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Estimating individual driving distance by car and public transport use in Sweden

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  • Johansson-Stenman, Olof

    (Department of Economics, School of Economics and Commercial Law, Göteborg University)

Abstract

How much to drive, and how much to use public transport, are modelled as three- and two level decisions, respectively, based on micro-data for Sweden. The choices whether to have a car, whether to drive given access to a car, and how much to drive given that the individual drives at all are then estimated using a three equation model. Also after correcting for other variables, such as income, men are driving much more, and using less public transport, compared to women. People living in big cities are less likely to drive, but those who do are on average driving about as much as others. Age and access to company-cars are also important determinants for travel behaviour, but being a member of an environmental organisation is not. Driving increases with income, but to a lower degree compared to most aggregated studies on national level. The difference is explained in a simple model with income-dependent structural changes, implying that it becomes more difficult to live without a car when average income increases. This indirect effect is found to be of a similar size as the ordinary income elasticity typically found in cross-section analysis within a country or region.

Suggested Citation

  • Johansson-Stenman, Olof, 2001. "Estimating individual driving distance by car and public transport use in Sweden," Working Papers in Economics 36, University of Gothenburg, Department of Economics.
    • Handle: RePEc:hhs:gunwpe:0036
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    Cited by:

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    3. Sun, Shanxia & Delgado, Michael S. & Khanna, Neha, 2019. "Hybrid vehicles, social signals and household driving: Implications for miles traveled and gasoline consumption," Energy Economics, Elsevier, vol. 84(C).
    4. Mercado, Ruben & Páez, Antonio, 2009. "Determinants of distance traveled with a focus on the elderly: a multilevel analysis in the Hamilton CMA, Canada," Journal of Transport Geography, Elsevier, vol. 17(1), pages 65-76.
    5. Commins, Nicola & Nolan, Anne, 2011. "The determinants of mode of transport to work in the Greater Dublin Area," Transport Policy, Elsevier, vol. 18(1), pages 259-268, January.
    6. Eakins, John, 2016. "An application of the double hurdle model to petrol and diesel household expenditures in Ireland," Transport Policy, Elsevier, vol. 47(C), pages 84-93.
    7. Galit Cohen-Blankshtain, 2008. "Institutional constraints on transport policymaking: the case of company cars in Israel," Transportation, Springer, vol. 35(3), pages 411-424, May.
    8. Winston Koh & Roberto Mariano & Yiu Kuen Tse, 2007. "Open vs. sealed-bid auctions: testing for revenue equivalence under Singapore's vehicle quota system," Applied Economics, Taylor & Francis Journals, vol. 39(1), pages 125-134.
    9. John Eakins, 2013. "The Determinants of Household Car Ownership: Empirical Evidence from the Irish Household Budget Survey," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 144, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    10. Nolan, Anne, 2010. "A dynamic analysis of household car ownership," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(6), pages 446-455, July.
    11. Hennessy, Hugh & Tol, Richard S. J., 2010. "The Impact of Climate Policy on Private Car Ownership in Ireland," Papers WP342, Economic and Social Research Institute (ESRI).
    12. Copenhagen Economics, 2010. "Company Car Taxation," Taxation Papers 22, Directorate General Taxation and Customs Union, European Commission.
    13. Menz, Tobias & Welsch, Heinz, 2012. "Population aging and carbon emissions in OECD countries: Accounting for life-cycle and cohort effects," Energy Economics, Elsevier, vol. 34(3), pages 842-849.
    14. Alexia Prskawetz & Jiang Leiwen & Brian C. O Neill, 2004. "Demographic composition and projections of car use in Austria," Vienna Yearbook of Population Research, Vienna Institute of Demography (VID) of the Austrian Academy of Sciences in Vienna, vol. 2(1), pages 175-202.
    15. Kwang-Sub Lee & Jin Ki Eom & Jun Lee & Sangpil Ko, 2021. "Analysis of the Activity and Travel Patterns of the Elderly Using Mobile Phone-Based Hourly Locational Trajectory Data: Case Study of Gangnam, Korea," Sustainability, MDPI, vol. 13(6), pages 1-23, March.
    16. Keller, Rose & Vance, Colin, 2013. "Landscape pattern and car use: Linking household data with satellite imagery," Journal of Transport Geography, Elsevier, vol. 33(C), pages 250-257.
    17. Berry, Carl & Börjesson, Maria, 2024. "Income and fuel price elasticities of car use on micro panel data," Energy Economics, Elsevier, vol. 135(C).
    18. Edward Bendit & Amnon Frenkel & Sigal Kaplan, 2011. "Knowledge-workers and the sustainable city: the travel consequences of car-related job-perks," ERSA conference papers ersa11p389, European Regional Science Association.
    19. Sun, Shanxia & Delgado, Michael & Khanna, Neha, 2017. "Hybrid Vehicles and Household Driving Behavior: Implications for Miles Traveled and Gasoline Consumption," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258502, Agricultural and Applied Economics Association.
    20. Bardazzi, Rossella & Pazienza, Maria Grazia, 2018. "Ageing and private transport fuel expenditure: Do generations matter?," Energy Policy, Elsevier, vol. 117(C), pages 396-405.
    21. Macharis, Cathy & De Witte, Astrid, 2012. "The typical company car user does not exist: The case of Flemish company car drivers," Transport Policy, Elsevier, vol. 24(C), pages 91-98.
    22. Steininger, Karl W. & Friedl, Birgit & Gebetsroither, Brigitte, 2007. "Sustainability impacts of car road pricing: A computable general equilibrium analysis for Austria," Ecological Economics, Elsevier, vol. 63(1), pages 59-69, June.

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    More about this item

    Keywords

    Transport demand; car ownership; car use; driving; public transport demand; multi-level decisions; social context; gender and transport;
    All these keywords.

    JEL classification:

    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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