nto a forecast for new construction (a complete discussion of the model and data sources is in Annex 4.A2). Generally, the model was developed in detail for road infrastructure, and then adapted for rail infrastructure. The discussion below will focus only on the road model. A key parameter is the elasticity of paved road capital stock with respect to GDPPC (or GDP). Table 4.1 shows some of the relevant elasticity relationship found in the literature. Under various assumptions, the different capital stock- to-income elasticities can be compared, giving rise to a range of estimates for the elasticity of paved road capital stock with respect to GDPPC of 0.12-0.90, with a mean estimate of 0.20 Much has been written about how high-income countries have seen a long-term downward trend of transportation investment (and capital stock to GDP) rates: EU: the transportation investment rate fell during the 1980s to 0.9% of GDP. OECD: public investment rates fell during 1967-90 to about 4% of GDP:7 transportation investment may be about 1.4% of GDP. US: the value of road capital stock fell during 1950-90 to about 13% of GDP.8 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 Table 4.1. Relevant elasticity relationships Study Variable Elasticity 1. Capital stock elasticity Fay-Yepes Paved road capital stock GDPpc 0.23 high income 0.14 low/medium income Canning Paved road capital stock GDPpc 0.90 panel data Munnell Public capital stock GDP per employee 0.30 cross-section Munnell Public capital stock 0.14 time-series Munnell Road capital stock 0.04 time-series 2. Road kilometre elasticity Canning Paved road km GDPpc 1.20 cross-section Ingram-Liu Paved road km GDPpc 1.00 cross-section 3. Road use Hanly et al. Vehicle stock Income 0.73 cross-section Johansson-Schipper Vehicle stock Income Ingram-Liu Vehicle stock per road (km) GDPpc 0.30 cross-section Hanly et al. Vehicle distance driven (vkm) Inco