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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

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

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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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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  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. P.A.V.B. Swamy & George S. Tavlas, 1993. "Random coefficient models: theory and applications," Finance and Economics Discussion Series 93-14, Board of Governors of the Federal Reserve System (U.S.).
  5. 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.
  6. Pesaran, M.H. & Smith, R., 1992. "Estimating Long-Run Relationships From Dynamic Heterogeneous Panels," Cambridge Working Papers in Economics 9215, Faculty of Economics, University of Cambridge.
  7. Bresson, Georges & Dargay, Joyce & Madre, Jean-Loup & Pirotte, Alain, 2003. "The main determinants of the demand for public transport: a comparative analysis of England and France using shrinkage estimators," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(7), pages 605-627, August.
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