Infrastructure Cost Drivers Study
The Future of Infrastructure Conference
19 August 2014
MELBOURNE
Joe Branigan, Senior Re...
Outline
(i) Description of Infrastructure Cost Drivers Study
(ii) Progress so far
(iii) Hypothesis “Perfect Storm”
(iv) To...
Study Description
3
Project Objectives
• The Need – we can’t learn if we don’t reflect, and we can’t reflect if we
don’t have the information
...
Methodology
5
- Attempt to bring regulatory issues into a S-D framework, along with scarcity
- Because prices/costs are al...
Project Participants and Data
• Queensland (2007-2013)
– Roads (21 case studies)
– Rail (6 case studies)
– Some previous s...
Progress so far
7
Early results from the case studies
• Wide variation in costs per km because wide variation in types of
infrastructure
– R...
Wide variation in costs per km (roads example)
9
0
10
20
30
40
50
60
Project
3
Project
1
Project
14
Project
10
Project
15
...
Productivity Commission found similar variation in
urban passenger rail
10
Information from interviews (1)
• Mining construction boom (esp. Queensland)
– Materials shortages
– Engineers/designers s...
• Technical standards
– Qld had its own standards for road building up until 2012, when it reverted to the
national standa...
“Perfect Storm” hypothesis
13
Conceptual Framework
14
- Attempt to bring regulatory issues into a S-D framework
- Ultimately, costs borne by governments...
The rise in public investment in Queensland through
the mining boom was unprecedented
15
Public investment competing with mining boom
investment led to resource scarcity and rising costs
per unit of infrastructu...
The mining boom states (Queensland and WA) were
more affected than other states…
17
80
90
100
110
120
130
140
150
160
Sep-...
By the end of the boom, road construction and
maintenance costs were 25% higher than the PPI
18
80
90
100
110
120
130
140
...
The boom has ended but Engineering Design and
Consulting Services wages have remained
relatively elevated
19
Another factor – increased complexity
• Higher population density and much higher land values
– Increases disruption assoc...
Magnitude of potential benefits from
infrastructure reform
21
“Top-Down” estimate of magnitude of potential
benefits – not based on Case Study data
22
- SMART has estimated a “top-down...
The Future of Infrastructure
23
A wish list…
• No more bad mega-projects
– Potential for significant welfare losses
– More independent and transparent pro...
Thank You
25
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Joe Branigan - University of Wollongong - Infrastructure cost drivers and financial sustainability – Lessons learned from past projects

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Joe Branigan delivered the presentation at the 2014 Future of Infrastructure Conference.

The Future of Infrastructure forum explored state and national challenges which impact the long term economic growth and future of infrastructure development in Australia at this time. It also addressed the latest proposals for changes within Australia's infrastructure.

For more information about the event, please visit: http://bit.ly/FutureofInfrastructure2014

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Joe Branigan - University of Wollongong - Infrastructure cost drivers and financial sustainability – Lessons learned from past projects

