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Economic and structural determinants of the demand for public transport: an analysis on a panel of French urban areas using shrinkage estimators

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  • Bresson, Georges
  • Dargay, Joyce
  • Madre, Jean-Loup
  • Pirotte, Alain

Abstract

This paper presents a panel data analysis of annual time series from 1975 to 1995 for 62 urban areas in France. It compares the results obtained from a conventional fixed-effects (FE) model with a Bayesian approach (shrinkage estimators), which allows the computation of elasticities for each urban area. First, considering only three economic determinants (vehicle km, income and price), we show the sensitivity of the estimates to the time period used for the estimation. On the basis of these models, public transport appears as an "inferior good" (i.e. its income elasticity is negative). By combining economic determinants with structural determinants (i.e. population ageing, urban sprawl and growing car ownership) synthesized in a single indicator, we show that this 'income effect' is in fact mainly a "motorization effect". The competition with the automobile also appears through the cross-elasticity to the fuel price. Finally, the impact of supply is decomposed into a dominant effect of quantity (seats kilometres), and the weaker effects of quality (frequency and density of network). The major conclusion is that the downward trend in public transport patronage is mainly due to increasing car ownership, and that this effect will be less and less important over time since the growth of the car stock is decelerating. In addition, the use of public transport is quite sensitive to the volume supplied and to its price, which makes the financial equilibrium of this industry problematic.

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Bibliographic Info

Article provided by Elsevier in its journal Transportation Research Part A: Policy and Practice.

Volume (Year): 38 (2004)
Issue (Month): 4 (May)
Pages: 269-285

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Handle: RePEc:eee:transa:v:38:y:2004:i:4:p:269-285

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References

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  1. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  2. Maddala, G S, et al, 1997. "Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators," Journal of Business & Economic Statistics, American Statistical Association, vol. 15(1), pages 90-100, January.
  3. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2002. "Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption," Economics Letters, Elsevier, vol. 76(3), pages 375-382, August.
  4. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
  5. Swamy, P A V B & Tavlas, George S, 1995. " Random Coefficient Models: Theory and Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 9(2), pages 165-96, June.
  6. Badi H. Baltagi & Georges Bresson & James M. Griffin & Alain Pirotte, 2002. "Homogeneous, heterogeneous or shrinkage estimators? Some empirical evidence from French regional gasoline consumption," 10th International Conference on Panel Data, Berlin, July 5-6, 2002 A6-4, International Conferences on Panel Data.
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Cited by:
  1. Stéphanie SOUCHE, 2009. "Un exemple d'estimation de la demande de transport urbain," Revue d'économie régionale et urbaine, Armand Colin, vol. 0(4), pages 759-779.
  2. Stéphanie Souche, 2010. "Measuring the structural determinants of urban travel demand," Post-Print halshs-00578019, HAL.
  3. De Witte, Astrid & Hollevoet, Joachim & Dobruszkes, Frédéric & Hubert, Michel & Macharis, Cathy, 2013. "Linking modal choice to motility: A comprehensive review," Transportation Research Part A: Policy and Practice, Elsevier, vol. 49(C), pages 329-341.
  4. Thøgersen, John, 2006. "Understanding repetitive travel mode choices in a stable context: A panel study approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(8), pages 621-638, October.
  5. Fullerton, Thomas M., Jr. & Walke, Adam G., 2012. "Border zone mass transit demand in Brownsville and Laredo," MPRA Paper 42990, University Library of Munich, Germany.
  6. Yusuke Sakata & Junyi Shen & Yoshizo Hashimoto, 2006. "The Influence of Environmental Deterioration and Network Improvement on Transport Modal Choice," Discussion Papers in Economics and Business 06-04, Osaka University, Graduate School of Economics and Osaka School of International Public Policy (OSIPP).
  7. Germa Bel & Daniel Albalate, 2009. "What shapes local public transportation in Europe? Economics, Mobility, Institutions, and Geography," RSCAS Working Papers 2009/34, European University Institute.
  8. Dirk Temme & Marcel Paulssen & Till Dannewald, 2007. "Integrating latent variables in discrete choice models – How higher-order values and attitudes determine consumer choice," SFB 649 Discussion Papers SFB649DP2007-065, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.

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