Introduction and Background
This project will undertake econometric research into cost and efficiency benchmarking for strategic roads. This is an under researched empirical area and now is an opportune time to undertake research given reforms in the sector within England. The structure of the sector potentially permits many interesting analyses including internal (regional) benchmarking, comparisons with other UK highway authorities and broader cost analysis. Further, the project will utilise appropriate state-of-the-art methodologies to yield detailed insight into the emerging data and also has the possibility of development of applied methodologies.
Of particular methodological interest is:
- The exploitation of multi-layered panel data sets, which is important to understand the breakdown of performance between organisations and within organisations. This builds on the work of the supervisors in this area such as Smith and Wheat (2012) and Smith et al (2015). Development in this area focuses on the distinction of unobserved heterogeneity from inefficiency building on a recent panel data literature (e.g. Kumbhakar et al (2014) and Filippini and Greene (2014)), but exploiting the extra structure of multi-layered datasets to yield more intuitive interpretations of error components.
- Enhancing the policy robustness of econometric modelling through the use of model averaging to aid the ‘triangulation’ of efficiency scores from models. This issue has recently surfaced in the econometric efficiency literature (e.g. Huang and Lai (2012)) however its application to economic regulation is yet to be explored which is an opportunity given the need for regulators to systematically consider the results from various modelling approaches.
- Developing a robust set of prediction intervals for firm inefficiency building on work by the supervisors on this topic (Wheat, Greene and Smith (2014)). Extensions to the received work include, but are not limited to, establishing prediction intervals for efficiency from the model averaging process mentioned above.
This project is a unique opportunity to influence at the outset the benchmarking framework for the new Strategic Highways Company, called Highways England (HE), which has replaced the Highways Agency in April 2015. As part of the reforms a new unit (within the ORR), the Strategic Road Network Monitor (SRNM), will be formed. Part of the activities of the SRNM will be to ensure the investment plans of HE are delivered with value for money. As such a range of efficiency benchmarking techniques will be needed to support this work. The student will have the opportunity to work directly with the SRNM and as such be in an excellent position to engage with HE and obtain the data required to undertake empirical analysis. A representative from the SRNM will play an active role as an external supervisor. Further the student will be provided with a 6 month secondment opportunity to the SRNM which provides both wider professional experience for the student, but also is a further avenue to embed the benchmarking approaches developed within the project within the SRNM to yield a strong pathway to impact for the PhD.
Aims and Approach
The aim of the project is to develop an econometric benchmarking framework and provide empirical results for the new SRNM in their task of regulating Highways England. In doing so, the project should develop state-of-the-art methods in efficiency analysis as described in the previous section.
The structure of the highways sector potentially permits many interesting empirical analyses and depending on the exact data availability and quality of data it is expected that the project will consider a number of these areas. These include:
1. Regional benchmarking: How do the management areas within England compare in terms of economic efficiency? Are there gains to be made, as is found in railways, from adopting best practice across the network? Are there economies of scale cost characteristics of regions which indicate certain geographical management structures would yield lower cost than others
2. Investment projects and their characteristics: How should road investment projects be structured to exploit economies of scale? What are the most effective contracting models?
3. International benchmarking: can data be sourced and analysed to begin the process of benchmarking England’s strategic roads with strategic roads in other countries?
4. Benchmarking against other industries.
5. Benchmarking against other highways authorities in the UK, including local authorities, Transport Scotland, Welsh Assembly and Transport Northern Ireland.
All these application areas permit the development of methods in line with the discussion in the introduction section.
Entry Requirements/Necessary Background:
The student should have a first degree in a quantitative background in subjects such as Economics, Mathematics and Statistics and/or a master’s degree in a relevant quantitative discipline such as economics, business or mathematics.
Please visit our LARS scholarship page for more information and further opportunities: https://www.environment.leeds.ac.uk/study/postgraduate-research-degrees/lars-scholarships/
Filippini, M., & Greene, W. (2014). Persistent and Transient Productive Inefficiency: A Maximum Simulated Likelihood Approach. ETH Zurich, Working Paper 14/197
Huang, C. J. and Lai, H. (2012). ‘Estimation of stochastic frontier models based on multi model inference’. Journal of Productivity Analysis 38:273–284
Kumbhakar, S.C. and Lovell, C.A.K (2000). Stochastic Frontier Analysis, Cambridge University Press, Cambridge UK.
Kumbhakar, S.C., Lien, G. and Hardaker, J.B. (2014). ‘Technical efficiency in competing panel data models: a study of Norwegian grain farming’, Journal of Productivity Analysis. 41 (2), 321-337.
Smith A; Buckell J; Wheat P; Longo R (2015) Hierarchical performance and unobservable heterogeneity in health: A dual-level efficiency approach applied to NHS pathology in England, Productivity and Efficiency Analysis, Springer Proceedings in Business and Economics, In: Greene WH; Sickles R; Khalaf L; Veall M; Voia MC (Ed) Productivity and Efficiency Analysis, Springer Proceedings in Business and Economics, Springer Verlag, pp.1-10.
Smith A. and Wheat P. (2012). ‘Estimation of Cost Inefficiency in Panel Data Models with Firm Specific and Sub-Company Specific Effects’, Journal of Productivity Analysis, 37 (1), 27-40.
Wheat, P., Greene, W.H. and Smith, A. (2014). ‘Understanding prediction intervals for firm specific inefficiency scores from parametric stochastic frontier models', Journal of Productivity Analysis. 42(1), 55-65.