Tim has just had a new paper published in Transport Policy in a special issue on Transport Poverty.
The paper is called “Financial Implications of Car Ownership and Use: a distributional analysis based on observed spatial variance considering income and domestic energy costs
Chatterton, T., Anable, J., Cairns, S. and Wilson, R. (2017) Financial implications of car ownership and use: A distributional analysis based on observed spatial variance considering income and domestic energy costs . Transport Policy. ISSN 0967-070X [In Press] http://dx.doi.org/10.1016/j.tranpol.2016.12.007
The paper is an output from the Motoring and vehicle Ownership Trends (MOT) project led by Jillian Anable at ITS, Leeds www.MOTproject.net
· If the paper is of interest, there is also a ‘follow-on’ conference paper with some additional analyses considering travel to work costs, and a segmented analysis based on ONS Output Area Classifications. Chatterton, T., Anable, J., Cairns, S. and Wilson, R. (2016) Financial implications of car use and the drive to work: A social and spatial distributional analysis using income data and area classifications . In: DEMAND Conference 2016, Lancaster, UK, 13-15 April 2016. Available from: http://eprints.uwe.ac.uk/28729
Abstract
This paper presents a new perspective on assessing the financial impacts of private car usage in England and Wales using novel datasets to explore implications of motoring costs (principally Vehicle Excise Duty and road fuel costs) for households as part of the overall costs of their energy budget. Using data from an enhanced version of the Department for Transport ‘MOT’ vehicle test record database, combined with data on domestic gas and electricity consumption from the Department for Business, Energy and Industrial Strategy (formerly the Department of Energy and Climate Change), patterns of car usage and consequent energy consumption are investigated, and the costs of Vehicle Excise Duty and road fuel examined as a proportion of total expenditure on household direct energy consumption. Through the use of these new datasets it is possible to analyse how these vary spatially and in relation to levels of median income. The findings indicate that motoring costs are strongly regressive, with lower income areas, especially in rural locations, spending around twice as much of their income on motoring costs as the highest income areas.
