1. Institute for Transport Studies FACULTY OF ENVIRONMENT How do organisations make decisions? The case of regulated and quasi-regulated industries Dr Andrew Smith Senior Lecturer in Transport Regulation and Economics Joint appointment, Institute for Transport Studies (ITS) and LUBS Economics Division, University of Leeds October 2012

2. Overview Economic Regulation Organisational and Institutional Separation Competitive tendering “Infrastructure” industries RPI-X model Value for money Resilience, sustainability EU reforms; break up “infrastructure” and operation Decision making in fragmented industries Competition “for the market” not “in the market” Typically applies to “operations” Does it work?

3. Economic regulation – behavioural assumptions Key formula Regulatory Price Change = RPI - X ProductivityInvestment Private firms assumed to profit maximise Implies, minimise costs subject to output and quality Large incentives to cut costs by more than the X factor

4. Graphically… Revenue (RPI-X) ROR = 8% 5 year price control periodNext control period T=0 Cost base P0P0 T=5 Costs ROR > 8%

5. Issues Asymmetries of information – firms know more than the regulator Gives opportunities for gaming in various ways

6. Can firms hoodwink the regulator

7. Issues Asymmetries of information – firms know more than the regulator Gives opportunities for gaming in various ways So regulators use benchmarking…

8. Conceptual approach Regulator eliminates inter-company efficiency differences Cost Output Cost frontier (T=0) B..... A Step 2: frontier shift Cost frontier (T=5) C D E Step 1: catch-up Data points can be regulated firms in same country, or different countries (or business units within a company) Stochastic Frontier Methods

9. Efficiency estimates for Network Rail (2008 review) Implies a gap against the frontier of 40% in 2006 40% gap

10. Hierarchies (top-level and business unit managers) Infrastructure Company Region (sub- company) IM1 IM2 … R1 1 R2 1 RS 1 … R1 2 R2 2 RS 2 … Inefficiency due to systematic differences between firms –external inefficiency Inefficiency due variation in performance at regional level– internal inefficiency

11. Britain’s rail reform experiment TOCs ROSCOs Railtrack / Network Rail FOCs Track Maintenance Track Renewal Train manufacture and maintenance British Rail Monopoly Competition “in the market” Competition “for the market”

12. Growth in Britain’s Train Operating Company Costs Unit costs have stabilised since then – roughly same in 2009 as 2006 35% unit cost growth since 2000 = £1.5bn annual cost FIGURE 1:TRAIN OPERATING COMPANY COSTS (EXCLUDING INFRASTRUCTURE ACCESS CHARGES) 0.0 20.0 40.0 60.0 80.0 100.0 120.0 140.0 0 1,000 2,000 3,000 4,000 5,000 6,000 1997199819992000200120022003200420052006 Unit cost index: 1997=100 Costs, £m, 2006 prices

13. Projected costs of vertical separation – EVES Rail Study So vertical separation may not be good for all situations Alternatives – Holding Company Or clearer, better aligned incentives – combined with alliances? How to model this complex system?

14. Final observations / research challenges The RPI-X regulatory model under strain – needs refreshing Incorporating and incentivising quality Capital bias – too much investment? 5 year planning - cycles in investment – leads to high cost? Modelling complex systems (within industries) and between industries (competing for same resources) Costs – climate – resilience – how much do we know? Incentivising the “right” behaviour?

15. Contact details Dr Andrew Smith Senior Lecturer in Transport Regulation and Economics Institute for Transport Studies (ITS) and Leeds University Business School Tel (direct): + 44 (0) 113 34 36654 Email: a.s.j.smith@its.leeds.ac.uk Web site: www.its.leeds.ac.uk

16. Back-up slides

17. Stochastic frontier analysis Y it - output measures P it - input prices N it - exogenous network characteristic variables E.g. Ln Costs =0.944Ln Track + 0.309*Ln(TRAIN/TRACK)… τ it represent time variables capturing technical change β - parameters to be estimated. v it - random noise term u it - inefficiency term Deterministic FrontierNoiseInefficiency

18. Stochastic frontier analysis: diagram Cost Output Deterministic frontier Firms observed cost Firms stochastic frontier