 .015 .003 .007
St. Error 612 699 152 48
VIF 1.050 1.050
On the basis of this analysis, the following model was derived:
AP = 1991.157 – 153.527 DR + 563.627 FDI
Where AP = Annual Profit; DR= Discount Rate and FDI = Foreign Direct
Investment.
The R² (0.72) was significant at F (2, 12) =15.412, p < .001. So, at least one of the independent variables had significant impact on profits. There was also no multicollinearity between DR and FDI. The value of VIF (1.05) was much less than 5. Data Analysis and Findings 98
After the collection of all supporting data, it was concluded that Discount Rate and
Foreign Direct Investment caused a substantial variation of 72% in the annual profit of the Pak Suzuki Company.
The above equation showed that beta coefficients were FDI (β1 =563.627, t =
3.706, p= 0.003) and DR (β2 =-153.527, t= 3.229, p= 0.007). The model was fit to explain the relations as both of independent variables were significant. The statistics elaborated that Foreign Direct Investment had positive impact on Annual Profit which means that greater the FDI, higher the annual profit and vice versa. However, Discount
Rate had negative effect on Annual Profit and rise in Discount Rate would reduce the
Profit of the firm. Keeping other factors constant, if there were a 1% rise in Discount
Rate, the profit would go down by Rs.154 million and 1% rise in FDI would increase the annual profit by Rs. 564 million.
The results were justified in theoretical framework as well. The negative relationship between Discount Rate and Annual Profit reflected that a rise in discount rate made the cost of borrowing higher for the firm. As a result, the annual profit tended to decline. The positive effect of FDI on Profit was well justified as explained in the last analysis.
4.1.3. Macroeconomic variables and ROA of Pak Suzuki. As depicted in Table 35, the only factor which was strongly correlated with ROA was Consumption
Rate (r= .522). The trend analysis in Appendix A for ROA (Figure A-10) and
Consumption Rate (Figure A-5) showed that there was an increasing trend between independent and dependent variables. The following single variable regression model was established. Data Analysis and Findings 99
Table 35 Model summary for ROA of Pak Suzuki
Model R R² Adj. F βₒ β1 R (Const.) (CR) CR .522 .273 .217 4.871 109.407 -1.193 t-statistics 2.366 -2.207 Sig .046 .034 .046
St. Error 4.95 46 0.54
The R² (0.273) was significant at F (1, 13) = 4.871, p = .046. The following equation is formed:
ROA = 109.407 – 1.193 CR
Consumption Rate (β1= -1.1.3, t = -2.207, p = .046) was significant with these statistics and it had a negative influence on Return on Assets of Pak Suzuki Company.
The model showed that there would be a small degree of variations of around 27.3% in the Return on Assets due to Consumption Rate. If CR rose by 1%, the ROA would go down by around 1%. The reason behind this negative relationship was the fact that most of the aggregate consumption was supposed to be channelized toward some other sectors and it was not used in the buying of Pak Suzuki Company vehicles. The rationale seemed to be true as there was no significant correlation between consumption rate and sale.
4.1.4. Macroeconomic variables and ROE of Pak Suzuki. There were two factors which were significantly correlated with ROE i.e. Unemployment
Rate(r=.564) and Consumption Rate (r=-.564). In Appendix A, ROE (Figure A-11) and
UR (Figure A-6) had decreasing trend and CR (Figure A-5) had an increasing trend. The multiple regression model for CR and UR had R² (.370) at F (2,12) = 3.525, p = .063. Data Analysis and Findings 100
Since p > .05, this multiple regression model was not fit for the analysis. Therefore, any one of the variables would be chosen as a predictor. The following comparative analysis was conducted for this purpose:
Table 36 Models comparison for ROE of Pak Suzuki UR CR Correlation 0.564 -0.564 R² 0.318 0.318 F Statistics 6.062 6.049 Sig 0.029 0.029 Constant -26.160 284.834 t-value -1.467 2.616 sig 0.166 0.21
β1 6.66 -3.132 t-value 2.462 -2.459 sig 0.029 0.029
` The above statistics revealed almost similar results for both the predictors.
However, UR (t=-1.467, p= 0.1666), had the p > 5% which increased the risk of committing type I error. So the preferable predictor was consumption rate which is expressed in table 37:
Data Analysis and Findings 101
Table 37 Model Summary for ROE for Pak Suzuki Company
Model R R² Adj. F βₒ β1 R (Const.) (CR) CR -0.564 0.318 .217 6.049 284.834 -3.132 t-statistics 2.616 -2.459
Sig 0.029 0.21 0.029
St. Error 11.67 109 1.27
ROE = 284.834 – 3.132 CR
The equation depicts that CR (β1= -3.132, t= -2.459, p= .029) was significant at
F(1, 13) = 6.049, p= .029 and it had a negative influence on ROE. The R² reflected that variations in CR would cause around 31.8% below average change in ROE. Further, a
1% rise in CR was likely to cause around 3% fall in ROE. It had the same interpretation that the aggregate consumption was less channelized towards the consumption of Pak
Suzuki vehicles.
4.1.5. Effect of macroeconomic factors on NPM of Pak Suzuki.
Amongst the selected macroeconomic variables, DR (r= -.53), UR (.647) and CR (r=-
.589) had significant correlation with Net Profit Margin. The scatter plots for NPM
(Figures A-12) showed slightly negative trend. The dependent variables DR (Figures A-
4), UR (Figures A-6) also had negative trend and CR (Figures A-5) had a positive slope.
The multiple regression analysis generated the table 38.
Data Analysis and Findings 102
Table 38 Model summary for NPM for Pak Suzuki
S.No Model R R² Adj. F βₒ β1 β2 β3 Decision R (Const.) (DR) (UR) (CR) 1 UR,DR,CR .659 .643 .342 3.34 32.827 -.201 .712 -.361 Not Fit
t-statistics .866 -.835 .728 -.950 Sig .056 .405 .421 .482 .362
VIF 1.749 3.103 2.119 2 UR,DR .664 .441 .348 4.732 -2.588 -.164 1.336 Not Fit
t-statistics .371 -.693 1.852 Sig .031 .717 .502 .089
VIF 1.703 1.703 3 UR,CR .671 .451 .359 4.92 22.848 1.185 -.310 Not Fit
t-statistics .643 1.505 -.836 Sig .028 .532 .158 .420
VIF 2.063 2.063 4 CR .589 .347 .297 6.904 64.769 -.710 Fit
t-statistics 2.803 - 2.628 Sig .021 .015 .021
VIF 1 5 UR .647 .419 .374 9.358 -6.699 -6.69 1.657 Best Fit
St. Error 2.33 3.5 0.542 t-statistics -1.876 3.059 Sig .009 .083 .009
VIF 1 6 DR .530 .281 .226 5.082 9.575 -.444 Fit
t-statistics 3.781 - 2.254 Sig .042 .002 .042
VIF 1 Data Analysis and Findings 103
Model no. 1 included three independent variables that were Discount Rate,
Unemployment Rate and Consumption Rate. The R² (.643) was not significant at F (3,
11) = 3.43, p = .056 because p> .05. Further, the t values of all three variables were insignificant (p> .05). Model no. 2 included UR and DR and its R² (.441) was significant at F (2, 12) = 4.732, p=.031. However, the coefficient DR (β1= -1.64, t= -.693, p=.502) was not significant and this model was not fit either. The same problem lied in model 3 where both of the variables i.e. UR (β1=1.185, t=1.505, p= .158) and DR (β2=-.310, t=-
.836, p= .42) were not acceptable due to p>.05.
Model no. 4 included CR whose R² (.347) which was significant at F (1, 13) = 6.904, p = .021. Model no. 5 took into account UR whose R² (.419) was also significant at F (1,
13) = 9.358, p = .009.
Model no. 6 also revealed a similar output by taking into account DR whose R²
(.281) was also significant at F (1, 13) = 5.082, p = .042. However, the best fit model was model no. 5 which had the highest R² (.419) and largest value of F (9.358) and lowest p value (.009). This equation is as follows:
NPM = -6.699 + 1.657 UR
In this model, unemployment rate was likely to case 41.9% positive variation in
Net Profit Margin. The coefficient UR (β1= 1.657, t= 3.059, p= .009) was significant at p<.01. It means if other factors were constant and Unemployment Rate rose by 1%, the net profit margin would increase by 1.6%. The positive impact of unemployment rate on
NPM reflected that the entrepreneurs of Pak Suzuki Company could not exploit the weak bargaining position of the labor or the labors of the firm were not affected by general unemployment conditions in the economy. Data Analysis and Findings 104
4.1.6. Conclusions and Decision. After a detailed correlation and regression analysis, the following models have been finalized for all five dependent variables for the performance of Pak Suzuki Company Limited.
AS = - 36129.072 + 778.906 ER + 10393.391 FDI
AP = 1991.157 – 153.527 DR + 563.627 FDI
ROA = 109.407 – 1.193 CR
ROE = 284.834 – 3.132 CR
NPM = -6.699 + 1.657 UR
At 5% level of significance (α = .05), significant R² value at F (p < .05) and t value for coefficients at p< .05, there existed enough evidences to conclude that all of the dependent variables for performance measurement were influenced by at least one of the seven independent variables. So, we reject the null hypothesis and conclude that there was a significant impact of at least one of the macroeconomic variables on the performance of Pak Suzuki Company Ltd.
The models also indicated that the Foreign Direct Investment and Consumption rate appeared to be the most influential factors for the performance of the firm.
4.2. Determinants of the Performance of Indus Motors
Company Ltd.
In order to find put the influence of macroeconomic variables on the performance of the company, the following hypothesis was established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Indus Motors Company Ltd. Data Analysis and Findings 105
H1: There is significant impact of macroeconomic factors on the performance of
Indus Motors Company Ltd.
In order to test the hypothesis, the data was retrieved for the variables of the company as shown in table 39. Similarly, the correlation analysis is shown in table 40.
Table 39 Variables for Indus Motors Company Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 4136.10 187.24 8.45 13.96 4.52 1996-97 4538.22 150.20 6.26 10.94 3.30 1997-98 4973.99 147.16 5.24 10.49 2.95 1998-99 6957.88 251.29 8.09 16.79 3.61 1999-00 8246.27 172.25 4.93 11.11 2.08 2000-01 9054.73 203.37 6.30 13.97 4.53 2001-02 8111.29 360.46 5.78 10.94 3.31 2002-03 15634.98 1257.61 11.36 10.50 2.96 2003-04 22521.34 1473.24 12.74 16.80 3.61 2004-05 27601.03 1484.65 12.17 11.11 2.09 2005-06 35236.54 2648.46 16.74 42.32 7.52 2006-07 39061.23 2745.70 17.53 34.13 7.03 2007-08 41423.84 2290.85 16.66 24.28 5.53 2008-09 37864.60 1385.10 6.70 13.45 3.66 2009-10 60093.14 3443.40 12.69 27.36 5.73 Note: Adapted from Annual Reports of Indus Motors Company, Karachi Stock Exchange and Economic Survey of Pakistan
On the basis of above data, the correlation analysis was conducted which is shown in table 39.
Data Analysis and Findings 106
Table 40 Correlation analyses for Indus Motors
GNI IR ER DR UR CR FDI
Sales Pearson .963** .622* .843** -.327 -.349 .641* .575* Correlation
Sig. (2-tailed) .000 .013 .000 .234 .202 .010 .025
Profit Pearson .851** .501 .804** -.424 -.282 .471 .672** Correlation
Sig. (2-tailed) .000 .057 .000 .115 .309 .077 .006
ROA Pearson .532* .406 .512 -.448 -.207 .201 .833** Correlation
Sig. (2-tailed) .041 .133 .051 .094 .460 .473 .000
ROE Pearson .517* .287 .433 -.236 -.420 .327 .782** Correlation
Sig. (2-tailed) .049 .300 .107 .396 .119 .235 .001
NPM Pearson .480 .336 .335 -.094 -.488 .372 .745** Correlation
Sig. (2-tailed) .070 .221 .222 .740 .065 .172 .001
4.2.1. Effects of macroeconomic variables on sales of Indus Motors.
On the basis of correlation analysis shown in table 40, the variables which were highly correlated with the annual sales of the firm included Gross National Income (r=
.963), Inflation Rate (r= .622), Exchange Rate (r=.843), Consumption Rate (r=.641) and
Foreign Direct Investment (r=.575). In the Appendix A, the scatter plots for Sales
(Figures A-13), GNI (Figures A-1), FDI (Figures A-7) and CR (Figures A-5) showed increasing trend over a period of 15 years. Data Analysis and Findings 107
In order to analyze the impact of these variables on sales, regression analysis was conducted which produced four possible models. The first model included all five independent variables. The t values had insignificant ER (p=.243), CR (p=.949), IR
(p=.538) and GNI (p=.06). Further, the Values of VIF> 5 for ER (VIF=10.775), CR
(VIF=5.18), IR (VIF=4.38) and GNI (VIF=19.93).Therefore, the model was not fit at all.
The best fit model is as follows:
Table 41 Model summary for sales of Indus Motors
Model R R² Adj. F βₒ β1 β2 β3 Variables R (Const.) (FDI) (ER) (CR) FDI. ER. CR .969 .939 .922 56.29 -241716.1 5108.78 1027.6 2331 t-statistics -5.062 4.073 8.017 4.000 Sig .000 .000 .002 .000 .002 St. Error 4893 47749 1264 128 582 VIF 1.116 1.174 1.191
The analysis produced the following equation:
AS = -241716.1 + 5108 FDI + 1027.6 ER + 2331 CR
According to this model, the R² (.939) was significant at F (3, 11) = 56.29, p=.000. Hence, at least one of the independent variables had significant influence on the annual sales of Indus Motors Company. FDI (β1= 5109, t=4.073, p=.002), ER (β2= 1028, t=8.017, p=.000) and CR (β3= 2331, t=4, p=.002) had significant influence over annual sales of the company. These variables collectively caused around 93.9% variations in the sales which was a very substantial influence.
It means if other factors were kept constant; an increment in FDI by 1% would boost the annual sales by Rs.5108 million. If there were a rise in Exchange rate by Re 1, Data Analysis and Findings 108
the sales would escalate by Rs. 1028 million. A 1% hike in consumption rate was likely to enhance the sale volume by Rs. 2331 million.
Theoretically, the model was justified as well. A positive relationship between
FDI and Sales seemed to be logical as the inflow of investment along with technology might enable the firm to improve the product quality and reduce the cost of production and price. Since vehicles were more elastic to price and technology, the diminishing trend in price led to upsurge in sales. The positive relationship between Exchange Rate and
Sales elaborated if US Dollar became expensive; there would be no negative impact on firm’s sales. Rather, the firm was successful in maintaining reasonable price of vehicles by absorbing the hike in the cost of production due to weakness of domestic currency.
Finally, Consumption Rate also influenced annual sales positively. It means a rise in aggregate consumptions was channelized to the buying of vehicles of the firm.
4.2.2. Effects of macroeconomic variables on profit of Indus Motors.
The variables which were significantly correlated with the annual profit included
Gross National Income Per Capita (r=.851), Exchange Rate (r=.804) and Foreign Direct
Investment (.672). The trend analysis in Appendix A showed that Annual Profit (Figure
A-14) was mutually dependent with GNI (Figure A-1), ER (Figure A-3) and FDI (Figure
A-7) as all of them had increasing trend over a period of time.
