What are the factors besides economic development that affect automobility?
What is their influence on automobility?
What will happen to automobility in developing countries if they develop along similar paths as developed countries?
The level of automobility, defined as travel in personal vehicles, is often seen as a function of income: The higher a country's per capita income, the greater the amount of driving. However, levels of automobility vary quite substantially between countries even at similar levels of economic development. This suggests that countries follow different mobility paths. The research detailed in this report sought to answer three questions: What are the factors besides economic development that affect automobility? What is their influence on automobility? What will happen to automobility in developing countries if they progress along similar paths as developed countries? To answer these questions, the authors developed a methodology to identify these factors, model their impact on developed countries, and forecast automobility (as defined by per capita vehicle-kilometers traveled [VKT]) in four developing countries. This methodology draws on quantitative analysis of historical automobility development in four country case studies (the United States, Australia, Germany, and Japan) that represent very different levels of per capita automobility, in combination with data derived from an expert-based qualitative approach. The authors used the latter to assess how these experiences may affect the future of automobility in the BRIC countries: Brazil, Russia, India, and China. According to this analysis, automobility levels in the four BRIC countries will fall between those of the United States (which has the highest per capita VKT level of the four case studies) and Japan (which has the lowest). Brazil is forecasted to have the highest per capita VKT and India the lowest.
Nine Non-Economic Factors Increase Automobility
Factors fall into two categories: transportation policy factors (car infrastructure, inexpensive fuel, pro-car policies, and lack of alternatives to driving) and exogenous factors (active population, domestic oil, domestic car industry, spatial dispersion, and car culture).
These nine factors seem to have affected automobility in developed countries.
These Factors Are Correlated with Automobility
The nine factors, each assigned a weight and scored, together yield an automobility score for each country.
The automobility scores were good predictors of automobility in the developed countries. Car infrastructure and spatial dispersion seem to be the most significant.
Developing Countries Are Forecasted to Have a Range of Automobility Outcomes
According to the automobility scores, Brazil is forecasted to have the highest levels of automobility, and India the lowest.
These Findings Have Policy Implications
The factors that influence travel demand, especially the exogenous ones, change only slowly over time. Substantial growth in personal vehicle travel is likely to occur no matter what transportation policies are adopted.
Although there is a strong correlation in developed countries between income growth and growth in vehicle travel, income levels alone are not good predictors of travel demand.
Understanding economic growth is quite helpful in understanding changes in travel demand over time within a country but far less so in understanding travel demand differences between countries.
Policymakers can influence travel demand through transportation policies. Especially in developing countries, where the infrastructure and spatial patterns are still being developed, there are opportunities to dampen the demand for driving.
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Ecola, Liisa, Charlene Rohr, Johanna Zmud, Tobias Kuhnimhof and Peter Phleps. The Future of Driving in Developing Countries. Santa Monica, CA: RAND Corporation, 2014. http://www.rand.org/pubs/research_reports/RR636. Also available in print form.Ecola, Liisa, Charlene Rohr, Johanna Zmud, Tobias Kuhnimhof and Peter Phleps, The Future of Driving in Developing Countries, Santa Monica, Calif.: RAND Corporation, RR-636-ifmo, 2014. As of July 15, 2014: http://www.rand.org/pubs/research_reports/RR636