Accounting for inertia in modal choices: Some new evidence using a RP/SP dataset

Elisabetta Cherchi, Francesco Manca

Research output: Contribution to journalArticlepeer-review


Inertia is related with effect that experiences in previous periods may have on the current choice. In particular, it has to do with the tendency to stick with the past choice even when another alternative becomes more appealing. As new situations force individuals to rethink about their choices new preferences may be formed. Thus a learning process begins that relaxes the effect of inertia in the current choice. In this paper we use a mixed dataset of revealed preference (RP)-stated preference (SP) to study the effect of inertia between RP and SP observations and to study if the inertia effect is stable along the SP experiments. Inertia has been studied more extensively with panel datasets, but few investigations have used RP/SP datasets. In this paper we extend previous work in several ways. We test and compare several ways of measuring inertia, including measures that have been proposed for both short and long RP panel datasets. We also explore new measures of inertia to test for the effect of "learning" (in the sense of acquiring experience or getting more familiar with) along the SP experiment and we disentangle this effect from the pure inertia effect. A mixed logit model is used that allows us to account for both systematic and random taste variations in the inertia effect and for correlations among RP and SP observations. Finally we explore the relation between the utility specification (especially in the SP dataset) and the role of inertia in explaining current choices.

Original languageEnglish (US)
Pages (from-to)679-695
Number of pages17
Issue number4
StatePublished - Jul 2011


  • Inertia
  • Modal choice
  • RP/SP dataset
  • Weighted experiences

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Development
  • Transportation


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