In order to realize the impact of these variables on sales, multiple step wise regression analysis was conducted. These three variables could not establish a fit model for annual profit as GNI (β3=455, t=1.119, p=.287) was not significant. Further, the F value (30.82) was less than the F value of other models. Following is the best fit model as shown in table 42: Data Analysis and Findings 109
Table 42 Model summary for annual profit of Indus Motors
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (FDI) (ER) FDI, ER. .939 .882 .862 44.65 -3365.186 4248.59 69.095 t-statistics -5.953 3.061 6.600 Sig .000 .000 .012 .000 St. Error 419 565 105 10.4 VIF 1.450 1.068
AP= -3365.186 + 4248.59 FDI + 69.095 ER
The R² (.882) was significant at F (2, 12) = 44.64, p=.000. So, the best fit model included FDI (β1=4249, t=3.06, p=.012) and ER (β2=69.095, t=6.6, p=.000). These variables were responsible for causing around 88.2% massive change in the annual profit of Indus Motors Company. Both FDI and ER had positive effects on profit. If other factors were kept constant, an upsurge in FDI by 1% would raise the annual profit by Rs.
4249 million. Further, if Exchange Rate against US dollar went up by Re 1, this would add Rs. 69 million in the profit of the firm.
The theoretical support to this model was quite clear. A rise in FDI generally raises profit as in this case. Further, rising trend in Exchange Rate would increase the profit as the firm maintained the demand despite the weakness of domestic currency.
4.2.3. Effects of variables on ROA of Indus Motors. Only two variables were found highly correlated with ROA i.e. GNI (r= .532) and FDI (r=.833). The scatter plots in Appendix A showed that ROA (Figure A-15) had strong relationship with GNI
(Figure A-1) and FDI (Figure A-7) due to the positive trend in 15 years. Data Analysis and Findings 110
After conducting the regression analysis, it was found that both the variables could not form a valid model as GNI (β2=4.124, t=1.256, p=.233) had p>.05. So the valid model’s summary is as follows:
Table 43 Model summary for ROA of Indus Motors
Model R R² Adj. F βₒ β1 Variables R (Const.) (FDI) FDI .833 .693 .670 29.36 4.877 3.370 t-statistics 4.165 5.419 Sig .000 .001 .000 St. Error 2.56 1.17 .622
ROA= 4.877 + 3.37 FDI FDI appeared as a major determinant of ROA which was supposed to cause around 69.3% above average change at F (1, 11) = 29.36, p=.000. FDI was also significant at p<.05. The model explained that 1% boost in FDI would give birth to around 3% rise in ROA.
The return on assets was likely to increase due to FDI for the same reasons as mentioned earlier. Since FDI raised profit, it also affected the ROA positively.
4.2.4. Effect of macroeconomic variables on ROE of Indus Motors.
There were two variables which were highly correlated with ROE and they were FDI
(r=.782) and GNI (r= .517). Appendix A showed that ROE (Figure A-16) appeared to have same increasing trend as GNI (Figure A-1) and FDI (Figure A-7) had. Data Analysis and Findings 111
FDI was found to be insignificant as the multiple regression model had smaller F
Value (2, 12) = 11.12 and GNI had p value = .272 which was much greater than .05. The summary of valid model is as follows:
Table 44 Model summary for ROE of Indus Motors
Model R R² Adj. F βₒ β1 Variables R (Const.) (FDI) FDI .782 .611 .581 20.40 7.091 6.946 t-statistics 2.450 4.517 Sig .001 .029 .001 St. error 6.33 2.9 1.5
The analysis produced the following equation:
ROE = 7.091 + 6.946 FDI
FDI was supposed to cause around 61.1% moderate fluctuation in ROE at F (1,
13) = 20.4, p=.001. There was a positive impact of FDI on ROE. So, there might be around 7% rise in ROE if FDI increased by 1 % keeping all other factors constant.
Theoretically, rise in FDI raised profit and ultimately ROE went up.
4.2.5. Effects of variables on NPM of Indus Motors.
The scatter plots in Appendix A showed that NPM (Figure A-17) and FDI (Figure A-
7) had the same increasing trend. FDI (r=.745) was significantly correlated with Net
Profit Margin. Following is the detail of model:
Data Analysis and Findings 112
Table: 45 Model summary for NPM of Indus Motors
Model R R² Adj. F βₒ β1 Variables R (Const.) (FDI) FDI .745 .555 .520 16.18 2.433 1.113 t-statistics 4.672 4.023 Sig .001 .000 .001 St.Error 6.33 2.89 1.53
The following equation is derived from the above analysis:
NPM = 2.433 + 1.113 FDI
FDI (β1=1.113, t=4.023, p=.001) was likely to bring about around 56% changes in
NPM at F (1, 13) = 16.18, p=.000. It explained that there would be around 1% increase in
NPM if FDI rose by 1% provided other factors are constant. Again, FDI played a positive role in raising NPM as the numerator of the ratio was profit and it was positively influenced by FDI.
4.2.6. Conclusion and decision. The regression analysis produces the following valid models.
AS = -241716.1 + 5108 FDI + 1027.6 ER + 2331 CR
AP= -3365.186 + 4248.59 FDI + 69.095 ER
ROA= 4.877 + 3.37 FDI
ROE = 7.091 + 6.946 FDI
NPM = 2.433 + 1.113 FDI
This is clear that FDI appeared to be the common determinant of all the variables of performance. It had a positive influence on the performance of Indus Motors Company Data Analysis and Findings 113
and a rise in FDI improved the overall performance of the firm. Another important element was Exchange Rate which also had a positive relationship with two of the variables for performance i.e. Annual Sales and Annual Profit. Consumption Rate also appeared as a determinant of Annual Sales.
Since the t values for all the predictors had p < 5%, we reject the null hypothesis and conclude that there was a significant impact of one or more of the independent variables on the performance of Indus Motors Company Ltd.
4.3. Determinants of the Performance of Honda Motors Ltd.
The variables of company are shown in table 46. The following hypotheses are designed:
Ho: There is no significant impact of macroeconomic factors on the performance of
Honda Motors Company Ltd.
H1: There is significant impact of macroeconomic factors on the performance of
Honda Motors Company Ltd.
Data Analysis and Findings 114
Table 46 Variables for Honda Motors Company Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 2783.23 201.17 11.31 27.00 7.23 1996-97 2556.49 48.07 2.26 6.06 1.88 1997-98 2763.46 158.18 10.57 17.40 5.72 1998-99 2561.01 207.69 13.57 20.11 8.11 1999-00 3506.53 191.29 10.69 16.78 5.46 2000-01 4485.44 204.47 10.04 16.23 4.56 2001-02 6519.00 432.00 17.61 28.74 6.63 2002-03 4901.00 346.00 11.59 20.33 7.06 2003-04 9358.00 409.00 5.84 21.17 4.37 2004-05 16587.00 162.00 1.37 7.74 0.98 2005-06 25638.70 705.29 7.69 26.07 2.75 2006-07 17055.12 -264.54 -3.19 -10.84 -1.55 2007-08 14715.50 75.01 1.10 2.32 0.51 2008-09 14149.65 -401.83 -4.04 -14.21 -2.84 2009-10 15854.14 -852.20 -9.51 -43.14 -5.38 Note: Adapted from Annual Reports of Honda Motors Company, Karachi Stock Exchange and Economic Survey of Pakistan
In order to test the hypothesis, the following correlation analysis is conducted as shown in the table 47:
Data Analysis and Findings 115
Table 47 Correlation analysis of Honda Motors GNI IR ER DR UR CR FDI
Sales Pearson .739** .429 .657** -.474 -.209 .393 .777** Correlation
Sig. (2-tailed) .002 .110 .008 .075 .454 .147 .001
Profit Pearson -.666** -.500 -.432 -.260 .526* -.685** -.024 Correlation
Sig. (2-tailed) .007 .057 .107 .349 .044 .005 .931
ROA Pearson -.816** -.705** -.555* -.043 .501 -.693** -.419 Correlation
Sig. (2-tailed) .000 .003 .032 .880 .057 .004 .120
ROE Pearson -.816** -.571* -.621* -.094 .482 -.726** -.195 Correlation
Sig. (2-tailed) .000 .026 .013 .740 .069 .002 .486
NPM Pearson -.880** -.674** -.647** .038 .457 -.716** -.451 Correlation
Sig. (2-tailed) .000 .006 .009 .892 .087 .003 .091
4.3.1. Effects of macroeconomic variables on sales Honda Motors.
The trend shown in Appendix A for Annual Sales (Figure A-18) appeared to be interdependent with the variables GNI (Figure A-1), FDI (Figure A-7) and ER (Figure A-
3). Annual sales was strongly correlated with GNI (r=.739), ER (r=.657) and FDI
(r=.777).
After conducting the regression analysis, the following best fit model summary was retrieved. Data Analysis and Findings 116
Table 48 Model summary for annual sales of Honda Motors
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (FDI) (ER) FDI, ER .911 .830 .802 29.34 -14746.979 4294.22 322.242 t-statistics -3.391 5.312 4.001 Sig .000 .005 .000 .002 St. Error 3224 4349 80.54 808 VIF 1.068 1.068
The equation on the basis of above model is as follows:
AS= -14746.979 + 4294.22 FDI + 322.242 ER
The Foreign Direct Investment (β1=4294, t=5.312, p=.000) and ER (β2=322, t=4, p=.002) were collectively supposed to bring about around 83% massive variations in the
Annual Sales of the firm. The F (2, 12) = 29.34 was also significant at p< .05. Both the predictors had positive impact on the Sales. If FDI and ER rise by 1 unit keeping other factors constant, the sales would rise by Rs. 4294 million and Rs. 322 million respectively.
4.3.2. Effects of macroeconomic variables on profit of Honda
Motors. In Appendix A, the scatter plot for Annual Profit (Figure A-19) showed a negative trend along with UR (Figure A-6). However, GNI (Figure A-1), and CR (Figure
A-5) had a positive trend. According to the correlation analysis, there were three variables which had strong correlation with annual profit and they were GNI (r= -.739),
CR ( -.657) and UR (.777). Data Analysis and Findings 117
The model which contained all three variables was not fit. The reason behind this was that the t values for GNI (p=.166), CR (p=.468) and U (p=.619) had p>.05. The same case was applicable for two-variable model. After conducting the step-wise regression analysis, the following best fit model was selected.
Table 49 Model summary for annual profit of Honda Car
Model R R² Adj. F βₒ β1 Variables R (Const.) (CR) CR .685 .469 .428 11.488 9085.122 -105.022 t-statistics 3.429 -3.389 Sig .005 .004 .005 St. Error 284 2650 31
AP= 9085 – 105 CR
The Consumption Rate (β1=-105, t=3.389, p=.005) was significant at F (1,
3)=11.488, p=.005. It was supposed to cause around 46.9% changes in the annual profit of Honda Motors Company.
If CR increased by 1%, the profit would go down by Rs.105 million. Surprisingly,
CR had a negative influence on Profit. It means that a rise in aggregate consumption was not diverted towards the buying of Honda Motors’ vehicles. Rather, a rise in consumption expenditure was likely to reduce profit of the firm. The reason might be the phenomenon of price elasticity of demand. When total consumption expenditure rises, the demand for goods and services increases. As a result, the price level goes up and rise in price reduces the demand for luxuries items such as vehicles. Therefore, decline in demand might be the responsible factor to reduce profit of the firm. Data Analysis and Findings 118
4.3.3. Effects of macroeconomic variables on ROA of Honda Motors.
The scatter plots in Appendix A showed that ROA (Figure A-20) had decreasing trend but all the dependent variables which were GNI (Figure A-1), ER (Figure A-3) and
IR (Figure A-2) and CR (Figure A-5), had increasing trend. The correlation analysis revealed that GNI (r= -.816), IR (r= -.705), ER (r= - .555) and CR (r=-.693) were significantly correlated with ROA.
The multiple regression model on the basis of these variables collectively was not fit due to the insignificant t values for coefficients GNI (p=.079), IR (p=.633), ER
(p=.307) and CR (p=.67). Further, there were strong evidences of multicollinearity between the variables as depicted by VIF for GNI (VIF= 17.668) and ER (VIF=10.137).
So, a multiple step regression analysis was conducted in order to explore the best fit model and the final model is as follows:
Table 50 Model summary for ROA of Honda Car
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (IR) ER, IR .809 .654 .597 11.363 28.921 -.280 -.933 t-statistics 4.449 -2.342 -3.468 Sig .002 .001 .037 .005 St. Error 4.79 6.5 .269 .119 VIF 1.061 1.061
Based on the above analysis, fowling model was selected.
ROA = 28.921 -0.28 ER -0.933 IR Data Analysis and Findings 119
Both ER (β1=-.28, t= -2.342, p=.037) and IR (β2=-.933, t=3.468, p=.005) collectively caused 65.4% moderate variations in ROA. The model was significant at F
(2, 12) = 11.363, p=.001. The Exchange Rate was likely to reduce the ROA by 0.28% for every one unit increase. It means that the rise in Exchange rate of US Dollar in terms of
Pakistani Rupee reduced the ROA as the cost of production on imported material went up. Similarly, a one unit rise in inflation rate reduced the ROA by 0.93% as the entrepreneurs were less capable to raise the profit in an inflated economy.
4.3.4. Effects of macroeconomic variables on ROE of Honda
Motors. According to the trend analysis in Appendix A, ROE (Figure A-21) had a decreasing trend as against the trends of GNI (Figure A-1), IR (Figure A-2), ER (Figure
A-3) and CR (Figure A-5). There were four variables which were highly correlated with
ROE and they were GNI (r=-.816), IR (r=-.571), ER (r=-.621) and CR (r=-.726).
Table 51 Model summary for ROE of Honda Car
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (CR) ER, CR .825 .681 .628 12.816 443.278 -.735 -4.604 t-statistics 3.911 -2.403 -3.334 Sig .001 .002 .033 .006 St. Error 11.85 113.327 .306 1.38 VIF 1.139 1.139
The regression analysis again showed that the four-variable model was not possible due to high VIF and insignificant t values of the coefficients of GNI (p=.243, VIF=
17.668), IR (p=.542), ER (p=.713, VIF=10.137), CR (p=.323). Data Analysis and Findings 120
Therefore, following is the best fit model:
ROE = 443.27 – 0.735 ER – 4.604 CR
The model predicted around 68.1% above average variations in the ROE due to ER
(β1=-.735, t=-2.403, p=.033) and CR (β2=-4.604, t=-3.334, p=.006). The F (2,12) =
12.816, p=.002 was also significant and ensured that at least one of the variables had significant influence on ROE. Hence if other factor were kept constant, one unit rise in
ER would reduce the ROE by 0.73%. Similarly, 1% rise in CR was also likely to diminish the ROE by around 4%. The theoretical justification was similar to the explanation of previous section.
4.3.5. Effects of macroeconomic variables on NPM of Honda
Motors. The same four variables were strongly correlated with NPM. The correlation of
GNI (r=-.880), IR (r=-.674) and ER (r=-.647) and CR (r=-.716) were highly but negatively associated with NPM. The correlation was also supported by Appendix A where the graphs of NPM (Figure A-22) had a negative trend and the independent variables GNI (Figure A-1), ER (Figure A-3) and CR (Figure A-5) had increasing trends.
