This chapter focuses on digital skills, and its aim is to empirically assess the Matthew effect in Italy. Thus, following an empirical point of view, microdata from Italian official statistical sources (Istat, Istituto Nazionale di Statistica) and harmonised European indicators have been used. The chapter is structured as follows: Section 2 overviews the various measurement approaches suggested in the literature. Section 3 presents the European framework that is the operating procedures – and the recent changes – proposed by the European Commission to detect and measure eSkills. Using Istat microdata, Sections 4 and 5 focus on the Italian context, to identify the synthetic indices of e-skills and an Internet user’s typology based on digital competence and socio-demographic characteristics. Finally, in Section 6 the main results are summarized and new lines of research are proposed. This chapter highlights the relation between e-inclusion and e-skills, starting with the complexity of these concepts, which are often applied with different semantic dimensions and different modalities of measurement. The different approaches to establishing indicators of e-skills inevitably introduce elements of diversification. To obviate this problem, setting up shared indicators, used in periodical extensive surveys, makes it possible to compare the results in cross-sectional and longitudinal studies. Thus, it seems appropriate to use indicators harmonised in the European Union. The results of this study, which was based on using data collected by the Italian official statistical sources (Istat), reveals the convergence of skills pertaining to computer use with those linked to the Internet use. A diversification of skills in terms of complexity and the different types of ability is clear, depending on a prevalently instrumental or prevalently relational use of the technology. Digital inequalities are thus reiterated, also within a segment of the population that represents the youngest, better qualified subgroup, better positioned in terms of the labour market and which can consider itself “included”, on the basis of the simple criterion of net access. Even within the connected group, the dynamics of the Matthew effect can be seen: those with the "richest" personal resources activate a cumulative multiplication of advantages that follows the logic of “those who have, will be given more”. Instead the “poorest” are victims of a cumulative multiplication of disadvantages according to the logic of “those who have nothing, will have even that taken away”. Thus, there are categories of subjects who are “richer” and “poorer” in e-skills, who again present the customary inequalities of gender (men/women), generations (young/old), and culture (low/high education). These elements reiterate the old, but not negligible, inequalities, heightened by the new technologies: "the rich get richer, and the poor get poorer".
Social Inequalities in Digital Skills: The European framework and the Italian case
Bracciale, Roberta;
2016-01-01
Abstract
This chapter focuses on digital skills, and its aim is to empirically assess the Matthew effect in Italy. Thus, following an empirical point of view, microdata from Italian official statistical sources (Istat, Istituto Nazionale di Statistica) and harmonised European indicators have been used. The chapter is structured as follows: Section 2 overviews the various measurement approaches suggested in the literature. Section 3 presents the European framework that is the operating procedures – and the recent changes – proposed by the European Commission to detect and measure eSkills. Using Istat microdata, Sections 4 and 5 focus on the Italian context, to identify the synthetic indices of e-skills and an Internet user’s typology based on digital competence and socio-demographic characteristics. Finally, in Section 6 the main results are summarized and new lines of research are proposed. This chapter highlights the relation between e-inclusion and e-skills, starting with the complexity of these concepts, which are often applied with different semantic dimensions and different modalities of measurement. The different approaches to establishing indicators of e-skills inevitably introduce elements of diversification. To obviate this problem, setting up shared indicators, used in periodical extensive surveys, makes it possible to compare the results in cross-sectional and longitudinal studies. Thus, it seems appropriate to use indicators harmonised in the European Union. The results of this study, which was based on using data collected by the Italian official statistical sources (Istat), reveals the convergence of skills pertaining to computer use with those linked to the Internet use. A diversification of skills in terms of complexity and the different types of ability is clear, depending on a prevalently instrumental or prevalently relational use of the technology. Digital inequalities are thus reiterated, also within a segment of the population that represents the youngest, better qualified subgroup, better positioned in terms of the labour market and which can consider itself “included”, on the basis of the simple criterion of net access. Even within the connected group, the dynamics of the Matthew effect can be seen: those with the "richest" personal resources activate a cumulative multiplication of advantages that follows the logic of “those who have, will be given more”. Instead the “poorest” are victims of a cumulative multiplication of disadvantages according to the logic of “those who have nothing, will have even that taken away”. Thus, there are categories of subjects who are “richer” and “poorer” in e-skills, who again present the customary inequalities of gender (men/women), generations (young/old), and culture (low/high education). These elements reiterate the old, but not negligible, inequalities, heightened by the new technologies: "the rich get richer, and the poor get poorer".I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