  1. 1. Infrastructure Cost Drivers Study The Future of Infrastructure Conference 19 August 2014 MELBOURNE Joe Branigan, Senior Research Fellow SMART
  2. 2. Outline (i) Description of Infrastructure Cost Drivers Study (ii) Progress so far (iii) Hypothesis “Perfect Storm” (iv) Top-Down estimate of magnitude of potential reform benefit (v) The Future of Infrastructure…A wish list 2
  3. 3. Study Description 3
  4. 4. Project Objectives • The Need – we can’t learn if we don’t reflect, and we can’t reflect if we don’t have the information • Understanding rising costs over time (longitudinal) – Ideally would like to test to what extent increasing regulation has caused rising costs • Environmental regulation • Technical and Design standards • Health and safety standards – Other relevant factors include: • Economic cycle (mining boom and GFC stimulus spending) • Dealing with increased Complexity (brownfields v greenfields, planning) • Understanding cost differences between Australian jurisdictions (cross- sectional) – Jurisdictions want to know how they compare with their neighbours – Likely that there will be common drivers as well as drivers unique to states 4
  5. 5. Methodology 5 - Attempt to bring regulatory issues into a S-D framework, along with scarcity - Because prices/costs are also affected by regulations that add to the cost of tendering for projects , designing projects, and building infrastructure - Cyclical Drivers - Economic cycle - Mining boom - GFC stimulus - Structural Drivers (more onerous regulation) - Environmental regulations - Planning requirements - Health & Safety - Increased density of cities - Matched case studies to isolate cost drivers e.g. 10 km road, same terrain, mining boom v non mining boom e.g. 10 km rail, same terrain, different jurisdiction - Interview to fill in the gaps - Particularly changes in standards and requirements over time
  6. 6. Project Participants and Data • Queensland (2007-2013) – Roads (21 case studies) – Rail (6 case studies) – Some previous studies – Interviews • NSW (early 1990s – 2013) – Roads (62 case studies) – Rail (10 case studies) – NSW Upper House Inquiry (Rail) – Interviews 6 • Victoria – Roads (Should receive data by end of August 2014) – Rail (Should receive data by end of August 2014) – Interviews • Wish List (Phase 2) – Other Australian jurisdictions (esp. WA and NT) – NZ – Singapore
  7. 7. Progress so far 7
  8. 8. Early results from the case studies • Wide variation in costs per km because wide variation in types of infrastructure – Roads, rail, tunnels, bridges, greenfields, brownfields, rock type etc – Obviously difficult to get exactly matched case studies and currently seeking a longer time series of data from participating jurisdictions • Early findings (not full dataset) – Clear increases in costs per km across time for matched projects; some suspects: • Property acquisition (increased brownfields development) • Technical and Project Management (inc. design costs, environmental management, stakeholder engagement) (both increased rates and more work to do) • Audit and Legal costs 8
  9. 9. Wide variation in costs per km (roads example) 9 0 10 20 30 40 50 60 Project 3 Project 1 Project 14 Project 10 Project 15 Project 13 Project 7 Project 9 Project 11 Project 17 Project 4 Project 5 Project 16 Project 8 Project 2 Project 12 Project 6 Cost per km ($millions) Average = $15.2 million per km Standard Deviation = $10.0 million per km Variation in Road Costs across 17 Case Studies
  10. 10. Productivity Commission found similar variation in urban passenger rail 10
  11. 11. Information from interviews (1) • Mining construction boom (esp. Queensland) – Materials shortages – Engineers/designers shortages – Cost escalation very common through mid- to late-2000s • Environmental legislation – EPBC – EIS requirements (massive increased workload) • Planning Approvals delays – Significant increase in waiting times over the 2000s • Design costs – Increased complexity (more brownfields, less greenfields) – Environmental management requirements – OH&S management requirements 11
  12. 12. • Technical standards – Qld had its own standards for road building up until 2012, when it reverted to the national standard • Health and Safety standards – More bright lights and witches hats, but where is the trade-off point • Lack of competition – Esp. during the boom – Alliance model is expensive • Public projects not cost controlled (scope creep and political interference) • Longer defect liability period and other legal costs • More substantial works to meet increased loads and traffic volumes (ie. ‘benefits’ greater) – See technical standards above • Fixed/Variable cost trade-offs – Loose fiscal environment (favour capex over opex) (mid-late 2000s) – Very different story now with constrained public finances 12 Information from interviews (2)
  13. 13. “Perfect Storm” hypothesis 13
  14. 14. Conceptual Framework 14 - Attempt to bring regulatory issues into a S-D framework - Ultimately, costs borne by governments reflect the prices paid for services - Prices rise and fall depending on resource scarcity (e.g. mining boom) - But prices are also affected by regulations that add costs to tendering for projects , designing projects and building infrastructure - Cyclical Drivers - Mining boom - GFC stimulus - Structural Drivers - More onerous regulation
  15. 15. The rise in public investment in Queensland through the mining boom was unprecedented 15
  16. 16. Public investment competing with mining boom investment led to resource scarcity and rising costs per unit of infrastructure built 16
  17. 17. The mining boom states (Queensland and WA) were more affected than other states… 17 80 90 100 110 120 130 140 150 160 Sep-2003 Jan-2004 May-2004 Sep-2004 Jan-2005 May-2005 Sep-2005 Jan-2006 May-2006 Sep-2006 Jan-2007 May-2007 Sep-2007 Jan-2008 May-2008 Sep-2008 Jan-2009 May-2009 Sep-2009 Jan-2010 May-2010 Sep-2010 Jan-2011 May-2011 Sep-2011 Jan-2012 May-2012 Sep-2012 Jan-2013 May-2013 Sep-2013 Australia NSW VIC Qld WA Index (Sept 2003 = 100) ABS Road & Bridge Construction Index, Impact of Mining Boom
  18. 18. By the end of the boom, road construction and maintenance costs were 25% higher than the PPI 18 80 90 100 110 120 130 140 150 160 170 180 1998-99 1999-00 2000-01 2001-02 2002-03 2003-04 2004-05 2005-06 2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 RCMPI ABS RBCI PPI The costs of Road Construction and Maintenance are running significantly ahead of the Producer Price Index. Index 1989-90 = 100 15-25% higher
  19. 19. The boom has ended but Engineering Design and Consulting Services wages have remained relatively elevated 19
  20. 20. Another factor – increased complexity • Higher population density and much higher land values – Increases disruption associated with major works – And cost of land resumption • More brownfields assets needing expanding or ‘decongesting’ • Fewer vacant corridors – Imposes very costly solutions such as tunnelling • Greater environmental restrictions and more responsiveness to community concerns 20
  21. 21. Magnitude of potential benefits from infrastructure reform 21
  22. 22. “Top-Down” estimate of magnitude of potential benefits – not based on Case Study data 22 - SMART has estimated a “top-down” ‘potential benefits’ from infrastructure reform figure - This estimate is based on publicly available data, including: - ABS National Accounts (State Accounts, Public GFCF) - State Budget Papers - Interviews with stakeholders - Recent PC Inquiry Report on Public Infrastructure - We find a potential benefit of between $4-$5 billion per year - This is around 12% of current total public new infrastructure investment, which translates to 6% of current total public investment (new construction + maintenance) - At 4%, the potential benefit is $2.8 billion per year - At 8%, the potential benefit is $5.7 billion per year - Clearly, the potential benefits from reform are significant
  23. 23. The Future of Infrastructure 23
  24. 24. A wish list… • No more bad mega-projects – Potential for significant welfare losses – More independent and transparent project prioritisation and selection processes • Independent review of CBAs – A counter-weight to short-term political imperatives • Economic regulation of roads – Like electricity and water, but (hopefully) without the mistakes – Improved price signal • Improved alignment of revenue-raising, spending and service delivery standards between Commonwealth and States – Remove regulatory duplication 24
  25. 25. Thank You 25

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