Vehicle ownership and income growth, worldwide: 1960-2030.

The speed of vehicle ownership expansion in emerging market and developing countries has important implications for transport and environmental policies, as well as the global oil market. The literature remains divided on the issue of whether the vehicle ownership rates will ever catch up to the levels common in the advanced economies. This paper contributes to the debate by building a model that explicitly models the vehicle saturation level as a function of observable country characteristics: urbanization and population density. Our model is estimated on the basis of pooled time-series (1960-2002) and cross-section data for 45 countries that include 75 percent of the world's population. We project that the total vehicle stock will increase from about 800 million in 2002 to more than two billion units in 2030. By this time, 56% of the world's vehicles will be owned by non-OECD countries, compared with 24% in 2002. In particular, China's vehicle stock will increase nearly twenty-fold, to 390 million in 2030. This fast speed of vehicle ownership expansion implies rapid growth in oil demand.

1. INTRODUCTION

Economic development has historically been strongly associated with an increase in the demand for transportation and particularly in the number of road vehicles. This relationship is also evident in the developing economies today. Surprisingly, very little research has been done on the determinants of vehicle ownership in developing countries. Typically, analyses such as IEA (2004) or OPEC (2004) make assumptions about vehicle saturation rates--maximum levels of vehicle ownership (vehicles per 1000 people)--which are very much lower than the vehicle ownership already experienced in most of the wealthier countries. Because of this approach, their forecasts of future vehicle ownership in currently developing countries are much lower than would be expected by comparison with developed countries when these were at comparable income levels.

This paper empirically estimates the saturation rate for different countries, by formalizing the idea that vehicle saturation levels may be different across countries. Given data availability, we limit ourselves to the influence of demographic factors, urban population and population density. A higher proportion of urban population and greater population density would encourage the availability and use of public transit, and could reduce the distances traveled by individuals and for goods transportation. Thus countries that are more urbanized and densely populated could have a lower need for vehicles. In this study we attempt to account for these demographic differences by specifying a country's saturation level as a function of its population density and proportion of the population living in urban areas. There are, of course, a number of other reasons why saturation may vary amongst countries. For example, the existence of reliable public transport alternatives and the use of rail for goods transport may reduce the saturation demand for road vehicles. Alternatively, investment in a comprehensive road network will most likely increase the saturation level. Such factors, however, are difficult to take into account, as they would require far more data than are available for all but a few countries.

This paper examines the trends in the growth of the stock of road vehicles (with at least four wheels, including cars, trucks, and buses) for a large sample of countries since 1960 and makes projections of its development through 2030. It employs an S-shaped function--the Gompertz function--to estimate the relationship between vehicle ownership and per-capita income, or GDP. Pooled time-series and cross-section data are employed to estimate empirically the responsiveness of vehicle ownership to income growth at different income levels. By employing a dynamic model specification, which takes into account lags in adjustment of the vehicle stock to income changes, the influence of income on the vehicle stock over time is examined. The estimates are used, in conjunction with forecasts of income and population growth, for projections of future growth in the vehicle stock.

The study builds on the earlier work of Dargay and Gately (1999), who estimated vehicle demand in a sample of 26 countries--20 OECD countries and six developing countries--for the period 1960 to 1992, and projected vehicle ownership rates until 2015.

The current study extends that work in four ways. Firstly, we relax the 1999 paper's assumption of a common saturation level for all countries. In our previous study, the estimated saturation level was constrained to be the same for all countries (at about 850 vehicles per thousand people); differences in vehicle ownership between countries at the same income level were accounted for by allowing saturation to be reached at different income levels.

Secondly, the data set is extended in time to 2002 and adds 19 countries (mostly non-OECD countries) to the original 26; these 45 countries comprise about three-fourths of world population. The inclusion of a large number of non-OECD countries--more than one-third of the countries, with three-fourths of the sample's population--provides a high degree of variation in both income and vehicle ownership. This allows more precise estimates of the relationship between income and vehicle ownership at various stages of economic development. In addition, the model is used for countries not included in the econometric analysis to obtain projections for the "rest of the world".

The third extension we make to our earlier study concerns the assumption of symmetry in the response of vehicle ownership to rising and falling income. Given habit persistence, the longevity of the vehicle stock and expectations of rising income, one might expect that reductions in income would not lead to changes in vehicle ownership of the same magnitude as those resulting from increasing income. If this is the case, estimates based on symmetric models can be misleading if there is a significant proportion of observations where income declines. This is the case in the current study, particularly for developing countries. In most countries, real per capita income has fallen occasionally, and in Argentina and South Africa it has fallen over a number of years. In order to account for possible asymmetry, the demand function is specified so that the adjustment to falling income can be different from that to rising income. Specifically, the model permits the short-run response to be different for rising and falling income without changing the equilibrium relationship between the vehicle stock and income. The hypothesis of asymmetry is then tested statistically.

Finally, the fourth extension is to use the projections of vehicle growth to investigate the implications for future transportation oil demand. This is based on a number of simplifying assumptions and comparisons are made with other projections.

Section 2 summarizes the data used for the analysis, and explores the historical patterns of vehicle ownership and income growth. Section 3 presents the Gompertz model used in the econometric estimation, and the econometric results are described in Section 4. Section 5 summarizes the projections for vehicle ownership, based upon assumed growth rates of per-capita income in the various countries. Section 6 presents the implications for the growth of highway fuel demand. Section 7 presents conclusions. Appendix A describes the data sources.

2. HISTORICAL PATTERNS IN THE GROWTH OF VEHICLE OWNERSHIP

Table 1 summarizes the various countries' historical data (1) in 1960 and 2002, for per-capita income (GDP, expressed in 1995 PPP-adjusted dollars), vehicle ownership (per 1000 people), and population. Comparisons of the data for 1960 and 2002 are graphed below (in Section 5, we present similar graphic comparisons between 2002 and the projections for 2030).

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The relationship between the growth of vehicle ownership and per-capita income is highly non-linear. Vehicle ownership grows relatively slowly at the lowest levels of per-capita income, then about twice as fast as income at middle-income levels (from $3,000 to $10,000 per capita), and finally, about as fast as income at higher income levels, before reaching its maximum level ("saturation") at the highest levels of income. This relationship is shown in Figure 1, using annual data over the entire period 1960-2002 for the USA, Germany, Japan and South Korea; in the background is an illustrative Gompertz function that is on average representative of our econometric results below. Figure 2 shows similar data for China, India, Brazil and South Korea--with the same Gompertz function, but using logarithmic scales. Figure 3 shows the illustrative Gompertz relationship between vehicle ownership and per-capita income, as well as the income elasticity of vehicle ownership at different levels of per-capita income.

3. THE MODEL

As illustrated above, we represent the relationship between vehicle ownership and per-capita income by an S-shaped curve. This implies that vehicle ownership increases slowly at the lowest income levels, and then more rapidly as income rises, and finally slows down as saturation is approached. There are a number of different functional forms that can describe such a process--for example, the logistic, logarithmic logistic, cumulative normal, and Gompertz functions. Following our earlier studies, the Gompertz model was chosen for the empirical analysis, because it is relatively easy to estimate and is more flexible than the logistic model, particularly by allowing different curvatures at low- and high-income levels. (2)