All four variables collectively failed to produce a valid model due high p value which were IR (p=.952), ER (p= .31), CR (p= .878). So, the best fit model is as follows:
Data Analysis and Findings 121
Table 52 Model summary for NPM of Honda Motors
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (IR) ER, IR .838 .703 .654 14.205 17.104 -.188 -.453 t-statistics 5.311 -3.174 -3.394 Sig .001 .000 .008 .005 St. Error 2.37 3.22 .133 .059 VIF 1.061 1.061
NPM= 17.104 – 0.188 ER – 0.453 IR
The ER (β1= -.188, t= -3.174, p=.008) and IR (β2=-.453, t=-3.394, p=.005) were responsible for causing a high degree variation of around 70.3% in NPM. One unit rise in
ER and IR might decrease the NPM by 0.188% and 0.453% respectively. Theoretically, both the variables caused logical fluctuation in the dependent variable which was discussed in the previous section.
4.3.6. Conclusion and Decision. The regression analysis produces the following valid models
AS= -14746.979 + 4294.22 FDI + 322.242 ER
AP= 9085 – 105 CR
ROA = 28.921 -0.28 ER -0.933 IR
ROE = 443.27 – 0.735 ER – 4.604 CR
NPM= 17.104 – 0.188 ER – 0.453 IR
All of the dependent variables were significantly influenced by one or more macroeconomic variables. Exchange Rate was found to be the most frequent predictor Data Analysis and Findings 122
which influenced four dependent variables. It means that the firm depended upon foreign exchange elements to a great extent.
As far as the hypothesis was concerned, all of the dependent variables were influenced significantly by one or more predictors. So, we reject the null hypothesis and conclude that the performance of Honda Motors Limited was significantly influenced by macroeconomic factors.
4.4. Determinants of the performance of Dewan Motors Ltd.
Another original equipment manufacturer is Dewan Farooq Motors Limited which was engaged in the production of four wheel vehicles. The data pertaining to the dependent variables of the firm is given below:
Table 53 Variables for Dewan Farooq Company Year Sale Profit ROA ROE NPM Rs.Mill Rs.Mill % % % 2000-01 1001.29 63.56 2.33 7.97 6.35
2001-02 3389.37 -31.15 -0.90 -4.06 -0.92
2002-03 4256.19 11.94 0.34 1.53 0.28
2003-04 4695.99 139.71 4.02 16.54 2.98
2004-05 6587.29 223.44 4.38 22.46 3.39
2005-06 8785.86 305.95 4.85 23.52 3.48
2006-07 9002.63 191.93 2.49 11.73 2.13
2007-08 7235.46 61.12 0.89 3.62 0.84
2008-09 5282.90 -399.50 -6.82 -37.60 -7.56
2009-10 1557.02 -1390.03 -30.91 294.90 -9.28
Note: Adopted from Annual Reports of Dewan Farooq Motors Company, Karachi Stock Exchange and Economic Survey of Pakistan
Data Analysis and Findings 123
Ho: There is no significant impact of macroeconomic factors on the performance of Dewan Farooq Motors Company Ltd.
H1: There is significant impact of macroeconomic factors on the performance of Dewan Farooq Motors Company Ltd. In order to test the hypothesis, the following correlation analysis was conducted as shown in the following table: Table: 54 Correlation Analysis for Dewan Farooq Motors
GNI IR ER DR UR CR FDI Sales Pearson .084 .272 -.206 -.218 -.427 .009 .895** Correlation Sig. (2-tailed) .817 .447 .568 .545 .219 .980 .000 N 10 10 10 10 10 10 10 Profit Pearson -.728* -.362 -.893** -.517 .546 -.665* .357 Correlation Sig. (2-tailed) .017 .304 .001 .126 .102 .036 .311 N 10 10 10 10 10 10 10 ROA Pearson -.742* -.369 -.921** -.492 .544 -.672* .284 Correlation Sig. (2-tailed) .014 .295 .000 .149 .104 .033 .426 N 10 10 10 10 10 10 10 ROE Pearson .570 .262 .854** .283 -.299 .477 -.146 Correlation Sig. (2-tailed) .086 .464 .002 .428 .401 .163 .687 N 10 10 10 10 10 10 10 NPM Pearson -.710* -.352 -.938** -.405 .454 -.619 .215 Correlation Sig. (2-tailed) .021 .318 .000 .245 .187 .057 .550 N 10 10 10 10 10 10 10
4.4.1. Effect of macroeconomic variables on Sales. The only significantly correlated factor with sales was FDI (r=.859). So, the single variable regression model is as follows: Data Analysis and Findings 124
Table: 55
Model Summary for Annual Sales of Dewan Motors
Model R R Adj. F β0 β1 Variables Square R (Const.) (FDI) FDI .895 .800 .775 32.053 1426.937 2016.369 t-statistics 1.826 5.662 Sig .000 .105 .000 St. Error 1309.7 781.5 356.1 AS = 1427 + 2016 FDI
So, FDI (β1= 2016, t=5.66, p= .000) was supposed to cause around 80% substantial changes in Annual Sales of Dewan Motors. The F Value (1, 8) =32.053, p=.000 was also significant to justify the model. If there were a 1% rise in FDI, the annual sales would be inflated by Rs.2016 million. The positive impact of FDI on sales was quite a logical phenomenon as the transfer of technology from abroad helped the firm to bring about innovations in the vehicles and to raise the sales.
4.4.2 Effect of macroeconomic variables on Profit. GNI (r=.-728), ER
(r=-.893) and CR (r=.665) were found to be significantly correlated with the annual profit of Dewan Motors. The three-variable regression model was not valid as the coefficients
GNI (p=.416, VIF = 13.265) and CR (p=.33, VIF = 8.458) were insignificant and auto correlated. The regression analysis produced the following single-variable regression model:
Data Analysis and Findings 125
Table 56
Model Summary for Annual Profit of Dewan Motors
Model R R Adj. F β0 β1 Variable Square R (Const.) (ER) s ER .893 .797 .772 31.39 3867.64 -64.902 6 6 t- 5.455 -5.603 statistics Sig .001 .001 .001 St. Error 238 709 11.5
AP = 3867.646 – 64.902 ER
The R2 (.797) was significant at F (1, 8)=31.396 , p= .001. It means that the model predicted 79.7 % big variations in the profit due to the changes in exchange rate
(β1=-64.9, t=-5.603,p=.001). A decrease of Rs 65 million would be observed in the annual profit if ER rose by Re 1.
The negative effect of exchange rate on the profit enunciated that the firm was more exposed to exchange fluctuation risk. If the domestic currency got weaker, the cost of production would increase and profit margins go down.
4.4.3 Effect of macroeconomic variables on ROA GNI (r=-.742), ER (r=-.921) and CR (r=-.672) were found to be significantly correlated with ROA. In the three variable model, GNI (p= .332. VIF=13.265) and CR (p= .265, VIF= 8.458) had the problems pertaining to p values and multicollinearity. Therefore, single-variable regression model was formulated which is as follows:
Data Analysis and Findings 126
Table 57
Model Summary for ROA of Dewan Motors
Model R R Adj. F β0 β1 Variables Square R (Const.) (ER) ER .921 .848 .829 44.575 85.859 -1.443 St. Error 4.44 13.224 .216 t-statistics 6.493 -6.676 Sig .000 .000 .000 VIF 1.00
ROA = 85.859 – 1.443 ER
The high R Square (.848), large value of F (1, 8) = 44.575, p=.000 and significant t values for ER (B1=-1.443, t=-6.676, p=.000) justified the model on statistical basis. A decrease of 1.4% would be observed in the ROA if ER rose by Re 1.
4.4.4 Effect of macroeconomic variables on ROE. Only ER( r=.854) appeared to be significantly correlated with ROE. A single regression model produced the following summary:
Table 58
Model Summary for ROE of Dewan Motors
Model R R Adj. F β0 β1 Variables Square R (Const.) (ER) ER .854 .730 .696 21.64 -674.12 11.636 t-statistics -4.403 4.652
Sig .002 .002 .002
St. Error 51.4 153 2.5
ROE = -674.126 + 11.636 ER Data Analysis and Findings 127
Exchange Rate (β1=11.636, t= 4.652, p=.002) with F (1, 8) = 21.642, p= .002 was responsible for causing 73% high degree variations in ROE. However, ER had positive impact on ROE which means that one Rupee increase in ER would raise the ROE by
11.63% assuming that other factors were constant. The positive impact reflected that the impact of rise in the cost of imported material due to higher Exchange Rate was absorbed by the firm quite nicely. As a result, the effect of ER on ROE was positive.
4.4.5. Effect of macroeconomic variables on NPM. GNI (r=-.71) and ER
(r=-.938) were significantly correlated with NPM. However, after running regression analysis, GNI (t= .49, p= .639) had p>.05 and it could not be included in the model. So, a single regression model was used which was as follows:
Table 59
Model Summary for NPM of Dewan Motors
Model R R Adj. F β β 0 1 Variables Square R (Const.) (ER) ER .938 .879 .864 58.364 232.556 -3.950
t-statistics 7.349 -7.640
Sig .000 .000 .000 St. Error 10.62 31.62 .517
NPM = 232.556 - 3.95 ER
Exchange Rate (β 1 = -3.95, t= -7.64, p=.000) was responsible to cause a massive variation in NPM which was around 87.9% at F (1, 8) = 58.364, p=.000). ER had a negative impact on NPM which means that one Rupee increase in Exchange Rate would reduce the NPM by 3.95%.
Data Analysis and Findings 128
4.4.6. Conclusion and Decision. The regression analysis produces the following valid models
AS = 1427 + 2016 FDI
AP = 3867.646 – 64.902 ER
ROA = 85.859 – 1.443 ER
ROE = -674.126 + 11.636 ER
ROE = 232.556 - 3.95 ER
All of the dependent variables were significantly influenced by one or more macroeconomic variables. Exchange Rate was identified as the most frequent predictor which influenced four dependent variables. It means that the firm depended upon foreign exchange factor to a great extent. As far as the hypothesis is concerned, all of the dependent variables were influenced significantly by one or more macroeconomic variables. So, we reject the null hypothesis and conclude that the performance of Dewan Farooq Motors Limited was significantly influenced by macroeconomic factors. 4.5. Determinants of the performance of Hinopak Company
The company is the original equipment manufacturers in Pakistan for heavy vehicles particularly buses. In order to conduct a detailed analysis, the following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Hinopak Motors Company Ltd.
H1: There is significant impact of macroeconomic factors on the performance of
Hinopak Motors Company Ltd.
In order to test the hypothesis, the following data is organized to display the variables of the firm. Data Analysis and Findings 129
Table 60 Variables for Hino Pak Company Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 2359.88 176.11 11.86 44.93 7.46 1996-97 2359.88 56.90 3.23 13.62 2.41 1997-98 907.75 -246.70 -26.50 -155.60 -27.17 1998-99 811.28 -181.24 -11.93 196.78 -22.33 1999-00 1442.58 4.79 0.38 -5.80 0.33 2000-01 1669.13 41.53 3.00 -101.38 2.49 2001-02 3078.15 439.41 24.56 105.63 14.28 2002-03 4261.27 352.13 11.71 52.77 8.26 2003-04 5521.80 335.73 9.82 32.78 6.08 2004-05 6367.61 242.96 7.67 20.81 3.82 2005-06 6392.28 323.88 7.82 22.76 5.07 2006-07 7826.78 590.05 12.01 31.24 7.54 2007-08 9897.26 71.77 1.12 4.30 0.73 2008-09 12151.02 69.92 1.40 4.19 0.58 2009-10 11127.55 -148.07 -2.58 -9.85 -1.33 Note: Adapted from Annual Reports of Hino Pak Company, Karachi Stock Exchange and Economic Survey of Pakistan
On the basis of above data, the following correlation analysis is conducted:
Data Analysis and Findings 130
Table 61 Correlation analysis for Hino Pak
GNI IR ER DR UR CR FDI
Sales Pearson .960** .716** .767** -.280 -.351 .681** .532* Correlation
Sig. (2-tailed) .000 .003 .001 .312 .199 .005 .041
Profit Pearson .048 -.178 .200 -.694** .343 -.392 .499 Correlation
Sig. (2-tailed) .864 .525 .475 .004 .211 .148 .058
ROA Pearson .065 -.094 .203 -.603* .391 -.194 .255 Correlation
Sig. (2-tailed) .818 .739 .467 .017 .150 .490 .360
ROE Pearson -.072 -.124 .033 -.200 .038 .041 .086 Correlation
Sig. (2-tailed) .800 .660 .907 .475 .892 .885 .760
NPM Pearson .189 .047 .273 -.573* .333 -.090 .269 Correlation
Sig. (2-tailed) .499 .867 .325 .026 .225 .749 .332
4.5.1. Effects of macroeconomic variables on sales of Hino Pak. The correlation analysis revealed that annual sales of Hino Pak was strongly correlated with five macroeconomic factors which included GNI (r= .96), IR (r=.716), ER (r=.767),
CR(r=.681) and FDI (r=.532). The scatter plots revealed that Sales (Figure A-23) had positive trend along with GNI (Figure A-1), CR (Figure A-5), ER (Figure A-2), IR
(Figure A-3) and FDI (Figure A-7). Data Analysis and Findings 131
All five variables did not collectively influence the sales of the firm due to insignificant levels of t value of IR (p=.081), ER (p=.424), CR (p=.102) and FDI
(p=.622). Therefore, the most appropriate model is as follows:
Table 62 Model summary for sales of Hino Pak
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (GNI) (IR) GNI, IR .972 .944 .935 102.031 -2115.318 .143 150.787 t-statistics -3.705 9.662 2.250 Sig .000 .003 .000 .044 St. Error 964 571 .015 67.01 VIF 1.627 1.627
AS = - 2115.32 + 0.143 GNI + 150.787 IR
A very high R² value (.944) at significant F (2, 12) = 102.031, p=.000 justified the model that GNI (β1=.143, t= 9.662, p=.000) and IR (β2=150.787, t= 2.25, p=.044) were the major determinants of annual sales of the firm. If other factors were kept constant, a hundred Rupee rise in GNI would boost the sales by Rs. 14.3 million. Similarly, if only
IR increased by 1%, the annual sale would go up by Rs.151 million.
The positive effect of GNI on sales is very logical. Rise in GNI means that the people have more amount of nominal income and any increase in income generally increases the sales of commodities. Inflation Rate also supports the enhancement in sales in case of Hinopak. It means that the firm did not take much influence of high inflation rate and the consumers were capable of buying the vehicles of the company. Since the products of the firm were heavy vehicles such as trucks and buses, the rise in price Data Analysis and Findings 132
generally did not change the decision of buyers. The rise in sales due to inflation may be understood in the way that the producers got the benefit of inflation by means of booking high prices. So, the firms tended to sell more and more so as to enjoy the benefits of price hike.
4.5.2. Effects of macroeconomic variables on profit of Hinopak. Just one variable was significantly associated with profit and that was Discount Rate (r=-
.694). The correlation was also justified by the scatter plots of annual profit (Figure A-24) that had increasing and DR (Figure A-4) had declining trend and they were mutually dependent. The following regression model was obtained:
Table 63 Model summary for profit of Hinopak
Model R R² Adj. F βₒ β1 variables R (Const.) (DR) DR .694 .481 .441 12.060 725.512 -47.105 t-statistics 4.163 -3.473 Sig .004 .001 .004 St Error 178.8 174 13.5 VIF 1.00
AP= 725.512 - 47.105 DR
Discount Rate (β1=-47.105, t= -3.473, p=.004) at F (1, 13) = 12.06, p=.004, was responsible of causing around 48% moderate change in the profit of the firm. The DR had negative influence on the profit and 1% rise in DR was likely to cut down profit by Rs.
