Institute for Transport Studies FACULTY OF ENVIRONMENT How do organisations make decisions? The case...
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Transcript of Institute for Transport Studies FACULTY OF ENVIRONMENT How do organisations make decisions? The case...
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: [email protected]
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