Identifying sources of systematic variation in direct price elasticities from revealed preference studies of inter-city freight demand

Freight demand elasticity studies vary significantly in terms of the demand measure, data type, estimation method, commodity type, etc. This wide variation makes it difficult to compare empirical estimates when the differences may arise in part from the methods and data used. In this paper we conduct a comparative analysis to identify systematic sources of influence on direct price elasticity estimates in the context of freight transport, distinguishing between road, rail, and sea transport, using published direct price elasticities from 12 elasticity-derivative studies from five countries. The study focuses on revealed preference elasticities defined by the freight rate for tonnes and tonne kilometres of inter-city freight movements. Systematic sources that explain differences in direct price elasticities include the demand elasticity measure, mode, commodity class, model estimation form, country, and temporal nature of data (e.g., cross-section). Analysts can utilise the model outputs to adjust the empirical evidence from specific studies to control for differences that impact on the behavioural implications of comparative evidence.

Research highlights
► An analysis to explore sources of systematic variation in freight elasticity estimates using published evidence from five countries sourced from 12 elasticity-derivative studies published between 1978 and 2010
► The paper highlights the challenges in meta type freight elasticity studies compared with passenger studies.
► Systematic sources of influence explain 54 percent of the variation in sampled elasticity estimates measured by tonnes or tonne kilometres with respect to the freight rate.
► These include demand elasticity measure, mode, commodity class, model estimation form, country, and temporal nature of data.
► Analysts can utilise the findings to adjust the empirical evidence from specific studies to control for differences that impact on the predicted behavioural implications