The complex phenomena of volunteering was mainly analyzed in economic literature with respect to its "economic value added", i.e the capability of this kind of activities to increase the level of productivity of some specific gods or services. In this paper, the point of view switches and voluntary organizations are analyzed as place of job market innovation, where new jobs arise and where people acquire new skills. Thus, volunteering can be thought as "social innovation" factor. In order to analyze the contents of voluntary works we use data coming from Istat survey "Multiscopo, Aspetti della vita quotidiana" (Multi-purposes survey, daily life aspects), for the year 2013. In our textual analysis, we use information included in the open answers given by people about the description of the tasks performed individually as volunteer. After stemming, lemmatization, and cleaning, data have been analyzed by means of Community Detection based on Semantic Network Analysis in order to discover patterns of jobs and through Correspondence Analysis on Generalized Aggregated Lexical Tables (CA-GALT) in order to discover profiles of volunteers. In particular, we look for differences given by gender, age, educational level, region of residence and type of voluntary association.
What volunteers do? A textual analysis of voluntary activities in the Italian context
SANTELLI, FRANCESCO;
2018-01-01
Abstract
The complex phenomena of volunteering was mainly analyzed in economic literature with respect to its "economic value added", i.e the capability of this kind of activities to increase the level of productivity of some specific gods or services. In this paper, the point of view switches and voluntary organizations are analyzed as place of job market innovation, where new jobs arise and where people acquire new skills. Thus, volunteering can be thought as "social innovation" factor. In order to analyze the contents of voluntary works we use data coming from Istat survey "Multiscopo, Aspetti della vita quotidiana" (Multi-purposes survey, daily life aspects), for the year 2013. In our textual analysis, we use information included in the open answers given by people about the description of the tasks performed individually as volunteer. After stemming, lemmatization, and cleaning, data have been analyzed by means of Community Detection based on Semantic Network Analysis in order to discover patterns of jobs and through Correspondence Analysis on Generalized Aggregated Lexical Tables (CA-GALT) in order to discover profiles of volunteers. In particular, we look for differences given by gender, age, educational level, region of residence and type of voluntary association.Pubblicazioni consigliate
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