In recent years a relevant issue in the analysis of regional innovation policies supporting collaboration between firms and other organizations (e.g.: other firms, universities, research centres, etc.) is to evaluate the impact of such policies on the personal network of the targeted firms in a given region. To deal with this issue, it becomes of primary importance to have a reliable estimates of the size of innovation networks. In this work, we propose a novel use of capture-recapture approach to estimate the size and the composition of the personal networks of firms involved in regional innovation networks. Capturerecapture is a statistical method widely used to estimate the size of hidden population using two or more samples drawn from the population. The number of individuals observed in a sample and the number of those observed in both samples is used to estimate the number of those not selected in any sample. The method can be also used if two or more incomplete and overlapping lists of ascertained cases from different sources are available. We apply the proposed method on data from a survey conducted on a sample of 536 companies located in Friuli Venezia Giulia (a region in the north-east of Italy) who participated in the POR-FESR 2007-2013 call, named “Innovation, research, technology transfer and entrepreneurship”. The collaboration ties they claimed to entertain with other organizations (also not regionally bounded) before and after their participation to the call will be considered as the two lists on which the proposed capture-recapture approach is applied. The idea is to account for unobserved ties (and thus to estimate the network size of companies) using the number of ties observed only before or only after the participation to the call and the number of ties common to both.
Estimating the size of regional innovation network through a capture-recapture approach / Pelle, Elvira; De Stefano, Domenico; Zaccarin, Susanna. - (2017), pp. 95-95. (Intervento presentato al convegno ARS '17 International Workshop: Challenges in Social Network Research tenutosi a Napoli nel 16-17 maggio, 2017).
Estimating the size of regional innovation network through a capture-recapture approach
PELLE, Elvira;
2017
Abstract
In recent years a relevant issue in the analysis of regional innovation policies supporting collaboration between firms and other organizations (e.g.: other firms, universities, research centres, etc.) is to evaluate the impact of such policies on the personal network of the targeted firms in a given region. To deal with this issue, it becomes of primary importance to have a reliable estimates of the size of innovation networks. In this work, we propose a novel use of capture-recapture approach to estimate the size and the composition of the personal networks of firms involved in regional innovation networks. Capturerecapture is a statistical method widely used to estimate the size of hidden population using two or more samples drawn from the population. The number of individuals observed in a sample and the number of those observed in both samples is used to estimate the number of those not selected in any sample. The method can be also used if two or more incomplete and overlapping lists of ascertained cases from different sources are available. We apply the proposed method on data from a survey conducted on a sample of 536 companies located in Friuli Venezia Giulia (a region in the north-east of Italy) who participated in the POR-FESR 2007-2013 call, named “Innovation, research, technology transfer and entrepreneurship”. The collaboration ties they claimed to entertain with other organizations (also not regionally bounded) before and after their participation to the call will be considered as the two lists on which the proposed capture-recapture approach is applied. The idea is to account for unobserved ties (and thus to estimate the network size of companies) using the number of ties observed only before or only after the participation to the call and the number of ties common to both.File | Dimensione | Formato | |
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