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Estimating Road Transport Fuel Demand Elasticities in the Uk: an Empirical Investigation of Response Heterogeneity

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Estimating Road Transport Fuel Demand Elasticities in the Uk: an Empirical Investigation of Response Heterogeneity 1 ESTIMATING ROAD TRANSPORT FUEL DEMAND ELASTICITIES IN THE UK: AN EMPIRICAL INVESTIGATION OF RESPONSE HETEROGENEITY Ahmad Razi Ramli Centre for Transport Studies Department of Civil and Environmental Engineering Imperial College London Submitted for the Diploma of the Imperial College (DIC), PhD degree of Imperial College London March 2014 2 DECLARATION OF ORIGINALITY I hereby declare that I am the sole author of this thesis and have personally carried out the work contained within. The contribution of my supervisor was only supervisory and editorial. I further declare that all sources cited or quoted are indicated and acknowledged in the list of references in this thesis. ……………………………………………… Ahmad Razi Ramli 3 COPYRIGHT DECLARATION ‘The copyright of this thesis rests with the author and is made available under a Creative Commons Attribution Non-Commercial No Derivatives licence. Researchers are free to copy, distribute or transmit the thesis on the condition that they attribute it, that they do not use it for commercial purposes and that they do not alter, transform or build upon it. For any reuse or redistribution, researchers must make clear to others the licence terms of this work’. 4 ABSTRACT The main aim of this dissertation is to estimate fuel demand elasticities for the UK road transport sector. Despite being extensively studied, there is a renewed need for the estimation of fuel demand elasticities so that they might be more reflective of recent trends and changes in consumption patterns. At present, understanding the fuel demand sensitivities is especially important for policy making purposes. A review of the empirical literature on fuel demand revealed three important areas of concern. First, fuel demand estimates tend to vary greatly in magnitude. The effect- size differences observed are probably related to the diversity of study-characteristics and data factors. Second, the reliability of past estimates may be questionable due to shortcomings in the modelling methodology employed. Obtaining reliable estimates does not only require the use of recent data but, beyond that, it is also important for the model to be based on sound methodological and theoretical foundations. Third, studies have often relied on the elasticity of petrol to define road transport fuel demand, assuming the absence of fuel type heterogeneity among road transport fuels. This is severely restrictive, however, since demand sensitivities are likely to vary between the respective fuels. This thesis undertakes a series of empirical analyses aimed at improving the current understanding of fuel demand for the UK road transport sector. Through meta- regression analysis, this research examines the underlying factors that can help explain the between-study variations found in the literature. The research then examines the sensitivity of fuel demand through the use of both time series and panel data econometric models. Special attention has been given to methodological issues and the use of recent econometric techniques to ensure the reliability of the estimates. In addition, this thesis does not assume that demand elasticity is homogenous for each respective transport fuels. To that end, fuel demand elasticities are estimated separately for each fuel type. 5 ACKNOWLEDGEMENTS This thesis was not what I originally intended at the beginning of the PhD. Despite that, I am really glad that it turned out the way it has. For this I am extremely grateful to my supervisor, Professor Daniel Graham, who has been tireless in encouraging me to move away from my comfort zone and who has continuously stressed the importance of thinking critically and conducting meaningful research. His knowledge and positive guidance throughout my PhD has allowed me to develop a greater understanding of the theoretical and fundamental ideas of my research and a stronger appreciation of the intricacies of econometric techniques and modelling. The journey through my PhD has also been made easier by the support and encouragement that I received from friends and colleagues in CTS. In particular, special thanks goes to the ‘gang of 602’: Patricia Melo, Hamed Jahromi, Bani Anvari, Jacek Pawlak, and Kriangkrai Arunotayanun; for simply being there every morning when I get to the office and for the meaningful discussions and friendship offered. I would like to extend my appreciation also to Ramin Moradi, Thalis Zis, Rocky Li Haojie, and K. C. Pien. Many thanks also to Jackie Sime and Fionnuala Dhonabhain, whose continued administrative support has made my stay in Imperial certainly easier. It would not have been possible for me to cope with the PhD on my own. On that account, I am extremely fortunate for having the support of my family. I am extremely indebted to my wife, for her continuous belief in me and for helping to manage the family when I was totally engrossed with the PhD. Her loving and nagging certainly helped me get through this journey. In addition, I am thankful also to my daughters: Aliah, Arissa, Aafreen, Aaira, and Alanis; for being such great supporters of everything that I do and for reminding me that there are other important things in life apart from work. My gratitude also goes to my parents and in-laws as well as my siblings for their understanding and crucial support over the last four years. 