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When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given e.g. AUTHOR (year of submission) "Full thesis title", University of Southampton, name of the University School or Department, PhD Thesis, pagination http://eprints.soton.ac.uk UNIVERSITY OF SOUTHAMPTON Faculty of Engineering and the Environment Civil, Maritime and Environmental Engineering and Science Evaluating the Long Term Impacts of Transport Policy: The Case of Bus Deregulation by Talal Almutairi Thesis for the degree of Doctor of Philosophy May_2013 UNIVERSITY OF SOUTHAMPTON ABSTRACT FACULTY OF ENGINEERING AND THE ENVIRONMENT Thesis for the degree of Doctor of Philosophy EVALUATING THE LONG TERM IMPACTS OF TRANSPORT POLICY: THE CASE OF BUS DEREGULATION Talal Almutairi The 1985 Transport Act, by which the British local bus industry outside London was deregulated, is considered as one of the most pioneering reforms of public transport policy in the world. The deregulation package (which also included privatisation and subsidy reduction) was controversial and was subject to heated academic debates over how successful it would be (and consequently has been) in reversing the deteriorating performance of local bus services since the Second World War. This debate specifically focused on issues concerning the efficiency of service provision, quality of service, and overall welfare. In addition, the contrasting regulatory system adopted in London (competitive tendering) has given opportunities for researchers to evaluate and compare the outcomes of these contrasting systems and draw conclusions over the impacts of such regulatory reforms on the local bus industry in Great Britain. Commentators began to evaluate regulatory experience as quickly as the end of the first year after deregulation. However, the amount of research has declined as time has passed. The key fundamental questions, which the current research is trying to answer, are: what are the longer term impacts of the deregulation policy, how successful was it in achieving its objectives and what lessons can be drawn after more than 20 years? These questions can be answered by carrying out cost-benefit analyses of deregulation policy compared to the counterfactual as well as to the alternative regime adopted in London. A key issue when examining long term changes is that of the counterfactual – what would have happened if the changes had not occurred? Econometric models of the demand, fare and cost for local bus services in Britain (London and the rest of the country) are outlined and used along with extrapolative methods for some key input variables such as bus kms and subsidy to determine counterfactuals. A large number of dynamic demand models have been estimated, considering both fixed and random effect, and using a variety of estimation methods including the Feasible Generalised Least Squares procedure (FGLS-AR(1)) and the Panel Corrected Standard Error (PCSE-AR(1)) method. In addition to Partial Adjustment Models (PAM), several Error Correction Models (ECM) were developed. Some analyses of subsidy and of costs are also outlined. The developed fares models are used to assess the impact of changes in subsidy (in terms of revenue support and concessionary fare reimbursements). The cost models are used to determine the extent to which costs are determined by external factors (such as fuel prices) or partially external factors (such as labour costs). This then permits the examination of welfare change by estimating changes in consumer and producer surpluses as well the impacts on government, bus workers, and society as a whole. Our finding is that there are net welfare decreases outside London, by contrast, welfare increases are found in London irrespective of whether subsidy changes impacts are included or excluded. We find that bus reforms in London have been more welfare enhancing than the reforms in Great Britain outside London, where deregulation led to substantial welfare losses in the first decade of the reforms (1985/6 to 1995/6). From the second decade onwards there are smaller losses. However, the results are sensitive to the specification of the modelling system and assumptions made concerning the counterfactual for the deregulated area in particular. This work confirms the sensitivity of the long term evaluation of transport policy to assumptions concerning the counterfactual and trends in demand, supply and prices. Any policy lessons inferred from these long term evaluations therefore need to take these sensitivities into account