Question

If increasing air fares increases revenues and decreasing them decreases revenues, then the demand
for air travel has a price elasticity of:

Answer 1

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a change in demand should not be confused with a change in the quantity demanded

Answer 2

Explanation:

♧The relation between price elasticity of demand for air transport and time horizon seems to be rather complex and to depend on various partial effects.

♧ An important issue here is the fact that the degree to which either one of these effects prevails under the given circumstances may be directly related to the degree to which substitution is available. For the specific case of the demand for passenger air transport, a number of aspects is relevant in this context. First, there is a lack of sufficient substitution transport modes for the air transport sector.

♧The mere travel speed of the mode is as yet still unmatched, whereas intra-modal substitution can hardly be regarded as a cost-evasive substitution due to differential fare structures and competition-related characteristics inherent in the air transport sector.

♧ Secondly, there are factors complicating possible long run adjustment strategies. Relocation costs, both pecuniary and non-pecuniary, as a means toevade increased transport costs tend to be relatively high due to the average flight distance and the often trans-cultural nature of the flight.

♧These aspects may imply that as far as possibilities for adjustment strategies are concerned, the use of long-run time horizons may not as obviously result in higher price sensitivity estimates compared to short-run time horizons as expected, while at the same time expressions of short-run price sensitivity will to a large degree be limited to demand changes on an aggregate modal level.

♧ The overall relationship between time-horizon and price sensitivity is, among other factors, determined by the ratio of leisure travellers to business travellers, since the former group generally disposes of more possibilities for demand adjustment on an aggregated level of the mode than the latter.

♧Most empirical research on airline demand has used cross-section data, notably a sample of city-pair data. This offers the advantage of larger samples than are often available in time series analysis, which is essential for studying dimensions of consumers’ travel demand such as time valuation and quality of service or for studying the modal choice behaviour of consumers.

♧ However, the disadvantage is that it does not always permit accurate estimation of price and income elasticities since cross-section data generally exhibit relatively little variation in air fares per unit of distance within a given fare class (see Straszheim, 1978). Time series data are more useful in estimating price and income elasticities, primarily since price (and income) changes have been dramatic during the last decades.

♧ But price changes, however significant they have been, are also relatively infrequent due to government regulation, whereas changes in service variables such as schedule frequencies, speed of aircraft, and density of seating also occur. Multicollinearity pervades clearly both cross-section and time series estimates.

♧ In cross-section models with gravity variables, fares tend to be strongly correlated with distance variables. This problem is most severe in time series studies. Variables such as price and income tend to be tightly correlated with a time trend. Parameter estimates therefore, are generally sensitive to changes in model specification and sample coverage.

♧ Moreover, the multi-collinearity, coupled with data limitations, leads to a persistent tendency to underspecify or oversimplify the model, with consequent biases in regression coefficients