47 million. The theoretical justification for the phenomenon is that the rise in discount Data Analysis and Findings 133
rate makes the cost of borrowing expensive for producers. As a result, the cost of production increases and the profit goes down.
4.5.3. Effect of macroeconomic variables on ROA of Hinopak.
Discount Rate (r=-.603) was again the only factor which was highly correlated with
ROA. The correlation was supported by scatter plots as ROA (Figure A-25) had slightly positive slope and DR (Figure A-4) had negative slope and they were mutually dependent. The following model explains the regression analysis:
Table 64 Model summary for ROA of Hino Pak
Model R R² Adj. F βₒ β1 Variables R (Const.) (DR) DR .603 .363 .314 7.416 28.416 -2.005 t-statistics 3.003 -2.723 Sig .017 .010 .017 St. Error 9.7 9.46 .736 VIF 1.00
ROA = 28.416 – 2.005 DR
Although the DR (β1= -2.005, t= -2.723, p= .017) was likely to cause just 36.3% low degree variation in ROA, its influence could not be ignored. It had a negative impact on ROA and 1% rise in DR would reduce ROA by 2%.
4.5.4. Effects of macroeconomic variables on ROE of Hinopak.
According to the Correlation analysis, there was no significant correlation identified between ROE and macroeconomic variables. Therefore, it was concluded that there was no significant influence of macroeconomic factors on ROE of Hino Pak Company. Data Analysis and Findings 134
4.5.5. Effects of macroeconomic variables on NPM of Hino Pak.
Discount Rate (r= -.573) was again the only factor which was significantly correlated with NPM. The trend analysis of NPM (Figure A-27) and DR (Figure A-5) had opposite relationship which was reflected by the negative correlation between the variables.
The following model explains the relationship:
Table: 65 Model summary for NPM of Hinopak
Model R R² Adj. F βₒ β1 Variables R (Const.) (DR) DR .573 .328 .277 6.359 22.812 -1.797 t-statistics 2.491 -2.522 Sig .026 .027 .026 St. Error 9.39 9.15 .713 VIF 1.00
NPM = 22.812 – 1.797 DR
The DR (β1= 22.812, t=2.491, p=.026) did not appear to be a major cause at F (1, 13)
= 6.395, p= .026 as it brought about just 32.8% changes in NPM of Hinopak Company
Limited. However, one percent rise in DR would be responsible for reducing NPM by
1.8%.
4.5.6. Conclusion and decision. The regression analysis produced the following valid models
AS = - 2115.32 + 0.143 GNI + 150.787 IR
AP= 725.512 - 47.105 DR
ROA = 28.416 – 2.005 DR Data Analysis and Findings 135
NPM = 22.812 – 1.797 DR
All of the dependent variables were influenced by one or more macroeconomic variables except ROE. Discount Rate was the most prominent predictor which influenced four dependent variables. It means that the performance of the firm was dependent upon the policies of the central bank extensively pertaining to the Discount
Rate.
As far as the hypothesis is concerned, all of the dependent variables were influenced significantly by one or more predictors except ROE. So, we reject the null hypothesis and conclude that the performance of Hinopak Motors Limited is significantly, though partially, influenced by macroeconomic factors.
4.6. Influence of Factors on the performance of Ghandhara
Industries Ltd.
The company is the original equipment manufacturers in Pakistan for heavy vehicles particularly buses and trucks. In order to conduct a detailed analysis, the dependent variables of the company are shown in table 58.
The following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Ghandhara Industries Ltd.
H1: There is significant impact of macroeconomic factors on the performance of
Ghandhara Industries Ltd.
Data Analysis and Findings 136
Table 66 Variables for Ghandhara Industries Ltd. Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 222.42 -116.10 -12.08 16.14 -52.20 1996-97 183.42 -65.10 -7.08 8.30 -35.49 1997-98 223.36 -53.49 -5.74 6.38 -23.95 1998-99 169.18 -89.29 -9.83 9.63 -52.78 1999-00 104.96 -18.87 -2.45 1.99 -17.98 2000-01 42.58 -44.01 -5.67 4.45 -103.36 2001-02 255.87 36.15 4.38 -4.10 14.13 2002-03 640.68 49.28 4.45 -5.95 7.69 2003-04 1006.30 96.29 12.30 -17.95 9.57 2004-05 1505.87 520.73 23.43 -30.01 34.58 2005-06 1872.33 126.48 5.53 64.46 6.76 2006-07 1857.06 18.13 0.85 8.32 0.98 2007-08 1313.81 -137.49 -7.15 -164.20 -10.46 2008-09 2086.52 135.56 4.79 60.89 6.50 2009-10 222.42 -116.10 -12.08 16.14 -52.20 Note: Adapted from Annual Reports of Ghandhara Industries, Karachi Stock Exchange and Economic Survey of Pakistan
In order to test the hypothesis, the following data is retrieved as shown in the table
67.
Data Analysis and Findings 137
Table 67 Correlation analysis of Ghandhara Industries GNI IR ER DR UR CR FDI
Sales Pearson .576* .388 .418 -.422 -.205 .232 .757** Correlation
Sig. (2-tailed) .025 .153 .121 .117 .463 .404 .001
Profit Pearson .109 -.129 .217 -.546* .451 -.230 .161 Correlation
Sig. (2-tailed) .699 .646 .437 .035 .092 .410 .568
ROA Pearson .095 -.183 .287 -.704** .587* -.387 .180 Correlation
Sig. (2-tailed) .736 .513 .299 .003 .021 .155 .520
ROE Pearson -.070 -.443 -.078 .035 .000 -.085 -.241 Correlation
Sig. (2-tailed) .805 .098 .782 .902 .998 .762 .387
NPM Pearson .222 .080 .320 -.574* .223 -.178 .391 Correlation
Sig. (2-tailed) .427 .778 .245 .025 .425 .525 .150
4.6.1. Effects of macroeconomic variables on sales of Ghandhara
Industries. After conducting the correlation analysis of sales and independent variables, it was found that sales was significantly correlated with GNI (r=.576) and FDI
(r= .757). The trend analysis showed that Sales (Figure A-28) and FDI (Figure A-7) had positive slope and they were mutually dependent. Data Analysis and Findings 138
However, the regression analysis revealed that both the variables could not collectively influence the sales of the firm due to the insignificant t value of GNI
(p=.135). So, the valid model summary is as follows:
Table 68 Model summary for sales of Ghandhara Industries
Model R R² Adj. F βₒ β1 variables R (Const.) (FDI) FDI .757 .573 .540 17.466 -20.094 515.594 t-statistics -.087 4.179 Sig .001 .932 .001 St. Error 508.5 232 123
AS = -20.094 + 515.594 FDI
The equation reflects a positive impact of FDI (β1=515.6, t=4.179, p=.001) on
Sales of Ghandhara Industries by causing a moderate variations of 54% at F (1, 3)=
17.46, p= .001. In addition to this, a one unit rise in FDI would boost up the sales by Rs.
515 million if all other factors are constant.
4.6.2. Effect of macroeconomic variables on profit of Ghandhara
Industries. A significant correlation between sales and Discount Rate (r=-.546) was identified. This relationship was also supported by the trend analysis depicted by the scatter plots of Profit (Figure A-29) that had slightly positive trend and DR (Figure A-4) had an inverse trend. The regression model summary is depicted below:
Data Analysis and Findings 139
Table 69 Model summary for profit of Ghandhara Industries
Model R R² Adj. F βₒ β1 Variables R (Const.) (DR) DR .546 .298 .244 5.517 336.617 -25.330 t-statistics 2.429 -2.349 Sig .035 .030 .035 St. Error 142.1 138.55 10.78
AP = 336.617 – 25.33 DR
The negative and low impact of DR (β1= -25.33, t=-2.349, p=.035) was observed on
Annual Profit as the R² was only .298 at a small value of F (1, 13) = 5.517, p= .035.
Hence DR was responsible for a small change in Annual Profit of Ghandhara Industries.
One percent rise in DR would reduce the profit by Rs. 25 million if other things were constant.
4.6.3. Effect of macroeconomic variables on ROA of Ghandhara
Industries. There was a significant correlation between sales and Discount Rate (r=
.704) and Appendix B also revealed that ROA (Figure A-30) and DR (Figure A-4) had opposite trends and they were negatively correlated. The most appropriate model included DR as depicted in table 70:
Data Analysis and Findings 140
Table 70 Model summary for ROA of Ghandhara Industries
Model R R² Adj. F βₒ β1 variables R (Const.) (DR) DR .704 .495 .457 12.767 23.896 -1.963 t-statistics 3.385 -3.573 Sig .003 .005 .003 St. Error 7.24 7.06 .549
ROA = 23.896 – 1.963 DR
A negative and moderate impact of DR (β1= 23.896, t= 3.385, p=.003) on ROA was identified which caused around 50% changes in ROA at F (1, 13) = 12.767, p=.005.
The model depicted that if there were one percent rise in Discount Rate, the ROA would go down by around 2% if all other factors were constant.
4.6.4. Effect of macroeconomic variables on ROE of Ghandhara
Industries. The Return on Assets was not significantly correlated with any of the macroeconomic variables. Therefore, it was concluded the performance of Ghandhara
Industries Ltd. would not be affected by the macroeconomic environment.
4.6.5. Effect of macroeconomic variables on NPM of Ghandhara
Industries. There existed a negative and significant correlation between sales and
Discount Rate (r = -.574) and the same was supported by Appendix A for NPM (Figure
A-31) and DR (Figure A-4) which had opposite trends. The model is depicted below:
Data Analysis and Findings 141
Table 71 Model summary for NMP of Ghandhara Industries
Model R R Adj. F βₒ β1 variables R² R (Const.) (DR) DR .574 .330 .278 6.402 54.589 -5.850 t-statistics 1.838 -2.530 Sig .025 .089 .025 St. Error 30.48 29.7 2.312
NPM = 54.89 – 5.85 DR
There was a negative and low degree effect of DR (β1= -5.85, t= -2.53, p= .025) on NPM which was likely to cause less than 28% variations in the dependent variable at
F (1, 13) = 6.40, p = .025. If all the factors kept constant, one percent rise in the Discount
Rate by the State Bank of Pakistan would cut down NPM by 6%.
4.6.6. Conclusion and Decision. The regression analysis produces the following valid models
AS = -20.094 + 515.594 FDI
AP = 336.617 – 25.33 DR
ROA = 23.896 – 1.963 DR
NPM = 54.89 – 5.85 DR
All of the dependent variables were significantly influenced by one of the macroeconomic variables except ROE. Discount Rate was the most frequent predictor which influenced four dependent variables. It means that the firm depended extensively upon the policies of the central bank pertaining to the Interest Rate.
As far as the hypothesis is concerned, all of the dependent variables were influenced significantly by at least one predictor except ROE. So, we reject the null Data Analysis and Findings 142
hypothesis and conclude that the performance of Ghandhara Industries Limited is significantly, though partially, influenced by macroeconomic factors.
4.7. Determinants of the performance of Ghandhara Nissan
Ltd.
In order to conduct a detailed analysis, the following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Ghandhara Nissan Ltd.
H1: There is significant impact of macroeconomic factors on the performance of
Ghandhara Nissan Ltd.
In order to test the hypothesis, the following data is retrieved as shown in the following table:
Data Analysis and Findings 143
Table 72 Variables for Ghandhara Nissan Ltd. Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 159.99 7.74 1.18 62.90 4.83 1996-97 225.29 -40.37 -3.14 -45.40 -17.92 1997-98 584.38 -186.11 -18.77 191.53 -31.85 1998-99 290.95 -285.45 -29.21 74.60 -98.10 1999-00 66.54 -137.53 -16.77 29.25 -206.60 2000-01 244.69 -206.40 -27.43 30.51 -84.35 2001-02 88.46 -112.15 -18.28 14.22 -126.78 2002-03 101.44 298.11 54.49 -68.85 293.89 2003-04 1237.15 242.26 14.12 214.30 19.58 2004-05 3504.21 -1.99 -0.08 -0.47 -0.06 2005-06 4440.21 132.71 4.64 24.94 2.99 2006-07 2894.83 187.23 7.81 25.03 6.47 2007-08 3708.89 178.16 5.74 17.65 4.80 2008-09 2053.96 -312.17 -9.20 -44.71 -15.20 2009-10 2402.62 -88.89 -2.65 -14.04 -3.70 Note: Adapted from Annual Reports of Ghandhara Nissan, Karachi Stock Exchange and Economic Survey of Pakistan
The correlation analysis is as follows:
Data Analysis and Findings 144
Table 73 Correlation analysis for Ghandhara Nissan GNI IR ER DR UR CR FDI
Sales Pearson .681** .571* .534* -.319 -.320 .443 .833** Correlation
Sig. (2-tailed) .005 .026 .040 .247 .245 .098 .000
Profit Pearson .088 .123 .224 -.504 .188 -.275 .491 Correlation
Sig. (2-tailed) .755 .663 .422 .055 .502 .322 .063
ROA Pearson .180 .104 .298 -.468 .206 -.160 .234 Correlation
Sig. (2-tailed) .521 .712 .281 .079 .462 .569 .401
ROE Pearson -.340 -.193 -.294 .129 .074 -.442 -.110 Correlation
Sig. (2-tailed) .215 .491 .288 .646 .794 .099 .698
NPM Pearson .162 .093 .241 -.282 .145 -.081 .139 Correlation
Sig. (2-tailed) .564 .743 .387 .308 .606 .774 .621
4.7.1. Impact of macroeconomic factors on sales of Ghandhara
Nissan. The variables which had significant correlation with sales included GNI (r=
.681), Inflation Rate (r= .571), Exchange Rate (r= .534) and FDI (r=.833). Appendix A revealed that Sales (Figure A-32) was mutually dependent with GNI (Figure A-1) and
FDI (Figure A-7). However, all the four variables did not produce a valid regression model due to the insignificant t values of GNI (p=.887), IR (p= .583) and ER (p=.494). Data Analysis and Findings 145
The valid multiple regression model included only two variables i.e. GNI and FDI and its summary is as follows:
Table: 74 Model summary for annual sales of Ghandhara Nissan
Model R R² Adj. F βₒ β1 Β2 Variables R (Const.) (GNI) (FDI) GNI, FDI .905 .819 .789 27.118 -1130.024 .028 935.534 t-statistics -2.678 2.885 4.851 Sig .000 .020 .014 .000 St. Error 716 423 .01 193 VIF 1.232 1.232
AS = - 1130.24 + 0.28 GNI + 935.534 FDI
Both the variables GNI (β1=.028, t= 2.885, p= .01) and FDI (β2=935, t=4.851, p=.000) brought about more than 81% changes in the annual sales. Hence, the model appeared to be a strong predictor of Sales at F (2, 12) = 27.118, p=.000. Further, annual sales would be raised by Rs. 0.028 million if GNI was increased by Rs. 1 million while other factors are constant. Similarly, if there were 1% increase in FDI, the sales would be appreciated by Rs. 935 million.
4.7.2. Conclusion and decision. Surprisingly, annual sale was the only dependent variable which was significantly influenced by the macroeconomic factors. It means that other variables of the firm for measuring the performance were not responsive to the macroeconomic environment.