6 Last but not least, I am also thankful for the scholarship and assistance from Universiti Teknologi MARA, without which, the dream of undertaking my PhD in Imperial would never have been possible to begin with. Ahmad Razi Ramli Centre for Transport Studies Imperial College London March 2014 7 TABLE OF CONTENTS Declaration of Originality 2 Copyright Declaration 3 Abstract 4 Acknowledgements 5 Table of Contents 7 List of Figures 11 List of Tables 12 List of Acronyms 14 CHAPTER 1 INTRODUCTION 16 1.1 Introduction 16 1.2 Background and Motivation 18 1.3 Problem Statement, Objectives and Scope of the Thesis 25 1.4 Structure and Overview of the Thesis 28 CHAPTER 2 ROAD TRANSPORT FUEL DEMAND: TRENDS AND STATISTICAL 31 BACKGROUND 2.1 Introduction 31 2.2 Road Transport Fuel Consumption Trends 32 2.3 Road Vehicles Statistics: Characteristics and Usage 34 2.3.1 Behavioural Differences: Fact of Fiction? 36 2.4 Summary 39 CHAPTER 3 FUEL DEMAND ELASTICITY MODELLING: A REVIEW OF ECONOMETRICS 40 AND METHODOLOGICAL ISSUES 3.1 Introduction 40 3.2 Model Specification 42 3.3 Data Characteristics 49 3.4 Contextual Characteristics 51 3.5 Time Series Models: Recent Estimation Issues 52 8 3.5.1 Non-stationarity, Co-integration and Error Correction Models 52 3.6 Panel Data Regression Models 56 3.6.1 Dynamic Panel Data Models: Estimation Issues 62 3.7 Summary 64 CHAPTER 4 ROAD TRANSPORT FUEL DEMAND ELASTICITIES: A REVIEW OF THE 65 LITERATURE 4.1 Introduction 65 4.2 Fuel Demand Elasticities: Overview of Empirical Evidence 67 4.3 Summary of Elasticities 72 4.3.1 Overall Summary of Elasticities Results 80 4.3.2 Variations between Time Series and Panel Data Models 82 4.3.3 Variations due to Estimation Techniques 84 4.4 Summary 86 CHAPTER 5 A META-REGRESSION ANALYSIS OF ROAD TRANSPORT FUEL DEMAND 88 5.1 Introduction 88 5.2 Meta-Regression: A General Discussion 90 5.3 Scope of the Meta-Regression 92 5.3.1 Meta-sample 95 5.4 Design of the Meta-Regression Analysis 99 5.5 Meta-Regression Results 103 5.5.1 Price Elasticity 103 5.5.2 Income Elasticity 109 5.6 Publication Bias 115 5.7 Summary 121 CHAPTER 6 ESTIMATION OF FUEL DEMAND ELASTICITIES USING ANNUAL TIME 122 SERIES DATA 6.1 Introduction 122 6.2 Time Series Data Sources 124 9 6.2.1 Dataset Construction 126 6.2.2 Dataset Limitations 127 6.3 Fuel Demand Model Specification 128 6.3.1 Extended Model 129 6.4 Time Series Cointegration Estimation Methodology 131 6.4.1 First Difference Regression 137 6.5 Estimation Results 138 6.5.1 Stationarity Tests 138 6.5.2 Model Cointegration Tests 139 6.5.3 Estimation Results for the Base Models 143 6.5.4 Estimation Results for the Extended Models 150 6.6 Summary of Elasticities 152 6.7 Summary 156 CHAPTER 7 AGGREGATE PANEL DATA ANALYSIS OF FUEL DEMAND ELASTICITIES 158 7.1 Introduction 158 7.2 Panel Data Sources and Variable Description 160 7.2.1 Dataset Construction 163 7.3 The Panel Data Model Specification 165 7.3.1 Static Model 167 7.3.2 Dynamic Model 168 7.4 Panel Data Estimation Techniques 169 7.4.1 Additional Estimation Validity Checks 172 7.5 Estimation Results 175 7.5.1 Diesel Fuel Estimates 175 7.5.2 Petrol Fuel Estimates 178 7.5.3 Total Fuel Estimates 181 7.6 Summary of Elasticities 185 7.7 Summary 189 CHAPTER 8 CONCLUSIONS 191 8.1 Introduction 191 10 8.2 Summary of the Main Findings 192 8.3 Research Contributions 197 8.4 Implications of the Research 199 8.4.1 Summary of Estimation Results 199 8.4.2 Methodological Implications 201 8.4.3 Policy Implications 203 8.4.4 Forecast of Road Transport Fuel Demand in the UK 204 8.5 Limitations of Study 210 8.6 Directions for Future Research 212 REFERENCES 214 APPENDICES 221 PUBLICATIONS 226 11 LIST OF FIGURES Figure 2.1 Road transport fuel consumption in the UK 33 Figure 2.2 Road transport vehicle stock categorised by fuel type 34 Figure 4.1 Short-run price elasticity of fuel demand 79 Figure 4.2 Long-run price elasticity of fuel demand 79 Figure 4.3 Short-run income elasticity of fuel demand 79 Figure 4.4 Long-run income elasticity of fuel demand 79 Figure 5.1 Relationship between price elasticity estimates (in absolute values) and their standard errors 117 Figure 5.2 Relationship between income elasticity estimates (in absolute values) and their standard errors 118
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