Institute for Transport StudiesFACULTY 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

Overview

Economic Regulation

Organisational and Institutional Separation

Competitive tendering

“Infrastructure” industries RPI-X modelValue for moneyResilience, sustainability

EU reforms; break up “infrastructure” and operationDecision making in fragmented industries

Competition “for the market” not “in the market”Typically applies to “operations”Does it work?

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

Graphically…

Revenue (RPI-X)

ROR = 8%

ROR = 8%

5 year price control period Next control period

T=0

Cost base

P0

T=5

Costs

ROR > 8%

Issues

• Asymmetries of information – firms know more than the regulator

• Gives opportunities for gaming in various ways

Can firms hoodwink the regulator

Issues

• Asymmetries of information – firms know more than the regulator

• Gives opportunities for gaming in various ways

• So regulators use benchmarking…

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

DE

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

Efficiency estimates for Network Rail (2008 review)

Implies a gap against the frontier of 40% in 2006

40%gap

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Sco

re a

ga

inst

fro

nti

er

Profile of Network Rail Efficiency Scores: Flexible Cuesta00 Model

Hierarchies (top-level and business unit managers)

Infrastructure Company

Region (sub-company)

IM1 IM2 …

R11 R21 RS1… R12 R22 RS2…

Inefficiency due to systematic differences between firms – external inefficiency

Inefficiency due variation in performance at regional level –internal inefficiency

Britain’s rail reform experiment

TOCs

ROSCOs

Railtrack /Network Rail

FOCs

TrackMaintenance

TrackRenewal

Train manufacture and maintenance

British Rail

Monopoly

Competition“in the market”

Competition“for the market”

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

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

Uni

t cos

t ind

ex: 1

997=

100

Cost

s, £

m, 2

006

pric

es

Projected costs of vertical separation – EVES Rail Study

Billions of Euros (2005 constant prices) Current

density

levels

Current

density

levels

+ 10%

Current

density

levels

+ 20%

Current

density

levels

+ 50%*

Yearly cost of imposing vertical

separation across EU (for those countries

not already separated)

5.8 7.8 9.6 14.5

Note: * It is recognised that higher growth would at some point require increased capacity

1 • 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?

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?

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

Back-up slides

Stochastic frontier analysis

itittitititit uvNPYfC );,,,(

• 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

Deterministic Frontier Noise Inefficiency

Stochastic frontier analysis: diagram

Cost

Output

Deterministic frontierFirms observed cost

Firms stochastic frontier

itv

itu