Network approach versus brain drain : lessons from the diaspora

Abstract : For the past two decades, network approaches have led to many conceptual and empirical developments in the studies of international migration as well as of technological innovation. However, surprisingly, such approaches have hardly been used for the study of what is at the intersection of both fields, namely the mobility of highly skilled persons or knowledge workers. This article draws on recent evidence brought by case studies on intellectual diaspora networks to bridge this gap and to explore the issue. These highly skilled expatriate networks, through a connectionist approach linking diaspora members with their countries of origin, turn the brain drain into a brain gain approach. These persons and groups also provide original information that questions conventional human capital based assumptions. The article argues that descriptions in terms of network open interesting perspectives for the understanding as well as management of the current global skills' circulation. The network approach under consideration combines input from migration as well as from innovation studies. This suggests an expanded version of the network approach, referring to actors and intermediaries, of which traditional kinship ties are but a part of more systematic associative dynamics actually at work.
Complete list of metadatas

Cited literature [24 references]  Display  Hide  Download

https://hal.ird.fr/ird-01730582
Contributor : Jean-Baptiste Meyer <>
Submitted on : Tuesday, March 13, 2018 - 2:00:58 PM
Last modification on : Thursday, March 15, 2018 - 1:22:52 AM
Long-term archiving on : Thursday, June 14, 2018 - 2:19:02 PM

File

Meyer network bran gain_versio...
Files produced by the author(s)

Identifiers

Collections

Citation

Jean-Baptiste Meyer. Network approach versus brain drain : lessons from the diaspora. International Migration, Wiley, 2001, 39 (5), p. 91-110. ⟨10.1111/1468-2435.00173⟩. ⟨ird-01730582⟩

Share

Metrics

Record views

78

Files downloads

778