ojections describe these in sufficient detail". 2. OECD (2005), p. 10. Studies with a broader range of forecast variables included UK-Royal Academy of Engineering (RAE) (2005) (i.e. ICT impact on transport efficiency); US-DoT (2000) (i.e. immigration impact); and DE-ifmo (2005) INFRASTRUCTURE TO 2030: TELECOM, LAND TRANSPORT, WATER AND ELECTRICITY - ISBN 92-64-02398-4 - © OECD 2006 4. KEY FACTORS DRIVING THE FUTURE DEMAND FOR SURFACE TRANSPORT INFRASTRUCTURE (i.e. security impact; climate change/extreme weather impact). The rationale for a limited set of forecast variables include: parsimony (i.e. Occam's Razor); co-integration (i.e. joint determinism of variables); and impact uncertainty (e.g. absence of historical evidence). 3. Fay-Yepes (2003). 4. Andrieu (2005). 5. This mean estimate excludes the Canning value. The transformations are: GDP per capita elasticity is about 3.1 times the GDP elasticity (for GDP growth of 3% and population growth of 2%); and GDP per employee elasticity should be equal to the GDP per capita elasticity with a constant employment rate. The equation is = where g is the GDP growth rate and p is the population growth rate. 6. Stevens-Michalski (1993), p. 1. This is for all forms of transportation. 7. Munnell (1993), Figure 3. Investment rates varied from 2-6% of GDP across OECD countries. If transport represents about 35% (as in the United States) of this investment, this would put the OECD range of road investment (i.e. new construction) at between 0.7 and 2.1% of GDP, with a mean of about 1.4%. 8. Munnell (1993), Table 2. Roughly 35% (1990) of US public (non-defence) public capital is road (highways and streets). 9. Evaluated for GDP growth rate of 3%. 10. Evaluated using the 3.1 ratio of GDP elasticity relative to GDP per capita elasticity. 11. Evaluated for GDP growth rate of 2.5%, base capital stock-to-GDP ratio of 12% (i.e. slightly lower than the US ratio), and assumptions that 35% of public investment is road transport, and 50% of that is for new construction. 12. Across a wide 