The demand for transportation fuels: Imperfect price-reversibility?

The demand for transportation fuels: Imperfect price-reversibility?

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Article ID: iaor19971995
Country: United Kingdom
Volume: 31B
Issue: 1
Start Page Number: 71
End Page Number: 82
Publication Date: Feb 1997
Journal: Transportation Research. Part B: Methodological
Authors: ,
Keywords: petroleum, energy, economics
Abstract:

This paper examines the price-reversibility of fuel demand for road transport. The analysis is based on an econometric model which utilizes price-decomposition techniques to measure separately the effects of different types of price increases and decreases. The methods proposed allow empirical testing of irreversibility and certain forms of hysteresis in demand relationships. The results lend strong support to the notion that consumers do not necessarily respond in the same fashion to rising and falling prices, nor equivalently to sudden and substantial price rises as to minor price fluctuations: demand is not necessarily reversible to price changes. This finding severely challenges the equilibrium basis of the traditional, reversible demand model. In the particular example used, the results indicate that consumers have reacted more strongly to the price rises of the seventies, than to other price rises, and that the resulting fuel reductions will not be totally reversed as prices return to lower levels. The results also show that, if irreversibilities do exist, the use of reversible, symmetric models will produce biased elasticity estimates, not only for prices, but for other variables as well. The methods used in this analysis should be applicable to more detailed analysis of travel behaviour, where asymmetry of response or persistence of effect may be relevant. The existance of price asymmetries will have important implications for fuel use in transport, as well as for traffic growth, and particularly for evaluating the impact of price-related transport policy. It will also affect the possibility of estimating price elasticities and forecasting demand on the basis of historic data.

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