1 / 18

How do organisations make decisions? The case of regulated and quasi-regulated industries

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. Overview.

alton
Télécharger la présentation

How do organisations make decisions? The case of regulated and quasi-regulated industries

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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 “Infrastructure” industries RPI-X modelValue for money Resilience, sustainability Economic Regulation Organisational and Institutional Separation EU reforms; break up “infrastructure” and operation Decision making in fragmented industries Competitive tendering 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 Productivity Investment • 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… 5 year price control period Next control period ROR = 8% P0 Revenue (RPI-X) Cost base Costs ROR = 8% ROR > 8% T=0 T=5

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

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

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

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

  12. FIGURE 1: TRAIN OPERATING COMPANY COSTS (EXCLUDING INFRASTRUCTURE ACCESS CHARGES) 6,000 140.0 120.0 5,000 100.0 4,000 80.0 Costs, £m, 2006 prices Unit cost index: 1997=100 3,000 60.0 2,000 40.0 1,000 20.0 0 0.0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 35% unit cost growth since 2000 = £1.5bn annual cost Growth in Britain’s Train Operating Company Costs Unit costs have stabilised since then – roughly same in 2009 as 2006

  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 EconomicsInstitute 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 { { { Deterministic Frontier Noise Inefficiency • Yit - output measures • Pit - input prices • Nit - exogenous network characteristic variables • E.g. Ln Costs =0.944Ln Track + 0.309*Ln(TRAIN/TRACK)… • τit represent time variables capturing technical change • β - parameters to be estimated. • vit- random noise term • uit- inefficiency term

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

More Related