As far as the hypothesis is concerned, only one of the dependent variables is influenced significantly by the predictor. So, we reject the null hypothesis and conclude Data Analysis and Findings 146
that only one aspect of performance of Ghandhara Industries Limited is significantly influenced by macroeconomic factors.
4.8. Determinants of the Performance of Millat Tractors Ltd.
In order to conduct a detailed analysis, the following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Millat Tractors Ltd.
H1: There is significant impact of macroeconomic factors on the performance of
Millat Tractors Ltd.
In order to test the hypothesis, the following data is retrieved as shown in the following table:
Data Analysis and Findings 147
Table 75 Variables for Millat Tractors Ltd.
Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 3330.32 96.81 14.23 21.22 2.91 1996-97 2049.39 65.90 5.17 14.26 3.22 1997-98 2751.72 138.10 8.09 25.95 5.02 1998-99 5322.90 230.46 9.75 34.58 4.33 1999-00 5679.23 262.40 14.34 32.66 4.62 2000-01 5451.73 287.70 13.14 29.63 5.28 2001-02 5115.60 187.80 8.13 17.80 3.67 2002-03 5260.79 265.61 10.15 21.93 5.05 2003-04 6984.92 394.62 11.11 26.16 5.65 2004-05 8326.23 453.86 7.18 22.98 5.45 2005-06 9737.38 730.58 9.80 30.53 7.50 2006-07 10961.44 636.90 10.17 23.62 5.81 2007-08 11174.01 810.46 11.15 26.67 7.25 2008-09 15910.62 1215.12 17.91 36.05 7.64 2009-10 22199.91 2284.50 19.42 54.49 10.29 Note: Adapted from Annual Reports of Millat TractorsLtd, Karachi Stock Exchange and Economic Survey of Pakistan
The correlation analysis is as follows:
Data Analysis and Findings 148
Table 76 Correlation analysis for Millat Tractors
GNI IR ER DR UR CR FDI
Sales Pearson .972** .550* .841** -.232 -.352 .721** .325 Correlation
Sig. (2-tailed) .000 .034 .000 .405 .199 .002 .236
Profit Pearson .928** .552* .803** -.124 -.368 .745** .224 Correlation
Sig. (2-tailed) .000 .033 .000 .660 .177 .001 .423
ROA Pearson .602* .292 .436 -.022 -.277 .514 -.146 Correlation
Sig. (2-tailed) .018 .291 .104 .938 .317 .050 .603
ROE Pearson .656** .233 .596* -.009 -.285 .571* -.107 Correlation
Sig. (2-tailed) .008 .403 .019 .975 .304 .026 .705
NPM Pearson .922** .508 .843** -.254 -.273 .622* .330 Correlation
Sig. (2-tailed) .000 .053 .000 .362 .326 .013 .230
4.8.1. Impact of macroeconomic factors on sales of Millat Tractors.
The Correlation analysis revealed that there were four variables which had significant correlation with sales and they included GNI (r= .972), Inflation Rate (r=.55),
Exchange Rate (r=.841) and Consumption Rate (r=.721). In Appendix A, Sales (Figure
A-38) appeared to be mutually dependent with GNI (Figure A-1), IR (Figure A-2) and
CR (Figure A-5) and ER (Figure A-3). Data Analysis and Findings 149
The multiple regression model based on these four variables was not valid due to high p value of ER (p= .906) and CR (p=.053). Therefore, a multiple regression model based on three independent variables was found fit whose results are shown as under.
Table: 77 Model summary for sales of Millat Tractors
Model R R² Adj. F βₒ β1 β2 β3 Variables R (Const.) (GNI) (IR) (CR) GNI, IR, CR .984 .969 .960 113.127 -48160.09 .229 -271.458 572.784 t-statistics -2.651 12.486 -2.682 2.570 Sig .000 .023 .000 .021 .026 St. Error 1078 18165 .018 101 223 VIF 1.999 2.972 3.593
AS = -48160 + 0.229 GNI – 271.458 IR + 572.784 CR
The selected variables collectively affected the sales substantially by causing 96% variation at F (3, 11) = 113.127, p=.000. A one million rise in GNP (β1=.229, t=12.486, p=.000) was likely to raise annual sales by Rs. 0.229 million if all other factors are constant. It means that increase in per capita income was channelized towards buying of tractors of the firm. Further, one percent increase in IR (β2= -271, t= -2.682, p= .021) would reduce the sales by Rs.271 million provided all other factors were constant. The rationale behind this was that the agriculturists were negatively affected by high rate of inflation and they had less amount of disposable income to spend on the purchase of tractors. Finally, if other factors were constant, a one percent rise in CR (β3= 573, t=2.57, p=.026) would increase the sales by Rs. 573 million. This upsurge in total consumption was also directed towards the agriculture activities including more buying of tractors. As a result, the sale went up. Data Analysis and Findings 150
4.8.2. Impact of macroeconomic factors on profit of Millat Tractors.
There were again four factors which had significant correlation with profit and they included GNI (r= .928), Inflation Rate (r=.552), Exchange Rate (r=.803) and
Consumption Rate (r= .745). The trends of graphs in Appendix A also supported that
Profit (Figure A-39) was mutually dependent with GNI (Figure A-1), IR (Figure A-2),
CR (Figure A-5) and ER (Figure A-3) as all of them follow the similar trend over a period of time.
The multiple regression model with all these variables contained the same problem of insignificant p value for GNI (p= .195), IR (p= .979) and ER (p= .489).
Therefore, another valid multiple regression model was selected in which CR, and ER were found to be significantly influential on the annual profit as other factors have p value greater than 5%. As a result, the following model is derived:
Table: 78 Model summary for profit of Millat Tractor
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (CR) (ER) CR, ER .943 .890 .872 48.568 -11876.57 124.561 32.272 t-statistics -6.010 5.173 6.050 Sig .000 .000 .000 .000 St. Error 207 1975 24 5.33 VIF 1.139 1.139
AP = -11876.57 + 124.561 CR + 32.272 ER
So, the two-factor model caused a substantial amount of variations in the annual profit by 89% at F (2, 12) = 48.568, p=.000. The high value of F with significant p value Data Analysis and Findings 151
reflected that the model is fit for interpreting the Annual Profit of Millat Tractors. Both consumption rate (β1=125, t=5.173, p=.000) and exchange rate (β2=32, t=6.05, p=.000) had positive influence on the annual profit. The per unit rise in consumption rate would increase the profit by Rs. 124 million which means that high consumption rate gave birth to more demand for tractors. As a result, sales revenue raised profit of the firm. Similarly, one unit rise in Exchange rate was supposed to raise the profit by Rs. 32 million. The upsurge in ER did not increase the cost of production more than the rise in revenue.
Therefore, the profit increased as ER went up.
4.8.3. Impact of macroeconomic factors on ROA of Millat Tractors.
There was only GNI (r=.602) which had significant correlation with ROA and the trend analysis also endorsed that ROA (Figure A-40) and GNI (Figure A-1) were positively correlated. The following model is derived from the regression analysis:
Table 79 Model summary for ROA of Millat Tractor
Model R R² Adj. F βₒ β1 variables R (Const.) (GNI) GNI .602 .363 .314 7.397 6.930 .000 t-statistics 3.816 2.720 Sig .018 .002 .018 St Error 3.23 1.81 .000
ROA = 6.93 + 0.000GNI
The analysis presented a negligible effect of GNI (β1=.000, t= 2.72, p=.018) on
ROA. So, the model failed to provide any meaningful data for interpretation and it was concluded that ROA was not affected by any of the macroeconomic factors. Data Analysis and Findings 152
4.8.4. Impact of macroeconomic factors on ROE of Millat Tractors.
There were three elements which had significant correlation with ROE and they included
GNI (r=.656), Exchange Rate (r=.596) and Consumption Rate (r=.571). The trends of graphs in Appendix A also supported that ROE (Figure A-41) was mutually dependent with GNI (Figure A-1), CR (Figure A-5) and ER (Figure A-3).
However, all three variables failed to form a valid model as GNI (p=.707), ER
(p=.28) and CR (p=.213) had p>.05. Therefore, single regression model was used to show the influence between the variables.
Table 80 Model Summary for ROE of Millat Tractor
Model R R² Adj. F βₒ β1 variables R (Const.) (CR) CR .571 .326 .274 6.275 -161.733 2.219 t-statistics -2.136 2.505 Sig .026 .052 .026 St. Error 8.11 75.7 .886
ROE = -162 + 2.219 CR
The model seemed to be not very effective as there was only 27.4% variations caused by the predictor at F (1, 13) = 6.257, p=.026. According to this model, CR
(β1=2.2, t=2.5, p=.026) had a positive impact on ROE and a one unit rise would raise
ROE by around 2%.
4.8.5. Impact of macroeconomic factors on NPM of Millat Tractors.
There were three variables which had significant correlation with NPM and they included
GNI (r= .922), Exchange Rate (r=.843) and Consumption Rate (r=.622). This positive Data Analysis and Findings 153
correlation between the variables was depicted by the scatter plots Appendix A where
NPM (Figure A-42) was interdependent with GNI (Figure A-1), ER (Figure A-3) and CR
(Figure A-5).
The multiple regression model based on the three variables was not valid due to the insignificant t value of GNI (p=.157) and ER (p= .383) and CR (p=.686). So, the following best fit model was obtained:
Table 81 Model summary for NMP of Millat Tractor
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (CR) ER, CR .912 .832 .804 29.745 -26.423 .125 .294 t-statistics -3.226 5.644 2.950 Sig .000 .007 .000 .012 St. Error .856 8.19 .1 .022 VIF 1.139 1.139
NPM= -26.423 + 0.125 ER + 0.294 CR
The model was likely to cause substantial variations up to 83.2% in the NPM due to ER (β1=.125, t=5.644, p=.000) and CR (β2=.294, t=2.95, p=.012) at F (2, 12) =
29.745, p= .000. If other factors were kept constant, rise in ER by one unit would raise
NPM by 0.125%. Similarly, one percent rise in CR was likely to increase NPM by
0.294%.
4.8.6. Conclusion and decision. The regression analysis produced the following valid models
AS = -48160 + 0.229 GNI – 271.458 IR + 572.784 CR Data Analysis and Findings 154
AP = -11876.57 + 124.561 CR + 32.272 ER
ROE = -162 + 2.219 CR
NPM= -26.423 + 0.125 ER + 0.294 IR
All of the dependent variables were significantly influenced by one of the macroeconomic variables except ROE. CR was the most frequent predictor which influenced three dependent variables.
As far as the hypothesis is concerned, all of the dependent variables are influenced significantly by one or more predictors except ROE. So, we reject the null hypothesis and conclude that the performance of Millat Tractors Limited is significantly, though partially, influenced by macroeconomic factors.
4.9. Determinants of the Performance of Al-Ghazi Tractors
Ltd.
In order to conduct a detailed analysis, the following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Al-Ghazi Tractors Ltd.
H1: There is significant impact of macroeconomic factors on the performance of Al-
Ghazi Tractors Ltd.
In order to test the hypothesis, the following data is retrieved as shown in the table 82:
Data Analysis and Findings 155
Table 82 Variables for Al-Ghazi Tractors Ltd. Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 2264.7480 107.6010 10.5989 27.9518 4.7511 1996-97 1717.0940 134.0000 14.8569 28.0124 7.8039 1997-98 2349.1500 207.3000 17.4565 32.7934 8.8245 1998-99 4232.8320 362.2510 15.8494 43.7974 8.5581 1999-00 8325.4880 963.5210 40.2927 79.9515 11.5731 2000-01 4562.9190 600.4050 26.6610 39.6886 13.1584 2001-02 3586.9650 400.4030 17.2889 26.2928 11.1627 2002-03 5460.2180 909.1020 29.8776 49.2346 16.6496 2003-04 6735.1950 964.7850 23.0801 36.8789 14.3245 2004-05 7739.3220 1060.8730 14.6419 34.5342 13.7076 2005-06 9022.5150 1229.3180 16.8900 34.6298 13.6250 2006-07 9081.3100 1267.4100 18.5638 32.9093 13.9562 2007-08 10107.8740 1113.2560 15.7106 25.1426 11.0138 2008-09 15764.8250 1743.5350 23.6230 32.1690 11.0597 2009-10 14936.0340 1908.8720 24.9000 30.0009 12.7803 Note: Adapted from Annual Reports of Al-Ghazi Tractors Ltd, Karachi Stock Exchange and Economic Survey of Pakistan
The correlation analysis is as follows:
Data Analysis and Findings 156
Table 83 Correlation analysis Al-Ghazi Tractors
GNI IR ER DR UR CR FDI
Sales Pearson .945** .523* .800** -.315 -.298 .651** .331 Correlation
Sig. (2-tailed) .000 .045 .000 .253 .280 .009 .228
Profit Pearson .933** .414 .878** -.478 -.151 .512 .358 Correlation
Sig. (2-tailed) .000 .125 .000 .071 .592 .051 .190
ROA Pearson .116 -.340 .323 -.361 .376 -.188 -.452 Correlation
Sig. (2-tailed) .680 .214 .240 .186 .167 .501 .091
ROE Pearson -.262 -.499 -.073 -.223 .315 -.361 -.419 Correlation
Sig. (2-tailed) .345 .058 .797 .424 .253 .186 .120
NPM Pearson .414 -.224 .735** -.863** .559* -.252 .169 Correlation
Sig. (2-tailed) .125 .422 .002 .000 .030 .366 .547
4.9.1. Effects of macroeconomic factors on sales of Al-Ghazi
Tractors. The Correlation analysis revealed that there were four variables which had significant correlation with sales and they included GNI(r= .945), Inflation Rate (r=.523), Data Analysis and Findings 157
Exchange Rate (r=.800) and Consumption Rate (r=.651). In Appendix A, Sales (Figure
A-43) appeared to have increasing trend along with GNI (Figure A-1), IR (Figure A-2) and CR (Figure A-5) and ER (Figure A-3).
The four-variable regression model was not valid due to high p value of ER (p=
.229), IR (p=.154) and CR (p=.993). Therefore, the best regression model was selected that was based on two independent variables whose results are shown as under.
Table 84 Model summary for sales of Al-Ghazi Tractors
Model R R² Adj. F βₒ β1 β2 variables R (Const.) (ER) (CR) ER, CR .893 .797 .763 23.582 -70400.157 254.151 743.416 t-statistics -3.515 4.701 3.046 Sig .000 .004 .001 .010 St. Error 2095 20027.218 54.065 244.069 VIF 1.139 1.139
AS = -70400 + 254.151 ER + 743.416 CR
The selected variables collectively affected the sales substantially by causing 79.7% variations at F (2, 12) = 23.582, p=.000. One unit increase in ER (β1= 254, t= 4.7, p=
.001) would increase the sales by Rs.254 million provided all other factors were constant.
Finally, if other factors were constant, a one percent rise in CR (β2= 743, t=3.046, p=.01) would increase the sales by Rs. 743 million. This upsurge in total consumption was also directed towards the agriculture activities including more buying of tractors. As a result, the sale went up. Data Analysis and Findings 158
4.9.2. Impact of macroeconomic factors on profit of Al-Ghazi
Tractors. There were only two factors which had significant correlation with profit and they were GNI (r= .933) and Exchange Rate (r=.878). The trends of graphs in Appendix
A also supported that Profit (Figure A-44) was mutually dependent with GNI (Figure A-
1), IR (Figure A-2) and CR (Figure A-5) and ER (Figure A-3).
