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Approaches to integrating measures of public satisfaction into cost benchmarking for regulation of network industries

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  • Full or part time
    Dr Wheat
    Dr Smith
  • Application Deadline
    No more applications being accepted
  • Competition Funded PhD Project (European/UK Students Only)
    Competition Funded PhD Project (European/UK Students Only)

Project Description

The problem of economic regulation of network industries is very important, due to the ‘market failure’ of natural monopoly that such industries face. Network industries constitute a wide range of essential services, such as highways, electricity, gas, water and sewerage, healthcare and public transportation. The expenditure of these industries is substantial. For example, the five year funding settlement for Network Rail (2009-2014) was £26.7 billion and for the water and sewerage sector in England and Wales £25 billion (2010-2015).

Satisfied customers is accepted as a key goal for the majority of businesses. However in the field of economic regulation of monopoly infrastructure managers, the tools to assess cost performance (which should take into account quality) rarely reflect the perceptions of final service users. Instead, cost benchmarking techniques tend to relate the cost to the physical outputs of the organisation. In highways (the focus of the empirical application in this project) this tends to relate to the amount of work done on the infrastructure as well as measured quality of the infrastructure (e.g. defects per km) (Wheat, 2015). This ignores however the final users of highways, the public, and how they perceive the product being provided by highways authorities. Such measures could be useful both from a public accountability perspective, but also in terms of differentiating between average quality of infrastructure and targeted quality improvements which impact on the most road users.

However incorporation of public satisfaction in benchmarking is not without barriers and requires fundamental research. Firstly, there are economic incentive issues in terms of using a benchmarking model which include public satisfaction for setting price or revenue controls (a la the common RPI-X regulation (Beesley and Littlechild, 1988)). For example if public satisfaction is included in benchmarking of highways authorities, they may be incentivised to adopt improved communication measures rather than invest more in the fabric of the highway. Evidence from local authorities suggests that such ‘soft’ measures have a more immediate impact on public satisfaction. Second, there is a raft of econometric (statistical) issues which present modelling challenges, including but not limited to:
• Difficulties in comparing public satisfaction scores across authorities given different demographics, population densities etc. This may indicate the need to ignore ‘between’ variation in the public satisfaction data and concentrate estimation on exploiting the variation over time (with-in) in the data
• The need to model lags over time in the evolution of citizen satisfaction
• Possible endogeneity in public satisfaction in the cost function.
The aim of the project is to consider how public satisfaction measures can be introduced into econometric performance benchmarking. This includes issues as to how to design regulation frameworks that utilise benchmarking techniques that include public satisfaction measures such that perverse incentives on companies are avoided. It also includes addressing the formulation and estimation of stochastic frontier models given the statistical challenges outlined above.

The research has potential for substantial impact. The empirical work will utilise data and connect with networks which are live in the CQC Efficiency Network (see http://nhtnetwork.org/cqc-efficiency-network/home/). The application to local highways authorities is topical given the move by central government to make a proportion of highway funding for local authorities conditional on meeting benchmarking criteria (DfT, 2015). The research will utilise data on local highways authorities in Britain as the principal empirical application. This panel data set is being collected/updated as part of the CQC Efficiency Network and is an established data set which has successfully been used for stochastic frontier analysis in the past. Other regulated sectors (e.g. water, rail, energy) will be explored for alternative empirical demonstrations if feasible.

Stochastic frontier methods are the workhorse of econometric performance analysis (Kumbhakar and Lovell, 2000). All the above issues are relatively little explored in the stochastic frontier literature to date. Some emerging work on endogeneity is appearing in the literature (e.g. Mutter et al, 2013) but is in its infancy. As such this PhD proposal envisages important fundamental research with a view to making significant methodological advances in the stochastic frontier / efficiency and performance benchmarking literature. These advances naturally have a strong link to regulatory policy in a range of sectors (not just highways) and we would therefore anticipate the potential for strong research impact.

Funding Notes

Successful applicants will receive payment to cover their University tuition fees up to three years, along with a maintenance grant matching the Research Council UK rate (£14,057 for session 2015/16)

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 contact Deborah Goddard ([email protected]) for details of how to apply.

References

Beesley, M., Littlechild, S. (1988). ‘The regulation of privatized monopolies in the United Kingdom’. RAND Journal of Economics 20, 454–472.

Department for Transport (DfT)(2015).’Highways Maintenance Funding Incentive Element.’ https://www.gov.uk/government/publications/highways-maintenance-funding-incentive-element

Kumbhakar, S.C. and Lovell, C.A.K (2000). Stochastic Frontier Analysis, Cambridge University Press, Cambridge UK.

Mutter, R, L, Greene, W.H., Spector, W. and Mukamel, D. B (2013). ‘Investigating the impact of endogeneity on inefficiency estimates in the application of stochastic frontier analysis to nursing homes’ Journal of Productivity Analysis, 39(2), 101-110.

Wheat, P. 2015 “Cost Quality Customer: Statistical Benchmarking, Report to stakeholders” http://nhtcqc.econtrack.co.uk/Content.aspx?2522

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