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The long-run behaviour of transport performance in India: a cointegration approach

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  • Ramanathan, R.

Abstract

The long-run relationships between variables representing transport performance and other macro-economic variables in India are analysed in this paper using the concepts of cointegration and error correction. The results show that passenger-kilometres (PKM) in India are likely to increase faster than gross domestic product (GDP), and much faster than urbanisation. The tonne-kilometres (TKM) are highly correlated to industrial growth, and are likely to increase faster than the index of industrial production. Both the passenger and freight performances are relatively inelastic to price changes. The error correction model (ECM) shows that both passenger and tonne-kilometres adjust to their respective long-run equilibrium at a moderate rate, with about 35% of adjustment in PKM and 40% of adjustment in TKMs occurring in the first year.

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  • Ramanathan, R., 2001. "The long-run behaviour of transport performance in India: a cointegration approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(4), pages 309-320, May.
  • Handle: RePEc:eee:transa:v:35:y:2001:i:4:p:309-320
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    2. Hultkrantz, Lars & A. Krüger, Niclas & Mantalos, Panagiotis, 2014. "Risk-adjusted long-term social rates of discount for transportation infrastructure investment," Research in Transportation Economics, Elsevier, vol. 47(C), pages 70-81.
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    5. Pradhan, Rudra P. & Bagchi, Tapan P., 2013. "Effect of transportation infrastructure on economic growth in India: The VECM approach," Research in Transportation Economics, Elsevier, vol. 38(1), pages 139-148.
    6. Kathrin Goldmann, 2017. "Time-declining risk-adjusted social discount rates for transport infrastructure planning," Working Papers 22, Institute of Transport Economics, University of Muenster.
    7. Achour, Houda & Belloumi, Mounir, 2016. "Investigating the causal relationship between transport infrastructure, transport energy consumption and economic growth in Tunisia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 988-998.
    8. Lu, I.J. & Lewis, Charles & Lin, Sue J., 2009. "The forecast of motor vehicle, energy demand and CO2 emission from Taiwan's road transportation sector," Energy Policy, Elsevier, vol. 37(8), pages 2952-2961, August.
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    10. Andersson, Fredrik N G & Elger, Thomas, 2007. "Freight Transportation Activity, Business Cycles and Trend Growth," Working Papers 2007:15, Lund University, Department of Economics.
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    12. Michaelides, Panayotis G. & Konstantakis, Konstantinos N. & Milioti, Christina & Karlaftis, Matthew G., 2015. "Modelling spillover effects of public transportation means: An intra-modal GVAR approach for Athens," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 82(C), pages 1-18.
    13. Pelayo Arbués & José F. Baños, 2016. "A dynamic approach to road freight flows modeling in Spain," Transportation, Springer, vol. 43(3), pages 549-564, May.

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