The multiple regression model with two variables contained the same problem of insignificant p value for ER (p= .146). Therefore, another valid regression model was selected which is as follows:
Table 85 Model summary for Profit of Millat Tractor
Model R R² Adj. F βₒ β1 Variables R (Const.) (GNI) GNI .933 .871 .861 87.662 -107.212 .023 t-statistics -.917 9.363 Sig .000 .376 .000 St. Error 208.0 116.894 .003 VIF 1.000
AP = -107.212+ 0.023GNI
So, the two-factor model caused a substantial amount of variations in the annual profit by 87.1% at F (1, 13) = 87.662, p=.000. The high value of F with significant p value reflected that the model was fit for interpreting the Annual Profit of Al-Ghazi
Tractors. The GNI (β1=.023, t=9.363, p=.000) had positive influence but a meager impact on the annual profit. A per unit rise in GNI would increase the profit by Rs. 0.023 million only. Data Analysis and Findings 159
4.9.3. Impact of macroeconomic factors on NPM of Al-Ghazi
Tractors. There were three variables which had significant correlation with NPM and they included Discount Rate (r= -.863), Exchange Rate (r=.735) and Unemployment Rate
(r=.599). This positive correlation between the variables was depicted by Appendix A where NPM (Figure A-47) was interdependent with GNI (Figure A-1), ER (Figure A-3) and CR (Figure A-5).
The multiple regression model based on three variables was not valid due to the insignificant t value of UR (p=.079) and ER (p= .286). So, the following best fit model was obtained:
Table 86 Model summary for NMP of Al-Ghazi Tractors
Model R R² Adj. F βₒ β1 variables R (Const.) (DR) DR .863 .744 .724 37.816 20.768 -.746 t-statistics 13.330 -6.149 Sig .000 .000 .000 St. Error 1.598 1.558 .121 VIF 1.000
NPM= 20.786 - 0.746 DR
The model was likely to cause substantial variations up to 86.3% in the
NPM due to DR (β1=.746, t=6.149, p=.000) at F (2, 12) = 37.816, p= .000. If other factors were kept constant, rise in DR by one unit would reduce NPM by 0.746%.
4.9.4. Conclusion and decision. The regression analysis produced the following valid models: Data Analysis and Findings 160
AS = -70400 + 254.151 ER + 743.416 CR
AP = -107.212+ 0.023GNI
NPM= 20.786 - 0.746 DR
Three of the five dependent variables were significantly influenced by one of the macroeconomic variables.
As far as the hypothesis is concerned, all of the dependent variables are influenced significantly by one or more predictors except ROA and ROE. So, we reject the null hypothesis and conclude that the performance of Al-Ghazi Tractors Limited is significantly, though partially, influenced by macroeconomic factors.
4.10. Determinants of the Performance of Honda Atlas
Bikes
The data for Honda Atlas Bikes is retrieved as shown in the table 87. In order to conduct a detailed analysis, the following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Honda Atlas Bikes.
H1: There is significant impact of macroeconomic factors on the performance of
Honda Atlas Bikes.
Data Analysis and Findings 161
Table 87 Variables for Honda Atlas Bikes
Year Sale Profit ROA ROE NPM (Rs.Mill) (Rs.Mill) % % % 1995-96 3092.45 101.46 9.76 35.29 3.28 1996-97 3498.14 124.94 10.34 31.83 3.57 1997-98 3423.50 123.39 8.02 25.62 3.60 1998-99 3424.85 601.82 49.12 108.63 17.57 1999-00 3397.44 117.78 8.30 20.13 3.47 2000-01 4704.53 117.78 7.92 18.28 2.50 2001-02 5523.95 270.50 14.77 34.14 4.90 2002-03 6977.44 139.71 5.26 12.98 2.00 2003-04 9948.09 544.75 13.25 36.50 5.48 2004-05 14120.85 630.46 10.35 30.34 4.46 2005-06 17420.26 676.83 8.87 25.94 3.89 2006-07 16608.41 553.59 6.89 18.60 3.33 2007-08 20855.54 703.01 8.08 20.65 3.37 2008-09 13747.82 224.53 3.00 6.76 1.63 2009-10 25554.77 712.46 8.36 18.31 2.79 Note: Adapted from Annual Reports of Honda Atlas Ltd, Karachi Stock Exchange and Economic Survey of Pakistan
The correlation analysis is as follows:
Data Analysis and Findings 162
Table 88 Correlation analysis for Honda Bikes GNI_ IR ER DR UR FDI Sales Pearson .918** .652** .811** -.330 -.333 .643** Correlation Sig. (2-tailed) .000 .008 .000 .230 .225 .010 Profit Pearson .583* .410 .589* -.319 -.209 .617* Correlation Sig. (2-tailed) .022 .129 .021 .246 .454 .014 ROA Pearson -.338 -.284 -.240 .216 -.034 -.196 Correlation Sig. (2-tailed) .219 .305 .389 .440 .903 .485 ROE Pearson -.425 -.266 -.357 .280 -.060 -.173 Correlation Sig. (2-tailed) .114 .339 .191 .313 .832 .537 NPM Pearson -.317 -.270 -.226 .186 -.050 -.138 Correlation Sig. (2-tailed) .250 .330 .418 .506 .860 .623
4.10.1. Impact of macroeconomic factors on sales of Honda Atlas.
There were four variables which had significant correlation with sales and they included
GNI (r=.981), Exchange Rate (r=.652), Inflation Rate (r=.811) and Foreign Direct
Investment (r=.643). The trend of Sales (Figure A-48) was increasing along with ER
((Figure A-3), IR (Figure A-2), GNI (Figure A-1) and FDI (Figure A-7) that had the same increasing trends and the result was positive correlation between the variables.
The multiple regression model based on these four variables was exposed to the violation of significant t values for GNI (p=.623). Therefore, a multiple regression model was selected that was considered the best fit model due to high value of R² along with high and significant value F with significant t values for coefficients.
Data Analysis and Findings 163
Table: 89 Model summary for sales of Honda Atlas Bikes
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (GNI) (FDI) GNI, FDI .958 .917 .903 66.536 -3871.104 .263 2024.85 t-statistics -2.858 8.544 3.271 Sig .000 .014 .000 .007 St. Error 2298 1354 .031 619 VIF 1.232 1.232
AS = -3871.104 +0.263 GNI + 2025 FDI
Both the variables caused a substantial variations of 91.7% in the sales at F (2, 12) =
66.536, p=.000. GNI (β1= .263, t=8.544, p=.000) had positive impact and if it rose by 1 million Rupees, the sales would increase by Rs. 0.263 million provided other factors were constant. Further, if FDI (β1=2024, t=3.271, p=.007) increased by 1%, the sales would go up by Rs.2025 million keeping other factors constant.
4.10.2. Impact of macroeconomic factors on profit of Honda Atlas.
Three variables were identified to be significantly correlated with annual profit which were GNI (r=.583), Exchange Rate (r=.589) and Foreign Direct Investment (r=.617).
This positive correlation analysis was also supported by the figures in Appendix A where
Profit (Figure A-49) had increasing trend over a period of time. Similar trends were followed by GNI (Figure A-1), ER (Figure A-3) and FDI (Figure A-7).
The insignificant t vale for GNI (p=.72) and ER (p=.164) made the model invalid for the interpretation. So, the valid multiple regression model is as follows:
Data Analysis and Findings 164
Table 90 Model summary for profit of Honda Atlas
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (FDI) ER, FDI .763 .582 .512 8.347 -388.987 10.689 115.918 t-statistics -1.618 2.400 2.593 Sig .005 .132 .033 .024 St. Error 178.2 240.4 4.453 44.7 VIF 1.068 1.068
AP= - 388.987 + 10.689 ER + 115.918 FDI
The model included two independent variables i.e. ER (β1=10.689, t= 2.4, p=.033) and FDI (β2=116, t=2.593, p=.024) at significant F (2, 12) =8.347, p=.005. The model was responsible to cause 58.2% above average variations in the profit. Both the variables had positive effect on profit. All other things were constant, if ER went up by Re 1, the profit would rise by Rs. 11 million. If FDI increased by 1%, the profit would rise by Rs.
116 million.
4.10.3. Conclusion and decision. Since ROA, ROE and NPM have no significant correlation with any of the macroeconomic factors; no regression model was formed due to this fact. So, the regression analysis produces the following valid models for two dependent variables:
AS = -3871.104 +0.263 GNI + 2025 FDI
AP= - 388.987 + 10.689 ER + 115.918 FDI Data Analysis and Findings 165
There were only two dependent variables which were significantly influenced by one or more macroeconomic variables and they were annual sales and profit. FDI was the most significant predictor which influenced both dependent variables.
As far as the hypothesis is concerned, two elements for measuring performance are influenced significantly by one or more predictors. So, we reject the null hypothesis and conclude that the performance of Honda Atlas is partially, influenced by macroeconomic factors.
4.11. Determinants of the Performance of Automobile
Industry- Panel Data Approach
One of the most significant and effective approach is the use of Panel Data. All the independent and dependent variables of 10 firms are grouped into panel data and regression models are derived. The following hypothesis is established:
Ho: There is no significant impact of macroeconomic factors on the performance of
Automobile Industry.
H1: There is significant impact of macroeconomic factors on the performance of
Automobile Industry.
The first step is to find out the correlation between the variables as depicted in the following table:
Data Analysis and Findings 166
Table 91 Correlation analysis for Automobile Industry N=145 LnGNI LnIR LnER LnDR LnUR LnFDI LnCR
LnSales Pearson .491** .322* .426** -.200* -.020 .372** .311** Correlation
Sig. (2-tailed) .000 .000 .000 .016 .811 .000 . 000
Profit Pearson .243** .095 .268** - .168* .304** -.048 Correlation .269**
Sig. (2-tailed) . 003 .256 . 001 . 001 .044 .000 . 567
ROA Pearson -.060 -.118 -.036 - .250** .051 -.183** Correlation .236**
Sig. (2-tailed) .475 .156 .667 .004 .002 .541 .028
ROE Pearson -.048 -.112 -.002 -.009 .061 -.057 -.045 Correlation
Sig. (2-tailed) .567 .180 .984 .115 .466 .497 .594
NPM Pearson .030 .002 .076 -.172* .182* .094 -.089 Correlation
Sig. (2-tailed) .718 .977 .364 .039 .028 .262 .285
4.11.1. Impact of macroeconomic factors on sales of Auto Industry.
There were six variables which had significant correlation with sales and they included
GNI (r=.491), IR (r= .322), Exchange Rate (r=.426), DR (r= -.2) CR (r=.311) and
Foreign Direct Investment (r=.372).
The multiple regression model based on these six variables was exposed to the violation of significant t values and higher VIF. Therefore, a multiple regression model Data Analysis and Findings 167
was selected that was considered the best fit model due to high value of R² along with high and significant value F with significant t values for coefficients.
Table: 92 Model summary for sales of Auto Industry
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (FDI) (CR) LnFDI, LnCR .431 .186 .174 16.2 -86031.3 3235.05 1055.59 t-statistics -2.761 3.942 2.867 Sig .000 .007 .000 .005 St. Error 10207 31158.10 820.62 368.190 VIF 1.081 1.081
LnAS = -86031.3 +3235.05 LnFDI + 1055.59 LnCR
Both the variables caused a variations of 18.6% in the sales at F (2, 142) = 16.2, p=.000. FDI (β1= 3235.05, t=3.942, p=.000). Both FDI and CR are likely to put positive impact on Sales. Other things remain constant, if FDI rose by 1 unit, the sales would increase by Rs. 3235 million. Further, if CR (β2= 1055.59, t=2.86, p=.005) increased by
1%, the sales would go up by Rs.1055 million keeping other factors constant.
4.11.2. Impact of macroeconomic factors on profit of Auto Industry.
Five variables were identified to be significantly correlated with annual profit which were
GNI (r=.243), Exchange Rate (r=.268), Discount Rate (r=.269), Unemployment Rate
(r=.168) and Foreign Direct Investment (r=.304).
The insignificant t vales and higher VIF made the model invalid for the interpretation. So, the valid multiple regression model is as follows:
Data Analysis and Findings 168
Table 93 Model summary for profit of Auto Industry
Model R R² Adj. F βₒ β1 β2 Variables R (Const.) (ER) (FDI) ER, FDI .364 .132 .120 10.83 -628.03 14.259 172.15 t-statistics -2.069 2.561 3.145 Sig .000 .040 .011 .002 St. Error 686 303.48 5.56 54.74 VIF 1.063 1.063
AP= -628.03 + 14.259 ER + 172.15 FDI
The model included two independent variables i.e. ER (β1=14.259, t= 2.561, p=.011) and FDI (β2=172.15, t=3.145, p=.002) at significant F (2, 142) =10.834, p=.000.
The model was responsible to cause 13.2% variations in the profit. Both the variables had positive effect on profit. If other things were constant and ER went up by Re 1, the profit would rise by around Rs. 11 million. If FDI increased by 1%, the profit would rise by Rs.
172 million keeping other factors constant.
4.11.3. Impact of macroeconomic factors on ROA of Auto Industry.
Three variables were identified to be significantly correlated with annual profit which were Consumption Rate (r=-.183), Discount Rate (r=-.236) and Unemployment Rate
(r=.250). The insignificant t vales and higher VIF made the model invalid for the interpretation. So, the valid regression model is as follows:
Data Analysis and Findings 169
Table 94 Model summary for ROA of Auto Industry
Model R R² Adj. F βₒ β1 Variables R (Const.) (DR) DR .236 .056 .049 8.410 17.204 -.845 t-statistics 4.644 -2.900 Sig .004 .000 .004 St. Error 11.79 3.705 .291 VIF 1.000
ROA= 17.204 -.845 DR
The model included one independent variables i.e. DR (β1=-.845, t= 4.664, p=.000) at significant F (2, 142) =8.14, p=.004. The model was responsible to cause 5.6% variations in the ROA. The variables had positive effect on ROA. If other things were constant and DR went up by 1%, the ROA would rise by around .845%.
4.11.4. Impact of macroeconomic factors on NPM of Auto Industry.
Two variables were identified to be significantly correlated with annual profit which were
Discount Rate (r=-.172) and Unemployment Rate (r=.182). The insignificant t vales and higher VIF made the model invalid for the interpretation. So, the valid regression model is as follows:
Data Analysis and Findings 170
Table 95 Model summary for NPM of Auto Industry
Model R R² Adj. F βₒ β1 Variables R (Const.) (UR) UR .182 .033 .026 4.911 -38.959 5.897 t-statistics -2.221 2.216 Sig .028 .028 .028 St. Error 36.8 17.541 2.661 VIF 1.000
NPM= -38.959 + 5.897 UR
The model included one independent variables i.e. UR (β1=5.897, t= 2.216, p=.028) at significant F (2, 142) =4.911, p=.028. The model was responsible to cause
3.3% variations in the NPM. The variables had positive effect on NPM. If other things were constant and UR went up by 1%, the ROA would rise by around 5.9%.
4.11.5. Conclusion and decision. Since ROE have no significant correlation with any of the macroeconomic factors; no regression model was formed due to this fact.
So, the regression analysis produces the following valid models:
AS = -3871.104 +0.263 GNI + 2025 FDI
AP= - 388.987 + 10.689 ER + 115.918 FDI
ROA= 17.204 -.845 DR
NPM= -38.959 + 5.897 UR
There were four dependent variables which were significantly influenced by one or more macroeconomic variables and they were annual sales, profit, ROA and NPM of Data Analysis and Findings 171
the industry. FDI was the most significant predictor which influenced both dependent variables.