Contents ABSTRACT Contents List of tables List of figures DECLARATION OF AUTHORSHIP 1. Introduction 1.1 Background to the mid-1980s policy reforms 1.2 Aims and Objectives 1.3 Key Methods 1.4 Structure of the Thesis 2. Background: The 1985 Transport Act 2.1 History of British local bus industry 2.1.1 The pre-1930 era 2.1.2 The Road Traffic Act 1930 2.1.3 The Post-War Era (1947-1979) 2.1.4 The local bus industry in 1979 - The Problem 2.1.5 1980 Transport Act – The proposed solution (the first step): 2.2 The deregulation of local bus industry 2.2.1 Introduction 2.2.2 The Transport Act - The proposed solution (the second step): 2.2.2.1 Limiting regulator’s power and expansion of commercial operations 2.2.2.2 Stricter quality control 2.2.2.3 Privatisation 2.2.3 London 2.2.4 The debates sparked by the “Buses” White Paper: 2.2.5 The proponent’s view 2.2.6 The Opponent’s View 2.2.6.1 Contestability Arguments 2.2.6.2 Competition would reduce costs 2.2.6.3 Efficient resource allocation 2.2.6.4 No undesirable spin-off effects 2.2.6.5 Central planning 2.2.7 Competitive tendering system 2.2.8 Theoretical Arguments 2.2.8.1 The view of regulating the industry 2.2.8.2 The view of free market’s mechanisms 2.2.8.3 Concluding remark 2.2.9 Conclusion 3. The policy outcomes 3.1 Introduction 3.2 The Early Outputs of Deregulation 3.2.1 Cost 3.2.1.1 Sources of cost reductions 3.2.2 Subsidy 3.2.3 Fare 3.2.4 Level of service (VKM) 3.2.5 Patronage 3.2.6 Bus industry restructuring 3.2.7 Competition 3.2.8 Conclusion 3.3 Medium-Term Impacts (The First Decade) 3.3.1 Cost 3.3.2 Subsidy 3.3.3 Fares 3.3.4 Service Level 3.3.5 Patronage 3.3.6 Comments on the medium-term impacts 3.4 The Longer Impacts 3.4.1 Cost 3.4.2 Subsidy 3.4.3 Fares 3.4.4 Service Level 3.4.5 Patronage 3.4.6 Comments on the long-term impacts 3.5 The final conclusion of in-depth literature review 4. The Methodology: Evaluation and Costs and Benefits Analysis 4.1 Evaluation 4.1.1 Introduction 4.1.2 Establish what is to be evaluated 4.1.3 What is the target outturn, alternative and counterfactuals? 4.1.3.1 Alternative 4.1.3.2 Counterfactuals 4.1.4 Comparative assessments against alternative and counterfactuals 4.2 Methodological approach 4.3 Cost-Benefit Analysis (CBA) 4.3.1 The use of CBA in transport 4.3.2 Nature of the CBA 4.3.3 Consumer surplus as measured in the cost and benefit calculations (in monetary terms) 4.3.3.1 Rule of half (RoH) 4.3.4 Producer surplus and other groups’ welfare changes 5. The Basic Demand Model 5.1 Introduction 5.2 Dynamic Approach 5.2.1 The dynamic process 5.2.2 Lagged adjustment 5.2.3 Short and long-term elasticities 5.2.4 Constant and variable elasticity 5.2.5 The dynamic (lagged) effects in the future 5.3 An aggregate demand model 5.4 Data shortage and pooling procedure (panel) 5.4.1 Advantages of panel data 5.4.2 Heterogeneity: Applied techniques to control for unobserved effects 5.4.2.1 Fixed Effect Model (FEM) 5.4.2.2 Random Effect Model (REM) 5.4.2.3 Differences between FE and RE (specification) 5.4.2.4 Tests 5.5 Model specification 5.5.1 What explanatory variables should be included 5.6 Functional form 5.7 Other modelling issues and priorities 5.7.1 Modeling Priorities 5.8 Estimation of basic model 5.8.1 Introduction 5.8.2 Estimation results 5.8.2.1 Static model 5.8.2.2 Concluding remarks on static models 5.8.2.3 Dynamic Model 5.8.3 Initial findings based on the empirical results of the conventional panel approach 5.8.4 Concluding remark on basic model estimations 6. Advanced Model Estimation 6.1 Demand Model 6.1.1 Introduction 6.1.2 Testing the presence of non-spherical error process 6.1.3 The choice of estimator 6.1.4 Estimation Results 6.1.4.1 Introduction 6.1.4.2 Dynamic models with AR(1) errors - FGLS and PCSE estimators 6.1.4.3 Panel A: including London 6.1.4.4 Panel B: excluding London 6.1.4.5 Conclusion on dynamic model estimation results 6.1.5 Model choice 6.1.6 London’s time series model 6.1.6.1 The Cochrane-Orcutt and Prais-Winsten estimators 6.1.7 Shortages of our models/modelling approach 6.2 The Fare Model 6.2.1 Introduction 6.2.2 Model specification 6.2.3 Model Estimation 6.2.3.1 The choice of estimator 6.2.3.2 Dynamic Specification 6.2.3.3 London Model 6.3 The Cost Model 6.3.1 Introduction 6.3.1.1 Why modelling bus costs 6.3.1.2 Trends by area 6.3.1.3 Source of reduction 6.3.1.3.1 Is competition or privatisation the reason for such reductions? 6.3.2 Data 6.3.3 Methodology 6.3.3.1 Model specification 6.3.3.2 Model Estimation 6.3.3.3 Estimation Results 6.3.3.4 Preferred cost model 6.3.4 Conclusion 6.4 The structure of the model system 7.