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Rarely Enjoyed? A Count Data Analysis of Ridership in Germany's Public Transport

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  • Frondel, Manuel
  • Vance, Colin

Abstract

Focusing on adult members of German households, this paper investigates the determinants of public transit ridership with the aim of quantifying the effects of fuel prices, fares, person-level attributes, and characteristics of the transit system on transport counts over a five-day week. The reliance on individual data raises several conceptual and empirical issues, the most fundamental of which is the large proportion of zero values in transit counts. To accommodate this feature of the data, we employ modeling procedures referred to as zero-inflated models (ZIMs), which order observations into two latent regimes defined by whether the individual never uses public transport. Our estimates reveal fuel prices to have a positive and substantial influence on transit ridership, though there is no evidence for a statistically signifi cant impact of the fare.

Suggested Citation

  • Frondel, Manuel & Vance, Colin, 2010. "Rarely Enjoyed? A Count Data Analysis of Ridership in Germany's Public Transport," Ruhr Economic Papers 209, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:209
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    References listed on IDEAS

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    1. Jorg Peters & Colin Vance, 2011. "Rural Electrification and Fertility - Evidence from Cote d'Ivoire," Journal of Development Studies, Taylor & Francis Journals, vol. 47(5), pages 753-766.
    2. Hensher, David A., 2008. "Assessing systematic sources of variation in public transport elasticities: Some comparative warnings," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(7), pages 1031-1042, August.
    3. Storchmann, K. -H., 2001. "The impact of fuel taxes on public transport -- an empirical assessment for Germany," Transport Policy, Elsevier, vol. 8(1), pages 19-28, January.
    4. Cameron, A Colin & Trivedi, Pravin K, 1986. "Econometric Models Based on Count Data: Comparisons and Applications of Some Estimators and Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 1(1), pages 29-53, January.
    5. Frondel, Manuel & Vance, Colin, 2010. "Driving for fun? Comparing the effect of fuel prices on weekday and weekend fuel consumption," Energy Economics, Elsevier, vol. 32(1), pages 102-109, January.
    6. Manuel Frondel & Jorg Peters & Colin Vance, 2008. "Identifying the Rebound: Evidence from a German Household Panel," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 145-164.
    7. Colin Vance & Ralf Hedel, 2007. "The impact of urban form on automobile travel: disentangling causation from correlation," Transportation, Springer, vol. 34(5), pages 575-588, September.
    8. Daniel J. Graham & Stephen Glaister, 2002. "The Demand for Automobile Fuel: A Survey of Elasticities," Journal of Transport Economics and Policy, University of Bath, vol. 36(1), pages 1-25, January.
    9. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    10. Holmgren, Johan, 2007. "Meta-analysis of public transport demand," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(10), pages 1021-1035, December.
    11. Manuel Frondel & Colin Vance, 2009. "Do High Oil Prices Matter? Evidence on the Mobility Behavior of German Households," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 43(1), pages 81-94, May.
    12. Kayser, Hilke A., 2000. "Gasoline demand and car choice: estimating gasoline demand using household information," Energy Economics, Elsevier, vol. 22(3), pages 331-348, June.
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    Cited by:

    1. Nowak, William P. & Savage, Ian, 2013. "The cross elasticity between gasoline prices and transit use: Evidence from Chicago," Transport Policy, Elsevier, vol. 29(C), pages 38-45.
    2. Xiaohong Chen & Xiang Wang & Hua Zhang & Jia Li, 2014. "The Diversity and Evolution Process of Bus System Performance in Chinese Cities: An Empirical Study," Sustainability, MDPI, vol. 6(11), pages 1-17, November.
    3. Boisjoly, Geneviève & Grisé, Emily & Maguire, Meadhbh & Veillette, Marie-Pier & Deboosere, Robbin & Berrebi, Emma & El-Geneidy, Ahmed, 2018. "Invest in the ride: A 14 year longitudinal analysis of the determinants of public transport ridership in 25 North American cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 116(C), pages 434-445.
    4. 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.
    5. Julian Dieler, 2016. "Effectiveness of Climate Policies: Empirical Methods and Evidence," ifo Beiträge zur Wirtschaftsforschung, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, number 68.

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    More about this item

    Keywords

    transit ridership; pricing policy; Zero-Inflated Models; household data;
    All these keywords.

    JEL classification:

    • D13 - Microeconomics - - Household Behavior - - - Household Production and Intrahouse Allocation
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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