As far as the hypothesis is concerned, four elements for measuring performance are influenced significantly by one or more predictors. So, we reject the null hypothesis and conclude that the performance of automobile industry is influenced by macroeconomic factors.
Conclusions and Recommendations 172
Chapter Five
Conclusions and Recommendations
The objective of this study was to find out the influential factors which were responsible for the performance of automobile industry. For this purpose, seven macroeconomic variables were identified and a sample of nine automobile companies was selected. The impact of selected predictors was explored on five dependent variables of each firm. For this purpose, regression and correlation analysis were conducted to test the hypothesis if there was any impact of macroeconomic variables on the performance of the firms or not. The analysis revealed that the performance of all of the selected firms was influenced by one or more variables. As a result, we conclude that the selected macroeconomic variables proved to be the determinants of the performance of automobile industry.
5.1 Performance of Industry-Panel Data Approach
The panel data analysis of automobile industry reveals that Annual Sales, Annual
Profit and Return on Equity were four performance related elements which were influenced by macroeconomic factors. The conclusion on the performance of industry is stated as under:
5.1.1. Annual sales of the industry. The Regression Model on Annual Sales of industry is as follows:
AS = -86031.3 +3235.05 FDI + 1055.59 CR Conclusions and Recommendations 173
The Annual Sales of industry was influenced by Foreign Direct Investment,
Exchange Rate and Inflation Rate. The impact of FDI on sales shows that the investment along with transfer of technology helped the industry to improve the process of production by means of better technology. The transfer of technology also helped to reduce the cost of production so as to make the selling price more competitive. Further,
FDI helped in the improvement of vehicles which also facilitated escalation in sales.
Consumption rate was another macroeconomic determinant that influenced annual sale positively. Aggregate consumption increased the sales of automobiles for obvious reasons. The rise in aggregate demand was channelized towards consumption of automobile. As a result, the automobile industry experienced a rise in sales.
5.1.2. Annual profit of the industry. Following is the model for profit of the industry:
AP= -628.03 + 14.259 ER + 172.15 FDI
The annual profit of the industry was influenced by FDI. A rise in FDI was likely to raise the annual profit of the industry. The rationale was that the inflow of investment with technology brought about improved product quality, reduced cost of production and better marketing depth. As a result, the profit margin went up.
The profit of automobile industry was influenced by exchange rate fluctuation to a great extent. The appreciation in the value of US Dollar increased the profit of industry.
So, the industry exploited the deprecation of Pakistani Rupee to a great extent. As soon as the rate of US Dollar increased, the firms raised the prices of vehicles immediately and substantially. As a result, the industry derived the benefit from the difference of cost of inventory and price. Conclusions and Recommendations 174
5.1.3. ROA of the industry. Following is the model of ROA:
ROA= 17.204 -.845 DR
Return on Assets is the ratio between net profit and total Assets. The ratio is directly influenced by this variable. However, it is indirectly affected by all those elements which are related to profit or equity. Return on Equity was negatively affected by discount rate.
It means that the rise in interest rate eroded the profit margin of the industry. As a result, a rise in DR reduced the ROE.
5.1.4. NPM of the industry. Following is the model of NPM:
NPM= -38.959 + 5.897 UR
Net Profit Margin is the ratio between net profit and sales. Net Profit Margin was positively affected by unemployment rate. It means that the rise in unemployment rate strengthened the bargaining power of the industry with respect to employees. So, the profit margin of the industry increased and UR influenced NPM positively.
5.2 Influential Macroeconomic Determinants of the Industry
There were seven macroeconomic variables whose effect was examined on the performance of automobile industry. The most important factor was foreign direct investment which influenced the profit as well as sales of the industry. It had a positive effect on profitability and sales of the industry. The investment and technology transfer by the Japanese and other foreign companies enabled the domestic firms to reduce cost of production; improve quality of vehicles; capitalize marketing opportunities and increase demand for automobiles. Conclusions and Recommendations 175
The second most frequent factor that influenced the performance was Exchange
Rate. It means that the performance of industry was exposed to exchange rate fluctuation to a great extent. Most of the companies were heavily dependent upon foreign technology and they assembled spare parts and units imported from abroad. Any fluctuation in foreign currency especially in US Dollar and Japanese Yen in terms of Pak Rupee caused substantial variation in the profit and sales of the industry. It reveals that the industry could not achieve the desired level of indigenization to reduce the dependence on foreign technology.
Consumption Rate was another influential factor on the performance. Its impact was positive. It means the consumption rate improved the performance as the aggregate consumption was channelized towards the vehicle industry.
Discount rate was the next influential factor. Its function for the industry was to make the cost of borrowing expensive. As a result, it worked for raising the cost of production which might reduce the sales volume due to price hike. In addition to this, it was also responsible for reducing the industry’s profit because of high cost of production.
The final effective factor was unemployment rate which had a positive impact on performance. The bargaining power enabled the employers to book high profits.
So, it is concluded that the most of the selected macroeconomic variables were found to be the determinants of the performance of automobile industry in Pakistan from
1995 to 2010. The influential determinants were Foreign Direct Investment, Exchange
Rate, Consumption Rate, Discount Rate, and Unemployment rate. However, Per Capita
Gross National Income and Inflation Rate had no impact on the performance of the industry. Conclusions and Recommendations 176
5.3. Analysis of Firms’ Performance
Following is the conclusion of study pertaining to the firms of automobile industry.
5.3.1. Annual sales. The data for annual sales was obtained from ten firms. After conducting regression and correlation analysis, following conclusion was derived that shows the nature and intensity of influential variables on sales of the firms.
Table 96 Determinants of annual sales GNI Inflation Exchange Discount Unempl. Cons. Foreign rate rate rate rate rate direct investment Frequency 4 3 4 0 0 3 7 % of 19% 14% 19% 0 0 14% 34% Frequency
The table depicts that annual sales of the industry was most frequently influenced by Foreign Direct Investment. FDI was responsible to bring about variation in the sales in seven out of ten firms.
The second most frequent influential factor was Per Capita Gross National Income.
Any rise in GNI was likely to boost sales volumes of the firms. Per head increment in income made the consumers more willing and able to buy goods including vehicles. The expansion in demand gave birth to rise in sales.
Another important determinant of sales was Exchange Rate of Dollar in terms of
Pakistani Rupee. Generally, it was assumed that increase in exchange rate would have a negative impact on sales. The reason behind this was anticipated rise in the cost of Conclusions and Recommendations 177
production due to expensive imports. As the cost of import and production increased, the firms were forced to raise the price and the price hike reduced demand as the vehicles were more elastic products. This decline in demand led to fall in sales volume. However, in this case, exchange rate appeared to be the determinant of sales for four firms. The annual sales of Pak Suzuki Company, Indus Motors and Honda Car Limited increased as the exchange rate of Pak Rupee in terms of US Dollar increased. There were several reasons for this phenomenon. The first reason was that the companies absorbed the upsurge in exchange rate to an extent and they did not shift the total burden of rise in the cost of import to consumers. Further, the rise in price did not reduce the demand for vehicles as it was governed by some other factors as well. Hence, the rise in exchange rate unexpectedly increased the sales. It was concluded that the annual sales volume of the company was not contracted due to rise in exchange rate.
The next influential factor on sales was inflation rate which appeared two times in the sales models. It means that the sales were moderately responsive to general price level of goods and services in the country. The impact of inflation on sales was mixed in different model. In one model, IR was responsible to raise the sales and in other model, it reduced the sales volume. The reason was the responsiveness of the firms to inflation.
Sometimes, the entrepreneurs, unlike other competitors, did not shift the burden of rising prices to the consumers and it resulted in the expansion of sales volume. On the other hand, when the burden of rise in inflation was shifted to the consumers by means of increasing price of vehicles, the demand went down and sales volume contracted.
Another significant element was consumption rate that influenced the sales by two times. The consumption rate had a positive impact on sales and it was well justified Conclusions and Recommendations 178
on theoretical ground. Since the aggregate consumption increased, the consumption demand for vehicles also went up and the sales volume expanded due to rise in demand.
Besides the influential elements, there were many macroeconomic factors which did not influence the sales of industry. They included Discount rate and Unemployment rate.
5.3.2. Annual profit. The profit of the firms generally depends upon three basic elements i.e. sales revenue, cost of goods sold and expenses. Any increase or decrease in these three variables causes fluctuation in the profit. The regression models identified the following factors which affected the profits:
Table 97 Determinants of annual profit
GNI IR ER DR UR CR FDI Frequency 2 0 4 3 0 2 3 % of 14% 0 30% 21% 0 14% 21% Frequency
One of the most significant elements which affected the profits was exchange rate of Pak Rupee with US Dollar. It means that the profit of automobile industry was influenced by exchange rate fluctuation to a great extent. Most of the times, appreciation in the value of US Dollar increased the profits. So, the firms exploited the deprecation of
Pakistani Rupee to an extent. As soon as the rate of US Dollar increased, the firms raised the prices of vehicles immediately and substantially. As a result, the industry derived the benefit from the difference of cost of inventory and price. Conclusions and Recommendations 179
Another equally important determinant was the Discount Rate of Central Bank.
There was a negative impact of Discount Rate on the profit of industry. An increase in the discount rate made the cost of borrowing expensive for the sellers. So, the rise in interest rate on bank loans raised the cost of production and the profit went down.
Foreign Direct Investment was again an important factor to boost the profitability for established reasons. The rationale was that any inflow of investment with technology brought about improved product quality, reduced cost of production and better marketing depth. As a result, the profit margin went up.
The second most frequent influential factor was Per Capita Gross National Income.
Any rise in GNI was likely to raise profit of the firms. Per head increment in income made the consumers more willing and able to buy goods including vehicles. The expansion in demand gave birth to sales hike which results in the rise of profit margin.
The final determinant of firms profit was consumption rate. This factor appeared only in two models and it caused mixed variations in the profit. The rationale for the mixed trend was the direction of consumption. When the aggregate consumption was directed towards the luxuries items such as automobiles, the demand and profit for vehicle industry surged and when it was channelized to some other type of goods and services, the profit went down.
In addition to this, the profit of most of the firms was not influenced by three other macroeconomic variables and they were GNI per capita, Inflation rate and
Unemployment rate.
5.3.3. Return on assets. Return on assets directly depends upon net profit and total assets of the firms. Indirectly, it depends upon all those elements which cause Conclusions and Recommendations 180
fluctuation in the net profit and assets of the industry. The following macroeconomic factors were found responsible for variations in ROA of the industry:
Table: 98 Determinants of return on assets GNI IR ER DR UR CR FDI Frequency 0 1 2 2 0 1 1 % of 0 14% 29% 29% 0 14% 14% Frequency
Discount Rate was the most frequent factors that brought about changes in the ratio.
Since the profit was reduced by this factor, the ROA of the firms went down. Further,
Consumption Rate, Exchange Rate and FDI also appeared as the determinants of profit.
Their effect on the Return on Assets was almost similar to the effects on profits.
Inflation Rate was likely to reduce the ratio which means that the assets of industry increased more rapidly in numeric terms due to rise in price level. As a result, the ratio went down.
5.3.4. Return on equity. Return on Equity is the ratio between net profit and total equity. The ratio is directly influenced by these two variables. However, it is indirectly affected by all those elements which are related to profit or equity. Following is the summary of the determinants of ROE.
Table 99 Determinants of Return on Equity GNI IR ER DR UR CR FDI Frequency 0 0 2 0 0 3 1 % of 33% 50% 17% Frequency Conclusions and Recommendations 181
The ratio was most frequently influenced by consumption rate because of the fact that the profit was influenced by the patterns of consumption. The same reasons were responsible for the positive impact of Exchange Rate on the variable. Foreign Direct
Investment was another significant factor which positively affected the ratio by increasing the value of numerator. Hence the rationale behind these influential factors was similar to the determinants of profit.
5.3.5. Net profit margin. Net Profit Margin is calculated as net profit divided by net sales. This is a very important ratio as both the significant variables of the research are included in it. The factors influenced the ratio has the following pattern:
Table 100 Determinants of net profit margin GNI IR ER DR UR CR FDI Frequency 0 2 3 3 1 0 1 %of Frequency 20% 30% 30% 10% 10%
Discount rate was the most significant factor that caused the variation in the ratio. As discussed earlier, DR had a negative influence on profit. So, a rise in DR would likely to reduce profit as the cost of borrowing increased. Hence, DR caused fluctuation in NPM as well.
The Inflation rate also caused variations in the ratio in two models as it affected the numerator i.e. net sales to an extent. The exchange rate also appeared in two models as it was also responsible to cause variations in sales or profit. The discount rate and Foreign
Direct Investment were also had some effects on sales and profit and ultimately affected Conclusions and Recommendations 182
NPM. However, a new factor i.e. unemployment rate came up for the first time to affect any of the dependent variables. The net profit margin increased due to unemployment rate which means that unemployment rate was either responsible for increasing profit or reducing sales due to the changes in the bargaining power of employers and labors.
However, the effect was not very significant in terms of frequency.
5.4. Recommendations
On the basis of above analysis and conclusions, the following recommendations are listed to improve the performance of the industry:
The analysis proves that inflow of FDI increases the profitability and sales of
the firms. In addition to this, rise in FDI provides better quality and choice to
consumers. So, the firms and regulatory authorities should take measures to
bring more foreign direct investment in the country.
The Industry increases the prices much more than the actual rise in rate of
exchange. In contrast, it does not pass on the benefits to consumers while the
rate of exchange goes down. So, the concerned authorities should take
measures to stop the industry to take undue benefits of rise in Exchange rate.
In order to reduce vulnerability of industry to exchange rate fluctuation, the
authorities should take measures to increase the indigenization level.
The research reveals that high unemployment rate enables the entrepreneurs
to take the undue advantages of their bargaining powers with labors. So, the
concerned authorities should check the high profits due to this reason and
regulate the industry by means of relevant labor laws. Conclusions and Recommendations 183
The discount rate has a negative impact on the Returns of Industry. So, a
favorable monetary policy is required by the central bank to improve the
returns of industry progressively.
5.5 Future Research Direction
The study lays down the foundation for many research studies in this domain as mentioned below:
The period taken in this research was from 1996 to 2010. There study may be
conducted to extend the period in past and future directions.
The performance of the industry may be measured by taking some other
variables related to equity, market share and ratios.
The methodology can be applied on some other industry as well.
