8 2b which plot the relationship between per capita income and motor vehicles per thousand population using the constant and variable elasticity equations, respectively. The variable elasticity estimates appear to be heavily affected by the behavior pattern of the lowest income countries (below US$1,000 per capita in 1987 constant US dollars). It is not clear, however, whether the higher income elasticities at the lower income level are an indication of different behavior (higher rates of capital accumulation), or merely the result of limited sample size (50 countries). Because the estimated quadratic log-log equations do not deviate much from the log-log equations at the national level (and not at all at the urban level), using constant elasticities to project motorization would not yield a substantially different result from using the variable elasticities.

It is fair to conclude that although elasticity estimates vary depending on the functional specification, a good point estimate is approximately 1 for the elasticity of national fleet growth with respect to per capita income and population. These values mean that country motor vehicle fleets grow at roughly the same rate as country incomes.

At the urban level, the estimates of the income elasticity of vehicle ownership should be interpreted with caution because vehicle ownership interacts strongly with population density, and both are strongly affected by income. Income elasticity estimates are about 0.8 if population density is omitted from the vehicle ownership equations (see, for example, Appendix Table 4, Eq. I b). When the historical spatial endowment index is considered, the estimate is about 0.7 (Appendix Table 5, Eq. la). A comparison of the two urban vehicle ownership equations in Appendix Table 5 (Eq. la and lb) indicates that motor vehicle ownership is basically linear with income on the log-log scale. And the estimates from the piecewise regression (Appendix Table 7, Eq. 1) suggest that the income elasticity for low-income cities is not statistically different from that for high-income cities. Finally, similar to the national equations, the income elasticity estimates from the firstdifference urban equations turns out to be about 1.0.

It should be noted that the per capita incomes used in the urban area data set are actually country income data. It is known that the urban and national difference in per capita income is much greater in developing countries than in high income countries. This implies that the true range of urban income levels for the urban sample is narrower than that suggested by the income data used. Therefore, the true income elasticities for urban areas may be greater than the current estimates. The use of country income data in urban equations may affect the cross-section estimates more than the first-difference estimates, because changes in urban incomes would be quite similar to changes in country incomes over a short period of time.

# Roads

Because roadways complement motorization, the growth of the road network is likely to be associated systematically with changes in the same economic variables that affect the growth of the motor vehicle fleet. Empirical analyses by Ingram and Liu (1997) at the national level found that income per capita is a major determinant of road length, and that road provision is quite responsive to demand. While total road networks expand more