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

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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.

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Fig. 1

Source: Based on the ISTAT “Aspects of daily life” database, 2001–2013

Fig. 2

Source: Based on the ISTAT “Aspects of daily life” database, 2001–2013

Fig. 3

Source: Based on the ISTAT “Aspects of daily life” database, 2001–2013

Fig. 4
Fig. 5

Source: Based on the ISTAT “Aspects of daily life” database, 2001–2013

Fig. 6

Source: Based on the ISTAT “Aspects of daily life” database, 2005, 2009, 2013

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Notes

  1. The “rich to get richer” (Kraut et al. 2002) and “accumulation of advantage (AOA) hypothesis” (De Haan 2004) labels identify models similar to the Matthew effect.

  2. In view of the specificity of sociocultural and territorial contexts that it must take into account in its analyses, Eurostat continues to define regular use as “at least once a week (i.e. every day or almost every day or at least once a week but not every day) on average within the last three months before the survey. Use includes all locations and methods of access and any purpose (private or work/business related)” http://ec.europa.eu/eurostat/web/products-datasets/-/tin00091.

  3. The empirical operationalisation of the concept is determined by certain documents that establish the current regulatory framework (Regulations (EC) Nos 808/2004 and 1006/2009) which govern empirical surveys on ICTs within Europe. This framework serves as a guideline on the items needed to build a useful regional benchmark for comparative analysis which is both longitudinal and transverse.

  4. There might also be a process of closing the gap between “rich” and “poor”, or intermediate combinations in the rate of enrichment and impoverishment. This rare dynamic may happen when the Matthew effect does not occur and when the absolute and relative terms are inverted: in the first case, the rich become poor and the poor become richer; in the second case, both the rich and the poor become richer, but the pace of enrichment of the poor is much faster (Rigney 2010).

  5. In this work, the words “poor” and “rich” are used broadly and do not refer specifically to the economic dimension.

  6. Je(2) is the sum of squared errors in resulting subgroups, while Je(1) is the sum of squared errors in the group that is to be divided.

  7. In 2009 the Internet skills were not detected.

  8. The analysis was conducted using SPAD-TM software.

  9. These are the homologous partial points which are more distant on the first factorial common plane.

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Authors and Affiliations

  1. Department of Communication and Social Research, Sapienza University of Rome, via Salaria 113, 00198, Rome, Italy

    Isabella Mingo

  2. Department of Political Science, University of Pisa, via Serafini, 56126, Pisa, Italy

    Roberta Bracciale

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  1. Isabella Mingo
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Correspondence to Isabella Mingo.

Appendix: Sociodemographic Categories

Appendix: Sociodemographic Categories

A: Cluster analysis

H * A (20–34) * M

LM * A (14–19) * F

H * A (20–34) * F

LM * A (20–34) * M

H * A (35–45) * M

LM * A (20–34) * F

H * A (35–45) * F

LM * A (35–45) * M

H * A (46–54) * M

LM * A (35–45) * F

H * A (46–54) * F

LM * A (46–54) * M

H * A (55–64) * M

LM * A (46–54) * F

H * A (55–64) * F

LM * A (55–64) * M

H * A (64+) * M

LM * A (55–64) * F

H * A (64+) * F

LM * A (64+) * M

UM * A (17–19) * M

LM * A (64+) * F

UM * A (17–19) * F

L * A (14–19) * M

UM * A (20–34) * M

L * A (14–19) * F

UM * A (20–34) * F

L * A (20–34) * M

UM * A (35–45) * M

L * A (20–34) * F

UM * A (35–45) * F

L * A (35–45) * M

UM * A (46–54) * M

L * A (35–45) * F

UM * A (46–54) * F

L * A (46–54) * M

UM * A (55–64) * M

L * A (46–54) * F

UM * A (55–64) * F

L * A (55–64) * M

UM * A (64+) * M

L * A (55–64) * F

UM * A (64+) * F

L * A (64+) * M

LM * A (14–19) * M

L * A (64+) * F

B: Multiple factorial analysis

H * A (20–34) * M

UM * A (55–64) * M

H * A (20–34) * F

UM * A (55–64) * F

H * A (35–45) * M

UM * A (64+) * MF

H * A (35–45) * F

LM * A (14–19) * M

H * A (46–54) * M

LM * A (14–19) * F

H * A (46–54) * F

LM * A (20–34) * M

H * A (55–64) * M

LM * A (20–34) * F

H * A (55–64) * F

LM * A (35–45) * M

H * A (64+) * MF

LM * A (35–45) * F

UM * A (17–19) * M

LM * A (46–54) * M

UM * A (17–19) * F

LM * A (46–54) * F

UM * A (20–34) * M

LM * A (55–64) * M

UM * A (20–34) * F

LM * A (55–64) * F

UM * A (35–45) * M

LM * A (64+) * MF

UM * A (35–45) * F

L * A (14–34) * MF

UM * A (46–54) * M

L * A (35–54) * MF

UM * A (46–54) * F

L * A (55–74) * MF

  1. Education (L low, LM lower middle, UM upper middle, H higher); A age; Gender (M male, F female)

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Mingo, I., Bracciale, R. The Matthew Effect in the Italian Digital Context: The Progressive Marginalisation of the “Poor”. Soc Indic Res 135, 629–659 (2018). https://doi.org/10.1007/s11205-016-1511-2

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