Instead of taking macroeconomic variables, some other factors or group of
factors may be taken to explore the impact on the industry. References 184
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Appendix-A 198
Appendix A- Scatter Plots
1. Scatter Plots of Independent Variables
Figure A-1. Scatter plot for GNI
Scatterplot of GNI vs Year
90000
80000
70000
60000
I 50000
N G 40000
30000
20000
10000
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-2. Scatter Plot for IR
Scatterplot of IR vs Year
20
15
R I 10
5
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 199
Figure A-3. Scatter Plot for ER
Scatterplot of ER vs Year 80
70
60
R E
50
40
30 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-4. Scatter Plot for DR
Scatterplot of DR vs Year 20
18
16
14
R D 12
10
8
6 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-5. Scatter Plot for CR
Scatterplot of CR vs Year 91
90
89
88
87
R C 86
85
84
83
82 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 200
Figure A-6. Scatter Plot for UR
Scatterplot of UR vs Year 8.5
8.0
7.5
7.0
R U 6.5
6.0
5.5
5.0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-7. Scatter Plot for FDI
Scatterplot of FDI vs Year 4
3 I
D 2 F
1
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 201
2. Scatter Plot for Dependent Variables
2.1. Pak Suzuki Company
Figure A-8. Scatter Plot for Sales of Pak Suzuki
Scatterplot of Sales vs Year
50000
40000
30000
s
e
l
a S 20000
10000
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-9. Scatter Plot for Profit of Pak Suzuki
Scatterplot of Profit vs Year 3500
3000
2500
2000
t
i
f
o r
P 1500
1000
500
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-10. Scatter Plot for ROA of Pak Suzuki
Scatterplot of ROA vs Year 18
16
14
12
10 A
O 8 R
6
4
2
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year Appendix-A 202
Figure A-11. Scatter Plot for ROE of Pak Suzuki
Scatterplot of ROE vs Year 40
30
E 20
O R
10
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-12. Scatter Plot for NPM of Pak Suzuki
Scatterplot of NPM vs Year 9
8
7
6
5 M
P 4 N 3
2
1
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
2. Indus Motors Company
Figure A-13. Scatter Plot for Sales of Indus Motors
Scatterplot of Sales vs Year
60000
50000
40000 s
e 30000
l
a S 20000
10000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year Appendix-A 203
Figure A-14. Scatter Plot for Profit of Indus Motors
Scatterplot of Profit vs Year 4000
3000 t
i 2000
f
o
r P
1000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-15. Scatter Plot for ROA of Indus Motors
Scatterplot of ROA vs Year
17.5
15.0
12.5
A
O R 10.0
7.5
5.0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-16. Scatter Plot for ROE of Indus Motors
Scatterplot of ROE vs Year 45
40
35
30
E O
R 25
20
15
10
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 204
Figure A-17. Scatter Plot for NPM of Indus Motors
Scatterplot of NPM vs Year 8
7
6
M 5
P N
4
3
2 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
3. Honda Motors Cars
Figure A-18. Scatter Plot for Sales of Honda Motors Car
Scatterplot of Sales vs Year
25000
20000
15000
s
e
l
a S 10000
5000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-19. Scatter Plot for Profit of Honda Motors Car
Scatterplot of Profit vs Year
500
t 0
i
f
o
r P
-500
-1000 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 205
Figure A-20. Scatter Plot for ROA of Honda Motors Car
Scatterplot of ROA vs Year 20
15
10
A 5
O R
0
-5
-10 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-21. Scatter Plot for ROE of Honda Motors Car
Scatterplot of ROE vs Year
30
20
10
0 E
O -10 R
-20
-30
-40
-50 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-22. Scatter Plot for NPM of Honda Motors Car
Scatterplot of NPM vs Year 10.0
7.5
5.0
M 2.5
P N
0.0
-2.5
-5.0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 206
4. Hinopak Company
Figure A-23. Scatter Plot for Sales of Hino Pak Company
Scatterplot of Sales vs Year
12000
10000
8000
s e
l 6000
a S
4000
2000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-24. Scatter Plot for Profit of Hino Pak Company
Scatterplot of Profit vs Year
600
500
400
300 t
i 200
f
o r
P 100
0
-100
-200
-300 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-25. Scatter Plot for ROA of Hino Pak Company
Scatterplot of ROA vs Year 30
20
10 A
O 0 R
-10
-20
-30 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 207
Figure A-26. Scatter Plot for ROE of Hino Pak Company
Scatterplot of ROE vs Year
200
100 E
O 0 R
-100
-200 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-27. Scatter Plot for NPM of Hino Pak Company
Scatterplot of NPM vs Year 20
10
0
M
P N -10
-20
-30 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
5. Ghandhara Industries
Figure A-28. Scatter Plot for Sales of Ghandhara Industries
Scatterplot of Sales vs Year
2000
1500
s
e l
a 1000 S
500
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 208
Figure A-29. Scatter Plot for Profit of Ghandhara Industries
Scatterplot of Profit vs Year 600
500
400
300
t
i f
o 200
r P 100
0
-100
-200 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-30. Scatter Plot for ROA of Ghandhara Industries
Scatterplot of ROA vs Year 25
20
15
10 A
O 5 R
0
-5
-10
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-31. Scatter Plot for ROE of Ghandhara Industries
Scatterplot of ROE vs Year
50
0 E
O -50 R
-100
-150
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 209
Figure A-32. Scatter Plot for NPM of Ghandhara Industries
Scatterplot of NPM vs Year 50
25
0
M -25
P N
-50
-75
-100
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
6. Ghandhara Nissan Ltd.
Figure A-33. Scatter Plot for Sales of Ghandhara Nissan
Scatterplot of Sales vs Year 5000
4000
3000
s
e
l a
S 2000
1000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-34.. Scatter Plot for Profit of Ghandhara Nissan
Scatterplot of Profit vs Year
300
200
100
t
i f
o 0
r P
-100
-200
-300
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 210
Figure A-35. Scatter Plot for ROA of Ghandhara Nissan
Scatterplot of ROA vs Year 60
50
40
30
20
A O
R 10
0
-10
-20
-30
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-36. Scatter Plot for ROE of Ghandhara Nissan
Scatterplot of ROE vs Year 250
200
150
100
E
O R 50
0
-50
-100 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-37. Scatter Plot for NPM of Ghandhara Nissan
Scatterplot of NPM vs Year
300
200
100
M
P N 0
-100
-200
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 211
7. Millat Tractors Ltd.
Figure A-38. Scatter Plot for Sales of Millat Tractors
Scatterplot of Sales vs Year 25000
20000
15000
s
e
l
a S 10000
5000
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-39. Scatter Plot for Profit of Millat Tractors
Scatterplot of Profit vs Year 2500
2000
1500
t
i
f o
r 1000 P
500
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-40. Scatter Plot for ROA of Millat Tractors
Scatterplot of ROA vs Year 20.0
17.5
15.0
A 12.5
O R
10.0
7.5
5.0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 212
Figure A-41. Scatter Plot for ROE of Millat Tractors
Scatterplot of ROE vs Year 60
50
40
E
O R 30
20
10 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-42. Scatter Plot for NPM of Millat Tractors
Scatterplot of NPM vs Year 11
10
9
8
7
M P
N 6
5
4
3
2 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
7. Al-Ghazi Tractors Ltd.
Figure A-43. Scatter Plot for Sales of Al-Ghazi Tractors
Scatterplot of Sales vs Year
16000
14000
12000
10000
s
e l
a 8000 S
6000
4000
2000
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 213
Figure A-44. Scatter Plot for Profit of Al-Ghazi Tractors
Scatterplot of Profit vs Year 2000
1500
t
i f
o 1000
r P
500
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-45. Scatter Plot for ROA of Al-Ghazi Tractors
Scatterplot of ROA vs Year
40
35
30 A
O 25 R
20
15
10 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year Figure A-46. Scatter Plot for ROE of Al-Ghazi Tractors
Scatterplot of ROE vs Year
80
70
60 E
O 50 R
40
30
20 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 214
Figure A-47. Scatter Plot for NPM of Al-Ghazi Tractors
Scatterplot of NPM vs Year 17.5
15.0
12.5
M P N 10.0
7.5
5.0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year 9. Honda Atlas Bikes
Figure A-48. Scatter Plot for Sales of Honda Bikes
Scatterplot of Sales vs Year
25000
20000
15000
s
e
l
a S 10000
5000
0
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-49. Scatter Plot for Profit of Honda Bikes
Scatterplot of Profit vs Year
700
600
500
t
i
f o
r 400 P
300
200
100
1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-A 215
Figure A-50. Scatter Plot for ROA of Honda Bikes
Scatterplot of ROA vs Year
50
40
30
A
O R 20
10
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-51. Scatter Plot for ROE of Honda Bikes
Scatterplot of ROE vs Year 120
100
80 E
O 60 R
40
20
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Figure A-52. Scatter Plot for NPM of Honda Bikes
Scatterplot of NPM vs Year 18
16
14
12
10
M P
N 8
6
4
2
0 1995.0 1997.5 2000.0 2002.5 2005.0 2007.5 2010.0 Year
Appendix-B 216
Appendix – B: Heteroscedasticity Tests
Figure B- 1. Histogram for Residuals of Pak Suzuki Sales
Figure B- 2. Histogram for Residuals of Pak Suzuki Profit
Figure B- 3. Histogram for Residuals of Pak Suzuki NPM
Appendix-B 217
Figure B- 4. Histogram for Residuals of Indus Motors Sales
Figure B- 5. Histogram for Residuals of Indus Motors Profit
Figure B- 6. Histogram for Residuals of Indus Motors ROA
Appendix-B 218
Figure B-7. Histogram for Residuals of Indus Motors ROE
Figure B-8. Histogram for Residuals of Indus Motors NPM
Figure B- 9. Histogram for Residuals of Honda Car Ltd. Sales
Appendix-B 219
Figure B- 10. Histogram for Residuals of Honda Car Ltd. Profit
Figure B- 11. Histogram for Residuals of Honda Car Ltd. ROA
Figure B- 12. Histogram for Residuals of Honda Car Ltd. ROE
Appendix-B 220
Figure B- 12. Histogram for Residuals of Honda Car Ltd. ROE
Figure B- 13. Histogram for Residuals of Honda Car Ltd. NPM
Figure B- 14. Histogram for Residuals of Hino Pak Sales
Appendix-B 221
Figure B- 15. Histogram for Residuals of Hino Pak Profit
Figure B- 16. Histogram for Residuals of Hino Pak ROA
Figure B- 17. Histogram for Residuals of Hino Pak ROE
Appendix-B 222
Figure B- 18. Histogram for Residuals of Ghandhara Industries Sales
Figure B- 19. Histogram for Residuals of Ghandhara Industries Profit
Figure B- 20. Histogram for Residuals of Ghandhara Industries ROA
Appendix-B 223
Figure B- 21. Histogram for Residuals of Ghandhara Industries NPM
Figure B- 22. Histogram for Residuals of Ghandhara Nissan Sales
Figure B- 23. Histogram for Residuals of Millat Tractors Sales
Appendix-B 224
Figure B- 24. Histogram for Residuals of Millat Tractors Profit
Figure B- 25. Histogram for Residuals of Millat Tractors ROA
Figure B- 26. Histogram for Residuals of Millat Tractors ROE
Appendix-B 225
Figure B- 27. Histogram for Residuals of Millat Tractors NPM
Figure B- 28. Histogram for Residuals of Al-Ghazi Tractors Sales
Figure B- 29. Histogram for Residuals of Al-Ghazi Tractors Profit
Appendix-B 226
Figure B- 30. Histogram for Residuals of NPM for Al-Ghazi Tractors NPM
Figure B- 31. Histogram for Residuals of Honda Bikes Sales
Figure B- 32. Histogram for Residuals of Honda Bikes Profit
Appendix-C 227
Appendix – C: Normality Tests
Table 101 Tests of Normality-Sales of Pak Suzuki Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig. Unstandardized Residual 0.203 15 .096 0.910 15 0.135
Table 102 Tests of Normality-Profit of Pak Suzuki Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.116 15 0.200* 0.968 15 0.826
Table 103 Tests of Normality-ROA of Pak Suzuki Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.142 15 0.200* 0.969 15 0.845
Table 104 Tests of Normality-ROE of Pak Suzuki Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.187 15 0.167 0.925 15 0.229
Table 105 Tests of Normality-NPM of Pak Suzuki Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual .097 15 0.200* 0.972 15 0.885
Appendix-C 228
Table 106 Tests of Normality-Sales of Indus Motors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.128 15 0.200* 0.944 15 0.434
Table 107 Tests of Normality-Profit of Indus Motors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.146 15 0.200* 0.960 15 0.690
Table 108 Tests of Normality-ROA of Indus Motors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.205 15 .090 0.890 15 .068
Table 109 Tests of Normality-ROE of Indus Motors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.130 15 0.200* 0.923 15 0.215
Table 110 Tests of Normality-NPM of Indus Motors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.161 15 0.200* 0.923 15 0.212
Appendix-C 229
Table 111 Tests of Normality- Sales of Honda Car Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.241 15 .019 0.926 15 0.238
Table 112 Tests of Normality-Profit of Honda Car Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized 0.152 15 0.200* 0.935 15 0.319 Residual
Table 113 Tests of Normality-ROA of Honda Car Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.134 15 0.200* 0.962 15 0.724
Table 114 Tests of Normality-ROE of Honda Car Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.134 15 0.200* 0.982 15 0.981
Table 115 Tests of Normality-NPM of Honda Car Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.109 15 0.200* 0.986 15 0.994
Appendix-C 230
Table 116 Tests of Normality-Sales of Hino Pak Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig. Unstandardized Residual 0.158 15 0.200* 0.918 15 0.182
Table 117 Tests of Normality-Profit of Hino Pak Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic Df Sig. Unstandardized Residual 0.146 15 0.200* 0.955 15 0.612
Table 118 Tests of Normality-ROA of Hino Pak Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic Df Sig. Unstandardized Residual 0.251 15 .012 0.909 15 0.130
Table 119 Tests of Normality-NPM of Hino Pak Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic Df Sig. Unstandardized Residual 0.244 15 .016 0.876 15 .051
Table 120 Tests of Normality-Sales of Ghandhara Industries Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig. Unstandardized Residual 0.155 15 0.200* 0.935 15 0.324
Table 121 Tests of Normality-Profit of Ghandhara Industries Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.114 15 0.200* 0.981 15 0.975
Appendix-C 231
Table 122 Tests of Normality-ROA of Ghandhara Industries Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.216 15 .057 0.899 15 .092
Table 123 Tests of Normality-NPM of Ghandhara Industries Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual 0.173 15 0.200* 0.836 15 .051
Table 124 Tests of Normality-Sales Ghandhara Nissan Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic Df Sig. Unstandardized 0.197 15 0.122 0.936 15 0.336 Residual
Table 125 Tests of Normality-Sales of Millat Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.155 15 0.200* 0.935 15 0.324
Table 126 Tests of Normality-Profit of Millat Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.114 15 0.200* 0.981 15 0.975
Appendix-C 232
Table 127 Tests of Normality-ROA of Millat Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.151 15 0.200* 0.939 15 0.368
Table 128 Tests of Normality-ROE of Millat Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual 0.111 15 0.200* 0.986 15 0.994
Table 129 Tests of Normality-NMP of Millat Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.129 15 0.200* 0.963 15 0.743
Table 130 Tests of Normality-Sales of Al-Ghazi Tractors Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Unstandardized Residual 0.122 15 0.200* 0.946 15 0.465
Table 131 Tests of Normality- Profit of Al-Ghazi Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.128 15 0.200* 0.965 15 0.780
Appendix-C 233
Table 131 Tests of Normality- Profit of Al-Ghazi Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.128 15 0.200* 0.965 15 0.780
Table 132 Tests of Normality-ROA of Al-Ghazi Tractors
Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.219 15 .052 0.851 15 .018
Table 133 Tests of Normality-ROE of Al-Ghazi Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.165 15 0.200* 0.917 15 0.170
Table 134 Tests of Normality-Profit of Honda Bikes Kolmogorov-Smirnova Shapiro-Wilk Statistic Df Sig. Statistic df Sig. Unstandardized Residual 0.187 15 0.168 0.940 15 0.379