The Matthew Effect in the Italian Digital Context: The Progressive Marginalisation of the “Poor”

Isabella Mingo () and Roberta Bracciale ()
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Isabella Mingo: Sapienza University of Rome
Roberta Bracciale: University of Pisa

Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, 2018, vol. 135, issue 2, No 11, 629-659

Abstract: Abstract The Matthew effect describes a model according to which, over time, inequalities fuel ever-widening gaps among individuals and social groups on the basis of the wellknown adage: “the rich get richer and the poor get poorer”. In this paper, we analyse the results of the Matthew effect in Italy in relation to first and second level digital divide, in order to determine the trajectories of closure, persistence or reinforcement of inequalities within the population. The central research question of the work aims to understand whether, when compared with a higher level of dissemination of technology over time, the adoption curves trace a model of progressive inclusion for the “poor” which approach the “richest”, or whether progressive increases are recorded in gaps. Considering a time span of more than a decade, microdata from the Istat multipurpose “Aspects of daily life” survey were used to find an empirically grounded answer to this research question. In terms of methodology, indices of absolute and relative digital exclusion and marginalisation which are necessary to take into account the changing nature of the phenomenon were proposed and used. Techniques of multivariate analysis (cluster analysis and multiple factor analysis) were also applied to detect any changes in the structure of variables and trajectories of the socio-demographic characteristics in question. The main results show the existence of a relative Matthew effect in Italy: despite the general increase in the spread of technologies, we are witnessing a progressive impoverishment of the weakest sectors of the population.

Keywords: Digital divide; Social inequalities; Matthew effect; Cluster analysis; Multiple factor analysis (search for similar items in EconPapers)
Date: 2018
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DOI: 10.1007/s11205-016-1511-2

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