Studies of diffusion have traditionally relied on specific distributions-primarily the logistic- to characterize and estimate those processes.We argue here that such approach gives rise to serious problems of comparability and interpretation, and may result in large biases inthe estimates of the parameters of interest. We propose instead the Gini's expected mean differenceas ameasure of diffusion speed, discuss its advantages over the traditional approach, and tackle with it the problems of truncated processes, inter-group comparisons, and related issues. We also elaborateon the use of the hazardrate, and suggest some possible extensions. The diffusion of CT scanners is presented as an illustration.
*Published:
Trajtenberg M. and S. Yitzhaki, Journal of Economics and Business Statistics, Vol. 7, no. 1, pp. 35-47, January 1989.
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