How mean rank and mean size may determine the generalised Lorenz curve: With application to citation analysis

Lucio Bertoli-Barsotti and Tommaso Lando

Journal of Informetrics, 2019, vol. 13, issue 1, 387-396

Abstract: Within the wide framework of information production processes, we present a conversion formula that expresses the generalised Lorenz (GL) curve of a size-frequency distribution as a function of the corresponding rank-size distribution using a fully discrete modelling approach. Based on this conversion formula, we introduce a somewhat universal model for the GL curve of the empirical size-frequency distribution. This study’s approach to determining the GL curve is indirect, as we obtain our model for the size-frequency framework by modelling the rank-size distribution and not by directly modelling the size distribution or the GL curve itself, as is usually done. Our GL curve model is particularly appealing because it provides a simple and economical description of the distribution that depends on only three quantities: the (i) mean size, (ii) mean rank, and (iii) maximal rank. The model’s performance in predicting the shape of the empirical GL curve is illustrated through a case study involving citation analysis.

Keywords: Lorenz curve; Stochastic dominance; Journal ranking; Journal impact factor; Geometric distribution (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:13:y:2019:i:1:p:387-396

DOI: 10.1016/j.joi.2019.02.003

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