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


  • Isabella Mingo

    () (Sapienza University of Rome)

  • Roberta Bracciale

    () (University of Pisa)


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.

Suggested Citation

  • Isabella Mingo & Roberta Bracciale, 2018. "The Matthew Effect in the Italian Digital Context: The Progressive Marginalisation of the “Poor”," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 629-659, January.
  • Handle: RePEc:spr:soinre:v:135:y:2018:i:2:d:10.1007_s11205-016-1511-2
    DOI: 10.1007/s11205-016-1511-2

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    References listed on IDEAS

    1. Vicente, María Rosalía & López, Ana Jesús, 2011. "Assessing the regional digital divide across the European Union-27," Telecommunications Policy, Elsevier, vol. 35(3), pages 220-237, April.
    2. Nicole Zillien & Eszter Hargittai, 2009. "Digital Distinction: Status‐Specific Types of Internet Usage," Social Science Quarterly, Southwestern Social Science Association, vol. 90(2), pages 274-291, June.
    3. Eszter Hargittai & Steven Shafer, 2006. "Differences in Actual and Perceived Online Skills: The Role of Gender," Social Science Quarterly, Southwestern Social Science Association, vol. 87(2), pages 432-448, June.
    4. Ira M. Wasserman & Marie Richmond‐Abbott, 2005. "Gender and the Internet: Causes of Variation in Access, Level, and Scope of Use," Social Science Quarterly, Southwestern Social Science Association, vol. 86(1), pages 252-270, March.
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    Cited by:

    1. Sarah Anrijs & Koen Ponnet & Lieven De Marez, 2020. "Development and psychometric properties of the Digital Difficulties Scale (DDS): An instrument to measure who is disadvantaged to fulfill basic needs by experiencing difficulties in using a smartphone," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-15, May.
    2. Naixia Mou & Chunying Wang & Tengfei Yang & Lingxian Zhang, 2020. "Evaluation of Development Potential of Ports in the Yangtze River Delta Using FAHP-Entropy Model," Sustainability, MDPI, Open Access Journal, vol. 12(2), pages 1-24, January.


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